Featured This month
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Increasing Referral Efficiency in Eye Care: Addressing Data Gaps, Wait Times, and more
Maria Martynova
04.07 20237 min readOphthalmology has the highest average number of patients waiting, but up to 75% of patients make preventable trips to eye hospitals and general practitioners. Some of these patients are referred by optometrists who, more often than not, receive no feedback on the quality of their referrals, perpetuating this cycle. This article examines the referral procedure and potential solutions for increasing referral efficiency in eye care that practitioners can implement.
More than 25% of U.S. counties lack a single practicing eye care provider, and the situation isn’t unique to the U.S. In the UK, ophthalmology has been the most overburdened healthcare sector for some time. With a globally aging population and an increasing prevalence of age-related diseases, ensuring accessible eye care is crucial. Unfortunately, the reality is quite the opposite. One contributing factor is the high number of failures in the referral process.
How did we arrive at this point, and what can be done to improve it?
Altris AI’s survey identified a lack of data and increased patient wait times as the top problems with referrals for practitioners, while lack of co-management tools and poor communication/feedback ranked lower.
Let’s dive into more details:
FDA-cleared AI for OCT analysis
What are the top problems with referrals in eye care?
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Lack of diagnostic data
The ultimate goal of a referral is to ensure patients receive appropriate treatment for their specific pathology or confirmation of its absence. The receiving specialist’s first step is to review the referral report, making its completeness and clarity paramount. While there is a clear need for specialised assessment and treatment, almost 80% of those attending eye casualty do not require urgent ophthalmic attention following triage, and up to 60% of patients are seen and discharged on their first visit.
In eye care, both text information and accompanying images are crucial in ensuring efficient and accurate diagnoses.
However, handwritten and fragmented data continue to pose significant challenges in the patient referral process. Despite the prevalence of electronic health records (EHRs), over half of referrals are still handled through less efficient channels like fax, paper, or verbal communication. This can lead to fragmented or doubled patient data, potential gaps in care, and delays in treatment.
The study on the Impact of direct electronic optometric referral with ocular imaging to a hospital eye service showed that, given some limitations, electronic optometric referral with images to a Hospital Eye Service (HES) is safe, speedy, efficient, and clinically accurate, and it avoids unnecessary HES consultations.
Direct electronic referrals with images reduced the need for hospital eye service appointments by 37% compared to traditional paper referrals. Additionally, while 63% of electronic referrals led to HES appointments, this figure was 85% for paper referrals.
While incorporating images like OCT scans can significantly enhance understanding, some subtle or early-stage pathologies might still be overlooked. This is where detailed and customized reports become invaluable.
To illustrate the point, here is a handwritten referral compared to one of the types of customised OCT report from the Altris AI system, a platform that automates AI-powered OCT scan analysis for 70+ pathologies and biomarkers. This screenshot, in particular, shows segmented retina layers and highlights biomarkers of Dry AMD alongside a comparison of the patient’s macular thickness over visits.
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Lack of experience and access to second opinion
Research reveals a notable inverse relationship between clinician experience and the frequency of false-positive referrals in optometry, echoing findings in other medical fields where diagnostic proficiency typically improves with experience. This highlights the importance of recognizing the learning curve inherent in optometric practice and supporting less experienced practitioners.
The challenge is amplified by the fact that optometrists often practice in isolation, lacking the immediate professional support network available to their hospital-based counterparts. Unlike colleagues in hospital settings who have ready access to peer consultation for other opinions or guidance, optometrists often face limited opportunities for collaborative decision-making and skill development.
Another problem specialists often face is a lack of confidence in diagnosing, which may or may not be linked to experience. Knowing that their patients could potentially suffer irreversible vision loss from a pathology not yet detected during an exam, they often err on the side of caution and refer to a hospital. While this “better safe than sorry” approach is understandable, it places a significant burden on hospitals, extending wait times for those already at risk of blindness.
These concerns primarily revolve around glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR). AI can help identify these and other eye diseases at their earliest stages during routine visits. Some retinal changes are so minute that they escape detection by the human eye, making the program’s ability to detect tiny retinal changes invaluable.
Another significant benefit of AI systems lies in their approach to OCT analysis for glaucoma. Traditional methods rely on normative databases to assess retinal normality, but these databases are often limited in size and represent a select group of individuals. This can result in missed diagnoses of early glaucoma in those who deviate from the “norm” or unnecessary referral from optometry to ophthalmology for those who don’t fit the “normal” profile but have healthy eyes. AI can overcome this limitation by providing more personalized and comprehensive analysis.
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Increased wait times for patients
The National Health Service (NHS) is grappling with significant backlogs in ophthalmology services, which account for nearly 10% of the 7.8 million patients awaiting treatment.
The consistently high average number of patients waiting per trust in Ophthalmology, with high follow-up waitlists, delays care that poses substantial risks. The Royal College of Ophthalmologists reported that the risk of permanent visual loss is nine times higher in follow-up patients than in new patients. With 30% more patients on ophthalmology waitlists than pre-pandemic, the number of people at risk of sight loss may have increased.
Community Eyecare (CHEC), a provider of community-based ophthalmology services, received around 1000 referrals per week before the pandemic, further highlighting the strain on the system.
An analysis of electronic waitlists revealed that administrative issues, such as deceased patients or those already under care remaining on the list, artificially inflate wait times by up to 15%.
Improving administrative processes and reassessing referrals for appropriateness could help address this problem. Additionally, interim optometric examinations could revise referral information or determine the necessity of hospital visits, further reducing wait times.
Artificial intelligence can significantly speed up the screening process while reducing the controversy around diagnoses. This faster and more accurate diagnostic tool will enable more patients to be seen, allow for quicker responses to pathologies that pose a risk to eyesight, and reduce the burden on strained hospitals with needless patient referrals, as well as free up patients from unnecessary stress and wasted time.
International studies have shown that collaborative care also can increase screening and detection rates of eye disease.
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Lack of comanagement tools for eye care providers
The increasing demand for Hospital Eye Services, projected to grow by 40% in the next two decades and currently accounting for 8% of outpatient appointments, necessitates a re-evaluation of referral pathways and comanagement strategies between optometrists and ophthalmologists.
The lack of digital connectivity between primary, community, and secondary care creates a significant barrier to effective collaboration. In many cases, optometrists cannot make direct digital referrals to Hospital Eye Service, often relying on general practitioners as intermediaries, causing delays in diagnosis and treatment.
The COVID-19 pandemic highlighted the vital role of optometrists as first-contact providers for eye health, relieving pressure on hospitals. However, better integration between primary and secondary care is essential to build upon this and create a more sustainable eye care system. The current lack of digital connectivity hinders efficient communication and impedes the timely transfer of patient records, potentially leading to unnecessary referrals and delays in care.
As David Parkins, the ex-president of the College of Optometrists, emphasizes, the solution lies in increased integration and streamlined communication between primary and secondary eye care services. Implementing integrated digital platforms for referrals and feedback can enhance collaboration, improve patient outcomes, and reduce the burden on hospitals.
Leveraging optometrists’ expertise through shared care programs and direct digital referral pathways can alleviate the strain on eye hospitals and ensure timely access to care for patients with eye conditions.
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Poor communication/lack of feedback
A recent study published in Ophthalmic and Physiological Optics revealed that in 73% of cases, the referring optometrist was unaware of the outcome of their referral.
This lack of closure can lead to unnecessary re-referrals, patient anxiety, and potential treatment delays that could result in preventable vision loss, especially considering the extended waiting times for hospital eye service appointments.
Effective referral in eye care requires a closed feedback loop, where referring providers receive timely updates and reports from specialists. However, studies have shown that up to 50% of primary care providers (PCPs) are unsure whether their patients have even been seen by the referred specialists. This disconnect necessitates time-consuming follow-up calls and manual data integration, increasing the risk of errors and jeopardizing patient care.
The absence of consistent feedback also impacts optometrists’ professional development. Without knowing the accuracy of their referrals, optometrists cannot identify areas for improvement or refine their diagnostic skills. This is particularly relevant for newly qualified practitioners who may benefit from feedback to enhance their clinical judgment.
Implementing electronic referral systems that include feedback mechanisms can significantly improve communication and close the feedback loop. This would enable optometrists to track the progress of their referrals, receive timely updates on patient outcomes, and make informed decisions about future referrals.
Technology is also bridging the gap in specialist communication by enabling secure online consultations, such as live chat with dedicated ophthalmologists. A notable example in the UK is Pocket Eye, a platform designed to empower eye care professionals with clinical advice, diagnostic and image support, and AI-powered OCT analysis.
Summing up
FDA-cleared AI for OCT analysis
Implementing digital platforms that foster collaboration between eye care providers, increasing confidence in complex cases, and utilizing AI technologies to expedite diagnostics is crucial in a world where an aging population will increasingly rely on healthcare.
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Customisable OCT Reports: Enhancing Diagnostic Accuracy
Maria Martynova
07.06. 20238 min readThe average OCT device is a significant investment, costing upwards of $40,000. As eye care specialists, we recognize the revolutionary power of OCT. However, patients often receive only a standard OCT report from this investment. Unfortunately, many patients are unaware of OCT’s true value and may not even know what it is. This raises a crucial question: are these standard reports truly reflecting the full diagnostic potential of such an expensive and sophisticated device? Are we, as professionals, maximizing the capabilities of this technology to ensure optimal patient care?
This article explores how customisable OCT Reports address these shortcomings, enhancing diagnostic accuracy, treatment monitoring, referral efficiency, patient education, and audit readiness.
FDA-cleared AI for OCT analysis
Common OCT reports and their limitations
How does the standard report look?
OCT has become a golden standard for diagnosing and monitoring many ocular pathologies, thanks to its unparalleled level of detail in ophthalmic imaging.
While retinal reports vary among OCT models, they typically include:- a foveally centered B-scan,
- a quantitative thickness map,
- and a semi-quantitative thickness map.
The B-scan offers a visual snapshot of foveal architecture and confirms proper scan centering. The quantitative thickness map employs the ETDRS sector map to measure retinal thickness within a 6mm circle around the fovea, with specific measurements for the foveal sector (1mm), inner macular ring (3mm), and outer macular ring (6mm).
Progression analytics enable comparison of serial macular scans, which is invaluable for managing vitreomacular interface disorders and macular edema. The semi-quantitative thickness map provides a broader overview of retinal thickness throughout the scan.
Given this amount of data, it is challenging to identify subtle and localized retinal pathological changes. As a result, entire OCT datasets are represented by few aggregated values, and the standard OCT reports generated by most devices often rely on significant data reduction to simplify interpretation, which you can usually not customize.
Three standard methods exist for displaying OCT data
Firstly, acquired 2D image slices are presented individually. This allows for detailed examination, but navigating through numerous images can be cumbersome, particularly with large datasets.
Secondly, a fundus image is displayed with superimposed retinal layers. This facilitates linking layers to the fundus, but only one layer can be examined at a time, hindering the analysis of multiple layers simultaneously.
Thirdly, the OCT tomogram is visualized in 3D, providing a comprehensive overview, but adjusting the visual representation often has limitations. Additionally, combined 3D visualizations of the tomogram and layers are typically unavailable, potentially obscuring spatial relationships.
While existing reports offer diverse approaches to managing, analyzing, and presenting OCT data, each solution focuses on specific aspects and lacks customization. The situation becomes even more complex if scans come from different OCT devices, as manufacturers only provide software for the data for proprietary OCT scanners. Consequently, no approved way of viewing, analyzing, or comparing data from different manufacturers exists.
Furthermore, there are limited possibilities for implementing prototypes to perform such tasks since software libraries are provided with exclusive licenses and incomplete data specifications. Hence, managing and analyzing OCT data and relating them to other information are challenging and time-consuming tasks.
Often, supplementary software is utilized to overcome these limitations by providing additional information, visualizing and emphasizing data differently, and enabling the selection of relevant subsets.
How can customized reports for OCT help?
Altris AI’s recent survey has revealed that the key benefits of OCT technology for eye care specialists lie in treatment monitoring, patient education, and referral optimization.
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Measuring treatment progress: biomarkers tracking, pathology progression
Imaging biomarkers are a particularly attractive option for clinical practice due to their non-invasive and real-time nature. Quantitative measurements of retinal thickness, fluid volume, and other biomarkers relevant to diseases like diabetic retinopathy and age-related macular degeneration aid in treatment monitoring.
OCT reports with customized measurements and selected biomarkers, retinal layers, or segments allow for precise focus on treatment monitoring and patient response to therapy. This personalized approach enhances clinical decision-making by highlighting each case’s most relevant information.
In current clinical practice, macular damage assessment typically involves measuring the distance between the ILM and RPE layers, summarized in a post-scan report.
However, these reports often fall short of visualization best practices, employing ineffective or inconsistent color schemes. Additionally, they lack flexibility, with static visuals preventing in-depth examination of specific details. Despite these limitations, these reports remain valuable for many clinicians by distilling complex data into a manageable format.
Enhanced OCT data visualization offers a promising solution to these challenges. It enhances report clarity and comprehensibility while preserving the richness of the underlying data.
Let’s explore how this applies to a clinical case, such as monitoring a patient with Wet AMD during follow-up visits.
Data demonstrates that OCT findings can reveal the onset or progression of neovascular AMD before a patient reports new symptoms or changes in visual acuity. In fact, OCT images are reported to have the best diagnostic accuracy in monitoring nAMD disease states. This underscores the importance of key OCT findings or biomarkers in personalizing anti-VEGF treatment, achieving disease control, and reducing monitoring burdens.
Central Retinal Thickness emerged as one of the earliest OCT biomarkers used as an outcome measure in clinical trials for nAMD.
However, due to confounding factors, CRT’s use in outcome-based assessments of nAMD varies. Thus, it is essential to evaluate additional morphological changes alongside retinal thickness and their relationships with functional outcomes.
It has been reported that OCT images have the best diagnostic accuracy in monitoring nAMD disease states.
Another finding that is correlated with a worsening VA due to the associated photoreceptor defects is any damage to the four outer retina layers, including the RPE, interdigitation zone (IZ), ellipsoid zone (EZ), and external limiting membrane band (ELM).
OCT is a valuable imaging tool for visualizing subretinal hyperreflective material (SHRM). It can automatically identify and quantify SHRM and fluid and pigment epithelial detachment to calculate the overall risk of worsening visual outcomes associated with SHRM.
Subsequent follow-up visits will then display the most relevant picture, highlighting the most pertinent biomarkers for tracking a particular pathology (wet AMD in our example) and comparing their volume, progression, or regression through visits.
Another helpful option is retinal layer segmentation, which focuses solely on the retinal layers of interest for the specific case.
This level of customization empowers clinicians with a comprehensive yet targeted view of the patient’s condition. It saves time from manually detecting anomalies on scans and facilitates informed decision-making and personalized treatment plans.
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Glaucoma risk evaluation
Millions risk irreversible vision loss due to undiagnosed glaucoma, underscoring the need for improved early detection. Current tests often rely on observing changes over time, delaying treatment assessment and hindering early identification of rapid disease progression. OCT frequently detects microscopic damage to ganglion cells and thinning across these layers before changes are noticeable through other tests. However, the earliest signs on the scan can still be invisible to the human eye.
AI algorithms offer insights into glaucoma detection by routinely analyzing the ganglion cell complex, measuring its thickness, and identifying any thinning or asymmetry to determine a patient’s glaucoma risk without additional clinician effort.
Another significant benefit of AI systems is that OCT for glaucoma usually utilizes a normative database to assess retinal normality. However, these databases are limited in size and represent an average of a select group of people, potentially missing early glaucoma development in those who deviate from the “norm.” Conversely, individuals may be unnecessarily referred for treatment due to not fitting the “normal” profile, even if their eyes are healthy.
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Crafting effective referral
In the UK, optometrists are crucial in initiating referrals to hospital eye services (HES), with 72% originating from primary care optometric examinations. While optometrists generally demonstrate proficiency in identifying conditions like cataracts and glaucoma, discrepancies in referral thresholds and unfamiliarity with less common pathologies can lead to unnecessary or delayed referrals.
At the same time, an evaluation of incoming letters from optometrists in a glaucoma service found that 43% of the letters were considered “failures” because they did not convey the necessity and urgency of the referral.
So, having an elaborate record of the entire clinical examination in addition to a referral letter is crucial.
Customized OCT reports solve this challenge by streamlining the referral process and improving communication between optometrists and ophthalmologists. These reports can significantly reduce delays and ensure patients receive timely care by providing comprehensive and relevant information upfront.
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Patient Education
Patient education and involvement in decision-making are vital for every medical field and crucial for ophthalmology, where insufficient patient engagement can lead to irreversible blindness.
Research specifically targeting the ophthalmology patient population, which often includes older and potentially visually impaired individuals, reveals a clear preference for materials their eye care provider endorsed.
Providing explicit visual representations of diagnoses can significantly improve patient understanding and compliance. Seeing photos of their condition, like glaucoma progression, builds trust and reinforces the importance of treatment recommendations.
Surveying eye care professionals specializing in dry eye disease revealed a strong emphasis on visual aids during patient education.
Photodocumentation is a favored tool for demonstrating the condition to asymptomatic patients, tracking progress, and highlighting treatment’s positive outcomes.
The visual approach provides tangible evidence of the benefits of their treatment investment, allowing for a deeper understanding of the “why” behind treatment recommendations and paving the way for ongoing collaboration with the patient.
Color-coded OCT reports for pathologies and their signs, severity grading, and pathology progression over time within its OCT analysis highlight the littlest bits that a patient’s unprepared eye would miss otherwise. With follow-up visits, patients can see what’s happening within their eyes and track the progress of any conditions during treatment.
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Updating EMR and Audit readiness
OCT reports are crucial components of a patient’s medical history and are essential for accurate diagnosis, personalized treatment, and ongoing monitoring. The streamlined process of integrating OCT data into EMR ensures that every eye scan, with its corresponding measurements, biomarkers, and visualizations, becomes an easily accessible part of the patient’s medical history.
This is crucial for continuity of care and simplifies the audit process, providing a clear and comprehensive record of the patient’s eye health over time. Just optometry chains alone can perform an imposing volume of OCT scans, with some reaching upwards of 40,000 per week. While this demonstrates the widespread adoption of this valuable diagnostic tool, it also presents a challenge: the increased risk of missing subtle or early-stage pathologies amidst the sheer volume of data.
Enhanced OCT reports offer a solution by providing a crucial “second look” at scan results. While not foolproof, this double-check significantly reduces the risk of overlooking abnormalities, ultimately improving patient outcomes and safeguarding the clinic’s reputation.
In audits, comprehensive OCT reports are critical in ensuring regulatory compliance. As the Fundamentals of Ophthalmic Coding states, “It is the responsibility of each physician to document the interpretations as promptly as possible and then communicate the findings with the patient… to develop a fail-safe way to ensure that your interpretations are completed promptly.”
Auditors typically look for several key elements in OCT reports:
- Physician’s Order: Document the test order, indicating which eye(s) and the medical necessity.
- Interpretation and Report: The physician analyzes the scan results, including any identified abnormalities or concerns.
- Timely Completion: Prompt documentation and communication of findings to the patient.
Customisable OCT reports can streamline this process by generating comprehensive reports that meet these requirements. These reports include detailed measurements, biomarker analysis, and clear visualizations, making it easier for physicians to review, interpret, and document their findings efficiently.
FDA-cleared AI for OCT analysis
Summing up
Standard OCT reports, while valuable, often need more customization due to data reduction and lack of customization. The inability to visualize multiple scans simultaneously or compare data from different devices hinders comprehensive analysis. Enhanced OCT reports address these limitations by offering detailed visualizations, customizable measurements, and biomarker tracking.
Customisable OCT reports aid in the early detection and monitoring of diseases like wet AMD and glaucoma, empowering clinicians with accurate diagnoses and personalized treatment plans. Additionally, they streamline referrals by providing focused information and clear visualizations, reducing delays and improving communication between optometrists and ophthalmologists.
These comprehensive reports also enhance patient education by offering clear visual representations of their conditions and treatment progress, fostering better understanding and compliance. Moreover, with detailed documentation and analysis, detailed reports ensure audit readiness for eye care professionals, mitigating the risk of missed pathologies and upholding regulatory compliance.
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Increasing Referral Efficiency in Eye Care: Addressing Data Gaps, Wait Times, and more
Maria Martynova
04.07 20237 min readOphthalmology has the highest average number of patients waiting, but up to 75% of patients make preventable trips to eye hospitals and general practitioners. Some of these patients are referred by optometrists who, more often than not, receive no feedback on the quality of their referrals, perpetuating this cycle. This article examines the referral procedure and potential solutions for increasing referral efficiency in eye care that practitioners can implement.
More than 25% of U.S. counties lack a single practicing eye care provider, and the situation isn’t unique to the U.S. In the UK, ophthalmology has been the most overburdened healthcare sector for some time. With a globally aging population and an increasing prevalence of age-related diseases, ensuring accessible eye care is crucial. Unfortunately, the reality is quite the opposite. One contributing factor is the high number of failures in the referral process.
