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Featured This month

  • Altris AI for Buckingham and Hickson Optometry, the UK

    Altris Team
    1 min.

    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.
    Book Demo + Free Trial

    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 regarding 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:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Experience and Training: Accurate interpretation of OCT scans in optometry and ophthalmology requires specialized training and experience.
    6. 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.

    The solution: Artificial intelligence (AI) can play a significant role in OCT (Optical Coherence Tomography) scan interpretation for ophthalmologists and optometrists in various ways. Artificial Intelligence (AI) provides eye care specialists with more accurate results, severity level detection ( to work only with pathological scans), and assists in early pathologies detection.
    According Ian, one of the owners of Buckingham and Hickson optometry, “they are using Altris AI to get a second opinion on OCT scans.”
    According to Altris AI Medical Director, Maria Znamenska, who is MD, Ph.D., Associate Professor of Ophthalmology, “It is getting more common to double-check the interpretation of OCT scans ( and other medical images) with modern AI tools as they are getting safer and more efficient. Altris AI has received FDA clearance recently apart from having a CE certificate.”
  • 8 Reasons why Optometry Groups Invest in Artificial Intelligence for OCT Scan Analysis

    Mark Braddon
    5 min.

    Optometry chains offer a wide range of eye care services, making it convenient for patients to access eye care locally. 

    However, the widespread accessibility of optometry chains has a reverse side for them. The shortage of employees, new unfamiliar equipment for diagnostics, and a large number of patients create an extremely challenging workflow for many optometrists. This, in turn, creates a number of challenges that can be more familiar to Optometry chains: low optometrist recruitment and retention, inconsistent quality of examination throughout the practices, lack of communication with patients, etc. 

    Automation of routine processes and digitalization have always served as answers to challenges like these in any industry, and healthcare is no exception. Luckily, automation of one of the most complex tasks for optometrists – OCT examination is already available to optometry chains with Artificial Intelligence (AI).   

    OCT proves to be one of the most efficient diagnostic tools for many modern top-notch optometry practices, however, mastering it requires skills and time. Artificial intelligence tools, such as AI for OCT analysis platform, can automate many routine processes which will have enormous benefits for any optometry chain. The top 8 benefits are the following: 

    • #1 AI for OCT increases clinical efficiencies

    Automating OCT scan analysis through AI reduces the time optometrists spend on image interpretation. This allows optometrists to focus on more complex cases, patient interactions, and personalized treatment plans. For any large optometry chain, saving time means providing more patients with high-quality service. 

    How does it work in practice?

    For instance, Altris AI has a severity grading of b-scans. Severity grading means that 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

    • #2 AI for OCT provides consistently high standard of quality throughout the chain

    AI algorithms provide consistent and standardized analysis regardless of the individual interpreting OCT scans. This reduces variability in diagnoses and ensures that patients receive uniform care across different clinics and practitioners within the optometry chain.

    AI algorithms can analyze OCT scans with incredible precision and consistency. They can detect subtle changes in retinal structures that might be missed by human observers, leading to earlier and more accurate diagnoses of various eye conditions such as macular degeneration, glaucoma, diabetic retinopathy, and more.

    This will help younger less experienced optometrists and will serve as a second opinion tool for more experienced specialists. 

    Test how Altris AI analyzes OCT

    • #3  AI for OCT enables better retention of employees

    The shortage of optometrists in the world is staggering. 14 million optometry specialists are needed worldwide according to the WHO, while today there are only 331K ready to work.

     It is equally difficult to hire and retain a good optometrist for a company in 2023. However, more and more young optometrists choose innovative businesses that use technology to improve the workflow. Top-notch equipment, convenient scheduling tools, and of course, Artificial Intelligence for OCT & fundus photo analysis might be the perks that will help optometrists to choose your optometry business. 

    Fresh from college optometrists feel more confident when they know that they will have a backup when reviewing OCT scans

    • #4 Reduced Workload Burden

    Optometrists often have heavy workloads, and AI can help alleviate some of this burden by handling routine tasks like initial image analysis. This enables optometrists to spend more time on patient consultations and treatment planning.

    According to a survey by the General Optical Council, 57% of optometrists worked beyond their hours in 2022. Optometrists were more likely to be working beyond their hours (60%) or finding it difficult to provide patients with the sufficient level of care they needed (34%) when compared to other registration types.

    It is possible to outsource preliminary image analysis to Artificial Intelligence tools but communication and empathy are human tasks only. 

    • # 5 AI promotes enhanced patient education

    Let’s not forget about the patients. AI-generated OCT reports can help explain complex medical conditions to patients in a more understandable, visual way. After all 80% of all the information we receive is visual: imagine your optometrists not only telling but also showing what is going on with patients.  

    Comprehensive, color-coded OCT reports may improve patient education and engagement, leading to better treatment adherence and loyalty. 

    When patients don’t understand what they are paying for they are not likely to return for annual checkups. At Altris AI we created smart OCT reports that are comprehensible for patients as well as optometrists. We visualize all the pathologies and the patients can trace the dynamics of 

    #6 Reducing a clinical risk. No chances of getting a legal inquiry because of a pathology missed

    Optometry chains can perform around 40K OCT scans a week. Statistically speaking, the chance of missing a minor early pathology is huge simply because of the big number.

    With the double-check that AI for OCT scan analysis provides, It is not possible to wipe the risk out for 100%, but it is possible to diminish the risk to the absolute minimum. 

    For the optometry chain, it might mean no bad PR and weird stories in the papers and subsequently, a better brand image.

    • #7 AI makes early detection of pathologies possible on OCT

    AI algorithms can identify early signs of eye diseases that might not be easily recognizable in their early stage. This early detection can lead to timely interventions, preventing or minimizing patient vision loss.

    Glaucoma, 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.

    Early detection of pathologies means that it is possible to stop or reduce the risk of total blindness which is the best result in any sense. Early detection will allow optometrists to give valid recommendations, and advise on dieting and supplements right at the optical store. 

    • #8 Competitive Edge

    AI is a buzzword, and it’s not accidental. All major players understand its enormous value and invest in it. During the last presentation, the CEO of Google said “AI” 140 times, and let’s be honest, it is not to show off. It is because AI can actually make changes in business: automation of repetitive processes, workflow optimization, and human error reduction. 

    Adopting AI technology for OCT analysis showcases the optometry chain’s commitment to staying at the forefront of technological advancements in healthcare. Gaining a real competitive edge is another big goal. 

    This can attract patients who value cutting-edge approaches to diagnosis and treatment. A younger generation of patients are curious about new technologies, and this can be an additional lead magnet for them.

    Conclusion

    Incorporating AI for OCT analysis into optometry chains can enhance patient outcomes, make the workflow more efficient, and improve the performance of each optometry center. However, it’s important to ensure that the AI systems are properly validated, integrated into clinical workflows, and monitored to maintain their accuracy and effectiveness. More than that, it should complement, not replace, the expertise of optometrists. The technology should be used as a tool to aid optometrists and make OCT examination more effective.

     

popular Posted

  • Altris AI for Buckingham and Hickson Optometry, the UK

    Altris Team
    1 min.

    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.
    Book Demo + Free Trial

    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 regarding 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:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Experience and Training: Accurate interpretation of OCT scans in optometry and ophthalmology requires specialized training and experience.
    6. 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.

    The solution: Artificial intelligence (AI) can play a significant role in OCT (Optical Coherence Tomography) scan interpretation for ophthalmologists and optometrists in various ways. Artificial Intelligence (AI) provides eye care specialists with more accurate results, severity level detection ( to work only with pathological scans), and assists in early pathologies detection.
    According Ian, one of the owners of Buckingham and Hickson optometry, “they are using Altris AI to get a second opinion on OCT scans.”
    According to Altris AI Medical Director, Maria Znamenska, who is MD, Ph.D., Associate Professor of Ophthalmology, “It is getting more common to double-check the interpretation of OCT scans ( and other medical images) with modern AI tools as they are getting safer and more efficient. Altris AI has received FDA clearance recently apart from having a CE certificate.”
  • 8 Reasons why Optometry Groups Invest in Artificial Intelligence for OCT Scan Analysis

    Mark Braddon
    5 min.

    Optometry chains offer a wide range of eye care services, making it convenient for patients to access eye care locally. 

    However, the widespread accessibility of optometry chains has a reverse side for them. The shortage of employees, new unfamiliar equipment for diagnostics, and a large number of patients create an extremely challenging workflow for many optometrists. This, in turn, creates a number of challenges that can be more familiar to Optometry chains: low optometrist recruitment and retention, inconsistent quality of examination throughout the practices, lack of communication with patients, etc. 

    Automation of routine processes and digitalization have always served as answers to challenges like these in any industry, and healthcare is no exception. Luckily, automation of one of the most complex tasks for optometrists – OCT examination is already available to optometry chains with Artificial Intelligence (AI).   

    OCT proves to be one of the most efficient diagnostic tools for many modern top-notch optometry practices, however, mastering it requires skills and time. Artificial intelligence tools, such as AI for OCT analysis platform, can automate many routine processes which will have enormous benefits for any optometry chain. The top 8 benefits are the following: 

    • #1 AI for OCT increases clinical efficiencies

    Automating OCT scan analysis through AI reduces the time optometrists spend on image interpretation. This allows optometrists to focus on more complex cases, patient interactions, and personalized treatment plans. For any large optometry chain, saving time means providing more patients with high-quality service. 

    How does it work in practice?

    For instance, Altris AI has a severity grading of b-scans. Severity grading means that 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

    • #2 AI for OCT provides consistently high standard of quality throughout the chain

    AI algorithms provide consistent and standardized analysis regardless of the individual interpreting OCT scans. This reduces variability in diagnoses and ensures that patients receive uniform care across different clinics and practitioners within the optometry chain.

    AI algorithms can analyze OCT scans with incredible precision and consistency. They can detect subtle changes in retinal structures that might be missed by human observers, leading to earlier and more accurate diagnoses of various eye conditions such as macular degeneration, glaucoma, diabetic retinopathy, and more.

    This will help younger less experienced optometrists and will serve as a second opinion tool for more experienced specialists. 

    Test how Altris AI analyzes OCT

    • #3  AI for OCT enables better retention of employees

    The shortage of optometrists in the world is staggering. 14 million optometry specialists are needed worldwide according to the WHO, while today there are only 331K ready to work.

     It is equally difficult to hire and retain a good optometrist for a company in 2023. However, more and more young optometrists choose innovative businesses that use technology to improve the workflow. Top-notch equipment, convenient scheduling tools, and of course, Artificial Intelligence for OCT & fundus photo analysis might be the perks that will help optometrists to choose your optometry business. 

    Fresh from college optometrists feel more confident when they know that they will have a backup when reviewing OCT scans

    • #4 Reduced Workload Burden

    Optometrists often have heavy workloads, and AI can help alleviate some of this burden by handling routine tasks like initial image analysis. This enables optometrists to spend more time on patient consultations and treatment planning.

    According to a survey by the General Optical Council, 57% of optometrists worked beyond their hours in 2022. Optometrists were more likely to be working beyond their hours (60%) or finding it difficult to provide patients with the sufficient level of care they needed (34%) when compared to other registration types.

    It is possible to outsource preliminary image analysis to Artificial Intelligence tools but communication and empathy are human tasks only. 

    • # 5 AI promotes enhanced patient education

    Let’s not forget about the patients. AI-generated OCT reports can help explain complex medical conditions to patients in a more understandable, visual way. After all 80% of all the information we receive is visual: imagine your optometrists not only telling but also showing what is going on with patients.  

    Comprehensive, color-coded OCT reports may improve patient education and engagement, leading to better treatment adherence and loyalty. 

    When patients don’t understand what they are paying for they are not likely to return for annual checkups. At Altris AI we created smart OCT reports that are comprehensible for patients as well as optometrists. We visualize all the pathologies and the patients can trace the dynamics of 

    #6 Reducing a clinical risk. No chances of getting a legal inquiry because of a pathology missed

    Optometry chains can perform around 40K OCT scans a week. Statistically speaking, the chance of missing a minor early pathology is huge simply because of the big number.

    With the double-check that AI for OCT scan analysis provides, It is not possible to wipe the risk out for 100%, but it is possible to diminish the risk to the absolute minimum. 

