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.

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.

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.   

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.

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

• area and volume calculations

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.

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. 

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.

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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.

Top modern optometry centers are not afraid of embracing innovations. 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 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. 
• 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 technologies 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 innovative eye care.

Warby Parker

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 features 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.

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

Lenskart is a fast-growing eyewear company 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.

One of the Lenskart features 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

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 feature also allows to share images of themselves wearing the glasses with their friends and family to get feedback on which pair looks best on them.

Another 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

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 features 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.

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

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.

To use the Virtual Try-On feature, 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

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.

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And as every company from this article, VSP Global has a strong focus on technology and innovation. 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+

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 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 innovative technology 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 mobile apps to enhance patient care, improve clinical outcomes, and thrive in a rapidly changing healthcare environment.

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.

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.

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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.

• 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.

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.

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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.

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.

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.

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.

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.

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.

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. 

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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.

To make an optometric practice truly profitable it is necessary to juggle many things at the same time: marketing, employees, clients, best brands of lenses, training, new equipment…So easy to lose something! In this article, you will read about 10 optometry centers that managed not only to survive the competition but also to increase their revenue. How? Read optometry practice management tips from 10 real centers and find out.

Let’s start with the challenges of the optometry business. Key challenges in 2023 in the optometry business according to Optometric Management and Eyecare Leaders are the following. There are 8 of them, but the retention of specialists, competition with large chains and retailers as well as marketing and sales are the most tangible for the majority of optometry businesses.

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RETENTION OF EMPLOYEES

This problem seems crucial considering the huge lack of optometry specialists in the world. According to WHO,14 million optometrists are needed globally when there are only 331K available. There are several strategies that optometry businesses can use to retain optometrists:

• Delegating more examinations to technicians helped the partner with Mountain View Eye to generate $2,600 in additional revenue per week. Work like performing pupil testing, versions (EOMs) and dilation were added to the pretesting protocols.

• Using artificial intelligence for retina scan analysis.  For the majority of optometrists, OCT scan analysis is a challenging and problematic task and more than half of optometrists are not confident about their interpretation skills. Using Artificial Intelligence for automated OCT scan analysis can make the work of optometrists more efficient: increasing the number of patients who undergo OCT examination and subsequently increasing the revenue.

MARKETING AND SALES ACTIVITIES

• Concentrating on eyewear sales. OD Perspective CEO claims that 2 simple techniques can add $75.000 to the annual revenue of any optometry center. Decreasing Patient’s Own Frame (POF) Glasses Sales by communicating the need of updating the frame with the lens. Communicating the need for all types of lenses (for computers, reading, sunglasses) can be a very effective technique for revenue generation which is often neglected by optometry owners. This is one of the optometry practice management tips that seems to work for any center.
• Providing exquisite luxury experience. The owner of Eye Boutique in Houston claims that a new strategy when he decided to concentrate on VIP clients turned out to be much more effective. Now his optometry averages 3 $500+ purchases per patient annually”

• Using social media and digital marketing tools extensively. Instagram, Facebook, Google Ads, SEO for promotion. Simply because your clients are doing it. For instance, “an average sale from a patient who has come into an office solely from social media marketing is around $750” which is a very good result according to a Corona Vision Center.

Instagram, Facebook, and Google Ads are the most effective digital marketing channels for communication with potential clients

• Providing a small warranty to all products, among other small changes, can bring $60000 annually. That is the experience of the owner of Brilliant Eyes Vision Center.

• Educating patients leads to $55K annually for the owner of Bright Family Eye Care. What does it mean? The employees of the center take time to explain what they are examining the patients for in detail so the last would prioritize screening. This has a financial benefit, as well, since the center charges patients for wide-field imaging on a screening basis

COMPETITION WITH LARGE OPTOMETRY CHAINS

Private optometry centers find it hard to compete with chains, like Specsavers, in terms of prices or the speed of service. Chains often have better locations and can spend much more money on marketing. So how can private optometry centers win this competition? There are several things that big companies don’t have:

• Offering personalized service and building a relationship with patients. The key to winning the competition with large chains is building a local presence. Your optometrist center can be known and valued if you really care about the community, know each of your clients personally and understand their pains and needs. More than that, 97% of marketers witnessed a rise in business outcomes as a result of personalization according to Salesforce.
• Providing unique and high-quality products not available at chain stores. That is also an interesting opportunity for a small but flexible business. For instance, some optometry centers build their presence relying on rare brands of glasses with sophisticated designs. The global therapeutic contact lenses market is expected to grow at a CAGR of 4.90% from 2021-2027 and designer brands will play a crucial role in this growth.
• Providing exceptional customer service and after-care. Communication with customers is the core of relationships. Today it’s easier to communicate with customers using the abundance of social media, messengers, and telemedicine. Optometry centers that take care of their clients not only during visits but afterward as well are much more profitable. The best messengers for communication with clients are

WhatsApp (offers several business solutions as well), Facebook Messenger, Instagram, Viber, WeChat.

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• Storing all the patients’ data effectively and securely is the key to fast and reliable services inside the optometry centers. There is a variety of EHR systems for optometry centers, and it’s hard to find the best optometry practice software. However, it is always wise to rely on testimonials.

Here is the review of the best optometric practice management software. Acuitas activEHR 2.0, MedFlow EHR, Liquid EHR, EyePegasusEHR, Eye Cloud Pro, OD Link, ManagementPlus, Medesk are the best optometric practice management software according to our reserach and reviews.

PATIENTS’ NO SHOWS

A patient no-show is a critical problem today for the majority of optometry centers. Patients tend to ignore yearly checkups and forget about follow-up visits whenever they feel a little better.

• Using virtual check-ins increased profitability and reduced our cost of goods sold (COGS) for the partner at Wichita Optometry. Virtual check-ins mean that patients can use audio or video technology for communication with the OD instead of visiting the optometry. This helps to decrease staff, become less dependent on employees and reduce payroll expenses. More than that, it gives more freedom to patients in terms of the time of the “virtual visit”. There are plenty of tools for virtual check-ins, but Zoom, Skype, Teams can be one of the most reliable.

• Using software to remind about future visits can be the solution. For instance, Weave software helped Serenity i Care optometry to reduce the number of no-shows up to 30% from 75%. This software allows informing clients about future visits automatically via e-mails and texts. No need for a team to have an endless amount of calls that are not responded to. Other solutions that might work are:

Demandforce, Solutionreach, and Simplifeye can be a great software for reminding patients about visits. This is the best optometry prcatice management software to deal with forgetfullness. 

Optometry practice management software is designed for eye care specialists to manage their practices more efficiently and effectively. The software can automate a wide range of administrative tasks, making it easier for practitioners to focus on patient care.

Unlike other medical practices, optometry involves the management of a much larger number of optical instruments, processes and aids. Therefore, software for optometrists is more complex and multifunctional. It usually includes features such as appointment scheduling, patient registration, billing and insurance claims processing, patient data management, and secure messaging and email communication. The software can also integrate with other technologies, such as electronic health records (EHRs), OCT image management systems and diagnostic equipment.

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By streamlining administrative tasks and providing practitioners with patient data, optometry practice management software can help eye care clinics improve their operations, increase efficiency, and provide better patient care. The software can be customized to fit the specific needs of individual practices and is often offered on a subscription basis, making it an affordable and accessible solution for eye care clinics of all sizes.

In this article, we will highlight the main benefits of practice management optometry soft, and provide you with a list of the Top 8 software to look at.

What are the benefits of practice management optometry software?

Optometry practice management software can help doctors in multiple ways besides increasing their revenue, efficiency, and productivity. Some of the key benefits of optometry practice management software include the following items.