How did we arrive at this point, and what can be done to improve it?
Altris AI’s survey identified a lack of data and increased patient wait times as the top problems with referrals for practitioners, while lack of co-management tools and poor communication/feedback ranked lower.
Let’s dive into more details:
FDA-cleared AI for OCT analysis
What are the top problems with referrals in eye care?
-
Lack of diagnostic data
The ultimate goal of a referral is to ensure patients receive appropriate treatment for their specific pathology or confirmation of its absence. The receiving specialist’s first step is to review the referral report, making its completeness and clarity paramount. While there is a clear need for specialised assessment and treatment, almost 80% of those attending eye casualty do not require urgent ophthalmic attention following triage, and up to 60% of patients are seen and discharged on their first visit.
In eye care, both text information and accompanying images are crucial in ensuring efficient and accurate diagnoses.
However, handwritten and fragmented data continue to pose significant challenges in the patient referral process. Despite the prevalence of electronic health records (EHRs), over half of referrals are still handled through less efficient channels like fax, paper, or verbal communication. This can lead to fragmented or doubled patient data, potential gaps in care, and delays in treatment.
The study on the Impact of direct electronic optometric referral with ocular imaging to a hospital eye service showed that, given some limitations, electronic optometric referral with images to a Hospital Eye Service (HES) is safe, speedy, efficient, and clinically accurate, and it avoids unnecessary HES consultations.
Direct electronic referrals with images reduced the need for hospital eye service appointments by 37% compared to traditional paper referrals. Additionally, while 63% of electronic referrals led to HES appointments, this figure was 85% for paper referrals.
While incorporating images like OCT scans can significantly enhance understanding, some subtle or early-stage pathologies might still be overlooked. This is where detailed and customized reports become invaluable.
To illustrate the point, here is a handwritten referral compared to one of the types of customised OCT report from the Altris AI system, a platform that automates AI-powered OCT scan analysis for 70+ pathologies and biomarkers. This screenshot, in particular, shows segmented retina layers and highlights biomarkers of Dry AMD alongside a comparison of the patient’s macular thickness over visits.
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Lack of experience and access to second opinion
Research reveals a notable inverse relationship between clinician experience and the frequency of false-positive referrals in optometry, echoing findings in other medical fields where diagnostic proficiency typically improves with experience. This highlights the importance of recognizing the learning curve inherent in optometric practice and supporting less experienced practitioners.
The challenge is amplified by the fact that optometrists often practice in isolation, lacking the immediate professional support network available to their hospital-based counterparts. Unlike colleagues in hospital settings who have ready access to peer consultation for other opinions or guidance, optometrists often face limited opportunities for collaborative decision-making and skill development.
Another problem specialists often face is a lack of confidence in diagnosing, which may or may not be linked to experience. Knowing that their patients could potentially suffer irreversible vision loss from a pathology not yet detected during an exam, they often err on the side of caution and refer to a hospital. While this “better safe than sorry” approach is understandable, it places a significant burden on hospitals, extending wait times for those already at risk of blindness.
These concerns primarily revolve around glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR). AI can help identify these and other eye diseases at their earliest stages during routine visits. Some retinal changes are so minute that they escape detection by the human eye, making the program’s ability to detect tiny retinal changes invaluable.
Another significant benefit of AI systems lies in their approach to OCT analysis for glaucoma. Traditional methods rely on normative databases to assess retinal normality, but these databases are often limited in size and represent a select group of individuals. This can result in missed diagnoses of early glaucoma in those who deviate from the “norm” or unnecessary referral from optometry to ophthalmology for those who don’t fit the “normal” profile but have healthy eyes. AI can overcome this limitation by providing more personalized and comprehensive analysis.
-
Increased wait times for patients
The National Health Service (NHS) is grappling with significant backlogs in ophthalmology services, which account for nearly 10% of the 7.8 million patients awaiting treatment.
The consistently high average number of patients waiting per trust in Ophthalmology, with high follow-up waitlists, delays care that poses substantial risks. The Royal College of Ophthalmologists reported that the risk of permanent visual loss is nine times higher in follow-up patients than in new patients. With 30% more patients on ophthalmology waitlists than pre-pandemic, the number of people at risk of sight loss may have increased.
Community Eyecare (CHEC), a provider of community-based ophthalmology services, received around 1000 referrals per week before the pandemic, further highlighting the strain on the system.
An analysis of electronic waitlists revealed that administrative issues, such as deceased patients or those already under care remaining on the list, artificially inflate wait times by up to 15%.
Improving administrative processes and reassessing referrals for appropriateness could help address this problem. Additionally, interim optometric examinations could revise referral information or determine the necessity of hospital visits, further reducing wait times.
Artificial intelligence can significantly speed up the screening process while reducing the controversy around diagnoses. This faster and more accurate diagnostic tool will enable more patients to be seen, allow for quicker responses to pathologies that pose a risk to eyesight, and reduce the burden on strained hospitals with needless patient referrals, as well as free up patients from unnecessary stress and wasted time.
International studies have shown that collaborative care also can increase screening and detection rates of eye disease.
-
Lack of comanagement tools for eye care providers
The increasing demand for Hospital Eye Services, projected to grow by 40% in the next two decades and currently accounting for 8% of outpatient appointments, necessitates a re-evaluation of referral pathways and comanagement strategies between optometrists and ophthalmologists.
The lack of digital connectivity between primary, community, and secondary care creates a significant barrier to effective collaboration. In many cases, optometrists cannot make direct digital referrals to Hospital Eye Service, often relying on general practitioners as intermediaries, causing delays in diagnosis and treatment.
The COVID-19 pandemic highlighted the vital role of optometrists as first-contact providers for eye health, relieving pressure on hospitals. However, better integration between primary and secondary care is essential to build upon this and create a more sustainable eye care system. The current lack of digital connectivity hinders efficient communication and impedes the timely transfer of patient records, potentially leading to unnecessary referrals and delays in care.
As David Parkins, the ex-president of the College of Optometrists, emphasizes, the solution lies in increased integration and streamlined communication between primary and secondary eye care services. Implementing integrated digital platforms for referrals and feedback can enhance collaboration, improve patient outcomes, and reduce the burden on hospitals.
Leveraging optometrists’ expertise through shared care programs and direct digital referral pathways can alleviate the strain on eye hospitals and ensure timely access to care for patients with eye conditions.
-
Poor communication/lack of feedback
A recent study published in Ophthalmic and Physiological Optics revealed that in 73% of cases, the referring optometrist was unaware of the outcome of their referral.
This lack of closure can lead to unnecessary re-referrals, patient anxiety, and potential treatment delays that could result in preventable vision loss, especially considering the extended waiting times for hospital eye service appointments.
Effective referral in eye care requires a closed feedback loop, where referring providers receive timely updates and reports from specialists. However, studies have shown that up to 50% of primary care providers (PCPs) are unsure whether their patients have even been seen by the referred specialists. This disconnect necessitates time-consuming follow-up calls and manual data integration, increasing the risk of errors and jeopardizing patient care.
The absence of consistent feedback also impacts optometrists’ professional development. Without knowing the accuracy of their referrals, optometrists cannot identify areas for improvement or refine their diagnostic skills. This is particularly relevant for newly qualified practitioners who may benefit from feedback to enhance their clinical judgment.
Implementing electronic referral systems that include feedback mechanisms can significantly improve communication and close the feedback loop. This would enable optometrists to track the progress of their referrals, receive timely updates on patient outcomes, and make informed decisions about future referrals.
Technology is also bridging the gap in specialist communication by enabling secure online consultations, such as live chat with dedicated ophthalmologists. A notable example in the UK is Pocket Eye, a platform designed to empower eye care professionals with clinical advice, diagnostic and image support, and AI-powered OCT analysis.
Summing up
FDA-cleared AI for OCT analysis
Implementing digital platforms that foster collaboration between eye care providers, increasing confidence in complex cases, and utilizing AI technologies to expedite diagnostics is crucial in a world where an aging population will increasingly rely on healthcare.
-
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Customisable OCT Reports: Enhancing Diagnostic Accuracy
Maria Martynova
07.06. 20238 min readThe average OCT device is a significant investment, costing upwards of $40,000. As eye care specialists, we recognize the revolutionary power of OCT. However, patients often receive only a standard OCT report from this investment. Unfortunately, many patients are unaware of OCT’s true value and may not even know what it is. This raises a crucial question: are these standard reports truly reflecting the full diagnostic potential of such an expensive and sophisticated device? Are we, as professionals, maximizing the capabilities of this technology to ensure optimal patient care?
This article explores how customisable OCT Reports address these shortcomings, enhancing diagnostic accuracy, treatment monitoring, referral efficiency, patient education, and audit readiness.
FDA-cleared AI for OCT analysis
Common OCT reports and their limitations
How does the standard report look?
OCT has become a golden standard for diagnosing and monitoring many ocular pathologies, thanks to its unparalleled level of detail in ophthalmic imaging.
While retinal reports vary among OCT models, they typically include:- a foveally centered B-scan,
- a quantitative thickness map,
- and a semi-quantitative thickness map.
The B-scan offers a visual snapshot of foveal architecture and confirms proper scan centering. The quantitative thickness map employs the ETDRS sector map to measure retinal thickness within a 6mm circle around the fovea, with specific measurements for the foveal sector (1mm), inner macular ring (3mm), and outer macular ring (6mm).
Progression analytics enable comparison of serial macular scans, which is invaluable for managing vitreomacular interface disorders and macular edema. The semi-quantitative thickness map provides a broader overview of retinal thickness throughout the scan.
Given this amount of data, it is challenging to identify subtle and localized retinal pathological changes. As a result, entire OCT datasets are represented by few aggregated values, and the standard OCT reports generated by most devices often rely on significant data reduction to simplify interpretation, which you can usually not customize.
Three standard methods exist for displaying OCT data
Firstly, acquired 2D image slices are presented individually. This allows for detailed examination, but navigating through numerous images can be cumbersome, particularly with large datasets.
Secondly, a fundus image is displayed with superimposed retinal layers. This facilitates linking layers to the fundus, but only one layer can be examined at a time, hindering the analysis of multiple layers simultaneously.
Thirdly, the OCT tomogram is visualized in 3D, providing a comprehensive overview, but adjusting the visual representation often has limitations. Additionally, combined 3D visualizations of the tomogram and layers are typically unavailable, potentially obscuring spatial relationships.
While existing reports offer diverse approaches to managing, analyzing, and presenting OCT data, each solution focuses on specific aspects and lacks customization. The situation becomes even more complex if scans come from different OCT devices, as manufacturers only provide software for the data for proprietary OCT scanners. Consequently, no approved way of viewing, analyzing, or comparing data from different manufacturers exists.
Furthermore, there are limited possibilities for implementing prototypes to perform such tasks since software libraries are provided with exclusive licenses and incomplete data specifications. Hence, managing and analyzing OCT data and relating them to other information are challenging and time-consuming tasks.
Often, supplementary software is utilized to overcome these limitations by providing additional information, visualizing and emphasizing data differently, and enabling the selection of relevant subsets.
How can customized reports for OCT help?
Altris AI’s recent survey has revealed that the key benefits of OCT technology for eye care specialists lie in treatment monitoring, patient education, and referral optimization.
-
Measuring treatment progress: biomarkers tracking, pathology progression
Imaging biomarkers are a particularly attractive option for clinical practice due to their non-invasive and real-time nature. Quantitative measurements of retinal thickness, fluid volume, and other biomarkers relevant to diseases like diabetic retinopathy and age-related macular degeneration aid in treatment monitoring.
OCT reports with customized measurements and selected biomarkers, retinal layers, or segments allow for precise focus on treatment monitoring and patient response to therapy. This personalized approach enhances clinical decision-making by highlighting each case’s most relevant information.
In current clinical practice, macular damage assessment typically involves measuring the distance between the ILM and RPE layers, summarized in a post-scan report.
However, these reports often fall short of visualization best practices, employing ineffective or inconsistent color schemes. Additionally, they lack flexibility, with static visuals preventing in-depth examination of specific details. Despite these limitations, these reports remain valuable for many clinicians by distilling complex data into a manageable format.
Enhanced OCT data visualization offers a promising solution to these challenges. It enhances report clarity and comprehensibility while preserving the richness of the underlying data.
Let’s explore how this applies to a clinical case, such as monitoring a patient with Wet AMD during follow-up visits.
Data demonstrates that OCT findings can reveal the onset or progression of neovascular AMD before a patient reports new symptoms or changes in visual acuity. In fact, OCT images are reported to have the best diagnostic accuracy in monitoring nAMD disease states. This underscores the importance of key OCT findings or biomarkers in personalizing anti-VEGF treatment, achieving disease control, and reducing monitoring burdens.
Central Retinal Thickness emerged as one of the earliest OCT biomarkers used as an outcome measure in clinical trials for nAMD.
However, due to confounding factors, CRT’s use in outcome-based assessments of nAMD varies. Thus, it is essential to evaluate additional morphological changes alongside retinal thickness and their relationships with functional outcomes.
It has been reported that OCT images have the best diagnostic accuracy in monitoring nAMD disease states.
Another finding that is correlated with a worsening VA due to the associated photoreceptor defects is any damage to the four outer retina layers, including the RPE, interdigitation zone (IZ), ellipsoid zone (EZ), and external limiting membrane band (ELM).
OCT is a valuable imaging tool for visualizing subretinal hyperreflective material (SHRM). It can automatically identify and quantify SHRM and fluid and pigment epithelial detachment to calculate the overall risk of worsening visual outcomes associated with SHRM.
Subsequent follow-up visits will then display the most relevant picture, highlighting the most pertinent biomarkers for tracking a particular pathology (wet AMD in our example) and comparing their volume, progression, or regression through visits.
Another helpful option is retinal layer segmentation, which focuses solely on the retinal layers of interest for the specific case.
This level of customization empowers clinicians with a comprehensive yet targeted view of the patient’s condition. It saves time from manually detecting anomalies on scans and facilitates informed decision-making and personalized treatment plans.
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Glaucoma risk evaluation
Millions risk irreversible vision loss due to undiagnosed glaucoma, underscoring the need for improved early detection. Current tests often rely on observing changes over time, delaying treatment assessment and hindering early identification of rapid disease progression. OCT frequently detects microscopic damage to ganglion cells and thinning across these layers before changes are noticeable through other tests. However, the earliest signs on the scan can still be invisible to the human eye.
AI algorithms offer insights into glaucoma detection by routinely analyzing the ganglion cell complex, measuring its thickness, and identifying any thinning or asymmetry to determine a patient’s glaucoma risk without additional clinician effort.
Another significant benefit of AI systems is that OCT for glaucoma usually utilizes a normative database to assess retinal normality. However, these databases are limited in size and represent an average of a select group of people, potentially missing early glaucoma development in those who deviate from the “norm.” Conversely, individuals may be unnecessarily referred for treatment due to not fitting the “normal” profile, even if their eyes are healthy.
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Crafting effective referral
In the UK, optometrists are crucial in initiating referrals to hospital eye services (HES), with 72% originating from primary care optometric examinations. While optometrists generally demonstrate proficiency in identifying conditions like cataracts and glaucoma, discrepancies in referral thresholds and unfamiliarity with less common pathologies can lead to unnecessary or delayed referrals.
At the same time, an evaluation of incoming letters from optometrists in a glaucoma service found that 43% of the letters were considered “failures” because they did not convey the necessity and urgency of the referral.
So, having an elaborate record of the entire clinical examination in addition to a referral letter is crucial.
Customized OCT reports solve this challenge by streamlining the referral process and improving communication between optometrists and ophthalmologists. These reports can significantly reduce delays and ensure patients receive timely care by providing comprehensive and relevant information upfront.
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Patient Education
Patient education and involvement in decision-making are vital for every medical field and crucial for ophthalmology, where insufficient patient engagement can lead to irreversible blindness.
Research specifically targeting the ophthalmology patient population, which often includes older and potentially visually impaired individuals, reveals a clear preference for materials their eye care provider endorsed.
Providing explicit visual representations of diagnoses can significantly improve patient understanding and compliance. Seeing photos of their condition, like glaucoma progression, builds trust and reinforces the importance of treatment recommendations.
Surveying eye care professionals specializing in dry eye disease revealed a strong emphasis on visual aids during patient education.
Photodocumentation is a favored tool for demonstrating the condition to asymptomatic patients, tracking progress, and highlighting treatment’s positive outcomes.
The visual approach provides tangible evidence of the benefits of their treatment investment, allowing for a deeper understanding of the “why” behind treatment recommendations and paving the way for ongoing collaboration with the patient.
Color-coded OCT reports for pathologies and their signs, severity grading, and pathology progression over time within its OCT analysis highlight the littlest bits that a patient’s unprepared eye would miss otherwise. With follow-up visits, patients can see what’s happening within their eyes and track the progress of any conditions during treatment.
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Updating EMR and Audit readiness
OCT reports are crucial components of a patient’s medical history and are essential for accurate diagnosis, personalized treatment, and ongoing monitoring. The streamlined process of integrating OCT data into EMR ensures that every eye scan, with its corresponding measurements, biomarkers, and visualizations, becomes an easily accessible part of the patient’s medical history.
This is crucial for continuity of care and simplifies the audit process, providing a clear and comprehensive record of the patient’s eye health over time. Just optometry chains alone can perform an imposing volume of OCT scans, with some reaching upwards of 40,000 per week. While this demonstrates the widespread adoption of this valuable diagnostic tool, it also presents a challenge: the increased risk of missing subtle or early-stage pathologies amidst the sheer volume of data.
Enhanced OCT reports offer a solution by providing a crucial “second look” at scan results. While not foolproof, this double-check significantly reduces the risk of overlooking abnormalities, ultimately improving patient outcomes and safeguarding the clinic’s reputation.
In audits, comprehensive OCT reports are critical in ensuring regulatory compliance. As the Fundamentals of Ophthalmic Coding states, “It is the responsibility of each physician to document the interpretations as promptly as possible and then communicate the findings with the patient… to develop a fail-safe way to ensure that your interpretations are completed promptly.”
Auditors typically look for several key elements in OCT reports:
- Physician’s Order: Document the test order, indicating which eye(s) and the medical necessity.
- Interpretation and Report: The physician analyzes the scan results, including any identified abnormalities or concerns.
- Timely Completion: Prompt documentation and communication of findings to the patient.
Customisable OCT reports can streamline this process by generating comprehensive reports that meet these requirements. These reports include detailed measurements, biomarker analysis, and clear visualizations, making it easier for physicians to review, interpret, and document their findings efficiently.
FDA-cleared AI for OCT analysis
Summing up
Standard OCT reports, while valuable, often need more customization due to data reduction and lack of customization. The inability to visualize multiple scans simultaneously or compare data from different devices hinders comprehensive analysis. Enhanced OCT reports address these limitations by offering detailed visualizations, customizable measurements, and biomarker tracking.
Customisable OCT reports aid in the early detection and monitoring of diseases like wet AMD and glaucoma, empowering clinicians with accurate diagnoses and personalized treatment plans. Additionally, they streamline referrals by providing focused information and clear visualizations, reducing delays and improving communication between optometrists and ophthalmologists.
These comprehensive reports also enhance patient education by offering clear visual representations of their conditions and treatment progress, fostering better understanding and compliance. Moreover, with detailed documentation and analysis, detailed reports ensure audit readiness for eye care professionals, mitigating the risk of missed pathologies and upholding regulatory compliance.
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AI for Ophthalmic Drug Development: Enhancing Biomarkers Detection
Maria Martynova
20.05.20238 min readDespite increased research and development spending, fewer novel drugs and biologics are reaching the market today.
Large pharmaceutical companies invest an average of over $5 billion and 12+ years in research and development for each new drug approval.
The high failure rate of drug candidates (only 15% of Phase I drugs reach approval) further exacerbates the issue. This risk often leads pharmaceutical companies to favor lower-risk investments like biosimilars or generic drugs over novel therapies.
Due to the eye’s specialized anatomy and physiology, ophthalmic drug development faces unique challenges. Ocular barriers like the tear film and blood-ocular barrier can hinder drug efficacy. Many therapeutic endpoints in ophthalmology are subjective, making controlled trials difficult. The imprecise nature of some measurements further complicates trial design. Rare ophthalmic diseases pose additional challenges, as clinical trials may group diverse conditions, like multiple types of uveitic, together despite their distinct underlying mechanisms and therapeutic needs.
Here is where AI enters the game. With its ability to rapidly analyze vast amounts of data and detect subtle patterns, AI is revolutionizing how we approach clinical trials for ophthalmic drugs.
In this article, we will explore how AI for ophthalmic drug development transforms the landscape by accelerating the identification of biomarkers for conditions like diabetic retinopathy and age-related macular degeneration, ensuring the right patients are enrolled in trials, and providing quantitative metrics for evaluating treatment efficacy.