    For the optometry chain, it might mean no bad PR and weird stories in the papers and subsequently, a better brand image.

    • #7 AI makes early detection of pathologies possible on OCT

    AI algorithms can identify early signs of eye diseases that might not be easily recognizable in their early stage. This early detection can lead to timely interventions, preventing or minimizing patient vision loss.

    Glaucoma, 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.

    Early detection of pathologies means that it is possible to stop or reduce the risk of total blindness which is the best result in any sense. Early detection will allow optometrists to give valid recommendations, and advise on dieting and supplements right at the optical store. 

    • #8 Competitive Edge

    AI is a buzzword, and it’s not accidental. All major players understand its enormous value and invest in it. During the last presentation, the CEO of Google said “AI” 140 times, and let’s be honest, it is not to show off. It is because AI can actually make changes in business: automation of repetitive processes, workflow optimization, and human error reduction. 

    Adopting AI technology for OCT analysis showcases the optometry chain’s commitment to staying at the forefront of technological advancements in healthcare. Gaining a real competitive edge is another big goal. 

    This can attract patients who value cutting-edge approaches to diagnosis and treatment. A younger generation of patients are curious about new technologies, and this can be an additional lead magnet for them.

    Conclusion

    Incorporating AI for OCT analysis into optometry chains can enhance patient outcomes, make the workflow more efficient, and improve the performance of each optometry center. However, it’s important to ensure that the AI systems are properly validated, integrated into clinical workflows, and monitored to maintain their accuracy and effectiveness. More than that, it should complement, not replace, the expertise of optometrists. The technology should be used as a tool to aid optometrists and make OCT examination more effective.

     

  • Why Eye Care Specialists Consider Innovative Tools in Addition to Normative Database

    Normative database OCT
    Maria Martynova
    06.09.2023
    6 min read

    The first normative database for OCT was created in the early 2000s and were based on small studies of mostly white patients. However, as OCT technology has evolved, so too have the normative databases. Recent databases are larger and more diverse, reflecting the increasing ethnic and racial diversity of the population.

    Free Trial

    FDA approved AI which detects 70+ retina pathologies on OCT

    Nowadays, eye care specialists use normative database to compare the characteristics of a patient to a population-wide norm. This allows them to quickly and easily assess whether a patient’s retinal dimensions fall within normal limits. According to our survey, 79% of eye care specialists rely on the normative databases for OCT verdict with every patient.

    Normative database OCT

    However, despite the fact that normative databases are very widespread among specialists worldwide, they are not perfect. They can be affected by factors such as age, gender, axial length, and refractive error.

    They can be influenced by low image quality due to different eye pathologies. It is essential to be aware of these limitations when interpreting normative data OCT parameters. That is why, in this article, we will discuss the benefits of the collaboration between AI decision-making tools and normative databases to improve patient outcomes.

    What is a normative database, and is there a difference between normative databases for different devices? 

    Before diving into the subject of the benefits and limitations of normative databases, we would like to remind you what a normative database is. From the moment of its invention, the OCT exam has rapidly gained widespread adoption and has become indispensable in the eye care practice. Critical to this success has been the ability of software to automatically produce important measurements, such as the thickness of the peripapillary retinal nerve fiber layer (RNFL) in tracking glaucoma progression or the total retinal thickness in the assessment of macular diseases. 

    In order to accurately interpret OCT scans, normative databases were created. These databases are now built into almost all commercial OCT devices, allowing eye care specialists to view colored reports and progression maps that assist in the rapid recognition and tracking of pathology.

    Summing up, a normative database for OCT is a set of data that provides references for OCT thickness measurements in a healthy population. These databases are used to compare the OCT measurements of your patient to a population-wide norm. 

    Here are some of the OCT parameters that are commonly measured and compared to normative databases:

    • Retinal nerve fiber layer (RNFL) thickness: the RNFL is a retinal layer that is measured around the optic nerve. This measurement is important for diagnosing optic nerve atrophy.
    • Macular thickness: the macula is responsible for sharp central vision.
    • Ganglion cell complex thickness: the ganglion cell complex is a group of cells in the retina that are responsible for transmitting visual information to the brain.
    • Cup-to-disc ratio, neuroretinal rim, and other optic nerve parameters: are very important for diagnosing glaucoma and other optic nerve pathologies

    These are just a few of the OCT parameters that are commonly measured in normative databases. The specific parameters that are measured can vary depending on the type of OCT device and the clinical application. 

    In addition, different OCT devices can have different measurement capabilities and resolutions. For example, a device that uses time-domain OCT (TD-OCT) technology may have a lower resolution than a device that uses spectral-domain or swept-source OCT (SD or SS-OCT) technology. This means that the normative database for a TD-OCT device may not be as accurate as the normative database for an SD or SS-OCT device.

    What is more, the normative database for a particular device may be based on a specific population of patients. What are the benefits and limitations of normative databases?

    Now that we have highlighted different aspects of the normative database definition let us discuss the benefits and limitations of this tool. Normative databases can sometimes be very helpful for eye care specialists in diagnosis, decision-making, and creating a treatment strategy for eye diseases such as glaucoma and macular degeneration.

    • The measurement provided by the normative database can be used as a baseline for tracking a patient’s response to medication or other treatment. Eye care specialists can track changes between a few visits and determine the impact on the patient.
    • Normative databases show deviations from the norm, which may be a reason for a more comprehensive examination.
    • Eye care specialists can also use normative databases to compare the results of different OCT devices. This can help to ensure that they are using the most accurate device for their patients.

    There are still challenges that must be overcome to develop normative databases sufficient for use in clinical trials. That is why current normative databases also have a lot of limitations.

    Does not detect pathology

    The normative database works only with the thickness of the retina and does not detect what is inside the retina. Therefore, it cannot detect all pathologies where there is no change in retinal thickness. In the early stages, these are absolutely all diseases. We can see deviations from the normative base only when the disease progresses to a later and more severe stage when the retinal thickness decreases or increases.

    Limited diversity

    Normative databases can be limited by factors like age, gender, and ethnicity of the population used to create them. This can result in reduced accuracy for patients who are not well-represented in the database.

    Population variation

    Even healthy patients can have some anatomical variations that fall within the range of normal. These variations may be falsely flagged as abnormalities when compared to the database.

    How Altris AI platform can complement the information provided by the normative database

    Normative databases in OCT play a crucial role in aiding diagnosis and treatment planning, but they also have limitations related to representation, disease progression, and data quality. Eye care specialists need to interpret the results in the context of the patient’s individual characteristics and other clinical information, using additional tools for scan interpretations.

    Sometimes, low-quality OCT scans can be inaccurately interpreted by the eye care specialist, and the normative database can showcase inaccurate measurements. Altris AI platform detects low-quality scans automatically and warns about the possibility of inaccurate results. In addition, the platform automates the detection of 70+ pathologies and pathological signs. Once the user uploads the scan, they can see visualized and highlighted pathological areas and pathology classification that the algorithm has detected. The user can also calculate the area and volume of detected biomarkers.

    Normative database OCT

    Artificial intelligence-based tools for OCT interpretation used along with normative databases can play a crucial role in clinical eye care. Altris AI, for example, can provide eye care specialists with additional and more precise information about separate retinal layer thickness. The system analyzes the thickness of each retina layer or several layers combined.

    Normative database OCT

    While normative databases provide information only about the thickness, AI tools equipped with deep learning models can detect pathological signs in OCT scans that might be missed by the normative database or the human eye, enhancing diagnostic accuracy. Altris AI algorithm classifies the OCT scans based on the degree of pathology found. It can distinguish green concern, which indicates normal retina, yellow – moderate with slight deviations, and red concern, which means high severity level.

    Normative database OCT

    Summing up

    Despite their limitations, normative databases are an essential tool for the clinical use of OCT. They provide a valuable reference point for assessing patients and can help to identify some diseases. However, the normative database measures only the thickness, which is not enough to accurately diagnose the patient and create a treatment plan.

    Make your eye care business technological

    That is why incorporating AI into OCT interpretation streamlines the decision-making process. By automating the initial analysis of OCT scans, specialists can focus their attention on more complex cases, making the best use of their skills and experience. Moreover, embracing AI technologies empowers eye care specialists to personalize patient care with greater precision.

  • AI Blindness Prevention: How We Can Use Artificial Intelligence to Help Prevent Blindness

    AI blindness prevention
    Maria Martynova
    07.08.2023
    9 min read

    The total number of people with near or distant vision impairment reaches 2.2 billion worldwide. Of these, 43 million people are blind, and 295 million are suffering from moderate to severe visual impairment. Although the numbers are constantly changing as new research is conducted, the global burden of blindness and visual impairment remains a significant problem of humanity in the fight against which specialists combine their forces with AI technologies.

    AI blindness prevention

    AI blindness prevention tools are being actively developed to transform the landscape of vision care in many ways. Eye care specialists use AI systems for screening and detecting diseases that lead to vision loss. AI-powered smart monitors assist specialists in finding proper contact lenses and glasses. In addition, many researches are held with the help of AI algorithms, as they are able to process vast amounts of data.

    In this article, we will discuss different applications of AI in blindness prevention, specifically how artificial intelligence tools can empower eye care specialists and extend beyond the clinical setting. 

    Book intro + free trial

    Make your eye care business technological

    Today’s conditions and risk factors of blindness you should pay attention to

    Before talking about the developments in the AI sector toward blindness prevention, we would like to discuss the most common causes and risk factors of this impairment. Many health and lifestyle factors can influence the risk of vision loss. Smoking, excessive alcohol consumption, sun exposure, and poor nutrition can contribute to diseases that lead to vision loss. 

    In addition, there are many conditions that can lead to blindness if left with no proper treatment, among which are the following. 

    Age-related eye diseases

    The global population is aging rapidly. The number of people aged 65 and over is projected to triple from 1 billion in 2020 to 2.1 billion in 2050. Considering this fact, age-related eye diseases have become a prominent cause of blindness. Such diseases as age-related macular degeneration (AMD), cataract, and glaucoma are more prevalent in older patients, and if left untreated, they can lead to fast and significant vision loss. Regular eye check-ups and timely interventions are crucial in managing these diseases and preventing severe visual impairment.

    AI blindness prevention

    Besides AMD, there are a lot of age-related conditions which can be a red flag when examining the patient. Among these are macular holes, mactel, and vascular diseases, for example,  central retinal vein occlusion (CRVO) and central retinal artery occlusion (CRAO). Detecting even one of these pathological conditions in the early stages of their development is crucial for preventing vision loss. 

    However, many eye care specialists sometimes don’t have enough resources to dedicate more time to analyzing patients’ images. Our recent survey detected that among more than 300 participating optometrists, 40% of them have more than 10 OCT exams per day. Meanwhile, 35% of eye care specialists have 5-10 OCT examinations per day. The greater the number of patients per day, the greater the likelihood that eye care specialists may miss some minor, rare, or early conditions.

    AI blindness prevention

    Fortunately, nowadays, there are a lot of ways to empower the clinical workflow, and AI blindness prevention tools are gaining popularity. Artificial intelligence systems like Altris AI can analyze retinal images and other diagnostic data to detect early signs of age-related eye diseases. Altris AI platform, for example, can detect 70+ pathologies and pathological signs, including the ones, that refer to age-related diseases.

    AI blindness prevention

    Altris AI platform allows eye care specialists to rely on its disease classification when diagnosing a patient. It detects all the most common age-related pathologies, such as AMD, mactel, and vascular diseases – CRVO, CRAO.

    AI blindness prevention

    Diabetes and diabetic retinopathy

    Diabetes and related conditions are also common causes of vision loss. In the United States, about 12% of all new cases of blindness are caused due to diabetes. Globally, diabetes is estimated to cause 4.8% of all blindness. In addition, the risk of blindness from diabetes increases with the duration of diabetes. People with untreated diabetes for years are 25 times more likely to be blind than people without diabetes.

    AI blindness prevention

    The complication of diabetes, called Diabetic retinopathy (DR), affects the blood vessels of the retina and can lead to impaired vision or blindness. With the rising prevalence of diabetes worldwide, DR has become a significant problem. Early detection, proper control of diabetes, and regular eye exams are essential to prevent vision loss. 