• Improved patient management. The software can store and organize patient data, including medical history, examination results, fundus or OCT images, and treatment plans. This information can be easily accessed by practitioners and used to inform patient care.
• Efficient appointment scheduling. The software can automate appointment scheduling, which can help to reduce the risk of double-booking and minimize wait times for patients.
• Accurate billing and insurance claims. The software can help to ensure that billing and insurance claims are processed accurately and efficiently, reducing the risk of errors and delays.
• Increased revenue. By streamlining billing and insurance claims processes, optometry practice management software can help eye care clinics to reduce errors and increase revenue.
• Easy access to patient records. The software can store and organize patient records, including OCT images, making it easy for doctors to access the information they need to provide the best care possible.
• Improved patient communication. Some optometry practice management software includes features that allow for secure messaging and email communication between patients and practitioners, making it easier to communicate outside of office visits. 
• Increased productivity. By automating repetitive tasks, such as appointment scheduling and billing, optometry practice management software can free up time for eye care practitioners to focus on providing an individual approach to each patient.
• Better patient outcomes. With access to patient data and treatment history, eye care practitioners can provide more informed and effective care. This can lead to better patient outcomes and increased patient satisfaction.

Overall, optometry practice management software can help eye care clinics to provide better patient care, increase efficiency and productivity, and improve their bottom line. Now let’s take a look at out Top 8 optometry practice management software.

Altris AI

Altris AI is an image management system based on artificial intelligence (AI) tools that assists eye care specialists in OCT scan analysis and interpretation. The solution was designed in cooperation with retina experts to help practitioners detect the pathology from the OCT scan. Altris AI also can be easily integrated with EHR systems or used standalone as a web application.

To create an Altris AI system, our specialists colored thousands of OCT scans and named more than 100 retinal pathologies and pathological signs to train an AI algorithm. May sound complicated, but the workflow of the image management system is pretty simple.

1. First, a user uploads an OCT b-scans to the platform, and the AI model evaluates the scans. 
2. After that, the model differentiates between normal scans and scans with moderate and severe pathology.
3. With the help of the second step, eye care specialists are able to focus only on serious (red) scans, saving their precious time.
4. After that, a user can highlight pathological signs with different colors, sort scans by severity level, and zoom.

It is important to mention that the patient’s diagnosis is always on the eye care practitioner’s side. Altris AI is a tool that provides assistance in support in decision-making and allows its users to see a broader perspective of a patient’s eye health. 

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

In addition, with Altris AI, users can work with all modern OCT equipment and popular data storage formats, such as DICOM of various lengths, png, and jpg. The patient data at all stages is tokenized and protected from disclosure. Eye care specialists can also actively use the Smart Reports feature, which allows users to select a single element (scan, layers, both eyes, etc.) that they want to see in their OCT report.

Acuitas activEHR 2.0

In case you are working at or owning a midsize or large optometry practice, this hybrid electronic health record solution will be quite useful. Acuitas activEHR 2.0 can be hosted in the cloud as well as deployed on-premise, depending on your preferences. This software offers its users a wide range of tools, including electronic medical records, billing software, scheduling, PACs, accounting software and billing services. 

What is more, Acuitas activEHR 2.0 can provide optometry clinics with various marketing and upselling features, and you can also customize BI reporting and track benefits. Healthcare providers can reach out to patients via either SMS or email, which makes it much easier to schedule an appointment.

In addition, the optometry practice management software supports such features as IDA (Immediate Data Access), which allows practitioners to automatically update the frames. Acuitas activEHR 2.0 also offers a variety of application integrations. 

MedFlow EMR

Next on our list — Medflow EMR software, which was designed to serve as either a standalone EMR (electronic medical record) or as a combination of EMR + practice management (PM) system. Like other optometry practice management software from our list, Medflow EMR was created specifically for eye care, but it can be used by eye care specialists providing both ophthalmology and optometry. 

Medflow has a bunch of features, but the main one is the software has built-in templates designed for comfortable and time-saving work, including retina scans and surgery, cataracts, glaucoma, digital drawings, eye measurements, LASIK procedures, and more. In addition, it also has a base package, where ASC and optical modules are included.

Overall, this practice management software will suit a clinic of any size, be it solo practice or a large hospital. The Medflow interface can be easily integrated with other practice management systems or image interpretation applications. Also, the software can be used as a hosted solution or installed on-premise.