FDA-cleared AI for OCT analysis
How AI for ophthalmic drug development can accelerate the search for biomarkers in clinical trials
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Biomarkers for quantitative analysis before and after treatment
A biomarker, as defined by the BEST Resource FDA-NIH Biomarker Working Group, is a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, disease processes, or responses to therapeutic intervention. Key characteristics of a useful biomarker include specificity, sensitivity, simplicity, reliability, reproducibility, multiplexing capability, and cost-effectiveness.
Determining a biomarker’s performance involves assessing its:
- analytical validity – how accurately it measures what it claims to measure;
- clinical validity – how well it reflects a clinical feature or outcome;
- clinical utility – how it improves patient outcomes or guides treatment decisions.
In the context of drug regulation, qualified biomarkers can serve as endpoints in clinical trials, potentially offering a more objective and less placebo-susceptible alternative to traditional patient-reported outcomes.
Imaging biomarkers are a particularly attractive option for clinical use due to their non-invasive, real-time, and cost-effective nature.
In ophthalmology, AI-powered analysis of OCT scans can provide precise, quantitative measurements of retinal thickness, fluid volume, and other biomarkers relevant to diseases like diabetic retinopathy and age-related macular degeneration. These measurements can aid in diagnosis, disease staging, treatment monitoring, and prediction of treatment response.
Systems like Altris AI for pathology detection and segmentation enabled automated disease characterization and longitudinal monitoring of therapeutic response in AMD. Multiple studies have demonstrated the value of volumetric fluid characterization, compartment-specific OCT feature evaluation, and subretinal fibrosis and hyperreflective material quantification.
A study has shown the potential of AI to predict conversion from early or intermediate non-neovascular AMD to the neovascular form, using quantitative imaging features like drusen shape and volume.
The extraction of quantitative fluid features and assessment of retinal multi-layer segmentation from OCT scans have offered valuable insights into disease prognosis and longitudinal dynamics of Diabetic Retinopathy.
A recent study demonstrated that quantitative improvement in ellipsoid zone integrity following anti-VEGF therapy for DME significantly correlated with visual function recovery. Furthermore, novel imaging biomarkers, such as the retinal fluid index (RFI), are emerging as tools for precisely monitoring treatment response. Studies have shown that early RFI volatility can predict long-term instability in visual outcomes after treatment.
Building on these advancements, researchers are now exploring the relationship between imaging biomarkers and underlying disease pathways. A recent study linked levels of various cytokines, including VEGF, MCP-1, and IL-6, with specific OCT-derived biomarkers like fluid parameters and outer retinal integrity.
By automating the analysis of OCT scans, AI not only streamlines the process but also uncovers subtle details and patterns that might be missed by human observation.
Enhanced by AI precision enables more accurate identification and quantification of biomarkers, leading to better patient stratification, treatment monitoring, and prediction of therapeutic responses.
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Data Annotation for Clinical Trials
An ophthalmologist’s report noting the presence of edema on an OCT scan is not the same as stating that its height and length are 411 and 3213 µm, accordingly.
Imaging biomarkers can range from simple measurements of size or shape to complex computational models, providing valuable information to complement traditional diagnostic methods. They can also determine the presence and severity of a disorder, assess its progression, and evaluate treatment response.
While biomarkers can be derived from various imaging modalities, OCT stands out in ophthalmology due to its high resolution and ability to visualize subtle retinal changes.
Parametric images, which visually represent the spatial distribution of biomarker values, further enhance the analysis of OCT scans. This combination of quantitative data and visual representation empowers clinicians and researchers to make more informed decisions about diagnosis, treatment, and disease management.
Traditionally, medical image interpretation has relied heavily on visual assessment by experts, who recognize patterns and deviations from normal anatomy based on their accumulated knowledge.
While semi-quantitative scoring systems offer some level of objectivity, the field is rapidly evolving towards more quantitative and automated approaches. This shift is driven by advancements in standardization, sophisticated image analysis techniques, and the rise of machine and deep learning.
In some clinical scenarios, automated image quantification can surpass manual assessment in objectivity and accuracy, interpreting subsequent changes with greater precision and clinical relevance by establishing thresholds for disease states. Unlike physical biomaterials, medical images are easily and rapidly shared for analysis, facilitating automated, reproducible, and blinded biomarker extraction.
This transition to quantitative analysis is particularly evident in the study of AMD. For instance, non-neovascular (dry) AMD has been extensively evaluated using various imaging biomarkers, such as intraretinal hyper-reflective foci, complex drusenoid lesions, subretinal drusenoid deposits, and drusen burden.
While SD-OCT has traditionally described these features qualitatively, recent studies have demonstrated the predictive power of quantitative measures like ellipsoid zone integrity, sub-RPE compartment thickness, and automated drusen volume quantification.
These quantitative biomarkers have shown stronger associations with disease progression than qualitative features, particularly in predicting the development of geographic atrophy. This predictive power of AI extends to diabetic retinopathy as well. In DR, quantitative measures like central subfield retinal thickness and retinal nerve fiber layer thickness have been linked to disease severity. Disruption of retinal inner layers has been associated with worse visual acuity, and its presence is highly specific for macular nonperfusion. Both DRIL and outer retinal disruption are linked to visual acuity in DR and diabetic macular edema.
Furthermore, morphological signs like hyperreflective foci, representing lipid extravasation and inflammatory cell aggregates, have emerged as potential biomarkers for monitoring inflammatory activity in diabetic eye disease. AI-powered segmentation and quantification of HRF can track changes in response to anti-VEGF and steroid injections.
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Enrollment of the right patients
Due to their complexity and scale, clinical trials, particularly Phase III trials, consume a significant portion of the budget required to bring a new drug to the market. However, the success rate for compounds entering clinical trials is dismal, with only about one in ten progressing to FDA approval. This high failure rate stems largely from ineffective patient recruitment, as each clinical trial has unique participant requirements, including eligibility criteria, disease stage, and specific sub-phenotypes.
Manual review of electronic medical records is time-consuming and prone to error, as staff must sift through vast amounts of data to identify eligible candidates.
AI can automate this process, rapidly analyzing medical imaging and extracting relevant information to determine patient eligibility. This reduces the burden on staff and allows for faster identification and enrollment of suitable participants, streamlining patient selection and ultimately leading to more efficient and successful clinical trials.
A targeted approach can dramatically improve recruitment efficiency by pinpointing ideal candidates and even revealing disease hotspots for geographically focused efforts.
In later phases of clinical trials (Phase II and III), AI-powered image analysis can also play a pivotal role. In ophthalmology, AI can analyze OCT scans to precisely quantify disease biomarkers, ensuring that the trial participants are those most likely to benefit from the investigated drug. This improves the success rate of trials and minimizes potential harm to patients who might not be suitable candidates.
AI-powered image analysis offers a crucial advantage: reducing variability in interpretation.
AI algorithms can standardize the imaging overview process by consistently identifying and quantifying key biomarkers, ensuring that different readers arrive at similar conclusions.
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Real World Evidence
Randomized controlled trials have long been the gold standard for evaluating the efficacy and safety of new therapies. However, controlled environments with strict inclusion and exclusion criteria may not fully reflect the diversity and complexity of real-world patient populations.
Real-world data (RWD) that is collected during routine clinical practice can provide critical insights into disease biomarkers and significantly impact the drug development process. This RWD can be transformed into real-world evidence (RWE) when appropriately analyzed.
RWE is bridging the gap between clinical trials and real-world patient care, providing a more representative view of disease progression, treatment patterns, and long-term outcomes in everyday clinical settings.
In ophthalmology, RWE already has played a crucial role in understanding the impact of anti-VEGF therapies for neovascular age-related macular degeneration. While RCTs demonstrated the initial efficacy of these treatments, RWE studies have shown variations in real-world outcomes and highlighted the need for continued and higher than previously provided treatment frequency and new treatment regimens such as treat-and-extend.
Big data, encompassing a vast array of structured and unstructured information, is now an integral part of modern medicine, including ophthalmology. By integrating RWE with traditional clinical trial data, researchers can better understand how a drug performs in the real world and conduct more pragmatic clinical trials designed to evaluate treatments in real-world settings with broader patient populations, ultimately accelerating the development of safer and more effective therapies.
FDA-cleared AI for OCT analysis
The future of ophthalmic drug trials
The global AI-in-drug discovery market is poised for significant growth, driven by advancements in machine learning, natural language processing, and deep learning.
Artificial intelligence has the potential to significantly impact drug discovery by enabling more creative and efficient experimentation. It can also reduce the cost and time associated with failures throughout the drug development process. By identifying promising leads earlier and eliminating less viable options, AI can streamline each stage, potentially halving the total cost of a single project.
Advanced simulation and modeling techniques powered by AI are also poised to revolutionize our understanding of disease mechanisms and accelerate the discovery of new drugs.
The promising potential of AI in clinical trials extends to the proactive identification and mitigation of adverse events, enhancing patient safety and reducing trial risks. Data-driven AI tools are poised to revolutionize the entire clinical trial process, from design to execution. By streamlining patient recruitment, continuously monitoring participants, and facilitating comprehensive data analysis, AI can increase trial success rates, improve adherence, and yield more reliable endpoints.
The future of ophthalmic drug trials is here, and it’s powered by AI. By embracing this technology, researchers and clinicians can unlock new possibilities for preventing blindness and preserving vision for future generations.
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AI-assisted OCT in Eye Care: Attracting and Educating Patients
Maria Znamenska
26.04.20239 min readToday patients are curious about AI, but they may also have some reservations. Researches suggest a cautious attitude towards autonomous AI in healthcare, but what happens when AI becomes a collaborative tool, assisting eye care professionals in educating and treating patients? This shift in focus can significantly affect patients’ comfort levels and acceptance of AI.
Patients have some concerns about AI in healthcare. Let’s delve into the patient perspective and discover how addressing these apprehensions and implementing AI-assisted OCT in eye care can lead to a better understanding of the technology and, ultimately, healthier outcomes.
FDA-cleared AI for OCT analysis
How do patients react to AI?
Interestingly, while surveys extensively document how eye care professionals feel about and interact with AI, the perspectives of the main beneficiary—the patient—remain less understood. The limited research available indicates mixed feelings towards this technology. Few studies examine patient attitudes toward AI in healthcare and eye care, suggesting a degree of caution.
However, these studies have focused on scenarios where AI fully replaces human healthcare providers. Patients demonstrated significant resistance to medical AI in these cases driven mostly by “uniqueness neglect” – concern that AI providers are less able than humans to account for a person’s unique characteristics and circumstances.
For example, in the “Resistance to Medical Artificial Intelligence” study, participants demonstrated less interest in using a stress assessment and were willing to pay less for it when administered by an automated system rather than a human, even with equivalent accuracy. Additionally, participants showed a weaker preference for a provider offering clearly superior performance if it was an AI system.
A survey of 926 patients reveals a mix of attitudes towards AI in healthcare but also gives us clues to understand the reasons behind it. While a majority believe AI could improve care, there’s also a significant undercurrent of caution:
- Desire for Transparency: Over 95% of respondents felt it was either very or somewhat important to know if AI played a significant role in their diagnosis or treatment.
- Unexplainable AI = Uncomfortable: Over 70% expressed discomfort with receiving an accurate diagnosis from an AI system that couldn’t explain its reasoning. This discomfort was more pronounced among those unsure about AI’s overall impact on healthcare.
- Application Matters: Patients were more comfortable with AI for analyzing chest X-rays than for making cancer diagnoses.
- Minority Concerns: Respondents from racial and ethnic minority groups expressed higher levels of concern about potential AI downsides, such as misdiagnosis, privacy breaches, reduced clinician interaction, and increased costs.
These findings highlight the importance of being transparent with patients about how AI is used in their care. Explaining the role of AI and reassuring patients that it’s a tool for assisting your clinical judgment (not replacing it) will be essential. Additionally, being mindful of potential heightened concerns among minority patients is crucial for providing equitable care.
A study solely focused on overcoming patients’ resistance to AI in healthcare found that demonstrating social proof (like highlighting satisfied customer reviews) increased trust in AI-involved help.
The team has identified several additional strategies for reducing patient apprehension of AI recommendations. One effective approach is to emphasize AI’s collaborative nature, where a human doctor endorses recommendations. This highlights AI as a tool to assist, not replace, physicians. Demonstrating AI capabilities through real-world examples where AI exhibits nuanced reasoning can also encourage greater reliance on the technology.
How to attract patients with AI in eye care
AI offers a powerful way to transform your practice and set yourself apart. It brings world-class diagnostic expertise directly to your community, potentially saving patients’ sight by catching eye diseases in their earliest stages. Here’s how to position AI for patients:
- Emphasize Early Detection
It brings world-class diagnostic expertise directly to your community, potentially saving patients’ sight by catching eye diseases in their earliest stages, including early signs of glaucoma, AMD, and many other pathologies that would often be invisible during a regular visit. Some retinal changes are so microscopic that they elude the human eye, making the program’s ability to detect tiny retinal changes invaluable. This makes AI a powerful tool during routine exams, potentially uncovering issues you may not even have been aware of as a patient.
- More time for personalized care
Patients expect personalized experiences, and AI empowers you to deliver exactly that. By analyzing each patient’s unique OCT image data, AI helps identify potential pathologies with greater accuracy.
Additionally, since AI acts as a meticulous assistant, double-checking your assessments and minimizing the risk of missed diagnoses, it frees up your time. This allows for more meaningful one-on-one conversations with patients, where you can explain their results and discuss the next steps, setting your practice apart regarding patient satisfaction.
- Your old good eye care professional, but with superpower
With AI-assisted OCT, you have the combined knowledge and experience of leading eye care specialists at your fingertips for every patient. This technology leverages massive datasets of medical images and clinical data meticulously analyzed by retinal experts during AI development. It is a valuable second opinion tool, helping you confirm diagnoses and identify subtle patterns the human eye might miss.
This offers your patients peace of mind – knowing their diagnosis has been informed by insights from a team of experts incorporated into the AI’s analysis.
It’s crucial to emphasize that AI will never replace the human touch. It’s a powerful tool that frees up your time for what matters most: building trust through personalized care and addressing patient concerns with empathy.
How to explain what AI is to patients
Patient understanding is vital for building trust with you and any technology you use. It is especially important when talking about a sophisticated instrument like AI.
For instance, we’ve found that patients sometimes struggle to understand how Altris AI, our AI-powered OCT analysis tool, works. We’ve crafted an explanation that helps them grasp the concept more quickly, covering how retinal specialists have taught the system to do its job, the AI’s role as a doctor’s help, and direct benefits for patients.
OCT scans provide incredibly detailed images of the retina, the important layer at the back of your eye. Eye doctors carefully analyze these scans to spot any potential problems. To make this process even more thorough, AI systems are now being used to assist with OCT analysis.
How does the system know how to do that? Real doctors have taught it. It works by first learning from thousands of OCT scans graphically labeled by experienced eye doctors.
The doctors analyzed images from real patients to detect and accurately measure over 70 pathologies and signs of pathology, including age-related macular degeneration and glaucoma, teaching the AI what to look for.
The system leverages a massive dataset of thousands of OCT scans collected from 11 ophthalmic clinics over the years. Carefully segmented and labeled by retinal professionals, these scans were used to train the AI. By analyzing each pixel of an image and its position relative to others, the AI has learned to distinguish between different biomarkers and pathologies.
The platform visualizes what is going on with the retina using color coding. This means that every problem on the OCT scan will be colored differently and signed so you will be able to understand what is going on with your retina.
As with any innovative tool, Altris AI partially automates some routine tasks, so clinicians have more time for what is important: talking to patients, learning more about their eye health, and providing treatment advice.
Why does this matter to you? Altris AI can help spot even the tiniest changes in your eyes, leading to earlier treatment and better protection of your eye health. Knowing a smart computer system is also double-checking your scans gives both you and your doctor extra confidence in the results.
With the help of Altris AI, you will be able to see how the treatment affects you. For example, if you have fluid in the retina (that is not supposed to be there), you will be able to see if its volume is decreasing or increasing with the help of color coding.
Altris AI was designed by eye doctors for eye doctors. It’s a tool to help us take even better care of patients.
AI color coding in eye care: how learning about diagnosis influences treatment adherence
Patient-centered care, a key principle outlined by the Institute of Medicine, emphasizes patient education and involvement in decision-making. This is vital in ophthalmology, where insufficient patient engagement can lead to irreversible blindness.
Research specifically targeting the ophthalmology patient population, which often includes older and potentially visually impaired individuals, reveals a clear preference for individualized education sessions and materials endorsed by their eye care provider.
According to Wolters Kluwer Health, patients crave educational materials from their providers, yet only two-thirds actually get them. This leaves patients searching for information, potentially exposing them to unreliable sources.
Providing clear, accessible patient education is crucial to ensure understanding and treatment adherence.
The human brain’s ability to process visual information far surpasses its speed with text, making visual aids a powerful tool for health education. In the field of eye care, this becomes even more critical. Patients often experience vision difficulties, potentially hindering their ability to absorb written materials. Providing clear visual representations of diagnoses can significantly improve patient understanding and compliance.
A study shows a strong preference for personalized educational materials, especially among older visually impaired patients. Seeing photos of their condition, like glaucoma progression, builds trust and reinforces the importance of treatment recommendations.
Surveying eye care professionals specializing in dry eye disease revealed a strong emphasis on visual aids during patient education. Photodocumentation is a favored tool for demonstrating the condition to asymptomatic patients, tracking progress, and highlighting the positive outcomes of treatment.
A visual approach is particularly motivating for patients. It provides tangible evidence of the benefits of their treatment investment, allowing for a deeper understanding of the “why” behind treatment recommendations and paving the way for ongoing collaboration with the patient.
Understanding complex eye conditions can be challenging for patients. Altris AI aims to bridge this gap by using color coding for pathologies and their signs, severity grading, and pathology progression over time within its OCT analysis.
With Altris AI, scans are color-coded for instant interpretation: all the detected pathologies are painted in different colors, highlighting the littlest bits that the unprepared eye of a patient would miss otherwise.
This easy-to-understand visual system empowers patients. They can clearly see what’s happening within their eyes and track the progress of any conditions during treatment.
Eye care professionals are enthusiastic about its impact.
The power of visuals goes beyond understanding a diagnosis. When patients see the interconnected structures that make up their vision, they gain a deeper appreciation for its complexity and the importance of preventative care. This understanding fosters a true partnership between doctor and patient, where the patient is an active, informed participant in their own eye health.
Summing up
FDA-cleared AI for OCT analysis
Patients are increasingly curious and open to AI’s potential in general healthcare and eye care in particular, but naturally, some questions and hesitation remain. They stem from a desire to ensure AI considers their individual needs. By addressing these concerns proactively and clarifying when and how AI is used in their care, emphasize the collaborative doctor-AI model—highlight that YOU review and endorse all AI recommendations.
You can successfully integrate this powerful technology into your practice by addressing patient concerns with empathy and highlighting AI’s benefits. This leads to a more informed and empowered patient experience, improving understanding, adherence to treatment, and, ultimately, better health outcomes.
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Early Glaucoma Detection Challenges and Solutions
Maria Martynova
09.04.202310 min readGlaucoma’s silent progression highlights a challenge we all face as clinicians. Millions of individuals remain at risk for irreversible vision loss due to undiagnosed disease – 50% or more of all cases. This emphasizes our responsibility to enhance early detection strategies for this sight-threatening condition.
Existing clinical, structural, and functional tests depend on both baseline exams and the need to observe changes over time, delaying the assessment of treatment effectiveness and the identification of rapid progression.
In this article, we will consolidate our knowledge as eye care professionals about Glaucoma, explore current clinical detection practices, and discuss potential areas to optimize early Glaucoma detection.
FDA-cleared AI-powered OCT Glaucoma Risk Assessment
What we know about Glaucoma
Glaucoma is a complex neurodegeneration fundamentally linked to changes occurring in two locations: the anterior eye (elevated pressure) and the posterior eye (optic neuropathy). Factors influencing glaucoma development include:
- age,
- ethnicity,
- family history,
- corneal thickness,
- blood pressure,
- cerebrospinal fluid pressure,
- intraocular pressure (IOP),
- and vascular dysregulation.
Early stages of Glaucoma are often asymptomatic, highlighting the importance of comprehensive eye exams, even without apparent vision issues. Current diagnostic criteria are insufficient and lack markers of early disease.
Glaucoma is broadly divided into primary and secondary types, with primary open-angle Glaucoma (POAG) representing approximately three-quarters (74%) of all glaucoma cases.
Primary glaucomas develop independently of other eye conditions, while secondary glaucomas arise as a complication of various eye diseases, injuries, or medications.
POAG is characterized by an open iridocorneal angle, IOP usually > 21 mmHg, and optic neuropathy. Risk factors include age (over 50), African ancestry, and elevated IOP. While IOP is a significant factor, it’s unpredictable – some patients with high IOP don’t develop Glaucoma, and some glaucoma progresses even at normal IOP.