    The American diabetes association (ADA) recommends that people with diabetes have an OCT scan of their eyes every year. This is because OCT can help to detect early signs of DR with high precision. In some cases, eye care specialists may recommend more frequent OCT scans. This may be the case if the patient has advanced diabetic retinopathy or a family history of diabetic retinopathy.

    AI blindness prevention

    AI algorithms such as Altris AI can assist in detecting the pathological signs of diabetic retinopathy or diabetic macular edema. Our web platform differentiates certain pathological signs that indicate diabetes-related diseases. Among these are:

    • Intraretinal fluid
    • Subretinal fluid
    • Hard exudates
    • Hyperreflective foci
    • Epiretinal fibrosis

    Genetic and inherited conditions

    Some patients are at a greater risk of developing visual impairment due to genetic factors or the inheritance of certain conditions. For example, retinitis pigmentosa is an inherited disease that affects the photoreceptor cells in the retina and gradually leads to night blindness and loss of peripheral vision. Genetic testing and counseling can help identify people at risk and provide early intervention.

    AI blindness prevention

    Some genetic eye conditions, such as myopia, vitelliform dystrophy, or retinoschisis, can be detected in the early stages with the help of OCT examination and artificial intelligence systems. Altris AI platform can help eye care specialists in their daily practice and make eye care more accessible, allowing specialists to perform regular eye check-ups, and provide timely treatment of genetic conditions.

    AI blindness prevention

    Current ways to prevent blindness with AI 

    As you can see, blindness risk factors encompass a wide range of conditions, pathologies, and circumstances that can significantly impact a patient’s health and increase the likelihood of severe visual impairment. Poorly managed age-related eye diseases, genetic and hereditary factors, and chronic health conditions can lead to eye-related complications, further elevating the risk of blindness.

    AI blindness prevention

    In the following paragraphs, we will describe in detail the modern ways of using artificial intelligence to detect and prevent blindness: from AI-based retinal imaging for early detection of eye diseases to personalized treatment recommendations and remote patient monitoring.

    AI for image interpretation

    AI blindness prevention

    It is important to understand that the timely detection of eye diseases is key to the effective treatment of visual impairments. However, today we have an unfortunate tendency to diagnose severe forms of disease too late. A large-scale survey by Eyewire conducted in 2021 found that about 40% of people in the USA said they had not had an eye exam in more than a year, and 10% said they had not had one in more than five years. 

    In addition, recent research by the British Journal of Ophthalmology found that 25.3% of people in Europe over the age of 60 have early signs of AMD. In the UK, about 200 people a day are affected by a severe form of AMD (wet AMD), which can cause severe blindness. 

    These studies show us that while eye care specialists around the world are trying to treat as many patients as possible, unfortunately, many patients are going blind due to delays in diagnosis. However, using advanced AI-based image analysis systems can speed up the detection of warning signs, allowing you to reach more patients.

    One of the advantages of AI for image analysis is its assistance in decision-making. Altris AI is a great example of how an image analysis system can help prevent blindness with AI. The platform allows eye care specialists to detect 74 retina pathologies and pathological signs, including risk conditions for vision loss, like AMD, Diabetic retinopathy, Vascular diseases of the retina, and others. 

    Diagnosing eye disease in children

    AI blindness prevention

    Today, one of the most important AI blindness prevention research is focused on teaching artificial intelligence algorithms to detect retinopathy in premature infants. Retinopathy of prematurity is the main cause of childhood blindness in middle-income countries. Some researches show that around 50,000 children all over the world are blind due to the disease.

    Unfortunately, experts’ forecasts show that these figures are likely to grow. Retinopathy of prematurity is becoming more and more common, especially in African countries. About 30% of children born in sub-Saharan Africa have this disease and, due to late detection and insufficient attention due to the lack of eye care specialists, can also go blind.

    An artificial intelligence model developed by an international team of scientists from the UK, Brazil, Egypt, and the US, with support from leading healthcare institutions, is able to identify children who are at risk of blindness if left untreated. The team of scientists hopes that this AI system will make access to screening and monitoring of young patients more affordable in many regions with limited eye care services and few qualified eye care specialists.

    AI monitors for eye strain control

    Another interesting application of AI to prevent blindness is eye care monitors. They are planned to be used to avoid eye strain due to prolonged computer work. Such monitors will be programmed to monitor the user’s facial expressions, blinks, and eye movements. They will also be able to assess the level of light in the room, and artificial intelligence will automatically adjust the screen brightness and image contrast.

    Since a huge number of the world’s population has switched to remote work since the pandemic and spends almost all day at the computer, such AI monitors are considered a huge help for users in preventing eye diseases that can lead to visual impairment.

    AI to determine better glasses or contact lenses

    AI blindness prevention

    In the field of developing and calculating suitable lenses, there are also a number of companies that have joined the development of AI tools. AI monitors will collect important information about the patient’s eye condition, analyze it, and prescribe suitable contact lenses or glasses. 

    In addition, these monitors will be able to analyze the patient’s medical history, including medical images, and create the most suitable treatment strategy to maximize visual acuity.

    AI for studying the human eye

    AI blindness prevention

    Today, AI is a promising tool for studying human eye tissue and developing new tools for diagnosing and treating eye diseases, including those that lead to vision loss. Artificial intelligence tools are used to analyze OCT images of the eye to detect changes that may indicate diseases such as diabetic retinopathy, macular degeneration, and glaucoma. AI is also used to predict the development of eye diseases based on genetic or risk factors. This is expected to help doctors identify people at risk of developing eye diseases at an early stage and prevent the progression of the disease.

    Summing up

    Today AI blindness prevention tools are already developing by many leading companies and institutions, and some companies, like Altris AI, are already using the potential of artificial intelligence to provide early detection and diagnostic advice for eye care specialists. But it’s worth noting that AI tools are not capable of coming up with innovative solutions for blindness prevention.

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    Only in close cooperation with eye care specialists AI blindness prevention tools can help in many ways, like early detection, providing access to medical care in underserved regions, detecting minor or rare conditions, and allowing to focus on personalized care and treatment of patients.

  • 5 Tips When Introducing the OCT Eye Exam to Patients

    OCT eye exam
    Mark Braddon
    24.07.2023
    8 min read

    As optometry technology evolves, many optometrists predict that utilizing OCT eye exam in practice will be vital in maximizing patient care. That is why successfully integrating an OCT device into your optometry practice workflow is instrumental to its clinical and commercial success.

    Optometrists from different countries often have the same questions about how to successfully integrate an OCT device into an Optometrist Practice, regardless of practice size or experience level. How to make patients feel comfortable? How to explain the importance of regular OCT scans? Will patients understand what is an OCT scan of the eye? How do we avoid patients thinking we want to perform OCT eye exams just to earn more money? The process of introducing OCT to patients is complex and covers many areas. 

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    If we speak to optometry practices, both those who are new to OCT and those who have had the OCT device for many years, most of them will want to improve the ROI and ensure the patients are gaining the full value of the OCT eye test. This article will show you 5 tips for successfully introducing the OCT eye exam to your patients.

    Remember why you invested in the OCT technology

    One may think that only novice optometrists tend to underestimate their work or do not feel confident about the value they give to patients. However, some experienced clinicians also avoid offering OCT eye tests because they think they are ‘overselling’ with additional fees for OCT, Optos, or other diagnostic exams. 

    That is why it is important to remember why you invested in OCT technology in the first place. In almost all cases, this is to improve the clinical standard of eye care that you offer to your patients. In fact, when I ask some optometrists if they want a member of their families to have an OCT eye exam, the answer is always ‘Yes, of course!’. So if you strongly recommend undergoing an examination to your relatives, why would you not recommend an OCT eye test for your patients?

    OCT eye exam

    Before a patient comes into the practice, one of the most important things you need to do is not undervalue your time, skills, and experience when charging for the additional time the OCT exam takes to interpret and discuss. 

    Implementing an OCT eye exam into regular practice improves clinical care and can generate a commercial benefit as well by increasing revenue through fees, patient retention, and loyalty. Moreover, word of mouth is often the most significant source of new patients for optometrists. If the patient feels you are confident in everything you do, it will make them more likely to recommend you to friends and family

    Explain the importance of OCT eye exam for early detection 

    From the first touch point, the patient should understand that your optometric practice takes its business seriously and provides additional diagnostic examinations, such as the OCT, to improve the quality of care. The first impression of your approach is very important, so it is crucial to start introducing the technology to the potential patient from the first point of contact. 

    As a rule, the beginning of a patient’s introduction to the OCT eye exam starts with several touch points. Whether they make their appointment for the eye examination through your website, mobile application, in person, or by phone, the most important thing you can do is create an integrated and comfortable patient journey.

    OCT eye exam

    Before a patient comes into the practice, you should explain the importance of the OCT device and its benefits compared to the standard examination. Even when the patient is fully acquainted with the OCT eye exam, they may still need help understanding why this particular imaging method is necessary. The ability of OCT eye exam to detect diseases in the early stages makes this technology indispensable for optometrists and patients and this is why it is such an excellent tool for diagnosing eye diseases. 

    More importantly, avoid frightening patients with stories about difficult-to-treat rare pathologies. Instead of talking about the pathology consequences, say that the OCT eye exam scan provides a clear map that helps locate areas of the eye with abnormalities or early changes.

    Understand the importance of a healthy-eye-as-a-baseline concept

    In this section, I want to discuss the concept of a healthy eye in more detail. When a patient comes to you for an examination, it is essential to use the correct narrative that the optometrist should use when discussing the results of an OCT eye exam with patients. It is important to emphasize that we are not looking for pathology but a healthy eye.

    We know that we will detect pathology in certain patients. The number of patients likely to have at least one pathology increases if you work with an older population. However, finding a healthy baseline scan is an important part of monitoring the long-term eye health of the patient.

    OCT eye exam

    Talking about baseline, make sure to emphasize how great it is to find a healthy eye in a patient. Explain that together you found a nice, healthy eye so you have the baseline to compare with the patient’s future scans. Emphasize that, hopefully, you will find a healthy eye at the next eye examination, but if anything does start to change, then with the help of an OCT eye exam, you will be able to detect these early and minor changes as you have the healthy baseline scan to compare to.

    It is necessary to develop your patient’s understanding through appropriate teaching and discussion. Giving the value of the baseline OCT eye exam to your patients is very important. Notice the difference between “We found nothing” and “We found a healthy eye”. The first statement is negative and undermines the reason for the scanning of patients for a healthy eye baseline. Meanwhile, the second statement is positive and clearly gives your patient more value as you have found what you are looking for.

    Integrate the OCT eye exam into the patient workflow

    Another one of my recommendations is to call the eye examination that includes the OCT eye exam the Advanced or Comprehensive Eye Examination. It is important to make sure all the staff members use the same terminology and your message to a patient is consistent from first contact to the end of the practice visit. The eye examination without the OCT exam can be called the ‘Standard Examination’ as we are not trying to make the ‘normal’ eye examination appear below standard, what we are trying to do is explain that the practice is invested in the latest technology to offer the most advanced (or comprehensive) examination for your patients benefit.

    OCT eye exam

    For example, when a patient books an appointment, make sure that the support staff uses the same terminology as written throughout the website, reminder letter/email, or mobile app if you have one.  

    When you review the OCT images with the patient, explain that you are going to look at the OCT images of the retina, which is part of the ‘Advanced examination’. When a patient pays at the end of the customer journey, make sure that the ‘Advanced Examination’ is mentioned again. When a patient rings up or books online for the next OCT eye exam, then they will understand what the ‘Advanced examination’  means and are more likely to select this option straight away for future examinations.

    Concentrate on giving more value to your patients

    Review the results with the patient to give them the actual value of an OCT scan. This will allow you to establish communication with the patient and improve their perception. Give them the “theatre” around the additional diagnostic testing so they understand how it applies to them and feel valued.

    OCT eye exam

    Remember that your knowledge, enthusiasm, and the extent to which the patient is involved in the process directly affect the clinical and commercial success. Dedicate time to each patient, involve them in the diagnostic process, and explain the OCT scans of their eyes on the screen.