Normal-tension Glaucoma (NTG) shares POAG’s optic nerve degeneration but with consistently normal IOP levels (<21mmHg). Vascular dysregulation and low blood pressure are risk factors. While rarer than POAG, IOP lowering can still be beneficial.
Primary Angle-Closure Glaucoma (PACG) is caused by narrowing the iridocorneal angle, blocking aqueous humor flow. More common in East Asian populations, it can be acute (severe symptoms, IOP often > 30mmHg) or chronic.
Secondary glaucomas are caused by underlying conditions that elevate IOP. Examples include pseudoexfoliative, neovascular, pigmentary, and steroid-induced Glaucoma.
Age is a central risk factor for glaucoma progression, linked to cellular senescence, oxidative stress, and reduced resilience in retinal ganglion cells and the trabecular meshwork. Intraocular pressure (IOP) remains the most significant modifiable risk factor. Understanding individual susceptibility to IOP-related damage is crucial. Existing IOP-lowering treatments have limitations in both efficacy and side effects.
Glaucoma has a strong genetic component, with complex interactions between genes, signaling pathways, and environmental stressors. For now, we know that mutations in each of three genes, myocilin (MYOC), optineurin (OPTN), and TANK binding kinase 1 (TBK1), may cause primary open-angle Glaucoma (POAG), which is inherited as a Mendelian trait and is responsible for ~5% of cases (Mendelian genes in primary open-angle Glaucoma).
More extensive effect mutations are rare, and more minor variants are common. Genome-wide association studies (GWAS) reveal additional genes potentially involved in pressure sensitivity, mechanotransduction, and metabolic signaling.
Recent research also suggests a window of potential reversibility even at late stages of apoptosis (a programmed cell death pathway, which is likely the final step in RGC loss). Cells may recover if the harmful stimulus is removed. This offers hope that dysfunctional but not yet dead RGCs could be rescued.
The Challenges of Early Glaucoma Detection
One of the most insidious aspects of Glaucoma is its largely asymptomatic nature, especially in the early stages. This highlights the limitations of relying on symptoms alone and underscores the importance of proactive detection strategies.
Relying on intraocular pressure (IOP) as a stand-alone glaucoma biomarker leads to missed diagnoses, especially in patients with normal-tension Glaucoma. Structural changes, such as optic disc cupping, also lack the desired sensitivity and specificity for early detection.
Optic nerve head evaluations remain subjective, with studies indicating that even experienced ophthalmologists can underestimate or overestimate glaucoma likelihood.
According to the research, even experienced clinicians can have difficulty evaluating the optic disc for Glaucoma. Both trainees and comprehensive ophthalmologists have been found to underestimate glaucoma likelihood in approximately 20% of disc photos. They may also misjudge risk due to factors like variations in cup-to-disc ratio, subtle RNFL atrophy, or disc hemorrhages.
Current Glaucoma Diagnosis in Clinical Practice
Eye care professionals typically encounter new glaucoma diagnoses in one of two ways:
- Firstly, during routine preventive examinations. A patient may come in for various reasons, including work requirements, and be found to have elevated intraocular pressure. This finding prompts further evaluation, potentially leading to a glaucoma diagnosis.
- Secondly, it is a finding in older patients (often over 50-60). A patient may present with significant vision loss in one eye, and examination reveals Glaucoma. Unfortunately, vision loss at this stage is often irreversible.
Alternatively, a patient may seek care for an unrelated eye problem. During the comprehensive examination, the eye care professional may discover changes suggestive of Glaucoma.
As it is statistically prevalent, we most often work with primary Glaucoma, where no other underlying eye diseases are present. Functional changes, specifically as seen on visual field testing, help diagnose and stage glaucoma. During the test, a patient indicates which light signals are visible within their field of vision, building a map of each eye’s visual function.
Vision Field Test for Glaucoma Detection
The optic nerve (a nerve fiber layer of the retina consisting of the axons of the ganglion neurons coursing on the vitreal surface of the retina to the optic disk) transmits visual information from the retina to the brain. Each part of the retina transmits data via a corresponding set of fibers within the optic nerve. Damage to specific nerve fibers results in loss of the associated portion of the visual field.
Challenges with this test include its complexity, especially for older patients, and its subjective nature.
Changes in the visual field determine glaucoma severity. These changes indicate how much of the visual field is already damaged and which parts of the optic nerve are compromised. We call these ‘functional changes‘ as they directly impact visual function.
Fundus photo for Glaucoma detection
Alongside functional changes, Glaucoma causes visible structural changes in the optic nerve that can be observed during a fundus examination. The optic nerve begins at a point on the retina where all the nerve fibers gather, forming the optic disc (or optic nerve head). The nerve fibers are thickest near the optic disc, creating a depression or ‘hole’ within it. As Glaucoma progresses, this depression deepens due to increased pressure inside the eye. This pressure causes mechanical damage to the nerve fibers, leading to thinning and loss of function.
Another crucial area on the retina is the macula, which contains a high density of receptors responsible for image perception. While the entire retina senses images, the macula provides the sharpest, clearest vision. We use this area for tasks like reading, writing, and looking at fine details. Therefore, the damage to the macular area significantly impacts a patient’s visual quality and clarity. Nerve fibers carrying visual information from this crucial region are essential when evaluating the visual field. We prioritize assessing the macula’s health because it directly determines the quality of a patient’s central vision.
Unfortunately, even if the macula is healthy, damage to the nerve fibers transmitting its signals will still compromise vision.
Glaucoma OCT detection
The most effective way to get information about nerve states is OCT, which allows us to penetrate deep into the layers to see the nerve fiber layer separately, making it possible to assess the extent of damage and thinning to this layer in much more detail.
The Glaucoma OCT test provides valuable information about ganglion cells. These cells form the nerve fiber layer and consist of a nucleus and two processes. The short process collects information from other retinal layers, forming the inner plexiform layer. The ganglion cell layer comprises the cell nuclei, while the long processes extend out to create the nerve fiber layer.
Damage to the ganglion cells or their processes leads to thinning across these layers, which we can measure as the thickness of the ganglion cell complex. OCT often detects these microscopic changes before we can see them directly. This enables the detection of structural changes alongside the functional changes observed with standard visual field tests.
Ideally, OCT would be more widely accessible, as the human eye cannot detect early changes. However, how often a patient undergoes OCT depends on various factors. These include the doctor’s proficiency with the technology, the patient’s financial situation (as OCT can be expensive), and the overall clinical picture.
Ways to Enhance Early Glaucoma Detection
We surveyed eye care specialists, and there was a strong consensus that the most efficient ways to boost early glaucoma detection are regular eye check-ups (47%) and utilizing AI technology (40%). Educating patients was considered less significant (13%).
AI as a second opinion tool
AI offers valuable insights into glaucoma detection, analyzing changes that may not be visible to the naked eye or even on standard OCT imaging.
The Altris AI Early Glaucoma Risk Assessment Module specifically focuses on analyzing the OCT ganglion cell layer, measuring its thickness, and identifying any thinning or asymmetry. These measurements help determine a patient’s glaucoma risk. If the ganglion cell complex has an average thickness and is symmetrical throughout the macula, the module will assign a low probability of Glaucoma.
Asymmetries or variations in thickness increase the calculated risk, indicated by a yellow result color. Glaucoma GCC is often characterized by thinning or asymmetry, suggesting glaucomatous atrophy, indicating a high risk, and triggering a red result color.
Changes are labeled as ‘risk’ rather than a diagnosis, as other clinical factors contribute to a confirmed glaucoma diagnosis. Indicators of atrophy could also signal different optic nerve problems, such as those caused by inflammation, trauma, or even conditions within the brain.
It’s crucial to remember that AI ganglion cell layer OCT detection tools like this are assistive – they cannot independently make a diagnosis. Similarly, while helpful in assessing risk, they cannot completely rule out the possibility of developing a disease. This limitation stems from their reliance on a limited set of indicators. Like other technical devices, the module helps flag potential pathology but does not replace the clinician’s judgment.
AI can be incredibly valuable as a supplemental tool, especially during preventive exams or alongside other tests, to catch possible early signs of concern. However, medicine remains a field with inherent variability. While we strive for precise measurements, individual patients, not just statistical averages, must be considered.
Therefore, it is unrealistic to expect devices to provide definitive diagnoses without the context of a complete clinical picture.
Public Health Education
The asymptomatic nature of Glaucoma in its early stages, paired with limited public awareness, creates a fundamental barrier to early detection.
For example, 76% of Swiss survey respondents could not correctly describe Glaucoma or associate it with eye health.
A Canadian study similarly shows that less than a quarter of participants understand eye care professionals’ roles correctly and that most people are unaware eye diseases can be asymptomatic.
Crucially, these studies also found a strong desire across populations for more information about eye care, including Glaucoma (e.g., 97% of Swiss respondents agreed the public lacks knowledge, and 71% want more information). This indicates a receptive audience for targeted education initiatives.
Health education programs, like the USA EQUALITY study, demonstrate the potential to address this challenge. This study combined accessible eye care settings with a culturally sensitive eye health education program, targeting communities with high percentages of individuals at risk for Glaucoma.
Participants showed significant improvements in both glaucoma knowledge (a 62% increase in knowledge questions) and positive attitudes toward the importance of regular eye care (52% improvement).
These results show us that improving glaucoma detection involves more than medical tools. Successful education strategies should prioritize community outreach, partnering with community centers, primary care clinics, and local organizations to reach those lacking access or awareness of regular eye care.
Information about Glaucoma must be presented clearly and accessible, focusing on the basics—what Glaucoma is, its risk factors, and the importance of early detection. Addressing common misconceptions, such as the belief that Glaucoma can’t be present if vision is good, is crucial, as is targeting high-risk groups, including older adults, those with a family history of Glaucoma, and certain ethnicities.
Screening Programs and Regular visits
Community-based studies consistently demonstrate the benefits of targeted screening programs for early glaucoma detection in high-risk populations.
These programs are essential, as traditional glaucoma screening methods often miss individuals with undetected disease.
The USA Centers for Disease Control and Prevention (CDC) funded SIGHT studies focused on underserved communities, including those in urban areas with high poverty rates (MI-SIGHT, Michigan), residents of public housing and senior centers (NYC-SIGHT, New York), and the rural regions with limited access to specialist eye care (AL-SIGHT, Alabama). These programs successfully reached populations who often don’t have regular eye care.
Notably, the results across all three studies demonstrate the effectiveness of targeted programs – approximately 25% of participants screened positive for Glaucoma or suspected Glaucoma.
The SIGHT studies recognize that screening is just the first step, highlighting the importance of follow-up care, testing ways to improve follow-through, using strategies like personalized education, patient navigators, financial incentives, and providing free eyeglasses when needed.
Summing up
FDA-cleared AI-powered OCT Glaucoma Risk Assessment
Glaucoma’s insidious nature demands better early detection strategies. While existing methods are essential, we must also invest in new technologies like AI, enhance public health education about Glaucoma, and focus on targeted screening within at-risk populations. Combining these approaches can protect sight and reduce the burden of glaucoma-related blindness.
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Effective Eye Care Innovation: Altris AI for the Eye Place
Altris AI
1 min.The Client: the Eye Place is an optometry center in Ohio, the United States. It is a renowned center that provides comprehensive eye examinations, infant and pediatric eye care, emergency care, LASIK evaluations, and cataract assessment. They offer precise personalized care plans to better treat and prevent ocular disease and chronic illness. Scott Sedlacek, the optometry center owner, is an experienced OD, an American Optometric Association member, and a true innovator who implemented AI for OCT in the optometry practice among the first in the USA.
The Problem: The Eye Place owner has always been searching for innovations to transform the center making it truly digital. The aim of the innovation was also to augment the analysis ability of the optometry specialists using it, while allowing for better visualization of the retinal layers affected for doctors and patients.
The Solution: The Altris AI system was introduced in the Eye Place and it transformed the practice making it more efficient. Scott Sedlacek, the owner of the practice admits that:
“We are one of the first Optometry offices with this AI technology. It is amazing at detecting and defining pathology in the 3D digital images I take with my Topcon Maestro2 OCT. We use Image Net6 software to export Dicom files to Altris AI. It’s fast and easy. If you want the right diagnosis, right away, this is the way to go.
I’ve been using this technology on every patient every day since the beginning of January 2024. There is no other technology in my 25 years being an optometrist that was easier to implement and more impactful immediately.”
FDA-cleared AI for OCT analysis
ROI of the AI for OCT scan analysis
Many eye care specialists worry about the ROI of Altris AI: will the system pay off? After all, it is an investment. That is the experience of Scott, the owner of the Eye Place:
“Altris AI identified and described pathology that I could not. Early detection changes the treatment from doing nothing to something. Also, Altris AI described something that I thought was worse than it was. Saved me from over-referring. Patients love to see the color-coded images which help as an educational tool and get buy-in on the treatment plan which helps compliance. There is a wow factor for me and my patients that sets your practice apart from the others.”
Effective Eye Care Innovation: What Else?
Apart from AI for OCT analysis, the Eye Place utilizes advanced technology for diagnostics.
- For instance, 3D OCT equipment is a highly advanced screening system that checks for serious conditions such as glaucoma, diabetes, macular degeneration, vitreous detachments, and more. Using this technology we can simultaneously take a digital photograph and a 3-D cross-section of the retina.
- Additionally, AdaptDX Pro can detect macular degeneration earlier than by any other means.
- Cognivue Thrive is a personalized, consistent, and reliable way to receive an overall screening of brain health.It is interactive, non-invasive, self-administered, secure, and confidential. It is a five-minute screening for patients of all ages, and you get immediate results in a simple 1-page report.
These are just some examples of innovative tools that optometry centers can use to automate and improve the level of diagnostics. If you want to imagine how Optometry Centers might look like in 2040, here is the article for you. The future is here, and those centers that digitalize have more chances of winning the competition and the hearts of the clients, much like the Eye Place which is highly appreciated by patients.
As you see, effective eye care innovations are an integral part of the work of the Eye Place which is why Artificial Intelligence for OCT analysis was seamlessly integrated into the workflow of the optometry center.
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Will AI have a Positive Effect on Eye Care Specialists?
Maria Martynova
18.03.202313 min readWill AI improve your practice or it’s another hype topic that will vanish like NFT or VR glasses?
This article examines present AI’s impact on eye care specialists, exploring its promises and challenges. To gain a realistic view, we surveyed eye care specialists on their experiences and expectations of this topic.
Let’s start with what has already been implemented in eye care and the results we can see already.
FDA-cleared AI for OCT Analysis
AI in Eye Care Industry: Current Status
Disease screening: DR, AMD, and rare pathologies & biomarkers
A 2022 study by the University of Illinois showed that eye care specialists mostly see AI helping with disease screening, monitoring, and patient triage tasks. Notably, a significant increase in willingness to incorporate AI in practice has emerged after the COVID-19 pandemic, presumably due to a need for remote consultations.
The growing interest in AI for disease screening and monitoring coincides with the development of sophisticated AI systems. Due to their significant causes of visual impairment, Diabetic Retinopathy and AMD are the primary targets for AI screenings.
With over 422 million people worldwide affected by diabetic retinopathy and an estimated 80 million suffering from age-related macular degeneration, the workload on eye care specialists is immense. Unsurprisingly, most AI-powered screening solutions focus on helping clinicians with these diagnoses.
AI algorithms are trained to recognize DR-related alterations on images: hemorrhages, exudates, and neovascularization. AI also offers significant advancements in Age-related Macular Degeneration screening. Algorithms accurately segment data in OCT scans, helping assess retinal structures and quantify fluids during treatment. Trained models predict disease progression risks and analyze treatment responses.
AI in eye care can segment retinal structures to distinguish between normal retina scans and pathology on OCT, detect atrophic changes, and follow all alterations over time. It can even highlight rare inherited retinal dystrophies. For example, Altris AI is trained to recognize Vitelliform dystrophy and Macular telangiectasia type 2.More Efficient Patient Triage
The number of eye scans clinicians are performing is growing at a pace much faster than human experts are able to interpret them. This delays the diagnosis and treatment of sight-threatening diseases, sometimes with devastating results for patients.
Our recent survey showed that among more than 1000 participating eye care specialists, 40% have more than 10 OCT exams daily. Meanwhile, 35% of eye care specialists have 5-10 OCT daily examinations. Unfortunately, more patients per day mean an increased risk that specialists may miss some minor, rare, or early conditions.
AI systems can quickly triage scans based on severity. Prioritized urgent cases can be flagged for immediate attention. Healthy patients can be monitored without urgency.
This ensures patients with time-sensitive conditions get the care they need, while less urgent cases receive a timely but less immediate review.
Optometrists can use AI systems to specify the need to refer patients based on eye image analysis.
Another advantage of AI used as a “copilot” is its continuous improvement. Providers that create such systems usually integrate new data and research findings into algorithms, resulting in an ever-evolving resource for eye care specialists.
In other words, the accuracy of the patients’ triage will get better and better with the data.
Early Glaucoma Detection
Glaucoma is a leading cause of vision-related morbidity worldwide. Although blindness is the most feared outcome, even mild visual field loss may harm the quality of life.
In a way, glaucoma is one of the most challenging eye diseases that specialists must treat; with most eye problems, the patient comes when something is wrong. Glaucoma, however, has no symptoms until it is advanced, and the damage can not be reversed.
One common reason glaucoma is not diagnosed early is the inability to recognize glaucomatous optic disc and RNFL damage. Ophthalmologists often rely primarily on intraocular pressure and visual fields and not on the appearance of the optic disc.
Combining optical coherence tomography imaging and artificial intelligence, Altris AI offers a solution to the problem. The platform performs Ganglion Cell Complex asymmetry analysis on OCT scan that categorizes the risk of developing glaucoma. Glaucoma Early Risk Assessment Module can help decrease the number of false-positive referrals and increase the standard of care by supporting early diagnosis to improve patients’ prognosis.
Better Education for Patients
Eye care specialists don’t always have time to explain to patients what is going on with their eye health.
Artificial intelligence can easily perform this task. AI systems will also enhance eye care education, offering innovative and immersive learning experiences: with the help of color-coding, user-friendly reports, and chat bots.
AI-generated OCT reports can propel patient education and engagement. By translating complex medical data into clear, visual formats, AI can help understand patients’ diagnoses, significantly improving treatment adherence and fostering greater patient loyalty.
For example, Altris AI employs smart reports with color-coded segmentation of pathologies that are easy for clinicians and their patients to understand.
When patients fully grasp the nature of their eye conditions and track therapy progress, they are far more likely to prioritize annual checkups and actively engage in their care.
Teleoptometry and teleophthalmology
The COVID-19 pandemic has accelerated the adoption of telemedicine, especially in the image-rich field of ophthalmology.
In recent years, many digital home measurement tests have been introduced. These include home-based and smartphone/tablet-based devices, which are cost-effective in specific patient cohorts.
One example is an artificial intelligence-enabled program for monitoring neovascular Age-related Macular Degeneration (nAMD) that uses a home-based OCT device. Patient self-measurements from home have proved to be a valuable adjunct to teleophthalmology. In addition to reducing the need for clinical visits, they serve as a collection of high-quality personal data that can guide targeted management.
Currently, most commercial providers of telemedical services and devices use artificial intelligence. However, these services are not autonomous. AI works simultaneously with so-called “backup” ophthalmologists. If a finding is unknown or unclear to the artificial intelligence, an ophthalmologist reads the image.
Non-medical AI: General Workflow Enhancements
COVID-19 made it crystal clear that healthcare worldwide has a full spectrum of problems, such as staffing shortages, fragmented technologies, and administrative complexities. So, the AI boom three years after the pandemic has come timely and handy.
Intelligent algorithms can solve the mentioned issues. For example, generative AI can enable easier document creation by digesting all types of reports and streamlining them. It can also ease the administrative workload for short-staffed clinicians (the average US nurse spends 25% of their work time on regulatory and administrative activities).
Probabilistic matching of data across different databases, typical for Machine Learning, is another technology that can take a burden off staff about claims and payment administration.
Patient engagement and adherence also can benefit from the technology. Providers and hospitals often use their expertise to develop a plan to improve a patient’s health, but that frequently doesn’t matter as the patient fails to make the behavioural adjustment. AI-based capabilities can personalize and contextualize care, using machine learning for nuanced interventions. It can be messaging alerts and targeted content that provokes actions at needed moments or better-designed ‘choice architecture’ in healthcare apps.
Another side of the coin: AI for OCT limitations
When discussing AI in eye care, it’s essential to recognize that AI is a tool. Like any tool, it is neutral. So, its effectiveness and potential for unintended consequences hinge not only on the quality of its design and the data used to train it but also on the expertise of the healthcare professionals interpreting its output. Here are some of the challenges to keep in mind when working with AI.
AI is fundamentally limited by the datasets used for training. An outsized amount of images can slow training and lead to overfitting, while a lack of demographic diversity compromises accuracy.