    How can Altris AI help with introducing OCT Eye Exam

    OCT eye test

    When talking about improving the clinical standard of care your practice offers to your patients, the Altris AI platform can also improve the standard of care you offer to your patients. The platform helps to quickly determine if the eye is healthy. If pathology is detected, then Altris AI identifies the very early, rare, or minor changes that can be the start of something more severe. Altris AI detects over 70 pathologies and pathological signs. If early pathology is identified, then the Altris AI platform can help educate the patient by clearly highlighting the areas of concern and then giving you the opportunity to discuss lifestyle changes, over-the-counter medications, or supplements, which may help the patient now rather than just monitoring until it is time to refer. 

    The Altris AI platform can improve the patient’s understanding of the OCT exam and add value to the Advanced Eye Examination.

    OCT eye test

    All you need to do is to upload an OCT macula exam to the platform and Altris AI will assess the exam by severity differentiating the b-scans between high, medium, and low severity levels.  The segmentation/classification module will highlight pathological signs on the OCT scan one by one and give the classification/s of any pathology found to support you with the diagnosis. Meanwhile, in the Comparison module of the platform, you are able to compare the baseline scan with the current one. 

    Summing Up

    Remember why you invested in the OCT technology in the first place — usually, this is to improve the clinical standard of care you can offer to your patients. The improvement in clinical care can also generate a commercial benefit as well by increasing revenue through OCT exam fees, patient satisfaction, patient retention and loyalty, and an increase in recommendations of friends and family. 

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    Build a patient journey in such a way that, at each stage, they know that they have received a new, exciting, and, important part for the most comprehensive examination you offer. Remember that the more skill and enthusiasm you show, the more you can interest the patient and increase the probability that they will return for their next examination with OCT.

    In addition, consider using modern AI tools to help you with decision-making. Image management systems like Altris AI can help you interpret the OCT scans faster and with more confidence. This will leave more time to add value for your patient, and integrating AI into practice can be another example of how you are investing in the latest technology to benefit your patients.

  • Business Case: Lux Zir Ophthalmic Clinic

    Altris Team
    11.07.2023
    2 min read

    The Client: Lux Zir is one of the best-known ophthalmic clinics in Ukraine which provides retina diagnostics and eye treatment services. The clinic currently employs 3 full-time eye practitioners 2 general ophthalmologists and a pediatric retina expert.

    The clinic normally sees between 15-20 per day with up to 10 OCT examinations performed.

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    The Problem:

    Luxzir uses Optical Coherence Tomography as one of its core diagnostic methods because of its high level of accuracy and non-invasiveness. However, the clinic needed to solve several typical problems related to OCT.

    • Some ECPs have less experience with OCT interpretation than others and this creates an inconsistent standard of care throughout the clinic.
    • Some ophthalmologists come across complex OCT scans that they are unable to interpret without the help of their more experienced colleagues.
    • It is difficult to maintain a high standard of care for diagnostics when the CMO is absent during the period of vacation or sick leave.
    • Take out wrong and start with an inaccurate diagnosis on the basis of OCT of the patients who are referred to the clinic from other eye care centers. 

    The Solution:

    Lux Zir Ophthalmic Clinic decided to implement the Altris AI platform as they understood how it can help resolve their problems. The results have been very positive with improvements with all issues above problems, and received very positive results.

    According to Marta Shchur, Chief Medical Officer at Lux Zir clinic, the implementation of the Altris AI system improved the level of OCT diagnostics inside the clinic or if to be precise:

    • OCT interpretation is now considerably faster allowing the ECPs to see 10% more patients per day.
    • OCT diagnostics has become much more efficient: supported by Altris AI, ophthalmologists now have confidence when diagnosing pathologies and pathological signs, even rare ones.
    • The quality of diagnostics is consistent regardless of the experience of the specialists.
  • Business Case: Altris AI for Jeff Sciberras Optometry

    Altris AI Team
    10.07.2023
    1 min read

    The Client: Canadian Optometry Clinic

    Jeff Sciberras Optometry Clinic is an established eye care facility in Mississauga, Canada. They have been recognized as the Top Choice Optometry Clinic for the past five years running in this large Canadian city. Dr. Jeff Sciberras is proud of his high patient satisfaction rate: 92% of those surveyed would refer a friend, colleague, or family member to this establishment.

    Dr. Sciberras aims to provide comprehensive eye care, with a desire to utilize leading technologies and the delivery of premium eye care products.

    Recent technology investments include OCT, which allows earlier diagnosis and greater in-house management capabilities.

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    The Challenge:

    The optometry clinic has just purchased a brand new Optopol Revo OCT equipment and the support was needed in OCT scan interpretation. OCT is one of the most accurate methods of retina diagnostics  however, the interpretation of OCT scans can be challenging and time-consuming,  for both doctor and patient.

    The Result:

    Dr. Sciberras has been extremely satisfied with the support that the Altris AI platform provides:

    • Increased confidence when working with the new OCT device · more profound analysis of OCT scans
    • More adequate referral of complex cases.
    • Scan summaries for the patient.
    • Earning patient confidence and trust: The image of the innovative optometry center is enhanced to their patients and families.
    • The AI Segmentation/Classification Module is invaluable for the optometry center as this module helps in the identification of 70+ pathologies and pathological signs.

    The introduction of OCT with Altris AI has transformed my practice literally overnight. The integration was seamless and Altris customer support has been outstanding.

    Overall, Dr. Sciberras has been impressed with the experience and support Altris AI provides and is happy to have chosen to partner with them for his leading eye care center.

  • DICOM File Format: Benefits of Managing DICOM images

    DICOM file format
    Mark Braddon
    31.05.2023
    6 min read

    DICOM file format (Digital Imaging and Communications in Medicine) was developed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) as a standard for exchanging medical images and related information across different healthcare systems. It serves as a universal language for medical imaging, enabling interoperability between various imaging devices and systems. DICOM ensures that medical images can be exchanged and viewed consistently regardless of the manufacturer or modality.

    DICOM image format supports a broad range of medical imaging modalities, including X-ray, MRI, OCT, ultrasound, nuclear medicine, and more. It also covers related data, such as patient information, study details, image annotations, and results.

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    As the DICOM format continues to evolve to keep up with advancements in medical imaging technology, our article aims to raise awareness among ophthalmologists and optometrists about the DICOM file format.

    You can also watch a short video about DICOM and non-DICOM file formats.

    What is DICOM file format?

    Image files that adhere to part 10 of the DICOM standard are commonly known as “DICOM format files” or simply “DICOM files,” and their file extension is “.dcm.” In ophthalmology, DICOM is a widely used file format for storing and transmitting medical images. DICOM files are used to store various types of ophthalmic images as well, including retinal images, optical coherence tomography (OCT) scans, visual field tests, and angiography images.

    DICOM files consist of two main components: the header and the image data. The header contains metadata that describes the patient, study, series, and image acquisition parameters.

    DICOM image format

    This metadata includes information such as patient demographics, image acquisition parameters (e.g., imaging modality, image orientation, pixel spacing), and any annotations or measurements made on the image. The image data itself is typically stored in a compressed format, such as JPEG or JPEG 2000, within the DICOM file.

    DICOM files also support the exchange of images and associated data between different medical imaging devices and systems. This enables eye care specialists to easily share and access ophthalmic images across different platforms, such as picture archiving and communication systems (PACS), ophthalmic imaging devices, and electronic health record (EHR) systems.

    By using DICOM, ophthalmologists and optometrists can efficiently store, retrieve, and analyze ophthalmic images, ensuring accurate diagnoses and effective patient care. In the next paragraphs, we will tell you more about the benefits of the DICOM file format for eye care specialists.

     

    Benefits of DICOM file format

    The DICOM standard ensures interoperability between different vendors’ OCT devices and facilitates seamless data sharing and analysis. The main difference between DICOM and other image formats is that it groups information into data sets. A DICOM file consists of several tags, all packed into a single file. It stores such info as:

    • demographic details about the patient
    • imaging study’s acquisition parameters
    • image dimensions
    • matrix size
    • color space
    • an array of additional non-intensity information necessary for accurate image display by computers.

    If you have to enter the patient’s information manually, there’s always a chance you can misspell the name or other information. However, when using a DICOM file to store patients’ information and monitor patients’ health, eye care specialists can be sure the chance of human bias is much lower.

    When you work in an optometry practice or a clinic, you may spend a lot of time filling in the details every time you upload a file. And if your clinic is busy and you do 30-50 uploads daily, it could take hours. Using DICOM image format significantly speeds up the process and reduces errors.   

    DICOM file format

    Another benefit of the DICOM image format is that the header data information is encoded within the file so that it cannot be accidentally separated from the image data. 

    DICOM files can be stored in a DICOM server or transmitted between DICOM-compliant systems using the DICOM network protocol (DICOM C-STORE or DICOMweb). DICOM SR (structure reporting) allows for the structured representation of measurement data and annotations in OCT images. It enables the storage of quantitative measurements, such as retinal thickness or optic nerve parameters, as structured data within the DICOM file.

    In addition, eye care specialists are able to manipulate the brightness of the image when using the DICOM viewing software. Some areas of an image can be increased or decreased for a better viewing and diagnostic experience.

    Is DICOM file format popular among OCT providers?

    When it comes to optical coherence tomography, many OCT device manufacturers and software providers support the DICOM standard for storing and exchanging OCT images. Some of the prominent OCT providers that offer DICOM support include:

    • Heidelberg Engineering is a well-known provider of OCT devices and software solutions for ophthalmology. They offer OCT devices like the Spectralis OCT, which supports DICOM connectivity. The DICOM capabilities of their systems enable seamless integration with PACS and other healthcare systems.
    • Carl Zeiss Meditec is a leading manufacturer of ophthalmic devices, including OCT systems. Their OCT devices, such as the Cirrus OCT, are DICOM-compatible, allowing for efficient storage and sharing of OCT images with other DICOM-compliant systems.
    • Topcon Medical Systems is another prominent provider of OCT devices. Their OCT systems, such as the Topcon 3D OCT, support DICOM connectivity, enabling interoperability with other DICOM-enabled devices and systems.
    • NIDEK offers a range of ophthalmic imaging devices, including OCT systems. Their OCT platforms, such as the NIDEK RS-3000, support DICOM, allowing for seamless integration with DICOM-compliant infrastructure, such as PACS and EHR systems.

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    These are just a few examples of OCT providers that support the DICOM standard. It’s important to note that DICOM support may vary among different models and versions of OCT devices from each manufacturer. We recommend you consult with the specific manufacturer or review their product documentation to confirm the DICOM capabilities of their OCT systems.

    Why do we recommend using DICOM file format with Altris AI?

    Modern DICOM viewer software extends beyond simple viewing. It can enhance image quality, generate additional data, take measurements, and more, and Altris AI is no exception. Using the DICOM image file gives you more opportunities within the platform.

    Such features as

    • retina layers thickness and linear measurements

    DICOM file format

    • area and volume calculations

    DICOM file format

    are only available when using the DICOM file format. This is because it contains the original image pixel data without modifying the study metadata. In case you upload an image, retina layers thickness won’t be available, as well as the measurements.

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    Another advantage of the DICOM format is that you can add patient and examination details in a few clicks by just uploading a DICOM file since this information is being pulled out automatically. 

    DICOM file format

    In the case of other image formats, when uploading an examination, you would have to manually fill in a bunch of information such as scan widths, eye type, etc.

    Considering all mentioned above, using DICOM format files saves time, increases efficiency, and gives you more opportunities within the Altris AI platform.

    Summing up

    In conclusion, the DICOM file format proves to be a valuable asset for eye care specialists. Its unique characteristics, such as grouping information into data sets and incorporating standardized tags within a single file, offer many advantages. 

    This format ensures the preservation of accurate and comprehensive data, reducing the potential for human error and minimizing the risk of data loss or misinterpretation. The DICOM file format streamlines the archival, organization, and display of images, optimizing the workflow of eye care specialists. 

    By adhering to the DICOM standard, OCT devices and software solutions ensure compatibility, interoperability, and consistent data representation across different platforms. This enables efficient communication and collaboration among healthcare professionals, enhances research capabilities, and promotes the broader use and exchange of OCT imaging data.

    Make your eye care business innovative

    With its widespread adoption and compatibility with various medical imaging systems, DICOM empowers ophthalmologists and optometrists to provide efficient and high-quality care while promoting seamless collaboration and knowledge sharing within the field. Ultimately, the DICOM file format plays a vital role in enhancing patient care, advancing research, and fostering innovation in the field of eye care.