One challenge facing AI implementation in medicine is the interdisciplinary gap between technological development and clinical expertise. These fields are developing separately and usually do not intersect. Therefore, cross-collaboration can suffer because tech experts may not understand medical needs, and clinicians may not have the technical knowledge to guide AI development effectively.
So, a successful AI solution requires bridging this breach to ensure AI solutions are grounded in medical realities and address the specific needs of clinicians (Clinical & Experimental Ophthalmology, 2019).
The commercialization of AI will also pose future issues. Trained models will likely be sold with and for implementation with certain medical technologies. Additionally, if AI does improve medical care, it will be essential to pass those improvements on to those who cannot afford them.
Overreliance on the technology can also be a problem.
AI is a tool, like any other equipment in the clinical environment. Decision-making is always on the side of an eye care practitioner who has to take into account many additional data: clinical history, other lab results, and concomitant diseases in order to make a final diagnosis.
And, of course, there are ethical dilemmas. Many practical problems can be solved relatively easily – secure storage, anonymization, and data encryption to protect patient privacy. However, some of them need a whole new field of law. The regulations surrounding who holds responsibility in case of a misdiagnosis by AI is still a significant question mark. Since most current AI algorithms diagnose not so many diseases, there is room for error by omission, and a correct AI diagnosis is not a comprehensive clinical workup.
Summing up
While AI in eye care isn’t without limitations and ethical considerations, its revolutionizing potential is hardly deniable. It already has proven itself working with disease screening, monitoring, and triaging, saving specialists time and improving patient outcomes. AI offers a “second opinion” for complex cases and expands access through telemedicine.
FDA-cleared AI for OCT Analysis
Yet, despite all its promises, the implementation of AI in practice should be seen as a new tool and technique, like the invention of the ophthalmoscope, IOL, OCT, and fundus camera. Optometrists and ophthalmologists will need to combine the best of their clinical skills and AI tools for best practices. Being an innovative tool does not make AI a magic wand, fortunately or not.
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Technologies in Optometry: Clare and Illingwort & Altris AI
Altris Team
3 min.3 min.The Client: Clare and Illingworth, renowned leaders in the field of optometry located in the UK.
The problem: The need to speed up the process of OCT interpretation and unburden the optometry team.
The Solution: Clare and Illingworth have embraced cutting-edge technology to enhance their Optical Coherence Tomography (OCT) analysis workflow. The introduction of Altris AI at this optometry center marks a significant milestone in their commitment to providing high-quality services to patients.
According to one of the owners of the optometry center, Richard, “We are adding a new OCT to one of our practices and will benefit from some extra support with AI to speed up the interpretation of results and assist the busy Optometry team.”
Altris AI, a leading provider of artificial intelligence solutions for healthcare, specializes in developing algorithms and software applications that augment medical imaging analysis. The integration of Altris AI into the British Optometry Center’s OCT workflow brings forth a host of advantages, revolutionizing the way eye conditions are diagnosed and managed.
FDA-cleared AI for OCT Analysis
Try it yourself in our Demo Account or get a Brochure
Technologies in Optometry and Ophthalmology: How AI Helps
One of the key benefits of Altris AI is its ability to automate and expedite the analysis of OCT scans. Traditionally, optometrists spent considerable time manually reviewing and interpreting OCT images.
FDA-cleared Altris AI is created to make the OCT workflow more effective
How does it work? Altris AI serves as a copilot, analyzing OCT scans in parallel to the eye care specialist. For instance, on this OCT scan, Altris AI detects Diffuse Edema, Floaters, Intraretinal Hyperreflective Foci, Posterior Hyaloid Membrane Detachment, RPE disruption, Shadowing, Hard Exudates, Intraretinal Cystoid Fluid.
- The classification in this case would be Diabetic Retinopathy.
With Altris AI, the process becomes significantly faster and more efficient. The AI algorithms can quickly analyze intricate details within the scans, providing clinicians with accurate and timely insights into the patient’s eye health.
Moreover, the use of Altris AI contributes to increased diagnostic accuracy. The algorithms are trained on vast datasets, learning to recognize subtle patterns and anomalies that may escape the human eye.
Thus, Altris AI recognizes 70+ retina pathologies and biomarkers, including DME, DR, GA, AMD, etc.
FDA-cleared AI for OCT Analysis
Try it yourself in our Demo Account or get a Brochure
Technologies in Optometry are paving the way to a new future where eye care specialists and AI will work together for better patient outcomes. AI will never be able to substitute eye care specialists because the final diagnosis must include clinical history, results of lab tests, and other diagnostic methods.
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OCT Layers of Retina
Maria Martynova
5 min.5 min.OCT Layers of retina: modern approach to segmentation
The knowledge about macular retinal layer thicknesses and volume is an important diagnostic tool for any eye care professional today. The information about the macular retinal layers often correlates with the evaluation of severity in many pathologies.
Manual segmentation is extremely time-consuming and prone to numerous errors, which is why OCT equipment manufacturers use automatic macular retinal layer thickness segmentation.
Test FDA-cleared AI for OCT analysis
Yet, retina layer segmentation in different OCT equipment manufacturers as well as in different OCT models varies significantly. It is sometimes difficult even for an experienced ECP to find the correlations and track the pathology dynamics. The normative bases refer only to the thickness of the entire retina, they are not related to segmentation. However, if the segmentation is performed incorrectly by the machine, it will lead to an incorrect calculation of the thickness of the retina or its layers, and then the assessment will be incorrect.
At Altris AI we aim to visualize retina layers for a more accurate understanding of pathological process localization. Such retina layers segmentation allows for defining the localization of the pathological process and tracing in dynamics the spread of the pathological process or the aftermath in the retina structure after its completion.
For instance, the EZ layer is important in terms of vision loss forecasting.
OCT Manufacturers & Retina Layers Analysis
From 2010 most eye care specialists have used the same OCT International Nomenclature for Optical Coherence Tomography. OCT equipment manufacturers rely on this nomenclature for retina layer thickness calculation and most ophthalmologists use it as well.
Taking into account retina structure, some layers can be united into complexes. For instance, the ganglion complex includes RNFL, ganglion cell layer & OPL.
Let’s take a look at various OCT equipment manufacturers and the way they perform retina layer segmentation analysis.
For instance, here is how Topcon Advanced Boundary Segmentation (TABSTM) automated segmentation differentiates between nine intraretinal boundaries:
- ILM
- NFL/GCL,
- GCL/IPL,
- IPL/INL,
- INL/OPL,
- ELM
- EZ
- OS/RPE
- BM
Zeiss CIRRUS uses two approaches to retina layer segmentation.
The existing segmentation algorithm (ESA) in CIRRUS estimates the positions of the inner plexiform layer (IPL) and outer plexiform layer (OPL) based on the internal limiting membrane (ILM) and retinal pigment epithelium (RPE). To improve the accuracy of the segmentation of these layers, a multi-layer segmentation algorithm (MLS) was introduced, it truly segments layers instead of estimating their position.
Heidelberg Engineering offers to learn about the following inner and outer retina layers on their website. There are 10 retina layers according to Heidelberg, and they are the following:
- ILM
- RNFL
- GCL
- IPL
- INL
- OPL
- ONL
- ELM
- PR
- RPE
- BM
- CC
- CS
Why accurate retina layer segmentation is important?
Retina layers segmentation helps eye care professionals to understand which pathology to consider in the first turn. For instance, changes in RPE and PR signify the development of Macular Degeneration.
Often such changes can also inform eye care specialists about the development of pathologies that lead to blindness, such as glaucoma, AMD, and Diabetic Retinopathy.
- Early Glaucoma Detection
Historically, evaluation of early glaucomatous change has focused mostly on optic disk changes. Modalities such as optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy (HRT) or scanning laser polarimetry (GDx) with specially developed software algorithms have been used to quantitatively assess such changes. However, glaucomatous damage is primarily focused on retinal ganglion cells, which are particularly abundant in the peri-macular region (the only retinal area with a ganglion cell layer more than 1 layer thick), constituting, together with the nerve fiber layer, up to 35% of retinal macular thickness.
Therefore, glaucomatous changes causing ganglion cell death could potentially result in a reduction of retinal macular thickness. Indeed, by employing specially developed algorithms to analyze OCT scans, previous studies have reported that glaucoma, even during the early stage, results in the thinning of inner retinal layers at the macular region.
According to this study, the RNFL, GCL, and IPL levels out of all the retinal layers, the inner-most layers of the retina: the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL) show the best discriminative power for glaucoma detection. Among these, the RNFL around the circumpapillary region has shown great potential for discrimination. The automatic detection and segmentation of these layers can be approached with different classical digital image processing techniques.
Test FDA-cleared AI for OCT analysis
- Detection of AMD
This first population-based study on spectral-domain optical coherence tomography-derived retinal layer thicknesses in a total of ∼1,000 individuals provides insights into the reliability of auto-segmentation and layer-specific reference values for an older population.
The findings showed a difference in thicknesses between early AMD and no AMD for some retinal layers, suggesting these as potential imaging biomarkers. When comparing layer thicknesses between early AMD and no AMD (822 eyes, 449 participants), the retinal pigment epithelium/Bruch’s membrane complex demonstrated a statistically significant thickening, and photoreceptor layers showed a significant thinning.
- Detection of DR
The depth and spatially resolved retinal thickness and reflectance measurements are potential biomarkers for the assessment and monitoring of Diabetic Retinopathy, one of the key reasons for blindness around the globe.
For instance, this study confirmed that decreased RNFL thickness and increased INL/OPL thickness in diabetics without DR or with initial DR suggest early alterations in the inner retina. On the contrary, the outer retina seems not to be affected at the early stages of DM. Automatic intraretinal layering by SD-OCT may be a useful tool to diagnose and monitor early intraretinal changes in DR.
Conclusion:
Retina layer segmentation is crucial for the accurate detection of pathologies in the eye, especially in the field of ophthalmology and medical imaging. Here are several reasons why it is important:
Precise Diagnosis: Retina layer segmentation provides a detailed map of the different retinal layers, which helps in the precise diagnosis of various eye conditions. It allows clinicians to identify the exact location of abnormalities, such as cysts, hemorrhages, or lesions, within the retina.
Quantitative Analysis: It enables quantitative analysis of retinal structures. By measuring the thickness, volume, and other characteristics of specific layers, clinicians can assess the severity and progression of diseases like diabetic retinopathy, macular degeneration, and glaucoma.
Early Detection: Some retinal pathologies manifest in specific layers of the retina before becoming visible on a fundus photograph. Retina layer segmentation can help detect these changes at an early stage, potentially leading to earlier intervention and improved outcomes.
Treatment Planning: Knowing the precise location of pathologies within the retina’s layers can aid in the planning of treatment strategies. For example, in cases of macular holes or retinal detachment, surgeons can use this information to guide their procedures.
Monitoring Disease Progression: Retina layer segmentation is valuable for monitoring how retinal diseases progress over time. Changes in the thickness or integrity of specific layers can be tracked to assess the effectiveness of treatments or the worsening of conditions.
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Altris AI for Buckingham and Hickson Optometry, the UK
Altris Team
1 min.Business case: Altris AI for Buckingham and Hickson Optometrists
The Client: Buckingham and Hickson is a family-run optometry practice that was established in 1960 in the United Kingdom. The optometry practice offers a number of services:
- Wide range of spectacle frames and lenses.
- Contact lenses.
- Glaucoma referral refinement.
- Cataract choice referral.
- OCT examination.
- NHS and private eye tests.
See how it works
FDA approved AI for OCT scan analysis
The challenge: The optometry owners wanted to test how Artificial Intelligence can assist them in OCT examination or, to be more precise, in providing a second opinion for OCT scans.
OCT examination is one of the best retina diagnostics methods, however in many cases OCT scan interpretation can be really challenging for several reasons:
- Variability in Anatomy: There is significant natural anatomical variation among individuals. What may be considered normal for one person may be abnormal for another. Eye care specialists need to account for these variations when interpreting OCT scans, but this often requires years of experience.
- Various Eye Conditions: Eye care specialists use OCT scans to diagnose and monitor a wide range of eye conditions, including macular degeneration, diabetic retinopathy, and retinal detachment, among others. Each of these conditions can manifest in different ways on OCT scans, making interpretation challenging.
- Progression Monitoring: Ophthalmologists often use OCT to monitor disease progression and the effectiveness of treatment. Tracking subtle changes over time can be difficult, as it requires precise comparisons of multiple scans.
- Artifacts: OCT scans are susceptible to artifacts, such as shadowing, motion artifacts, and signal dropout, which can obscure or distort the image. Recognizing and mitigating these artifacts is essential for accurate interpretation.
- Experience and Training: Accurate interpretation of OCT scans in optometry and ophthalmology requires specialized training and experience.
- Evolving Technology: OCT technology continues to advance, introducing new techniques and capabilities. Staying current with these advancements and understanding their clinical implications is an ongoing challenge for ophthalmologists.
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Will Artificial Intelligence Replace Ophthalmologists & Optometrists?
Maria Znamenska
17.11.20228 min readBack in 2019, at the RANZCO World Science Congress, Peter van Wijngaarden, Deputy Director of the Center for Eye Research, claimed that the eye sector is one of the leading areas of medicine in terms of artificial intelligence (AI) implementation. According to RANZCO, AI systems are already achieving incredible results and, in some cases, can even rival eye care specialists.
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There are a lot of similar examples of AI misconceptions when famous professors and specialists in the field of ophthalmology made predictions that artificial intelligence is rapidly gaining strength in the eyecare industry. This gives rise to many myths and fears around the introduction of AI in clinical practice. More and more eye care professionals have faced the question: will artificial intelligence replace ophthalmologists and optometrists in the near future?
The increased attention to the issue of optometrists and ophthalmologists replaced by AI was also provoked by a World Economic Forum (WEF) report. According to this report, people can lose 85 million jobs by 2025 due to the shifting division of labor between people and machines.
In this post, we will discuss the top 5 AI misconceptions that are most often faced by the owners of ophthalmological clinics and optometry centers in order to dispel them once and for all.
Do AI algorithms work exactly like a human brain?
The concept of optometrists and ophthalmologists replaced by robots is gaining popularity. Nowadays, eye care specialists often discuss the potential of AI training in human cognitive skills. It is no longer just about the ability of AI to detect Diabetic retinopathy or interpret OCT scans with greater accuracy. The question is, will AI ever be able to replicate human consciousness? And can AI replicate how the human brain works?
What do we know about such models in different areas? AI systems are already demonstrating the work of some human cognitive functions. For example, AI models successfully compete with humans in computer games by gradually learning successful strategies. There is also an AI model which creates enjoyable melodic music.
However, optometrists and ophthalmologists replaced by AI still seem unrealistic. Even with the above examples mimicking some aspects of human behavior, an AI algorithm still needs to learn what empathy is. Artificial intelligence does not understand and cannot make sense of its surrounding, nor can it learn from its surrounding as humans do. The most famous example that confirms this inability of AI is Siri or Alexa. Voice assistants can set up appointments but give strange answers when the conversation goes differently than their scenario.
While the human brain inspires modern AI techniques such as neural networks (NNs), the structure of NNs architectures is not biologically realistic.
First of all, there is a set of qualities that ophthalmologists and optometrists use every day. It is empathy for the patient, as well as creativity, teamwork, and adaptability. These qualities help doctors provide effective care to their patients. It is unlikely that the machines will ever be able to work with children, older adults, or patients with specific disabilities on par with humans. In addition, any patient would like to hear the diagnosis or discuss a treatment plan with a doctor, not a machine.
Therefore AI algorithm can’t work like a human brain, and the scenario where artificial intelligence replace ophthalmologists and optometrists will never happen. Nowadays, there are no developments that would make us think that AI image interpretation will ever be able at least to repeat important qualities of eye care specialists.
Is today’s state of AI dangerous for humans?
Today, AI algorithms can interpret retinal images and distinguish pathological from non-pathological scans. However, not all attempts at AI implementation have succeeded as well. One of the most popular non-medical examples is Facebook. Some time ago, Facebook tried to identify relevant news for certain groups of users. But the automated process could not detect the difference between real and fake news. Russian hackers managed to trick the system and bypass automatic filters. They posted fake news, forcing the Facebook team to come back to human editors.
This is just one example of how security lags behind performance when humans rely on AI too much. Artificial intelligence is a great tool, but in most cases, its abilities only give reliable and the most accurate results in collaboration with eye care professionals. Although machines are designed by humans, they often can’t predict human behavior and don’t know how to cope with situations or clinical cases that go beyond the scope of the algorithm.
Therefore AI is not dangerous for humans when ophthalmologists and optometrists periodically control the work of algorithms and review how the machine works. This is the number-two reason why artificial intelligence replace ophthalmologists and optometrists is unrealistic.
Will AI ever be 100% objective?
To honestly answer the questions of will artificial intelligence replace ophthalmologists and optometrists and whether it is 100% objective, you need to understand that an AI system will only be as good as its inputs. By loading unbiased training datasets, engineers can create an AI system that makes unbiased decisions. However, in the real world, AI is unlikely ever to be 100% objective.
For example, many well-known companies, such as Amazon or Facebook, still struggle with the gender gap in hiring. Some time ago, Amazon used historical data from the past ten years to train its AI recruiting model. The algorithm was supposed to process data and candidates and free recruiters from the routine viewing of hundreds of CVs. However, soon Amazon team discovered that the data was biased against women. AI algorithm was trained by outdated information when the technology industry used to be dominated by men. Thus, the new recruitment system selected only male candidates. This forced Amazon to abandon the algorithm and re-open many recruiter positions.
In the field of ophthalmology, AI models can already accurately predict diabetes risk factors or potential vision loss from OCT images. So when will artificial intelligence replace ophthalmologists? In Altris, we are sure the algorithm will never achieve adequate objectivity, as it will always be limited by input data, whether demographics, gender, or age.
Now we know that AI can’t be 100% objective. Indeed, ophthalmologists and optometrists can’t match the ability of algorithms to detect pixel-level patterns among the millions of pixels in the OCT scan. However, only the cooperation of eye care specialists and a quality AI model working together will allow for more accurate detection of diseases. The combined efforts of AI management systems and eye care specialists can help achieve the desired 100%.
Can AI make it without eye care specialists?
Various articles have speculated on whether artificial intelligence replace ophthalmologists and optometrists, raising concerns about unemployment. However, this never corresponded to the actual state of affairs. Carl Benedikt Frey, an Oxford Martin Citi Fellow at Oxford University, reported that while 47% of jobs are at risk of automation, the risk for doctors is estimated at only 0.4%.
In addition, in his book “Humans Are Underrated”, Geoff Colvin states that the most valuable skill for ophthalmologists is the ability to sense the thoughts and feelings of patients who are losing sight.
Many patients complain about the lack of contact with the doctor. They admit that the treatment would be more comfortable if doctors devoted more time to live communication. This mainly applies to children and the elderly, who need a lot of attention from eye care specialists. Empathy and similar human qualities are not only an understanding of the patient’s feelings but also an adequate response to them. Thus, a future in which optometrists and ophthalmologists are replaced by AI seems senseless.
Professor Tien Yin Wong, medical director of the Singapore National Eye Centre, claimed that AI holds great promise for retinal screening. And while AI for OCT interpretation will radically change clinical practice, the technology’s more significant impact will be to complement and enhance human capabilities rather than replace them. The field of ophthalmology demonstrates that the combined efforts of scientists and machines are more effective than either could achieve individually.
Artificial intelligence for OCT interpretation is just a recommendation system for an eye care specialist. Often one pathological sign, for example, Cystoid macular edema (CME), or Intraretinal fluid, can indicate many diseases, like Wet AMD, DR, DME, CRVO, and others. That is why AI is only an assistant to a doctor, especially when it comes to rare pathologies.
All in all, AI for OCT interpretation is just a tiny part of clinical practice and can never work without humans. In order to detect the pathological signs and diagnose a disease correctly, an eye care specialist must perform different examination methods. Among these exams are visual acuity, intraocular pressure, ophthalmoscopy, and a basic patient examination, which includes anamnesis. Moreover, ophthalmologists and optometrists may also need to perform other visualization methods, like Fundus photography, FFA, or OCTA.
Will artificial intelligence replace ophthalmologists and optometrists?
This is probably one of the key AI misconceptions. Automation has led to a significant change in many industries, and ophthalmology is no exception. So when will AI replace eye care specialists? The answer is quite simple — AI will never replace them. It will eventually take over routine tasks, allowing the careers of ophthalmologists and optometrists to advance in new and exciting directions.
Automated interpretation of OCT scans will significantly increase the circulation of patients in ophthalmic clinics or optometry centers, which is commercially attractive. Moreover, with increasing life expectancy, and expanding the range and effectiveness of treatment options offered, a collaborative effort between ophthalmologists and AI will improve patient outcomes. This will make ECPs more efficient, freeing up time for human interaction between doctor and patient, which has been a cornerstone of medicine for decades.