  • How 7 Leading Optometry Centers Provide Innovations in Eye Care

    innovations in eye care
    Maria Martynova
    08.05.2023
    9 min read

    Top modern optometry centers are not afraid of embracing innovations in eye care. Some offer home eye tests, others create mobile apps to try on frames remotely. There are optometry centers that use artificial intelligence to empower optometrists in OCT/ fundus interpretation. We’ve collected 7 optometry centers that are using technology now to win the competition. 

    From advanced diagnostic and treatment technologies to personalized care and patient education, these centers are transforming the way clients approach and bring innovations in eye care. 

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    Optometry meets technology: AI, AR, mobile apps, and home eye tests

    Augmented Reality (AR), mobile apps, and home eye tests are emerging trends that are changing the way people receive eye care.

    • AR technology uses the camera lens on a mobile device or your PC as the method to deliver information and graphics. A user accesses an AR application, and the camera viewpoint incorporates the data directly into the perspective in real time. With AR apps for eyewear and exams, anyone can have a large selection of glasses and other services from their homes, offices, or on the go.
    • Mobile apps offer a wide range of eye care services, from information on eye health and tips for maintaining healthy vision to virtual vision screenings. Moreover, mobile apps are also used to educate both young and experienced optometrists. We strongly believe that educational mobile apps inevitably become an additional efficient tool for OCT education because they are accessible and interactive. 
    • Another one of the innovations in eye care is Home eye tests are also often enabled by digital vision testing tools. They are becoming more and more common and offer a convenient and cost-effective way to monitor vision changes.
    • As for AI use in optometry practice, it allows its users to see a broader perspective of a patient’s eye health. Incorporating AI streamlines billing procedures, expands the input of electronic health records (EHRs), optimizes claims management, and improves cash flow. AI technology can also be used in cooperation with AR assisting in the glasses selection.

    Although these innovations in optometry and ophthalmology provide more comprehensive access to eye care and improve patient engagement, many optometry practices are still hesitating to add such innovations to their routine. That is why we prepared the info about 7 famous optometry practices that are already using innovations in eye care.

    Warby Parker

    innovations in eye care

    Warby Parker started its way in 2010 when the founders of the company were students. One of them lost his glasses during a tourist trip. The cost of replacing them was so high that he spent his first semester of graduate school without them. That is why the company’s mission is to provide affordable, high-quality eyewear to consumers, while also addressing the issue of access to vision care. 

    One of Warby Parker’s unique innovations in eye care is its Home Virtual Try-On program, which allows customers to try on up to five frames at home for free before making a purchase. This program makes it easier for customers to find the perfect pair of glasses and eliminates the need for them to go to a physical store to try on frames.

    innovations in eye care

    Warby Parker also offers an online eye exam called the Virtual Vision Test. It is designed to provide customers with a convenient and affordable way to obtain a prescription for glasses or contacts from the comfort of their own homes.

    The Virtual Vision Test is a telemedicine service that uses technology to allow customers to take an eye exam using their computer or smartphone. The test is not meant to replace a comprehensive eye exam performed by an eye doctor, but rather to provide a convenient option for those who need a prescription renewal or have mild refractive errors. 

    After completing the test, the results are reviewed by a licensed ophthalmologist or optometrist, who will issue a prescription if appropriate. The customer can then use the prescription to purchase glasses or contacts from Warby Parker or any other provider.

    Lenskart

    innovations in eye care

    Lenskart is a fast-growing company of innovations in eye care in India focused on making eyewear more affordable for everyone. To achieve this goal, the company has developed a number of innovative technologies and business models, including a mobile app that allows customers to try on frames virtually and a home vision testing service that allows to check their prescriptions from the comfort of their own home.

    One special feature of the Lenskart app is the “3D Try-On” feature, which uses 3D imaging technology to create a model of the customer’s face and allows them to try on different frames virtually. This feature helps get a better sense of how a particular frame will look on a customer’s face before making a purchase.

    innovations in eye care

    Another one of Lenskart’s innovations in eye care is the Home eye test, designed to provide people with a convenient and affordable way to obtain a prescription for glasses or contact lenses. To take the Lenskart Home Eye Test, customers must first book an appointment on the company’s website or mobile app. 

    The eye test includes a visual acuity test, a color vision test, and a refractive error test. The optometrist will also check the customer’s eye health and recommend any necessary follow-up exams or treatments. After the test, the optometrist will provide a prescription, which the customer can use to purchase glasses or contacts from Lenskart or any other provider.

    SmartBuyGlasses

    innovations in eye care

    SmartBuyGlasses is an online eyewear retailer that was founded in 2006. The company is headquartered in Hong Kong, but it operates in more than 20 countries worldwide. Company’s Virtual Try-On feature is available on the website and allows customers to upload a photo of themselves and try on glasses virtually using augmented reality.

    After the website generates a 3D model of the customer’s face, they can adjust the position and size of the glasses to get a better sense of how they will look on their faces. The virtual try-on innovations in eye care also allow to share images of themselves wearing the glasses with their friends and family to get feedback on which pair looks best on them.

    innovations in eye care

    Another eye care innovation of SmartBuyGlasses is a Lens scanner app that uses advanced technology to scan the user’s current eyeglasses lenses and analyze the prescription, allowing to order a new pair of glasses online without visiting an eye doctor.

    The app works by instructing the user to place their current eyeglasses on a flat surface and position their smartphone camera above the lenses. The app then captures a series of images and uses advanced algorithms to analyze the curvature, thickness, and other factors of the lenses to determine the prescription. 

    GlassesUSA

    innovations in eye care

    GlassesUSA is an innovative and socially responsible eyewear retailer that is committed to providing quality products and services to its customers. With its focus on technology, sustainability, and social impact, GlassesUSA has become a popular choice for customers in the United States and around the world.

    One of the innovations in eye care of GlassesUSA that is worth paying attention to is a Prescription Scanner app. The app works by guiding the user through a series of steps to scan their face and eyes using their smartphone camera. It uses advanced algorithms to analyze the user’s facial features and measure the distance between their pupils, which is a crucial factor in determining the correct prescription for eyeglasses.

    innovations in eye care

    Once the scanning process is complete, the GlassesUSA app provides the user with their personalized prescription and recommendations. The app also offers a Virtual Try-On feature that allows users to see how different frames will look on their faces before making a purchase.

    Another feature is a Find-your-Frame Quiz on the website. The quiz consists of a series of questions that ask users about their face shape, personal style, and preferences for eyeglass frames, such as color, material, and shape. Based on the user’s responses, the specially designed program generates a personalized selection of eyeglasses frames that are recommended for their face shape and style preferences.

    Zenni Optical

    innovations in eye care

    Zenni Optical offers a wide range of eyewear products, including prescription glasses, sunglasses, and sports eyewear. The company offers glasses at significantly lower prices than traditional brick-and-mortar stores, which has made it a popular choice for customers.

    Company’s Virtual Try-On feature uses advanced AR technology to create a 3D model of the user’s face, allowing them to see how different frames will fit and look on them.

    innovations in eye care

    To use the Virtual Try-On innovations in eye care, users simply need to upload a photo of themselves or use their computer or smartphone camera to take a live video. This feature then maps the user’s facial features and displays a selection of eyeglasses frames that can be tried on virtually. Users can then select different frames to see how they look from different angles, and can even compare different frames side-by-side.

    The Zenni Optical Virtual Try-On is a convenient and easy-to-use tool for anyone in the market for a new pair of glasses. It allows users to see how different frames will look on their faces without the need to visit a physical store or try on multiple pairs of glasses. 

    VSP Global

    innovations in eye care

    VSP Global is a leading eyewear company that was founded in 1955 by a group of optometrists who wanted to provide affordable eye care. Today, VSP Global is a major player in the optometric industry and offers its customers a wide range of services and products.

    The company works with a network of over 40,000 eye doctors and optometrists to provide affordable and accessible eye care to its customers. VSP Global also offers other eye care services, such as telehealth consultations, on-site eye exams for businesses and schools, and a mobile eye clinic that serves underserved communities.

    Book a free trial

    Make your eye care business technological

    And as every company from this article, VSP Global has a strong focus on technology and innovations in eye care. The company has developed a number of proprietary technologies, including an AI-powered platform called Eyeconic that helps customers find the right eyewear.

    Eyeconic uses machine learning algorithms to analyze a customer’s facial features and suggest frames that would fit their face shape and size. VSP Global has also developed a mobile app called myVSP that allows customers to manage their vision benefits, find an eye doctor, and order contact lenses online.

    iSight+

    OCT Imaging System

    Another AI-oriented optometry center is iSight+, located in Hong Kong. iSight+ is an excellent example of how an optometric eye care center didn’t hesitate and chose to provide innovations in eye care and a more in-depth examination of the macula.

    Andy Meau. Optometrist, the owner of ISight+ Optometric Eye Care center: 


    “Altris AI will be a great tool in helping to monitor patients with existing macular diseases. I am also honored to be the first EPC in Hong Kong to provide this service.”

    In addition, the eye care center is also equipped with advanced optometric technologies, digital photography systems, and optical coherence tomography (OCT), which helps to provide the highest quality eye examination.

    Summing Up

    Optometry centers can significantly benefit from incorporating innovations in eye care, such as augmented reality, artificial intelligence, and mobile apps, into their practice. These technologies enhance the patient experience, improve diagnostic accuracy, and streamline clinical workflows.

    Moreover, the use of innovative technology can help optometry centers stay competitive in a rapidly evolving healthcare landscape. Patients are increasingly tech-savvy and expect healthcare providers to offer convenient, digital solutions that meet their needs. By embracing innovative technologies, optometry centers can attract new patients and retain existing ones, while also increasing operational efficiency and reducing costs.

    Of course, there may be concerns about the cost and complexity of integrating new technologies into an optometry practice. However, the benefits of doing so can far outweigh these potential challenges. With careful planning and implementation, optometry centers can successfully leverage AR, AI, and other innovations in eye care to enhance patient care, improve clinical outcomes, and thrive in a rapidly changing healthcare environment.

  • New Technology in Optometry: How will Optometry Practice Look in 2040?

    technology in optometry
    Maria Znamenska
    29.03.2023
    9 min read

    In the next two decades, we can expect to see a paradigm shift in the way optometry is practiced. Advances in new technology in optometry, such as AI (artificial intelligence), machine learning, and virtual and augmented reality, are expected to revolutionize the way optometrists diagnose, manage, and treat eye-related problems. For example, smart contact lenses that can monitor blood sugar levels for diabetic patients or detect early signs of glaucoma are already in development, and they could become mainstream within the next 20 years.

    technology in optometry

    In addition to optometry technology advancements, changes in demographics will also play a significant role in shaping the future of optometry. The aging population will require more specialized eye care, particularly for conditions such as macular degeneration and cataracts, which are more prevalent in older adults. The rise of chronic diseases such as diabetes will also increase the demand for optometric services, especially in developing countries where access to healthcare is limited.

    Make your Optometry Business Innovative

    The future of new technology in optometry is exciting and holds great promise for patients and practitioners alike. In this article, we will explore some of the potential changes that ODs may face in the coming years, based on the survey that we have conducted.

    New technology in optometry: AI is here to help

    In the next 20 years, the technology in optometry will be represented by AI and is expected to revolutionize the field in several areas. Here are some ways AI is already helping optometry:

    • Diagnosis and treatment. AI algorithms can analyze large amounts of patient data and provide accurate and fast diagnoses of eye diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration. AI could also help in designing personalized treatment plans for individual patients.
    • Screening and monitoring. AI-powered optometry technology could help specialists to screen patients for eye diseases more accurately and quickly. For example, a patient could take a picture of their eyes with their smartphone and an AI algorithm could analyze the image for signs of eye disease. AI could also help in monitoring the progression of eye diseases over time.

    technology in optometry

    • Enhance patient care. AI-powered tools could help ODs to provide more personalized and comprehensive care to their patients. For example, AI technology in optometry could help in selecting the most suitable eyeglasses or contact lenses for a patient based on their unique vision needs and lifestyle factors.
    • Research and development. AI could help optometrists in developing new treatments for eye diseases. By analyzing large amounts of patient data, AI algorithms could identify new patterns and potential treatments for eye diseases.