In his concept of the future clinic, Eric Topol describes a system that the Altris AI team is already implementing today. AI labels, annotates and segments images. While ophthalmologists receive information about the structural and functional trends of the patient’s retina to track changes and develop a treatment plan. Altris AI allows ophthalmologists to focus on providing individualized care to each patient. Watch a short video by our team of how Altris AI assists ophthalmologists and optometrists with an interpretation.
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AI for Reading Centers: How it Boosts Workflow and Efficiency
Mark Braddon
05.10.20227 min readIn recent years reading centers have become an essential resource for facilitating imaging research in many fields, including clinical trials of ophthalmology drugs. And their importance will continue to grow.
Reading centers provide crucial information by evaluating images. That is why for conducting accurate clinical trials, they must hire ophthalmologists of high qualification. Moreover, to ensure consistent analysis, the materials that graders use for the research (be it fundus photographs, fluorescein angiograms, or OCT scans) must also undergo quality control. However, even such measures can’t completely exclude errors or biases.
Meanwhile, recent developments in the field of AI medical image analysis revolutionized the approach to clinical trials, which makes it possible to boost the workflow of reading centers. AI image analysis software works with thousands of images, efficiently providing the large amount of data needed to analyze the patient’s condition. In addition, evaluating images with AI is faster, cheaper, and more effective.
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In this article, we will discuss the top 5 benefits of AI medical image analysis software for reading centers and the way AI improves the image interpretation process.
Limitations of the manual evaluating procedure
Although several reading centers have already implemented AI for medical image analysis in their workflow, most organizations are far from evaluating automation and prefer classic image interpretation methods.
In most reading centers, ophthalmologists manually evaluate ocular images for drug safety studies, compile the images, and perform statistical analysis of the data. Research sizes for reading centers can range from 50 images to 3000 or more, and dozen of separate sets of images can be collected per research subject. Therefore reading centers have many obstacles to a quality evaluation process and accurate results.
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Large amount of images is hard to proceed
The vast number of images that need to be processed in the short term usually leads to the main problem for reading centers — most hire outsourced ophthalmologists to speed up the image grading and evaluation process. Outsourced specialists have different levels of qualification and different evaluating methods, which may lead to decreased accuracy. In addition, outsourced eye care specialists are not always interested in performing the work at the highest level.
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Human resources are expensive
Another limitation of the standard evaluating procedure is the high сost spent on ophthalmologists. Human resources are usually quite expensive and associated with the risk of staff turnover.
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High probability of human bias
Besides, hours spent in front of a computer screen evaluating thousands of images create a stressful environment for ophthalmologists and cause many errors, affecting the accuracy of the clinical trials. Even the FDA recognizes grader fatigue and its impact on potential errors in image interpretation.
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Inaccurate labeling
In addition, administrative problems also occur quite often. This happens due to deviations from study protocols and incorrect labeling of images, which can compromise the integrity of the analyses.
Fortunately, the pace of digitalization in reading centers is accelerating. Here is how AI medical image analysis can help reading centers cope with the growing workload.
The importance of implementing AI medical image analysis for reading centers
Usually, AI image analysis is made through a pattern recognition process that involves scanning images for specific pathological signs to interpret the patient’s condition. The AI image analysis software has precise and efficient evaluation protocols that allow the analysis and interpretation of images in terms of a variety of qualitative morphological parameters. For example, when analyzing images of a patient with diabetic retinopathy, the AI models recognize microaneurysms or hemorrhages.
AI algorithms allow reading centers to conduct trials of any size and duration, including various treatments for various eye diseases. Moreover, unlike the standard image interpretation process, which requires significant human resources, the introduction of AI for image analysis into the workflow of reading centers has many advantages.
- Quality control. Using AI algorithms ensures no errors in OCT scan analysis. AI image analysis software ensures that the desired parameters are classified based on certified imaging protocols.
- Less money spent. Implementing AI-assisted OCT analysis is less expensive than hiring outsourced ophthalmologists.
- Accurate quantification. AI in medical image analysis does not depend on patient characteristics or treatment group assignment knowledge, so the machine provides the most objective and accurate assessment possible.
- Increased efficiency. Improving the reading centers workflow with AI provides an objective and standardized classification of images. It means that any human bias is excluded, which increases the reputation of clinical research.
- No time wasted — no more hours spent at a computer screen. Evaluating images with AI medical image analysis provides faster and more sensitive identification of the patient’s condition, which can positively impact decision-making.
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How reading centers will benefit from AI image analysis software
In short, image evaluation with algorithms is fast, less expensive, and more reproducible. However, many companies that perform clinical trials in cooperation with reading centers are still afraid of implementing AI in medical image analysis and evaluating processes. Modern AI-based image management systems, such as Altris AI, unlike their predecessors, allow reading centers to overcome the challenges of the manual image interpretation process.
A lot of data available to train an algorithm
The more images with various pathological features the algorithm has for training, the more accurately it will detect the diagnosis. Modern AI image analysis software has the ability to obtain thousands of OCT images from different models of devices for comprehensive and correct training of algorithms. Although many medical centers keep their clinical practice confidential, many ophthalmic cases and images with various pathological signs in the public domain allow the training of AI algorithms.
For example, the Altris AI medical image analysis software was trained on 5 million unique OCT scans obtained in 11 practicing ophthalmology clinics through the years. Our retina experts took a responsible approach to annotating and labeling images for algorithm training. A thorough error detection and correction procedure gave our algorithm 91% accuracy.
Constant quality control
The responsibilities of the modern algorithms developers include not only the release of the model but also further diagnostics, which allows avoiding the problem of reproducibility. After all, constant quality control is necessary for algorithm development environments. Understanding the importance of quality control, the Altris AI team constantly tests the reproducibility of AI medical image analysis model diagnostics.
Collection of rare diseases
According to our research, 25% of ophthalmologists, on average, miss rare pathologies 3 times a week. However, modern AI image analysis software allows overcoming this challenge. For example, Altris AI excludes missing minor, early, rare pathologies. Our team created an algorithm that automates the detection of 54 pathological signs and 49 pathologies.
High percentage of algorithmic bias is avoided
Algorithmic bias is one of the biggest challenges in AI. Although algorithms themselves do not have biases, they inherit them from humans. However, today, AI for image analysis has learned how to overcome the lack of interoperability between medical record systems.
Although it is impossible to avoid algorithmic bias completely, as it can appear at any stage of the algorithm creation process, from study design and data collection to algorithm development and model selection, modern developers take a direction to fair AI. By using a technical and regulatory framework that provides the diverse data needed to train AI algorithms, the Altris AI team makes modern technologies inclusive and ensures algorithmic bias can be excluded.
The future of AI medical image analysis in reading centers
The ultimate goal of the ophthalmic AI system for reading centers is to improve the grading and evaluating process and obtain more accurate research results. However, instead of fully digitalizing image assessment, the ideal approach to analysis is integration — where the benefits of AI algorithms and human skills can be combined.
Technology will never fully replace humans, but it is already improving their work efficiency. For example, by taking over more routine and monotonous tasks, algorithms allow ophthalmologists to focus on specific eye areas and increase the evaluation speed. AI medical image analysis software can also be effective in determining compliance with the standardization of feature interpretation and determining image quality for requesting more images.
There are undoubtedly many challenges to integrating AI for image analysis into the workflow of reading centers. However, modern AI technologies can already overcome almost all of them. Altris AI image interpretation system is changing the future of clinical research by helping to classify images faster and increasing the efficiency, accuracy, and reproducibility of clinical trial data.
You can watch a short video of how Altris AI platform assists eye care specialists in detecting pathological signs on the OCT scans:
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The Role of AI Image Interpretation for Ocular Pathologies Detection
Maria Znamenska
28.09.202220 min readThe burden of timely diagnostics lies on the shoulders of eye care specialists: ophthalmologists and optometrists worldwide. According to the International Agency for the Prevention of Blindness, over 1 billion people live with preventable blindness because they can’t access the proper diagnostics and treatment. Almost everyone needs access to eye care services during their lifetime. Unfortunately, there are only 331K optometrists worldwide, while 14M optometrists are required to provide effective and adequate eye care services.
With the high prevalence of the population that needs eye care services and the lack of specialists, the goal of timely and accurate diagnostics and treatment seems unachievable.
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However, the empowerment of eye care specialists with Artificial Intelligence (AI) can be a real solution to this problem. As the larger part of the work of eye care specialists relies on retina image assessment and analysis, the support of this process can unburden ophthalmologists and optometrists all over the world. Modern AI image interpretation algorithms, such as Altris AI, can discover patterns among millions of pixels with high speed, accuracy, and zero human errors because of tiredness.
You can watch a short video of how Altris AI can assist you in detecting pathological signs on the OCT scans:
https://www.youtube.com/watch?v=Ehhwl6Q0O-A&ab_channel=Altris
In this article, we will talk about the capabilities of AI image interpretation for Optical Coherence Tomography (OCT) in detecting common pathologies, such as AMD or glaucoma, and less prevalent, such as Choroidal Melanoma. Despite the skepticism of the eye care community towards AI, multiple research works mentioned in this article prove the efficiency of AI. Moreover, there are market tools, capable of detecting 49 eye pathologies with 91% accumulative accuracy. Altris, the SaaS created by a team of retina experts based on 5 million OCT scans obtained in 11 clinics, is such a tool.
AI image interpretation for OCT
AI image interpretation for Asteroid Hyalosis
Asteroid hyalosis is a clinical condition in which calcium-lipid complexes are suspended throughout the vitreous collagen fibrils. Although it is a rare disease (1.2% prevalence according to the U.S. Beaver Dam Eye Study), it also may lead to unpleasant consequences, such as surface calcifications of intraocular lenses. Today OCT can help with the detection of this degenerative condition. For a higher confidence level, eye care specialists may use Altris AI image interpretation for OCT analysis to detect asteroid hyalosis.
AI for Central Retinal Artery Occlusion (CRAO)
Central Retinal Artery Occlusion (CRAO) presents as unilateral, acute, persistent, painless vision loss. It can be bilateral in 2% of the population. The vision loss is abrupt, and the treatment is only effective during the first hours. CRAO resembles a cerebral stroke. Therefore, its treatment should be similar to any acute event treatment: detecting the occlusion site and ensuring it won’t occur again. AI image interpretation models, such as Altris AI, can assist eye care specialists in detecting CRAO today.
AI for Central Retinal Vein Occlusion (RVO)
CRVO is one of the most widespread vascular diseases that affect the population over 45. There are two distinct types of CRVO: perfused (nonischemic) and nonperfused (ischemic). Each of these types has its symptoms and treatment prognosis. For instance, ischemic CRVO leads to sudden visual impairment, while nonischemic CRVO development takes time to develop mildly. The detection of CRVO is now done with the help of OCT predominately, and AI image interpretation systems shows promising results in spotting its symptoms, such as nonperfusion. Altris AI system defines CRVO with 91+% accumulative accuracy in detecting pathological signs that indicate the CRVO.
AI for Central Serous Chorioretinopathy (CSC)
Accumulation of fluid under the central retina is called central serous chorioretinopathy. Over time, this disease can lead to the distortion of vision. Fortunately, available AI models for OCT scan analysis show high accuracy in detecting CSC. This and other AI image interpretation models effectively discriminate between acute and chronic CSC, and their performance can be comparable to the performance of ophthalmologists. Altris AI is already helping eye care specialists worldwide to diagnose CSC cases.
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AI for Chorioretinal Scar
Chorioretinal scars are tiny scars in the back of the eye, the size of which may vary from 0,5mm to 2mm. In most cases, the chorioretinal scar appears as the result of virus infection, such as toxoplasmosis and toxocariasis, or trauma. It usually has no malignant potential. Modern AI image interpretation algorithms allow ophthalmologists and optometrists to diagnose chorioretinal scars more accurately by relying on OCT images.
AI image interpretation for Chorioretinitis
The inflammation of the choroid is called chorioretinitis. Often, the inflammatory process can be caused by congenital viral, bacterial, or protozoan infections. Chorioretinitis is characterized by vitreous haze, fine punctate gray to yellow exudation areas, pigment accumulation along the optic nerve and blood vessels, and flame-shaped hemorrhages with chorioretinal edema. The goal of the eye care specialist is to detect chorioretinitis which can potentially lead to blindness, and to eliminate inflammation. Altris AI image interpretation system can be an excellent decision-making support tool in detecting chorioretinitis.
AI image interpretation for Choroidal Melanoma
Today, choroidal melanoma is the second most common intraocular tumor in the adult population. Patients with choroidal melanoma don’t have distinct symptoms but can have impaired visual acuity, visual field defects (scotomas), metamorphopsia, photopsia, and floaters.
OCT is a relatively new method for the detection of choroidal melanoma, which is nevertheless gaining popularity. OCT cannot be the only diagnostic method for melanoma detection – FA is also needed for final diagnosis. However, optical shadowing, thinning of overlying choriocapillaris, subretinal fluid, retina local elevation, subretinal lipofuscin deposits, and disrupted photoreceptors can be detected with the help of OCT.
Such pathological signs will indicate possible choroidal melanoma. Altris AI image interpretation system can assist eye care specialists with detecting pathological b-scans and locating this disease.
AI for Choroidal Neovascularization (CNV)
Choroidal neovascularization (CNV) is part of the spectrum of exudative age-related macular degeneration (AMD) and some other conditions. CNV is an abnormal growth of vessels from the choroidal vasculature to the neurosensory retina through Bruch’s membrane.
Modern OCT systems can detect even a tiny amount of fluid leaking into the retina. Empowered by Altris AI image interpretation algorithm, eye care specialists can spot pathological signs of choroidal neovascularization much faster or detect the pathologies that accompany CNV, resulting in better patient outcomes.
AI image interpretation for Choroidal Rupture
Traumatic choroidal rupture is common after blunt ocular trauma (5 to 10%). It is a defect in the Bruch membrane, the choroid, and the retinal pigment epithelium. The location of the choroidal rupture will define the symptoms: if the fovea and parafoveal retina are included in the rupture area, patients experience impaired vision. In other cases, the rupture can be asymptomatic. OCT is used to diagnose choroidal rupture as it can show the loss of continuity of the RPE layer and the thinning of the choroid. AI image interpretation is exceptionally accurate in layers segmentation and volume/area calculation, so missing the symptom of choroidal rupture with AI is almost impossible.
AI image interpretation for Choroidal Nevus
Choroidal nevus is a benign melanocytic tumor of the choroid and is found in 5 to 30% of white people. It can be found accidentally because it is asymptomatic. Artificial intelligence methods are used not only for identifying choroidal nevus but also for early signs of its transformation into malignant melanoma. The earlier the small melanoma is detected, the better the treatment prognosis is for the patient. Altris AI image interpretation system is one of the systems capable of detecting choroidal nevus before its transformation into melanoma.
AI for Cone-Rod Dystrophy (CORD)
CORD is an inherited retinal disease caused by a genetic mutation characterized by cone photoreceptor degeneration. It may be followed by subsequent rod photoreceptor loss. CORD symptoms include loss of central vision, photophobia, and progressive loss of colored vision. OCT diagnostics help to diagnose CORD by pointing at the absent interdigitation zone and progressive disruption and loss of the ellipsoid zone (EZ). Today AI image interpretation is helping to detect Cone-Rod Dystropthy to eye care specialists more confidently, even in controversial cases.
AI for Cystoid Macular Edema (СME)
Cystoid macular edema (CME) is a painless condition in which cystic swelling or thickening occurs of the central retina (macula) and is usually associated with blurred or distorted vision. CME can be caused by many factors, including diabetic retinopathy and age-related macular degeneration (AMD). OCT diagnostics help to spot СME by detecting retinal thickening with the depiction of the intraretinal cystic areas. CME is not irreversible. Vision loss caused by macular edema can be reversed if detected early. Combining OCT diagnostics with AI image interpretation, eye care specialists can detect CME with higher accuracy at an earlier stage.
AI image interpretation for Degenerative Myopia
Degenerative or pathological myopia is the condition during which axial lengthening occurs, especially in the posterior pole. It leads to retina stretching, the sclera’s thinning, choroidal degeneration, and potential loss of vision. AI image interpretation systems have demonstrated excellent results in detecting pathologic myopia and identifying myopia-associated complications on OCT. AI helps ophthalmologists improve the monitoring of pathology treatment and classify different cases of myopia.
AI for Diabetic Macular Edema
Diabetic macular edema (DME) is the presence of excess fluid in the extracellular space within the retina in the macular area, typically in the inner nuclear, outer plexiform, Henle’s fiber layer, and subretinal space. DME can develop during any stage of diabetic retinopathy in patients with diabetes.
Unfortunately, the early symptoms of DME can be unnoticeable or include impaired vision and reading and color perception problems, which some people may ignore. Taking into account its asymptomatic nature, patients with diabetes need regular OCT examinations to determine the presence of DME. OCT has become a golden standard in DME detection within the last few years, and AI can be an excellent decision-making support tool in OCT scans interpretation. According to recent research, AI-powered OCT analysis provides an accurate diagnosis of DME with a cumulative accuracy of over 92%.
AI image interpretation systems can unburden ophthalmologists and optometrists who have a lot of patients due to their convenience and can be used in remote regions of the world in the future.
AI image interpretation for Diabetic Retinopathy
Diabetes can affect the eyes in various ways, most commonly corneal abnormalities, glaucoma, iris neovascularization, cataracts, and neuropathies. However, diabetic retinopathy (DR) is the most common and potentially the most blinding of these complications. Early treatment of both proliferative and non-proliferative DR can improve patient outcomes significantly. OCT is a common diagnostic method for diabetic retinopathy. It relies on the localization of intraretinal and/or subretinal fluid and can help to diagnose diabetic retinopathy through pathological signs detection and layer thickness measurement.
AI image interpretation is a step in the future of detection that shows high sensitivity in identifying DR, and studies prove its effectiveness. AI-assisted analysis of OCT scans helps eye care specialists today and will definitely be more widespread tomorrow.
AI image interpretation for Dry AMD
Dry AMD is a more common type of AMD (80% of people have this type), during which patients slowly lose their central vision. It is the aging of the macula and the appearance of deposits called drusen. There is no treatment for Dry AMD yet. However, early detection can help patients to change their lifestyles and slow down the development of this disease. Modern AI solutions make it possible to diagnose Dry AMD faster and develop successful methods of treating the disease. AI image interpretation systems also exclude the possibility of human error.
AI for Dry AMD – Geographic Atrophy
Geographic atrophy is an advanced form of the late stage of Dry AMD development. In this condition, retina cells will degenerate and finally die, leading to the patient’s central vision loss.
AI image interpretation is being widely used to detect Geographic Atrophy with the help of OCT. In this meta research, there are numerous studies that focus on lesion segmentation, detection, and classification of geographic atrophy and even its prediction. They vary in accuracy, but the overall trend of AI for geographic atrophy detection is very positive. The use of artificial intelligence has several advantages, including improved diagnostic accuracy and higher processing speed.
AI for ERM or Epiretinal Fibrosis
Epiretinal fibrosis (epiretinal membrane or macular puckering) is a treatable cause of visual impairment. It is a macula disease caused by fibrous tissue growth on the retina surface. AI image interpretation model for detecting ERM on OCT can outperform non-retinal eye care specialists with a cumulative accuracy of 98+%. For more professional retina experts, AI can be a decision-support tool. Early detection and treatment of this disease are crucial to prevent the growth of fibrous tissue and the worsening of the patient’s condition.
AI image interpretation for Epiretinal Hemorrhage
Epiretinal hemorrhages result from a serious trauma: car or sports accidents, falls, and direct physical impact. Mild hemorrhages unrelated to a serious traumatic event can disappear on their own, but they can be a symptom of a more complex pathology. Epiretinal hemorrhages can be detected with the help of OCT, and AI image interpretation systems can make this process more accurate.
AI for MTM (Foveoschisis)
Myopic foveoschisis or myopic traction maculopathy is the thickening of the retina that reminds schisis in patients with high myopia with posterior staphyloma.
Untreated foveoschisis often leads to vision loss due to secondary complications, which is why this disease should be detected in time. Today when OCT is becoming more widespread, detection of foveoschisis is more common and accurate. More than that, the studies show that combining the power of AI image interpretation and OCT diagnostics for MTM detection is equal to the junior ophthalmologist’s knowledge. Using AI-powered OCT, it is possible to deal with the shortage of specialists that can guarantee timely diagnostics.
AI for Full-thickness Macular Hole
A macular hole is a full-thickness defect of the retina involving the foveal region. Patients usually present a reduction of central visual acuity. A complete ophthalmic examination, including OCT, should be performed to diagnose a full-thickness macular hole. So far, the research of AI image interpretation algorithms for a full-thickness macular hole is dedicated to OCT(A), but there are available tools on the market that can help define full-thickness macular hole on OCT scans as well. Altris AI is one of them.
AI for Hypertensive Retinopathy
People with high blood pressure, older people, and patients with diabetes often develop hypertensive retinopathy. OCT examination can be used for the detection of hypertensive retinopathy.