    In addition, the implementation of AI in optometry technology can present various prospects for improving clinic operations, simplifying billing procedures, accelerating the input of EHRs (electronic health records), optimizing claims management, and boosting cash flow. As high-deductible health plans (HDHPs) gain popularity among employers and patients, revenue cycle management can be seamlessly integrated with AI technology, considering the increasing number of patients defaulting on their medical bill payments.

    technology in optometry

    Altho artificial intelligence is about to bring significant changes to the industry, it is important to remember that the effectiveness of AI is limited to tasks that it has been specifically trained to perform, while it may not perform well in areas outside its training. 

    Therefore, it is crucial to focus on enhancing ODs’ proficiency in utilizing AI instead of worrying about the possibility of job replacement. The integration of AI technology in optometry provides specialists with an opportunity to enhance patient outcomes on a global scale.

    Make your Optometry Business Innovative

    To be proficient in utilizing cutting-edge technologies, ODs specialists must possess critical thinking skills and the ability to manage complex cases in real-time. Additionally, communication skills are also essential, including cultural sensitivity, multilingualism, and familiarity with alternative communication platforms such as mobile technology. These skills will be particularly important for optometry specialists from 2040.

    technology in optometry

    Overall, AI has the potential to greatly improve the accuracy and speed of diagnosing and treating eye diseases, leading to better patient outcomes and a more efficient healthcare system.

    The evolution of OD and MD roles

    Back in 2019, Richard C. Edlow, OD, claimed that nearly 20mln more routine and medical eye exams will be required in 2025 compared to 2015. The volume of surgery that will be required for the aging US population will increase as well. What is more, the number of cataract surgical procedures will also significantly increase — from 3.6 million in 2015 to 5 million in 2025. Add here the fact that the number of ophthalmologists will increase by only 2.1% in this same period. 

    Given these facts, in the not-too-distant future, ophthalmologists will need to focus on surgical procedures, while optometrists will provide more medical care.

    technology in optometry

    The field of ophthalmology must be fully prepared to meet the huge and growing demand for surgical procedures and therapeutic intravitreal injections. This brings us to the fact that the field of optometry, in turn, must be ready to manage the ever-increasing demand for medical ophthalmic services.

    The roles of OD and MD are changing as with the advent of electronic healthcare, ophthalmologists are already spending more time on the computer instead of providing proper patient care. The ability to use innovative technologies, digital thought processes and critical thinking will create new opportunities in eye care as optometrists are moving further towards ‘data analysis’ and away from ‘data collection’. OD specialists must ensure that they are properly trained in new technology in optometry and its advances to enhance, not inhibit, the quality of patient care.

    technology in optometry

    It is also worth mentioning that despite the speed of new technology in optometry, the human relationship between patient and doctor remains the most powerful tool. To properly care for patients, ODs will need more than clinical skills, knowledge, or the latest technological advances. Patients need thoughtful, professional, kind, trusting, understanding, and caring optometrists.

    As technology advances, there will also be changes in optometry education. There may be more need for data analysis, less need for data collection, and an increased need for interpersonal skills (such as empathy, compassion, and bedside manner).

    The role of OCT technology in optometry

    OCT has become an important diagnostic tool for the detection and treatment of various eye diseases, such as glaucoma, macular degeneration, and diabetic retinopathy. The ability of OCT to obtain high-resolution cross-sectional images of the retina and optic nerve will broaden the horizons of optometry technology and help optometrists detect and track changes in ocular structures that may not be visible during the normal eye examination. 

    As technology advances and the use of AI and imaging techniques increases, the demand for OCT in the field of optometry is expected to continue to grow.

    technology in optometry

    Here are some ways in which optometrists will benefit from implementing OCT in their practice:

    • Improved diagnosis. OCT provides highly detailed images of the structures of the eye, allowing ODs to detect and diagnose eye conditions much earlier than with traditional methods. In fact, OCT is also called an optical retinal biopsy. This method makes it possible to examine 18 zones of the retina and detect minor or rare pathologies. This enables optometrists to provide timely treatment and prevent further damage to the eye. 
    • Better management of eye diseases. OCT allows optometrists to monitor the progression of eye diseases such as glaucoma, ARMD, and diabetic retinopathy by taking detailed retinal images. It helps to determine the severity and stage of the disease, compare images after examination with documented results, and track disease progression. Moreover, with OCT examinations, ODs can also monitor the same patient to choose the most accurate diagnosis.
    • Enhanced patient care. OCT is a non-invasive and painless procedure that is easy for patients to undergo. It uses safe laser light, avoiding all the side effects or risks. As the procedure is comfortable and effortless both for the ODs and patients, it helps to build stronger relationships by providing a less intimidating experience than other examinations.
    • Increased revenue. Offering OCT in their practice can provide optometrists with an additional revenue stream, as they can charge for the procedure and use it to attract new patients.

    Summing up, implementing OCT in their practice can help optometrists provide better patient care, improve their diagnostic accuracy, and increase revenue.

    Focusing on myopia management

    According to a survey conducted by the American Optometrists Association, nearly 70% of optometrists reported an increase in patient requests for myopia treatment in the last two years. Myopia is a rapidly growing problem worldwide. Only in the USA, it is predicted that by 2050 the number of patients will increase to 49.8%. As unfortunate as it may be, such a global epidemic of myopia will undoubtedly create an opportunity to expand the practice of specialized treatment.

    technology in optometry

    In the future, optometrists may manage myopia using a combination of approaches, and one of the most discussed is orthokeratology (ortho-K). This non-surgical approach that involves wearing specially designed contact lenses has been used to reduce the degree of myopia since the 1960s. Although this method is not new in optometry practice, many companies are still working hard to create new approaches and upgrade them. For example, 2 years ago, Johnson & Johnson Vision announced FDA approval of its Acuvue Abiliti Overnight Therapeutic Lenses for the management of myopia. That same year, CooperVision announced that its Procornea DreamLite night lenses for ortho-k had received the CE Mark from European regulators for slowing the progression of myopia in children and young adults. 

    Overall, the future of myopia management in optometry is likely to involve a personalized, multi-faceted approach that combines various strategies to reduce the progression of myopia and improve vision.

    Game-changing contact lenses

    In the research published in Advanced Materials Technologies, was claimed that in the near future, contact lens sensors can be used to monitor many common diseases. The fact is that in the lacrimal fluid, there are biomarkers, the presence of which will make it possible to create diagnostic contact lenses. Such lenses would analyze these biomarkers and detect and treat systemic and ocular diseases such as diabetes, cancer, and dry eye syndrome.

    It is predicted that in the near future, lenses will be able to monitor intraocular pressure, detect glaucoma, and even create images of retinal vessels for early detection of hypertension, stroke, and diabetes. For patients with diabetes, these lenses would be incredibly useful because of the measurement of blood glucose levels. Some companies, like Google, have already dedicated years to creating such lenses. Nowadays, scientists are even working on lenses that change color to alert about changes in glucose levels.

    technology in optometry

    However, according to Advanced Intelligent Systems, one of the limitations of these lenses to date is that they can typically only detect one biomarker in the eye, such as glucose or lactic acid. Lenses capable of detecting multiple chemical components are predicted to be developed in the future.

    Summing up

    Predicting the exact way optometry practices will look in 20 years is challenging, as technological advancements and societal changes can rapidly alter the way healthcare is delivered. However, in this article, we tried to predict ​​some potential trends and developments that could shape optometry practices in the next 20 years based on the opinion of the leading experts in the industry. 

    To put it simply, AI and technology will slowly gain popularity among eye care specialists. However, in 2040 artificial intelligence and machine learning still will be only an assistant, while ODs will be responsible for the diagnosis and treatment. 

    Check how artificial intelligence assists in OCT interpretation

     

    This brings to the forefront the important principles of patient education, empathy, and personal contact with patients (virtue ethics). Innovations in technology should allow ODs to have more personal contact and more time to improve outcomes for patients-not to improve productivity.

    In addition, optometric education will need to address these interpersonal skills so future generations of ODs are able to adequately educate patients on findings and ensure the quality of care.

    There will always be a business of health care, but the challenge for the optometric profession is for ODs to place the well-being of all patients as their first priority.

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  • Eye Hospital Management Software: Top 8 Solutions for your Clinic

    Eye Hospital Management Software: Top 8 Solutions for your Clinic

    Maria Znamenska
    04.01.2023
    10 min read

    The term “Eye hospital management software” can have numerous meanings. Some soft can be a part of larger EMR (electronic medical records) systems, some can help with scheduling and billing, and some can help with patients’ information management. There is also an eye clinic management system that can even advise on diagnosis based on the patient’s history and medical images. Because of dozens of different soft on the market, it can be quite complicated to choose a proper set of tools for your practice.

    If you are an ophthalmologist or manage an ophthalmic diagnostic center/hospital, you may have trouble choosing the right software. That is why we’ve decided to prepare a list of solutions for patients’ health recording and diagnosis. We will highlight the benefits of the ophthalmic practice management system and help you choose the right solution.

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    FDA approved AI for OCT analysis

    Why eye hospital management software is worth using

    Eye hospital management software has become extremely important for eye clinics or medical centers looking to streamline their workflows, automate processes, and provide higher-quality care with less effort. You can have piles of paper and numerous excels, but when someone is on vacation, it will be impossible to make sense of all data and use it quickly.

    However, many clinics still work according to the old scheme and refuse to introduce new technologies into their clinical practice. There may be several reasons for this: mistrust of modern tools, reluctance to spend the money buying licensed eye clinic management system, or  reluctance to spend staff time learning how to work with the program. But, in fact, today, there are systems designed specifically for ophthalmologists to function flawlessly in eye care settings. Here are some benefits that an eye clinic management system can provide to your medical practice. Let’s take a closer look at some of them:

    Eye hospital management software

    • High level of data protection. Another important benefit of the ophthalmic practice management system is a high level of data protection. High-quality soft gives access to data only to authorized persons. The software also has security systems that guarantee no risk of data loss and full protection of medical history or information about the patient’s condition.
    • Increasing diagnostic accuracy. Using an eye clinic management system, ophthalmologists improve the quality of diagnosis and treatment, as they get access to the whole patient’s history from the past to the present. An ophthalmologist can learn about the previous treatment their patient received and about chronic illnesses. By learning this, doctors can create a better treatment plan.
    • Increased revenue. Depending on the number of employees in your clinic, you may need dozens to hundreds of personnel to smoothly handle manual processes. And more human resources mean more expenses. However, by using best practice management software for ophthalmology, you can significantly reduce spending and let your employees and doctors focus on the more creative tasks that require empathy and communication.   

    These are the most common benefits of an eye clinic management system. However, each system has its unique features, so let’s look at the top 8 eye clinic management systems. 

    Altris AI System

    eye hospital management software

    Altris AI is a unique eye clinic management system that allows eye care specialists to analyze OCT scans with the help of artificial intelligence (AI) tools. 

    How does it work? Putting it simply, retina specialists have colored thousands of OCT scans and named more than 100 retinal pathologies and pathological signs to train an algorithm, so it can assist specialists in detecting the disease. After loading an OCT scan in the eye hospital management software, the AI model evaluates the b-scans (up to 512) and differentiates between normal scans and scans with moderate and severe pathology. It gives eye care professionals the ability to focus only on serious (red) scans, saving patients valuable time.

    In addition, Altris AI allows its users to see a broader perspective of a patient’s eye health. All the reports are dynamically editable: the ophthalmologist can add/revise/delete items in the OCT report. Eye care specialists also can add segmentation/classification results to the OCT report in 1 click. And what’s even more important, Altris AI OCT report is understandable for both ophthalmologists and patients. 

    Eye clinic management system features of Altris AI

    • The system allows working with all popular OCT equipment and all data storage formats, including DICOM of various lengths, png, and jpg.
    • Altris AI ophthalmic practice management system can be easily integrated with EHR systems or run standalone as a web application.
    • The system also takes care of user security, as all important patient data is tokenized and protected from disclosure at all stages.
    • The artificial intelligence program can independently identify more than 100 retinal pathologies and pathological signs.
    • The Smart Reports feature allows ophthalmologists to select the elements (single scan, layers, both eyes, etc.) that they want to see in their OCT report.
    • This All Scans feature allows the user to view all scans of a single OCT examination, sort them by severity level, and zoom.

    Watch a short overview of how Altris AI assists eye care specialists with OCT diagnosis and decision-making.

    DrChrono Software

    eye hospital management software

    DrChrono EHR is an iPad and iPhone-compatible platform that offers fully customizable form templates or ready-made forms to help users track patient information. 

    DrChrono EHR is an iPad and iPhone-compatible platform that offers fully customizable form templates or ready-made forms to help users track patient information. 

    Eye clinic management system features of DrChrono Software

    • The system allows medical practices to manage patient admissions, patient care, clinical charts, and billing.
    • Healthcare professionals can add patient notes to the medical record. The Vital Flowsheets module provides the ability to create basic health data and monitor the health indicators of each patient.
    • The DrChrono eye hospital management software also offers a variety of application integrations. 
    • Doctors can use the Free Draw module to annotate charts, OCT scans, or other files.

    RXNT Software

    eye hospital management software

    RXNT is a comprehensive billing, practice management, and EHR solution. This system improves patient care and simplifies clinical management. Access patient health history and prescriptions at the point of care, schedule patients and providers, and request and review lab or imaging orders with multi-site single sign-on (SSO).

    Eye clinic management system features of RXNT Software

    • Any RXNT ophthalmic practice management system products (EHR, ERX, PM, Billing, Scheduling) can be combined into a fully integrated “Full Suite” system.
    • Ophthalmologists, managers, or staff can add and organize documents in patient charts for clinical care plans and follow-up.
    • The system has developed customizable “smart” forms and short keys that improve work processes.
    • RXNT can share real-time data with other doctors to better coordinate care and support.

    In addition, an ophthalmic clinic can integrate RXNT eye hospital management software with the Altris AI system to create and dynamically edit OCT reports.

    Medfiles Software

    eye hospital management software

    Medfiles Software is a multi-task cloud-based solution that ensures compliance for ophthalmology clinic employees. The key features of this eye hospital management software are drug screening management, medical record tracking, case management, training tools, reporting, and safety documentation.

    Eye clinic management system features of Medfiles Software

    • Medfiles tracks patient treatment plans, open cases, treatment plans, medical expenses, and cash reserves and creates conclusions based on all the information.
    • The system can be easily integrated with different software so a doctor or staff can see scans of specific OCT examinations.

    Medfiles eye clinic management system allows to compare annual summary reports with benchmarks.

    IntelleChartPRO Software

    eye hospital management software

    Another cloud-based ophthalmic electronic medical record (EMR) solution is IntelleChartPRO. This system is very popular among ophthalmology clinics and centers. IntelleChartPRO helps professionals record and manage a patient’s treatment and medical history more effectively.

    Eye clinic management system features of IntelleChartPRO Software

    • Physicians or ophthalmology clinic management can customize the EHR themselves to fit their unique workflows.
    • IntelleChartPRO eye hospital management software developed adaptive template technology that allows offices to generate templates for each patient.
    • In combination with other eye clinic management system tools, the software becomes more relevant and allows more accurate diagnoses of patients and the creation of detailed reports.

    MaximEyes Software

    eye hospital management software

    MaximEyes is a comprehensive, unified electronic health record (EHR) and practices management solution designed exclusively for ophthalmology practices. It has a modern and intuitive user interface. The system will work on any computer OS. If users do not want to use cloud technologies or the clinic has a weak Internet connection, MaximEyes can be deployed through a local server

    Eye clinic management system features of MaximEyes Software

    • For each patient, the system allows ophthalmologists to set up an individual template according to different types of visits.
    • The eye hospital management software EHR includes a flexible rules engine that will suggest or automatically generate post-diagnosis codes, procedure codes, and output documents.
    • The First Insight module also offers an ophthalmic imaging management solution that works with any EHR.

    75health Software

    eye hospital management software

    One more fully-fledged eye clinic management system is 75health, which is also a cloud-based solution that provides its users with electronic health record tools. 75health system will be most suitable for managing health records and patient information for ophthalmologists working in small and mid-sized clinics.

    Eye clinic management system features of 75health Software

    • 75health eye hospital management software allows ophthalmic clinic staff to download and save patients’ medical images, such as consent forms, handouts, or scans.
    • Doctors can also create a treatment plan for their patients right in the system and scan records for allergies, medications, lab results, and symptom lists.
    • 75health solution provides smooth integration of ophthalmic management systems, which helps ophthalmologists in decision-making.

    myCare Integrity Software

    eye hospital management software

    Another cloud-based eye hospital management software that is worth your attention is myCare Integrity. It was created specifically for eye care specialists and contains a strong set of tools and modules that can cover the needs of any member of the ophthalmic clinic staff: from doctors to managers.

    Eye clinic management system features of myCare Integrity Software

    • The myCare Integrity system has an IntegriVIEW functionality that allows practitioners to link medical images directly to every screen of EMR.
    • There is also an IntegriDRAW module inside the eye clinic software, where templates are included in the application. It allows users to rely on the previously created stamps.
    • The IntegriLINK module allows ophthalmologists to link the diagnostic equipment to the system.
    • What is more, myCare Integrity eye hospital management software allows you to customize and personalize the dashboard.

    Summing up

    Eye hospital management software is extremely important for any clinic, whether there are 10 or 500 employees. It can help you improve your workflow by keeping a lot of data in one place. Imagine how easily you can get rid of unnecessary paperwork, forget about administrative costs, and speed up processing. In addition, with an ophthalmic practice management system, you can get 24/7 access to patients’ data.

    Free Trial

    FDA approved AI for OCT analysis

     

    However, the key benefit of practice management software for opticians is the improvement of diagnosis and treatment. There are already ophthalmic image management systems, like Altris AI, that can not only help to manage patients’ data but also provide a second opinion regarding medical image analysis. Using this knowledge, doctors can have better access to patients’ health problems and reports, ultimately enabling them to provide the best care to their clients.

  • Application of ML in ophthalmology

    The Application of Machine Learning in Ophthalmology: The View from the Tech Side

    Philip Marchenko
    30.11.2022
    15 min read

    According to the World Health Organization (WHO), artificial intelligence (AI) and machine learning (ML) will improve health outcomes by 2025. There are numerous digital technologies that shape the health of the future, yet AI and machine learning in ophthalmology and medical image analysis look like one of the most promising innovations.

    The healthcare industry produces millions of medical images: MRI, CT, OCT, images from the lab, etc. The right diagnosis depends on the accuracy of the analysis by the specialists. Today AI can back up any medical specialist in medical image analysis: providing confidence and much-needed second opinion.

    Try Altris AI for free

    Check how artificial intelligence assists in OCT interpretation

    Altris AI team decided to improve medical image analysis for just one type of medical image: Optical Coherence Tomography scans of the retina. To do it, the Altris AI team collected thousands of OCT scans and graphically labeled them, defining more than 100 pathologies and pathological signs. Watch the video to discover more features of Altris AI platform. 

    Then all this data was fed into the AI model. Further, I will tell how exactly we train the AI model of Altris AI so that it can detect more than 100 pathologies with 91% accuracy, but first, let’s discuss why it is important for the healthcare industry.

    Why are automation and machine learning in ophthalmology important?

    Due to the delicate anatomy of the eye, its treatment carries a high risk of complications. Sometimes these complications can be the result of a medical error by an eye care specialist. But how often?

    According to the Altris team research, 20.2% of eye care practitioners miss minor, early, and rare pathologies on OCT scans 1- 3 times a week, and 4.4% miss them 3-5 times a week. But the worst thing is that 30.5% of ophthalmologists and optometrists are not even sure if they are missing any pathology at all.

    Some medical errors may be minor, but some may cause significant harm to patients. Such medical errors can lead to medical malpractice lawsuits. That is why most ophthalmic clinics consider implementing AI to double-check the diagnosis of the ophthalmologist. 

    Besides, different tools of machine learning in ophthalmology have a high level of accuracy and can provide eye care specialists with a second opinion. 

    How to reach a high level of accuracy?

    It is almost always necessary to conduct many experiments to achieve a high level of model accuracy (in the case of Altris AI, it is 91%).  It is often done with the help of a machine learning pipeline.

    machine learning in ophthalmology

    High level of ML pipeline accuracy

    The machine learning pipeline is programmed by a team of engineers to perform certain steps automatically. It systematically trains and evaluates models, monitors experiments, and works with datasets.

      1. ML and Medical teams collect, annotate and preprocess data. It’s crucial to ensure the data quality is at its highest level because the model’s quality heavily depends on it. To do this, the teams developed a process and annotation guideline, which ensures that the number of errors in the annotation is minimized.
      2. ML team chooses the appropriate approach (model) depending on the collected data and the tasks. Each team member is well-versed in the most modern and high-quality approaches that solve emerging tasks.
      3. The selected model is trained on the annotated data.
      4. In the model evaluating and testing stage, we develop tests aimed at helping us understand whether the model is trained properly to perform the needed tasks.
      5. After the ML team is satisfied with the result, we deploy the model, which means the model is ready for production.
      6. While the model is running in production, we monitor its performance to ensure everything goes well.

    This workflow allows engineers to continuously fine-tune existing models alongside constant performance evaluations. The most significant advantage of this process is that it can be automated with the help of available tools. 

    Try Altris AI for free

    Check how artificial intelligence assists in OCT interpretation

     

    What tasks does machine learning in ophthalmology have?

    Within the Altris AI platform, we solve 2 main tasks: segmentation and classification of OCT scans. 

    Classification task

    Classification is the task of determining which category a particular object belongs to. We assign each pathology to a certain class of pathologies (for example, glaucoma class).

    Segmentation task

    The image segmentation problem can be stated as the division of an image into regions that separate different objects from each other, and from the background.

    Key metrics of Altris ML pipeline

    When discussing classification and segmentation metrics in medical imaging machine learning, it is essential to mention the Confusion matrix (CM). CM is a visualization of our performance, which helps us understand whether the model is performing well in terms of predicted and real data. For a better explanation, let’s take a look at the picture. 

    machine learning in ophthalmology

    Let’s consider 4 possible outcomes from model predictions. Say we need to create a classifier to diagnose or predict if a patient has a disease (positive / 1 or TRUE) or not (negative/ 0 or FALSE). In such a case, the model can predict “yes” or “no”, and we can have an actual “yes” or “no”. Based on this, we can get 4 categories of results:

    • TP — true positive. The patient that actually has a disease has been diagnosed with this disease. A class was predicted to be true, and it is actually true.
    • TN — true negative. The patient is actually healthy and has been diagnosed as healthy. A class was predicted to be false, and it is actually false.
    • FP — false positive (type 1 error). The patient that is actually healthy has been diagnosed as having a disease. A class was predicted to be true, but it is actually false.
    • FN — false negative (type 2 error). The patient that actually has a disease has been diagnosed as healthy. A class was predicted to be false, but it is actually true.

    With the help of the confusion matrix, our ML engineers get specific metrics needed to train our medical imaging machine learning model properly. We discuss each metric in more detail in the following paragraphs.

    Classification metrics

    • Accuracy

    To find out how many of our predictions were correct, we divide the number of correct predictions by the total.

    machine learning in ophthalmology

    While being intuitive, the accuracy metric heavily relies on data specifics. If the dataset is imbalanced (the classes in a dataset are presented unevenly), we won’t get trustful results.

    For example, if we have a training dataset with 98% samples of class A (healthy patients) and only 2% samples of class B (sick patients). The medical imaging machine learning model can easily give you 98% training accuracy by predicting that every patient is healthy, even if they have a disease. Such results may have destructive consequences as people won’t get needed medical treatment.  

    • Precision

    Precision shows what proportion out of all positive predictions was correct.

    machine learning in ophthalmology

    Precision metric helps us in cases when we need to avoid False Negatives but can’t ignore False Positives. A typical example of this is a spam detector model. As engineers, we would be satisfied if the model sent a couple of spam letters to the inbox. However, sending an important non-spam letter to the spam folder (False Positive) is much worse.

    • Sensitivity/Recall

    Recall shows how many of all really sick patients we predicted and diagnosed correctly. It is a proportion of correctly positive predictions out of all positives.

    machine learning in ophthalmology

    In our case, you want to find all sick people, so it would not be so critical if the model diagnoses some healthy people as unhealthy. They would probably be sent to take some extra tests, which is annoying but not critical. But it’s much worse if the model diagnoses sick people as healthy and leaves them without treatment. 