AI-based OCT analysis shows promising results in detecting hypertensive retinopathy by defining retinal vessels and other pathological signs in the retina.
AI for Intraretinal Hemorrhage
Among patients with DR, RVO, or ocular ischemic syndrome, there are often those who develop side pathologies. One of these pathologies is intraretinal hemorrhage. AI image interpretation systems help ophthalmologists and optometrists identify intraretinal hemorrhages in the retina.
AI image interpretation for Vitreous Hemorrhage
Vitreous hemorrhage results from bleeding into one of the several potential spaces formed around and within the vitreous body. This condition can follow injuries to the retina and uveal tract and their associated vascular structures. Eye care specialists should perform a complete eye examination, including OCT, slit lamp examination, intraocular pressure measurement, and dilated fundus evaluation. Timely diagnosis and treatment are essential: it can significantly reduce concomitant diseases of intravitreal hemorrhage. AI image interpretation systems can help eye care specialists detect vitreous hemorrhage supporting them in case of controversial OCT scans.
AI for Lamellar Macular Hole (LMH)
Lamellar macular hole is one of the types of macular holes known in eye care practice. The problem is that the stage 0 macular hole is a clinically silent finding detected on OCT where a parafoveal posterior hyaloid separation is present and a minimally reflective preretinal band is obliquely inserted at one end of the fovea. Eye care specialists may have problems identifying lamellar macular hole on OCT. That is where AI image interpretation models can come into play.
AI for Laser-induced Maculopathy
Since 2014, the number of laser injuries reported worldwide has more than doubled because of the widespread use of laser technologies. Depending on the damage, the patient may have a quick recovery or long-term vision loss with the development of diseases such as photoreceptor’s damage, macular hole, ERM, or others. OCT is one of the methods that help to detect laser-induced maculopathy without human errors and doubts. AI image interpretation models have a reasonable prospect of helping eye care specialists define laser-induced maculopathy based on OCT scans.
AI for Age-related Macular Degeneration (ARMD)
Age-related macular degeneration is one of the leading reasons for blindness in people of older age, especially among women and people with obesity. Patients usually present with a gradual, painless vision loss associated with delayed dark adaptation, severe metamorphopsia, and field loss. In other words, in the early stages of AMD, patients may not have any signs or symptoms, so they may not even know they have the disease. Regular OCT screening (among other diagnostic methods) can be a life-saving vest for older people.
AI image interpretation has shown great promise in detecting AMD, and research papers show that its capabilities are similar to those of ophthalmologists. AI-powered automated tools provide significant benefits for AMD screening and diagnosis.
AI for Macular Telangiectasia Type 2
Macular telangiectasia (Mac Tel) results from the capillaries abnormalities of the fovea or perifoveal region related to the retina nuclear layers and ellipsoid zone.
Macular Telangiectasia Type 2 can have negative consequences and develop into cystic cavitation-like changes in all the layers of the retina or even transform into a full-thickness macular hole. OCT is an effective diagnostic method of macular telangiectasia type 2 as the tomograph can localize foveal pit enlargement. Which is a result of secondary loss of the outer nuclear layer and ellipsoid zone that can progress into large cysts (often called ‘cavitation’) that can encompass all retinal layers.
Automating the detection of macular telangiectasia type 2 with the help of AI image interpretation systems for OCT scan analysis is already possible thanks to Altris AI.
AI for Myelinated Retinal Nerve Fiber Layer
Myelinated nerve fiber layer (MRNF) is a disease that occurs in 1%. It is a benign clinical condition that results from an embryologic developmental anomaly whereby focal areas of the retinal nerve fiber layer fail to lose their myelin sheath.
OCT is an effective method of MRNF detection with the help of the detection of the RNFL layer. Such tools as Altris AI image interpretation models are even more accurate in retina layers detection and volume measurement thanks to their growing level of accuracy.
AI image interpretation for Myopia
Myopia is not an eye disease. It is an eye-focusing disorder that affects 25% of the world population at a younger age. There are 2 distinct types of myopia: pathological and non-pathological — each of the types has its symptoms and treatment prognosis. The visual function of the patients, as well as the high quality of life, can be preserved if myopia is detected early enough and treated appropriately. Myopia is often diagnosed by ophthalmologists and optometrists with the help of OCT, thanks to its fine cross-sectional imagery of retinal structures. Unlike biomicroscopy, angiography, or ultrasonography, OCT can reveal undetectable retinal changes in asymptomatic patients with myopia.
Current AI image interpretation models show great promise in detecting myopia on OCT scans, and their results can be compared to the results of junior retina specialists. Altris AI is an accurate AI tool for myopia.
AI for Pigment Epithelium Detachment
Retinal pigment epithelial detachment (PED) is often observed in Wet AMD and other conditions. It is determined as a separation of the RPE layer from the inner collagenous layer of Bruch’s membrane. With its capability to visualize retinal layers, OCT helps eye care specialists with timely PED diagnostics. Powered with AI image interpretation systems, OCT diagnostics can promise zero human errors and exceptional accuracy.
AI for Polypoidal Choroidal Vasculopathy (PCV)
Polypoid Choroidal Vasculopathy is a disease of the choroidal vasculature. Serosanguineous detachments of the pigmented epithelium and exudative changes that can commonly lead to subretinal fibrosis are the main OCT signs of PCV. AI image interpretation systems show great potential in establishing a difference in diagnostics between PCV and AMD.
AI for Preretinal Hemorrhage
Preretinal hemorrhage is a complication of many pathologies, such as leukemia or ocular/head trauma. Missing preretinal hemorrhage means putting a patient at risk. Preretinal hemorrhage can be a presenting sign of some systemic diseases. In any case, OCT diagnostics are performed to determine preretinal hemorrhage and its real reason.
AI image interpretation for Pseudohole
Sometimes the pulling or wrinkling of the epiretinal membrane (ERM) can result in a gap called a pseudohole. A pseudohole can look like a macular hole; sometimes, it can turn into one, so it is essential to distinguish between these two phenomena. Optical coherence tomography can accurately determine a pseudohole revealing an epiretinal membrane with contraction of the retina or suppression of retinal layers. Combined with AI image interpretation, OCT diagnosis can guarantee higher accuracy in pseudohole detection.
AI for Retinal Angiomatous Proliferation (RAP)
RAP is a subtype of AMD, which is neovascularization that starts at the retina and progresses posteriorly into subretinal space. There are 3 stages of RAP: intraretinal neovascularization (IRN), subretinal neovascularization (SRN), and choroidal neovascularization (CNV). OCT is effective for detecting IRN only since changes beneath the pigment epithelium are challenging to assess. AI image interpretation models effectively differentiate between RAP and polypoidal choroidal vasculopathy (PCV), comparable to the performance of eight ophthalmologists. AI cannot substitute eye care specialists but can be an excellent decision-making support tool.
AI for Retinal Detachment
Retinal detachment is a serious eye condition that happens when the retina pulls away from the tissue around it. It can be a result of trauma or another disease. OCT has become a new standard for detecting early retinal detachment and defining the best time for surgical operation, for example. OCT powered with AI image interpretation systems can give eye care specialists the confidence they need to determine the degree of detachment and make the correct prognosis.
AI for Retinitis Pigmentosa
RP is a hereditary diffuse pigment retinal dystrophy characterized by the absence of inflammation, progressive field loss, and abnormal ERG. OCT diagnostics allows assessing morphological abnormalities in RP, providing insights into the pathology of RP and helping to make a good prognosis. AI image interpretation applied for the OCT analysis shows promising results in Inherited Retinal Diseases detection and future management.
AI image interpretation for Retinoschisis
Retinoschisis is an eye condition characterized by a peripheral splitting of retinal layers. OCT is an effective method of retinoschisis diagnostic. The application of AI image interpretation tools for OCT analysis for identifying retinoschisis (among other myopia conditions) is comparable to the performance of experienced ophthalmologists.
AI for Retinal Pigment Epithelial (RPE) Tears (Rupture)
RPE rupture or RPE tears is the condition when this retinal layer acutely tears from itself and retracts in an area of a retina, usually overlying a pigment epithelial detachment (PED). OCT is an effective method of diagnostics of RPE tears. OCT scans will show a discontinuity of the hyperreflective RPE band, with a free edge of RPE usually wavy and scrolled up overlying the PED, contracted back towards the CNVM.
AI image interpretation systems can provide eye care specialists with confidence when detecting RPE tears. Systems such as Altris AI can distinguish between retinal layers with exceptional accuracy, exceeding the accuracy of eye care specialists.
AI for Solar Retinopathy (Maculopathy)
Solar retinopathy is photochemical toxicity and the consequent injury to retinal tissues located in the fovea in most cases. OCT helps to diagnose solar retinopathy by indicating changes and focal disruption at the level of the subfoveal RPE and outer retinal bands. The overall retinal architecture remains intact. AI image interpretation models can confidently assist eye care specialists in detecting solar retinopathy, even when they are in doubt.
AI for Subhyaloid Hemorrhage
Subhyaloid hemorrhage is diagnosed when the vitreous is detached from the retina because of blood accumulation. This type of hemorrhage is rare and is different from intraretinal hemorrhage caused by trauma or diabetes. OCT helps to detect subhyaloid hemorrhage. For eye care specialists who don’t have experience in detecting subhyaloid hemorrhage, the AI image interpretation model can become a great support tool.
AI for Subretinal Fibrosis
Subretinal fibrosis appears due to wound healing reaction to the choroidal neovascularization in nAMD or other conditions. Early diagnostics of subretinal fibrosis are critical because a neovascular lesion’s transformation into a fibrotic lesion can be very rapid. OCT is regarded as the most accurate method of diagnostics today.
AI image interpretation systems can help eye care specialists who use OCT with early diagnostics of subretinal fibrosis and improve patient outcomes.
AI for Subretinal Hemorrhage
Subretinal hemorrhages are a complication of various diseases which arise from the choroidal or retinal circulation. It is most often caused by AMD, trauma, and retinal arterial macroaneurysm. OCT will be an effective tool for determining the level at which subretinal hemorrhage occurred. Powered with AI image interpretation models, OCT can become the decision-making support tool eye care specialists need for subretinal hemorrhage identification.
AI for Sub-RPE (Retinal Pigment Epithelial) Hemorrhage
Sub-RPE (retinal pigment epithelium) hemorrhage is located between the RPE and Bruch’s membrane. OCT is an essential tool for validating the hemorrhage’s diagnosis and localization. AI image interpretation tools, such as Altris AI, will ensure that Sub-RPE hemorrhage is not missed.
AI for Tapetoretinal degeneration or dystrophy
Tapetoretinal dystrophy or tapetoretinal degeneration (TD) is exogenous destruction of the retina caused by a genetic mutation. Eye care specialists might easily miss such rare conditions as tapetoretinal degeneration. It is often advisable to have AI image interpretation systems as a decision-making support tool not to miss TD or other uncommon diseases.
AI image interpretation for Vitelliform Dystrophy
It is autosomal dominant degenerative maculopathy wherein a mutation in the bestrophin gene leads to lipofuscin accumulation in RPE cells manifested in a yellow spot. Detecting vitelliform dystrophy is critical at the early stages as it can lead to vision loss. OCT provides essential information on the lesion’s morphology, location, and dynamics. Empowered with AI image interpretation tools, such as Altris AI, eye care specialists won’t miss such a rare disease as vitelliform dystrophy at the early stage.
AI for Vitreomacular Traction Syndrome
Vitreomacular traction syndrome is a pathological condition characterized by a posterior vitreous detachment that leads to blurred vision or serious vision impairment. OCT is an essential method of diagnostics of vitreomacular traction syndrome as it can show the amount of involvement and tension on the macula caused by VMT. Combined with AI image interpretation tools, OCT analysis can give incredible results.
AI image interpretation for Wet AMD
Wet AMD is the most widespread disease among the elderly population in developing countries. It is a disease characterized by abnormal blood vessel growth under the retina. Understanding that this disease can lead to rapid and severe vision loss, its early detection and treatment are very important. OCT is a golden standard for the diagnostics of wet AMD as it shows fluid or blood underneath the retina without dye, among other pathological signs.
Today AI shows promising results in predicting the development of wet AMD based on OCT images. For instance, the DARC algorithm designed for detecting apoptosing retinal cells could predict new wet-AMD activity. Another effective AI image interpretation algorithm determines the location and volumetric information of macular fluid within different tissue compartments in wet AMD, providing eye care specialists with the ability to predict visual acuity changes.
AI for X-linked Juvenile Retinoschisis (XLRS)
XLRS is a rare congenital retina disease caused by mutations in the RS1 gene, which encodes retinoschisin, a protein involved in intercellular adhesion and likely retinal cellular organization. The disease usually affects younger males in their teenage years who complain about blurred vision. OCT is used to detect schisis in the superficial neural retina and thinning of the retina. Despite the lack of research articles on AI in OCT diagnostics of XLRS, there are AI image interpretation tools that already cope with this task effectively.
Final Words
Artificial intelligence can identify, localize, and quantify pathological signs in almost every disease of the macula and retina. That is how AI image interpretation systems can provide decision-making support with the pathologies at their early stages or rare pathologies. AI can help to detect many pathologies that are invisible to the human eye because of their size or that are at their early stage.
The overall potential of artificial intelligence for ophthalmologists and optometrists is enormous and includes pathological scan selection and scan analysis with the probability of existing pathologies and pathological signs. One trial is worth a thousand words in the case of AI tools for ophthalmologists and optometrists.
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Types of Optometry Practices & the Role of OCT
Mark Braddon
14.09.20227 min. readVarious types of optometry practices have always played a crucial role in diagnosing many eye diseases and promptly referring to a retinal expert. According to Essilor International research, poor vision is the most common disability in the world today. The good news is that 90% of vision loss cases are treatable or preventable if discovered in their early stages.
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However, by performing only traditional types of optometry practices, such as anterior and posterior segment examinations, optometrists may miss the complete picture of a patient’s eyes. That is why optometry specialists are embracing a new technique: optical coherence tomography (OCT) examination.
Optometry OCT practice helps go beyond standard eye examination procedure by better visualizing the eye’s structures and providing an additional quantitative assessment.In this article, I will discuss the most important types of optometry practices and emphasize the role of OCT scan interpretation in optometry.
Types of optometry practices
When performing a full optometric examination, the optometrist should not only assess the visual acuity with an eye chart but also check their eye health. The types of optometry practices and tools are now very diverse and depend on the application field and the qualification level. Nowadays, there are a few eye examination techniques, although they may vary from country to country, that help diagnose a patient more accurately and improve follow-up care.
Ophthalmoscope eye examination
Ophthalmoscopy plays a crucial role in detecting the conditions of the retina, blood vessels, and optic disc. This is a basic eye examination procedure that optometrists usually perform to evaluate many diseases, such as diabetic retinopathy or retinal vein occlusion.
During the direct ophthalmoscopy, the optometrist shines a light into the patient’s eyes to see the inside. Binocular indirect ophthalmoscopy also involves shining a light into the patient’s eyes, however, it allows eye care specialists to take a better look at the retina and its parts that are difficult to see with other eye examination techniques. The indirect ophthalmoscopy is usually combined with pupil dilation and another optometry practice called scleral depression.
Slit lamp optometric examination
A slit lamp consists of a microscope, light source, and frame on which a patient lies their head. This regular eye examination procedure lets an optometrist focus on the eye by working with the light: expand or narrow it, increase brightness, and filter with colors. Sometimes the procedure also includes putting a few dye drops in a patient’s eye to examine some of its parts.
Slit lamp examination is pain-free and allows an optometrist to view the sclera, iris, or cornea to detect diseases related to allergies, autoimmune disorders, gout, or even melanoma. Such eye examination procedure also allows to view the retina of the eye to detect the pathological signs of diabetes. Optometrists usually use a slit lamp along with an ophthalmoscope examination.
Refraction eye examination procedure
One more type of types of optometry practices is a refraction test, usually performed to detect if a patient needs glasses or contact lenses. This test made with a phoropter is quick and painless. During the optometric examination, the optometrist adjusts the power of the lenses by moving or turning them back and forth until a patient can clearly see the letters on the chart.
An optimal value of 20/20 is considered ideal vision, while a deviation means a refractive error. This may indicate that when light passes through the lens of the patient’s eye, it is not refracted properly. An optometrist can detect astigmatism, myopia, presbyopia, and a refractive eye problem during a refraction test. This, in turn, helps detect macular degeneration, retinal vein occlusion, retinitis pigmentosa, and retinal detachment.
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Cycloplegic refraction
Sometimes the optometrist may decide that the normal refraction is insufficient or inaccurate due to error. During refraction, the patient may unconsciously focus, affecting the test result and showing nearsightedness or farsightedness.
Then the optometrist performs cycloplegic refraction using cycloplegic eye drops. This eye examination procedure paralyzes the muscles that focus the eye to determine the refractive error. Сycloplegic refraction exam is especially useful for children, patients with pre-presbyopia, and LASIK patients.
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Autorefraction
Autorefraction is an eye examination procedure performed using a special autorefractor device, also called an optometer. This exam automates the estimation of refraction and determines its error. Usually, the indications for the procedure are myopia, farsightedness, astigmatism, presbyopia, and prescription of glasses and contact lenses.
Retinoscopy optometric examination
Among different types of optometry practices usually performed to detect farsighted, nearsighted, or astigmatism, and the need for glasses is retinoscopy. This procedure is pain-free and quick. Using a retinoscope, the optometrist projects a beam of light into the patient’s eye. This light moves along a horizontal and vertical trajectory, reflecting off the back of the eye. The eye care practitioner observes the movement of light with the help of lenses they place in front of the eye. Then the optometrist changes the lens’s power and tracks the reflection’s direction and pattern. This test is performed to find a possible anomaly.
Role of optometry OCT practice
The types of optometry eye examination techniques described above are fundamental for any diagnosis. However, adopting modern optometry OCT practice systems already complements clinical practice perfectly and has the prospect of widespread distribution among optometrists worldwide.
Knowing that the prevalence of some eye conditions, such as Myopia or Dry AMD, has increased with the pandemic, specialists need to implement modern methods and eye examination techniques in their clinical practice. Current optical coherence tomography devices allow optometrists to perform consistent analysis and furthermore have special software and a database for storing patient information. Compared to other retinal examination methods, such as fundus photography, OCT scan interpretation enhances patient care by improving the quality of diagnosis.
High-quality information provided
Modern optometry OCT diagnostics allow optometrists to quickly obtain a huge amount of information about the patient’s eye. Built-in software collects images and compares results to normative databases. This allows optometrists to easily track patient progress or regression and generate reports that ophthalmologists or surgeons may need for follow-up treatment.
For example, suppose a patient has a disorder with the optic nerve, macula, or vascular system. In that case, the optometrist can send data to the ophthalmologist promptly, highlight important aspects of the patient’s condition, and provide abnormal OCT scan results for additional clarity.
No missed pathologies
Optometry OCT practice provides higher diagnostic standards, ensuring fewer pathologies or pathological signs are missed. OCT scan interpretation helps detect early vision-threatening eye conditions. For example, the system can detect AMD in the early stages, which is crucial for preventing vision loss due to subretinal fibrosis. With optometry OCT practice, the thickness of the retina over the macula and posterior pole can be analyzed to detect retinal edema or atrophy. Optometrists can also confirm diabetic macular edema (DME) and decide on further treatment based on the results of its examination. In addition, OCT perfectly visualizes the retinal pigment epithelium (RPE) and choroid.
More patients served with comfort
By better visualization of the eye structures, optometrists provide each patient with an individual approach. This level of service ensures comfort for patients and trust for a specialist. Optometry OCT practice allows optometrists to avoid routine work and devote more time and energy to patients. More importantly, the OCT scan interpretation helps establish contact, allowing patients to understand the examination and treatment plan.
Impact of AI on optometry OCT practice
OCT scanning allows optometrists to accumulate large amounts of patient data. However, a large amount of information can be difficult and time-consuming to process, even for experienced specialists. The collaboration of optometry OCT practice and artificial intelligence (AI) gives optometrists a unique opportunity to analyze a large amount of data and make better clinical decisions. Here are 4 key benefits of AI which completely transform the OCT scan interpretation process for optometrists:
- Gaining confidence. 16.3% of interviewed eye care practitioners still avoid using OCT in their daily practice because of the lack of confidence in their interpretation skills. However, with AI, this problem will be solved.
- Fast examination. Implementing AI-powered management systems in daily clinical practice reduces the time optometrists have to spend on non-pathological scans.
- Clear diagnosis. 59% of specialists acknowledge that they have to interpret controversial scans around 1-3 times a week. AI helps optometrists with controversial and abnormal OCT scans, so they don’t need to guess the diagnosis.
- High diagnostic standards. 30,5% of interviewed ECPs admit they are unsure how often they miss pathologies. When working with OCT, AI systems ensure no minor, early, rare pathologies are missed.
OCT scanning allows specialists to easily, quickly, and safely obtain many images, producing a lot of data. As AI aims to work with large volumes of data, more and more AI models are being created to help optometrists.