      The sensitivity of Altris AI is 92,51%

    • Specificity

    The specificity shows how many of all healthy patients we predicted correctly. It is the proportion of actual negatives that the medical imaging machine learning model has correctly identified as such out of all negatives.

    machine learning in ophthalmology

    Specificity should be the metric of choice if you must cover all true negatives and you can’t tolerate any false positives as a result. For example, we’re making a fraud detector model in which all people whose credit card has been flagged as fraudulent (positive) will immediately go to jail. You don’t want to put innocent people behind bars, meaning false positives here are unacceptable. 

    The specificity of Altris AI is 99,80%

    Segmentation metrics

    Segmentation also can be thought of as a classification task. For each pixel, we make predictions about whether it is a certain object or not. Therefore, we can talk about Accuracy, Precision, Recall, and Specificity in terms of segmentation. 

    Let’s say we have a Ground Truth (what is really an object) and a Segmented (what the model predicted). The intersection in the picture below is the correct operation of the medical imaging machine learning model. All that is the difference (FN and FP) is the incorrect operation of the model. True negative (TN) is everything the model did not mark in this case.

    machine learning in ophthalmology

    Quite often, even after looking at such metrics, the problem of non-symmetricity remains in the segmentation tasks. For example, if we consider a tiny object, the Accuracy metric doesn’t work. Therefore, segmentation tasks also refer to additional metrics that allow taking into account the size of the object of the overall quality assessment. Let’s look at additional metrics in more detail.

    • Intersection over Union (IoU)/Jaccard

    Intersection over Union is an evaluation metric used to measure segmentation accuracy on a particular image. This metric is considered quite simple — the intersection zone is divided by the union of Ground Truth and Segmented.

    machine learning in ophthalmology

    Sometimes we get such results, like if the object was determined to be very large, but in fact, we see that it is small. Then the metric will be low, and vice versa. If the masks are approximately equal to each other, everything works correctly, and the metric will be high.

    • Dice score/F1

    The dice coefficient is 2 times the area of overlap divided by the total number of pixels in both images.

    machine learning in ophthalmology

    This metric is a slight modification of the previous one. The difference is that, in this case, we take the intersection area twice.

    Calculating scores over dataset

    We calculate the metrics described above for each scan. In order to count them over the entire dataset, we take each picture in this dataset, segment it, calculate the metric, and then take the average value of the metrics on each image.

    What is model validation in ML?

    In addition to evaluating the metrics, we also need to design the model validation procedure suitable for a specific task.

    When we have determined the metric that suits the task of machine learning for medical image analysis, we also need to understand what data to use for calculation. It will be wrong to calculate the metric on the training data because the model has already seen it. This means that we will not check the ability of the model to generalize in any way. Thus, we need a specific test dataset so that we can carry out quality control according to the selected metrics.

    The main tasks of the model validation are:

    • To provide an unbiased estimation of the accuracy that the model can achieve
    • To check whether the model is not overfitted

    Picking the correct model validation process is critical to guarantee the exactness of the validation method. In addition, there is no single suitable validation method for machine learning in ophthalmology — each task requires different validation. Engineers separately examine each task to see if data has leaked from the train dataset to the test dataset because this may lead to an overly optimistic estimate of the metrics.

    For example, we can take OCT images in different resolutions. We may need a higher image resolution for some diseases. If the medical imaging machine learning model overfits at the resolution of this OCT, it will be called a leak because the model should behave the same at any resolution.

    Overfitting and underfitting

    The model also has such an important property as a generalization. If the model did not see some data during training, it should not be difficult for the model to determine which class a certain image belongs to.

    At this stage, engineers may have two problems that they need to solve. The first problem is overfitting. When the model remembers the training data too well, we lose the ability to make correct predictions. The picture below illustrates this problem. The chart in the middle is a good fit when the model is general enough and has a positive trend, and the trend is well-learned. But the chart on the right shows a too-specific model that will not be able to guess the trend.

    machine learning in ophthalmology

    Another problem to solve is underfitting. This problem arises when we have chosen a model that is not complex enough to describe the trend in the data (left chart).

    Bias variance trade-off

    Another important concept we use in machine learning model validation is the bias variance trade-off. We want our models to always make accurate predictions and have no ground truth scattered. As shown at first/second circle.

    However, there are situations when we have a model that predicts something close to the target, but from dataset to dataset, it has a strong scatter. This is showcased in the second circle. 

    In circle three, you can observe a situation where the model has heap predictions on different datasets, but they are inaccurate. This situation usually indicates that we need to almost entirely rebuild the model.

    machine learning in ophthalmology

    Overfitting and bias variance trade-off are very important in working with the model, as they allow us to track errors and select a model that will balance between spread and hitting the target.

    Unbiased estimation

    In addition, within each model, we evaluate a set of parameters. We made a certain estimate (graph on the left), but in real life, the distribution of parameters differs (graph on the right). Thus, seeing that our estimate turned out to be shifted, we find another problem that needs to be solved. Machine learning in ophthalmology needs to make the estimate as unbiased as possible.

    machine learning in ophthalmology

    How do we validate the Altris AI model?

    There are three main steps in choosing a validation strategy for machine learning in ophthalmology:

    • we got familiar with ophthalmology, understood the nature of data, and where the leakages are possible;
    • We estimated the dataset size and target distribution;
    • understood the model’s training complexity (amount of operations/ number of parameters/ time) to pick the validation algorithms.

    After that, we have a reliable strategy for the machine learning model validation. Here are some fundamental concepts we use in the validation of models’ performance.

    Train/test split

    Train/test split is the most simple and basic strategy that we use to evaluate the model quality. This strategy splits the data into train and test and is used on small datasets. For example, we have a dataset of 1000 pictures, 700 of which we leave for training and 300 we take for the test.

    This method is good enough for prototyping. However, we don’t have enough datasets with it to do a simple double-check. This phenomenon is called high sampling bias: this happens when we encounter some kind of systematic error that did not fit into the distribution in the train or test.

    By dividing data into train and test, we are trying to simulate how the model works in the real world. But if we randomly split the data into train and test, our test sample will be far from the real one. This can be corrected by constructing several test samples from the number of data we have and examining the model performance. 

    Train/test/holdout set

    We leave the holdout as the final validation and use the train and test to work with the medical imaging machine learning model. After optimizing our model on the train/test split, we can check if we didn’t overfit it by validating our holdout set.

    machine learning in ophthalmology

    Using a holdout as a final sample helps us look at multiple test data distributions and see how much the models will differ.

    K-fold cross validation

    There is also a more general approach that Altris AI team use for validation — k-fold cross validation. This method divides all of our data equally into train and test.

    machine learning in ophthalmology

    We take the first part of the data and declare it as a test, then the second, and so on. Thus, we can train the model on each such division and see how it performs. We look at the variance and standard deviation of the resulting folds as it will give information about the stability of the model across different data inputs.

    Do we need ML models to perform on par with doctors?

    Here I will try to answer a question that worries many ophthalmologists and optometrists: can machine learning for medical image analysis surpasses an eye care specialist in assessing quality?

    In the diagram below, I have drawn an asymptote called the Best possible accuracy that can be achieved in solving a particular problem. We also have a Human level performance (HLP), which represents how a person can solve this problem. 

    HLP is the benchmark that the ML model strives for. Unlike the Best possible accuracy, for which there is no formula, HLP can be easily calculated. Therefore, we assume that if a model crosses the human quality level, we have already achieved the best possible quality for that model. Accordingly, we can try to approximate the Best possible accuracy with the HLP metric. And depending on this, we understand whether our model performs better or worse.

    machine learning in ophthalmology

    For those tasks that people do better and the ML model does worse, we do the following:

    • collect more data
    • run manual error analysis
    • do better bias/variance analysis

    But when the model crosses the HLP quality level, it is not entirely clear what to do next with the model and how to evaluate it further. So, in reality, we don’t need the model to outperform a human in interpreting images. We simply won’t know how to judge the quality of this model and whether it can be 100% objective and unbiased.

    Avoidable bias

    Let’s say we need to build a classifier for diabetic retinopathy based on OCT scans, and we have a control dataset prepared by people. In the first case, doctors are wrong 5% of the time. At the same time, the model on the train set is wrong in 10% of cases and on the test set — in 13%.

    machine learning in ophthalmology

    The difference between the model’s and the human’s performance is usually taken as the minimum difference between the train/test set and the human. In our case, it is 5% gap (10% – 5%) of avoidable bias. It is called avoidable bias because it can be fixed theoretically. In such a case, we need to take a more complex model and more data to better train the model.

    In the second case, doctors determine the disease with a 9% error. If the model defines a disease with the same rates as the previous example, then the difference between the train/test set and the human will be 1% (10% – 9%), which is much better than avoidable bias

    Looking at these two cases, we must choose a strategy that will lower the variance for the machine learning model so that it works stably on different test sets. Thus, taking into account the avoidable bias and the variance between the samples, we can build a strategy for training the model so that it could potentially outperform the HLP someday. However, do we need it now?

    Understanding HLP

    To better understand the HLP metric, let’s consider the task of determining dry AMD on OCT scans. We have a fixed dataset and 4 train sets, each one determining dry AMD with a specific accuracy:

    • ML engineers – 20%
    • ophthalmologists – 5%
    • 2 ophthalmologists – 3%
    • 2 ophthalmologists and 1 professor of ophthalmology – 2%

    machine learning in ophthalmology

    We take a result of 2% as the best HLP possible. To develop our model, we can choose the performances we strive to get. The 20% error result is irrelevant, so we discard this option. But the level of 1 doctor is enough for model version number 1 model. Thus, we are building a development strategy for model 1.

    Summing up

    Machine learning will revolutionize the eye care industry. It provides confidence and second opinion to eye care specialists in medical image analysis. 

    If you are looking for ways to use machine learning in your eye care practice, feel free to contact us. At Altris AI, we improve the diagnostic process for eye care practitioners by automating the detection of 54 pathological signs and 49 pathologies on OCT images.

  • Altris AI Builds Partnership with Academic Institutions

    Maria Znamenska
    21.11.2022
    2 min read

    At Altris AI we are determined to set higher diagnostic standards for the eye care industry, which is why we are searching for partnerships with academic institutions.

    We would be happy to provide full access to the Altris AI platform in the clinic and education settings with access to the severity differentiation, segmentation and classification, progression, and comparison analysis.

    We can also provide access to the additional feature which will be released over the coming months which includes retinal layer identification and separation, thickness, and volume measurement tools.

    What do you get?

    • The opportunity to test the unique artificial intelligence-powered platform for OCT scans analysis

    • Validation and feedback on the platform with an opportunity for our Head of Product and Chief Medical Officer to have calls with your team to discuss areas about the system development and performance

    • Work with Altris to gain specific scans of pathology that we request so we can train the model – obviously, always GDPR compliant.

    • The opportunity for us to use your logo on our website and promote the partnership with the University on our website, social media, etc.

    • Discuss additional ways that we can work together on future projects.

    Contact us

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  • artificial intelligence replace ophthalmologists

    Will Artificial Intelligence Replace Ophthalmologists & Optometrists: Top 5 AI Misconceptions

    Maria Znamenska
    17.11.2022
    8 min read

    Back 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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    FDA approved AI that detects 70+ retina pathologies

    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?

    artificial intelligence replace ophthalmologists

    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?

    artificial intelligence replace ophthalmologists

    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?

    artificial intelligence replace ophthalmologists

    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?

    artificial intelligence replace ophthalmologists

    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.

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    FDA approved AI that detects 70+ retina pathologies

    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.

  • AI medical image analysis

    AI for Reading Centers: How it Boosts Workflow and Efficiency

    Mark Braddon
    05.10.2022
    7 min read

    In 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 image analysis in their workflow, most organizations are far from evaluating automation and prefer classic image interpretation methods.

    AI medical image analysis

    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.

    • 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. 

    • 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. 

    • 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. 

    • 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 medical image analysis

    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:

  • The use of AI for image analysis

    The Role of AI Image Interpretation for Ocular Pathologies Detection

    Maria Znamenska
    28.09.2022
    20 min read

    The 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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    Check how artificial intelligence assists in OCT interpretation

    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:

    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 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)

    AI image interpretation

    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)

    AI image interpretation

    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

    AI image interpretation

    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)

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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)

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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

    AI image interpretation

    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.