Altris AI has developed an artificial intelligence platform to assist ECPs during their optometric examination and already plays a significant role in diagnosing and treating eye diseases using optometry OCT techniques. We have trained an AI algorithm on 5 million OCT scans collected in 11 ophthalmic clinics with a 91% accuracy. Watch a short video to see how to detect pathological signs with Altris AI:
https://www.youtube.com/watch?v=Ehhwl6Q0O-A&ab_channel=Altris
Future of optometry oct practices
The integration of OCT into the clinical practice of optometrists is beneficial and shows great promise. However, to gain the most accurate diagnosis, the interpretation of scans should be carried out in cooperation with other optometry eye examination tools. Optical coherence tomography implemented with other eye examination techniques, including gonioscopy or slit lamp biomicroscopy, boosts diagnostic performance and provides valuable data.
Optometry oct practices are becoming routine for providing improved examination and patient care. This technology can also improve the confidence of eye care specialists. Detecting many pathologies using optical coherence tomography has an immediate practical benefit. Due to its high resolution, it defines and identifies early pathological signs before patients even notice any symptoms.
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Top 11 Optometry & Ophthalmology Mobile Apps for Eye Care Specialists
Maria Znamenska
15.08.202210 min. readToday, there are hundreds of ophthalmology mobile apps available to both experienced eye care specialists and beginners. Some of them assist in learning and practice as clinical tools, and some of them are educational apps for opticians. Some mobile applications are basically a database of useful materials, ophthalmic atlases, so to say.
Register in a free Demo Account to see how AI for OCT works. AMD, DR, early glaucoma examples.
In this post, I will focus on educational ophthalmology and optometry apps and highlight their main features and functions.
Altris Education OCT
Altris Education OCT is a unique free ophthalmology mobile app that contains millions of OCT scans labeled by a team of retina experts. More than 9000 eye care specialists have already joined the application.
The app is interactive, which means that eye care specialists can highlight pathological signs on the scan 1 by 1 to learn about their location. The database of OCT scans is updated every day with a new labeled OCT scan, so users can gather their library right within the app.
Watch a short video and learn how to interpret scans with Altris Education OCT ophthalmology mobile app:
Interactive eye atlas
The home page of the Altris Education OCT ophthalmology mobile app consists of 4 sections:
- In the Feed section, users will find millions of OCT scans of the retina to practice and improve their skills.
- In the Folders sections, there are 41 folders with various hereditary diseases, pathologies, and pathological signs. If an eye care specialist uploads the app for a specific reason, for example, to learn how to detect Epiretinal Fibrosis, he/she can easily find a folder with needed scans and work on them.
- In the News section, users can find recent news from the OCT world and current researches.
- In the Community section, a user can create a post and discuss curious cases with their colleagues.
Community interaction
A team of Altirs Education OCT has the aim to build a real community of ophthalmologists and optometrists worldwide who share their passion for learning. Most eye care specialists often face difficulty while interpreting OCT scans in their everyday clinical practice. We created a community where each app user can discuss problematic scans or ask OCT-related questions ( what OCT equipment to choose?).
Moreover, the Altris team will engage experienced OCT experts in the forums to give a professional assessment of the scans.
In addition, the Altris ophthalmology mobile app allows its users to like, comment and share OCT scans, as well as save them in a personal library.
Special features
In Altis ophthalmology mobile app, each pathological sign is highlighted with a different color so eye care specialists can easily learn how to interpret OCT scans. Each scan contains two tabs: pathologies and diagnosis, so users are able to highlight the pathologies in the first place and then guess the diagnosis. To check himself/herself, a user switches to the diagnosis tab and finds out the name of the disease. What is more, he/she can zoom in on OCT scans to view pathological signs in detail.
Register in a free Demo Account to see how AI for OCT works. AMD, DR, early glaucoma examples.
Membership options/perks
Altris ophthalmology mobile app not only provides its users with a huge database of educational materials. It also engages eye care specialists to invite friends, gain budges and upgrade their level. To reach the next level, there are tasks like “Search your first scan” or “Learn 5 scans in detail”. When a user level up, he/she gets access to new folders with pathological scans.
Another great feature of the app is that it constantly sends its users an unfamiliar OCT scan, so they can explore something new on a daily basis. The basic functionality of the app is completely free. However, ophthalmologists and optometrists can also become Pro users of Altris Education OCT and unlock more scans and app features for $4 monthly or $25 annually.
Please upload this FREE app if you are interested:
👉 Android link: https://bit.ly/3YarBQa
👉iOS link: https://apple.co/3NLyPZ7Eye Handbook
Being on the market since 2010, Eye Handbook is well known and loved by many ophthalmologists and optometrists. Eye Handbook is used worldwide for both diagnosis and treatment, as the app provides eye care professionals with tools for acuity testing, children’s target fixation, or color vision testing. Now let’s take a closer look at the app’s functionality.
Eye atlas
The overview of diseases in the mobile ophthalmology app begins with the Eye Atlas tab, which is a database of various pathologies arranged in alphabetical order. The description of each disease is accompanied by fundus photos, OCT images, or fluorescein angiography. Users can sort pathologies by category choosing, for example, retinal diseases, glaucoma, or oculoplastics.
Moreover, with the Eye Handbook ophthalmology mobile app, users can view videos of ophthalmic surgeries, such as posterior polar cataract surgery, and many more. Users are also able to sort videos by most relevant or ranked. In addition to videos, the application provides ophthalmologists and optometrists with access to audio materials, flash cards, and slides.
Community interaction
The Eye Handbook mobile ophthalmology app has a forum with topics open for discussion. Users can become a part of the community, add their posts, choose the appropriate category and invite like-minded eye care specialists to discuss the latest news in the field of ophthalmology.
Educational materials
The Eye Handbook is a very useful application not only for ophthalmologists but also for optometrists. Not to mention a bunch of study materials, the application has collected a large number of vision tests such as Amsler grids, duo-chrome test, OKN drum, and a lot more.
The ophthalmology mobile app contains a variety of calculators, like the Glaucoma risk calculator, which eye care specialists can use in their clinical practice right from their smartphones. Eye Handbook gathered even coding, like ICD-10 or CPT. In the app, they are also able to find detailed information about ophthalmic meds, check the EHB manual, and get access to a constantly updating news feed.
Eye Emergency Manual
Eye Emergency Manual mobile ophthalmology app is a great emergency aid because it quickly provides basic information about eye diseases. The application has several features, which I will explain in more detail below.
Eye atlas
This mobile ophthalmology app provides structured and detailed information about many eye traumas and treatments. Users can find fundus photos, photographs of real people’s eyes, or scans of each trauma and read about their initial treatment. In some cases, the developers even created Eye Trauma Communication Checklists to help eye care specialists come to a medical conclusion many times faster.
The Eye Emergency Manual app also contains a database of acute red eye or eyelid cases. All the information is presented clearly and plainly.
Special features
Each pathology overview can be saved so the app users can later explore their favorite pages or favorite glossary terms. The app also provides eye care professionals with the ability to search for a needed term, pathology, or assessment.
Educational materials
One of the unique features of the Eye Emergency Manual app is a variety of checklists, both for a certain pathology or a patient in general. In the app, users can find a comprehensive list of questions to ask their patients, which is useful both for ophthalmologists and optometrists. Eye Manual also contains pediatric assessment and injured patient assessment.
What is more, the app developers created a diagnostic tree that is aimed to help users by suggesting diagnoses. After answering a few questions, the app showcases a few diseases and suggests reading about them in the eye atlas.
OCTaVIA
One of the main differences between the OCTaVIA mobile ophthalmology app and other apps is the fact that it isn’t free. Some other apps for opticians, which I mention in this article, have a paid subscription, but OCTaVIA itself costs $5.99 yearly. However, it is interesting to explore how this price is justified.
Eye atlas
This ophthalmology app contains a constantly updated database of diseases from A to Z. Needless to mention that the application covers only retinal pathologies and provides information about retinal diseases, from Chorioretinal scars to VMT (Vitreo-Macular Traction).
Educational materials
One of the advantages of the OCTaVIA mobile ophthalmology app is that for each pathology it provides two views — fundus photo and OCT scan. They may be colored or not, but each fundus photo and OCT scan contains markers, which are explained in the text. What is curious, there are always a few useful links, so users can discover more trustworthy information about the disease.
Atlas of Ophthalmology Onjoph
The Atlas of Ophthalmology Onjoph app offers a clinical picture for almost all eye diagnoses. It includes more than 6,000 pathologies, from glaucoma to macular degeneration, and even includes such rare diseases as Stargardt syndrome. The image database is constantly being expanded and updated to include other eye diseases.
Eye atlas
Using the search function, eye care specialists can find specific clinical pictures and display them in lists based on diagnoses, ICD-10 code, or keywords. In the Atlas of Ophthalmology Onjoph, users will also find:
- accompanying diagnosis;
- code according to ICD-10;
- brief comment.
Atlas users can also change the font size, save essential images, or forward images by email.
Educational materials
The mobile ophthalmology app has a clear structure for all images. All pathological cases are arranged according to eye regions (conjunctiva, cornea, retina, lens, etc.). Within the eye area, the images are listed according to the type of disease (degeneration, inflammation, tumors, etc.).
Membership options
The mobile application also allows its users to save their favorite articles in the Favorites folder, but this feature is paid and has two types of subscription:
- $3.99 for a Silver plan
- $29.99 for a Gold plan
Other ophthalmology & optometry apps tools worth mentioning
Ophthalmology Guide
In case an eye care specialist needs a topic-oriented mobile ophthalmology app, they may check Ophthalmology Guide. Its users are allowed to choose the desired topic and find out the key characteristics of pathologies. In addition, they can also find several fundus photos, scans, and pathology charts.
Unfortunately, I can’t say that the Ophthalmology Guide app is user-friendly. It contains a few bugs and lacks some additional options, like eye atlases or lectures.
However, the app is promising thanks to the clear categorization of topics, it can be very convenient for ophthalmologists and optometrists to quickly find specific information about examination and management of the pathology.
Easy Ophthalmology Atlas
Easy Ophthalmology Atlas is one of those ophthalmology and optometry apps that are also worth mentioning. It is an offline color atlas of the most common eye diseases. The app contains 13 chapters, where users can find clinical features, diagnosis, and treatment management for different pathologies.
Easy Ophthalmology Atlas lacks quite a lot of features compared to other ophthalmologist tools on the list.
However, this mobile ophthalmology app has the potential to replace the heavy paper versions of the ophthalmology guides if the information is updated regularly in it.
Ophthalmology & Optometry Guide
Another representative of ophthalmology and optometry apps was created to assist students in learning the clinical signs, symptoms, and complications of different pathologies. It provides users with basic knowledge of eye diseases and pathologies, their causes, and treatment.
Ophthalmology & Optometry Guide has up to 18 sections, each stands for a specific eye region (conjunctiva, cornea, retina, optic nerve, pupil, etc.). Each section explains the importance of eye region examination and highlights various abnormalities.
I would recommend this ophthalmology mobile app for beginners or students of the 1st course because it contains a lot of general information that can be useful for those who have just started their careers. However, in the long run, the app lacks media content, real-life examples, and other important features.
Ophthalmology Atlas
Ophthalmology Atlas is a database for ophthalmologists and optometrists, showcasing up to 12 areas of eye diseases from A to Z.
Here users can find diseases of the cornea, lens, retina, and 9 more. The app is a digital variant of a paper atlas with a bunch of real photos and a lot of complicated cases, which is great for beginners.
Clinical Ophthalmology
The Clinical Ophthalmology mobile app has a very simple interface and a list of 20 pathologies to read about. Although the application has only one feature and lacks media content, the team has provided users with the ability to share content.
3D Atlas of Ophthalmology
The app is a collection of various 3D photos and videos, mostly created by Dr. John Davis. One of the distinctive features of the app is that to watch media content users will need to wear Red-Blue 3D glasses or VR Headset.
Will Ophthalmology Mobile Apps Replace Webinars and Conferences?
According to our research on OCT education, 36% of optometrists and ophthalmologists around the world choose webinars to study OCT interpretation. 36% prefer conferences as the source of new information, 18% choose atlases, and only 11% of eye care specialists trust ophthalmology mobile apps.
On the one hand, mobile ophthalmology app cannot replace atlases, webinars, internships, and clinical practice. On the other hand, interactive mobile application contribute to the assimilation of information much better than printed materials and have unlimited data storage capacity. Another of their advantages is that users can learn on the go for little money, while internships and clinical practice takes much time and can be expensive.
Summing up, any ophthalmologist and optometrist who has worked at least a little with OCT knows that practical skills are more important than theory. That is why our team believes that ophthalmology mobile apps will inevitably become an additional effective tool for learning OCT interpretation.
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OCT Interpretation & Eye Examination: How AI can Solve 4 main Problems
Maria Znamenska
10 July 20225 min. readDespite being a relatively recent advancement, OCT has rapidly become the gold standard for diagnostics in eye care practice. Its non-invasive nature and ability to visualize microscopic changes in the earliest stages of disease are among its key advantages. However, eye care specialists must be proficient in OCT interpretation scans to maximize its effectiveness. This is where challenges arise, as there is no single, easy path to mastering OCT interpretation.
For this article, we surveyed eye care practitioners to understand how they learned to interpret OCT scans, how often they encounter challenging or controversial scans in their practice, and what major pain points could be addressed with AI-assisted OCT interpretation.
AI for OCT Analysis
FDA approved AI that detects 70+ retina pathologies
According to our survey, there are four main ways to get OCT education: webinars, conferences, atlases, and mobile apps.
But even after attending courses or webinars, ophthalmologists and optometrists often feel that while they possess theoretical knowledge, they lack the practical experience necessary to feel fully confident in interpreting real-world OCT eye examinations. Thus, they may avoid working with OCT, even though they know its revolutionary value.
By surveying 1034 seasoned and newly practicing optometrists and ophthalmologists worldwide, encompassing a broad spectrum of clinical experience, we’ve identified the four main barriers to embracing OCT technology.
Four pain points of OCT interpretation
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Lack of confidence
Our survey revealed that 16.3% of eye care specialists avoid offering OCT eye examinations to their patients due to a lack of confidence in their interpretation skills. This mirrors a similar situation where even experienced practitioners may over-refer patients to eye hospitals out of an abundance of caution. While this approach might be justifiable in individual cases, it ultimately proves detrimental in the long run. For practitioners, it leads to a decline in clientele, while patients suffer from not receiving timely care at their initial point of contact.
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Slow OCT scan reading
While a machine can capture thousands of high-resolution scans in mere seconds, a clinician’s subsequent interpretation of OCT is far more time-consuming. They must meticulously analyze each scan, not only for any signs of pathology but also in the context of the patient’s complete medical history.
Some eye care specialists may spend up to 40 minutes per OCT examination, which can negatively impact their practice’s efficiency and overall quality. However, on average, specialists dedicate about 10 minutes per OCT eye exam, assuming they are satisfied with the report generated by their device’s OCT interpretation and are not faced with ambiguous or difficult-to-interpret scans.
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Minor, early, rare pathologies missed
Another common challenge in OCT scan interpretation is the potential for overlooking minor, early, or rare pathologies. Our survey reveals that 20.2% of eye care specialists miss such findings 1-3 times per week, while 4.4% miss them more frequently, 3-5 times a week. However, these figures only represent acknowledged errors. A concerning 30.5% of ophthalmologists and optometrists admit they are unsure whether they miss any minor, early, or rare pathologies.
Failing to identify pathologies in their early stages can have devastating consequences for patients. For example, missing early signs of glaucoma, an irreversible condition, can lead to blindness. Similarly, overlooking rare or minor pathologies can result in inadequate patient follow-up and treatment, potentially exacerbating the condition. Accurate OCT interpretation and timely diagnosis are paramount for positive patient outcomes. This discussion focuses solely on the devastating impact on patients’ lives and doesn’t even delve into the potential legal ramifications of missed signs on OCT scans.
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Controversial Scans
Most eye care specialists encounter challenging or ambiguous OCT scans that they find difficult to interpret in their practice. In the vast majority of cases (99%, to be precise), eye care specialists seek a second opinion from their colleagues when faced with an uncertain scan.
However, not all clinicians have equal access to this valuable resource. Optometrists and ophthalmologists (practicing in remote or rural areas) often work in isolation, lacking the readily available professional support network that their colleagues in hospital settings enjoy. While those in hospitals can quickly consult peers for additional insights or guidance, the mentioned group often faces limited opportunities for collaborative decision-making and professional development.
In many professions, sharing challenging information with colleagues online could easily overcome these obstacles. However, the highly sensitive nature of medical data prevents eye care professionals from utilizing such convenient solutions.
How AI can help with OCT interpretation
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Workflow optimization
Our recent survey showed that among more than 1000 participating eye care specialists, 40% have more than 10 OCT exams daily. Meanwhile, 35% of eye care specialists have 5-10 OCT daily examinations. Unfortunately, more patients per day mean an increased risk that specialists may miss some minor, rare, or early conditions.
Artificial intelligence can significantly speed up the screening process and OCT interpretation while reducing the controversy around diagnoses. This faster and more accurate diagnostic tool will enable more patients to be seen, allow for quicker responses to pathologies that pose a risk to eyesight, and reduce the burden on strained hospitals with needless patient referrals, as well as free up patients from unnecessary stress and wasted time.
For instance, the Altris AI platform, which offers AI-powered interpretation of OCT for 70+ pathologies, has a severity grading of b-scans. Severity grading means it is easy to see if the eye is healthy (removing any need to spend time interpreting) or highlight where the pathology is and the degree of severity.
- Green – no pathology detected
- Yellow – mild to medium level of severity
- Red – severe pathology detected
AI tools also offer interpretation of OCT in reports with customized measurements and selected biomarkers, retinal layers, or segments, allowing precise focus on treatment monitoring and patient response to therapy. This fastens the exam procedure and provides patients with educational materials they can understand.
Customizable and enriched OCT reports also enhance a patient’s medical history: the streamlined process of integrating OCT data into EMR ensures that every eye scan, with its corresponding measurements, biomarkers, and visualizations, becomes an easily accessible part of the patient’s medical history.
This is crucial for continuity of care and simplifies the audit process, providing a clear and comprehensive record of the patient’s eye health over time. Just optometry chains alone can perform an imposing volume of OCT scans, with some reaching upwards of 40,000 per week. While this demonstrates the widespread adoption of this valuable diagnostic tool, it also presents a challenge: the increased risk of missing subtle or early-stage pathologies amidst the sheer volume of data.
Enhanced OCT reports offer a solution by providing a crucial “second look” at scan results. While not foolproof, this double-check significantly reduces the risk of overlooking abnormalities in OCT interpretation, ultimately improving patient outcomes and safeguarding the clinic’s reputation.
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Identification of minor, early, and rare pathologies, including Glaucoma
AI systems that include pathology detection and segmentation in OCT scan interpretation enable automated disease characterization and longitudinal monitoring of therapeutic response. Wet AMD, Diabetic Retinopathy, and genetic diseases are among the pathologies that lead to blindness if not detected in time. Detecting pathological signs and pathologies related to these disorders in time can literally save patients from future blindness.
Another significant benefit of AI systems with early detection is OCT analysis for early glaucoma. Current tests often rely on observing changes over time, delaying treatment assessment and hindering early identification of rapid disease progression. OCT frequently detects microscopic damage to ganglion cells and thinning across these layers before changes are noticeable through other tests.
Another benefit of AI systems is that OCT interpretation for glaucoma usually utilizes a normative database to assess retinal normality. However, these databases are limited and represent an average of a select group of people, potentially missing early glaucoma development in those who deviate from the “norm.” Conversely, individuals may be unnecessarily referred for treatment due to not fitting the “normal” profile, even if their eyes are healthy.
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Second opinion
With AI-assisted OCT, you have the combined knowledge and experience of leading eye care specialists for every patient. This technology leverages massive datasets of medical images and clinical data meticulously analyzed by retinal experts during AI development. It is a valuable second opinion tool, helping you confirm diagnoses and identify subtle patterns the human eye might miss.
For example, the Altris AI mentioned above leverages a massive dataset of thousands of OCT scans collected from 11 ophthalmic clinics over the years. Carefully segmented and labeled by retinal professionals, these scans were used to train the AI. By analyzing each pixel of an image and its position relative to others, the AI has learned to distinguish between different biomarkers and pathologies.
AI for OCT Analysis
FDA approved AI that detects 70+ retina pathologies
Summing up
While OCT has revolutionized eye care diagnostics, its full potential is hindered by challenges in interpretation, ranging from a lack of confidence to time constraints and the risk of missing subtle pathologies. The survey of eye care professionals underscores a critical need for innovative solutions, particularly for practitioners who lack access to immediate peer consultation. These pain points highlight the potential for AI-assisted OCT interpretation to not only streamline workflows but also significantly enhance diagnostic accuracy and patient care.
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