Liquid EHR

Liquid EHR software will be a perfect solution rather for small and midsize optometry practices than large hospitals. The broad range of its features includes medical records management, medical billing, scheduling and a lot more. The optometry practice management software provides eye care specialists with the ability to generate a mailing list, track systems workflow, manage documents, do compliance checks, integrate e-prescribing, and configurable exam records. 

What is more, Liquid EHR has a number of specific optometry tools, such as historical IOP charts, drawing tools, built-in eye charts, frames data integration and image management. Optometrists can incorporate lab test results, view clinical summaries and send patient reminders. 

In addition, the software also allows practitioners to have instant access to electronic insurance filing tools, patient recalls, drug interactions and allergy interaction checks, problem lists, active medication lists, medication recommendations, educational resources, smoking status, vital signs and more.

EyePegasusEHR

The EyePegasus optometry practice management software offers a solid number of tools and features for optometry practices. You can schedule appointments online, turn on the automatic appointment reminders, work with a patient portal, scan documents, use an optical calculator and an iOS app with patient check-in features. 

Using EyePegasus, eye care specialists can customize different tabs by choosing a proper layout, and create templates for treatment documentation. Moreover, optometrists are able to scan medical images and upload them directly into a patient’s chart. The is also a possibility to create referral letters using auto-populated EHR data. Other EyePegasus tools include building and dispensing optical orders and online appointment management. 

In addition, the optometry practice management software allows managing inventory of different items, such as lenses. EyePegasus also can be integrated with a variety of applications. 

Eye Cloud Pro

Another optometry practice management software created for optical professionals is Eye Cloud Pro. The list of its data managing tools is really impressive and includes e-prescribing, inventory management, integrated credit card processing, electronic claims submission, device integrations, two-way texting (SMS), and ECP Billing.

The system also provides improved patient communication via secure messaging and email services. Clinic managers can configure various appointment types and lets clients request bookings via mobile or desktop devices. The software can be customized mailing lists, referral reports, account information, and sales reports to help with business strategy.

In addition, one more benefit of Eye Cloud Pro software is that it has an integrated payment processing system with automated invoice and receipt generation. It will make a clinic’s data safe and retained. 

OD Link

Taking about comprehensive optometry practice management software, OD Link is one of the most suitable variants for any clinic. It has both PM and EMR/EHR tools, helping to manage patient records, exams, appointments, inventory, billing/insurance information, and much more.

OD Link software allows optometry practitioners to communicate with patients via SMS or email, work with electronic insurance claim processing centers, and create automated patient entrance forms.

It also has a mobile app for iOS users, can accept data input from electronic optometry equipment, and can be integrated with different applications.

ManagementPlus

Last but not least, ManagementPlus practice management software for optometrists was designed as a fully-fledged and customizable solution with a bunch of functions. With the help of this soft, eye care specialists can work with EHR, PM, ASC forms and inventory. It is also quite helpful in managing revenue cycle services, practice building and reputation management, business analytics and capital funding.

What is more, ManagementPlus solutions allow optometrists and clinic managers to work in one platform, which makes communication clear and unified. Users can track workflows and handle all billing from eligibility to collections. 

In addition, ManagementPlus has an in-built reporting tool, which allows specialists to report on most fields in the system, while the practice management system provides a choice of two scheduling modules. Users have the option of choosing either cloud-based or on-premise deployment. 

Summing up 

Optometry management software is a perfect choice for any medical practice, including solo practices, midsize clinics, and large hospitals. It is a perfect tool not only for managing patients, optical instruments and aids. The software is also helpful in improving operations, increasing efficiency and revenue and streamlining the working process. Such solutions keep all the data in one place, powering optometrists to document the patient history directly from diagnosis, and managers to avoid unnecessary paperwork.

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Overall, optometry management software is a need for modern practice, as it improves the diagnosis and treatment, and even can be integrated with image management systems, like Altris AI. This integration assists in managing patient data, helps with controversial OCT scans, differentiate between pathological and non-pathological scans, and, most importantly, gives confidence to eye care specialists.

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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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 ophthalmic practice management system can provide to your medical practice. Let’s take a closer look at some of them:

• 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 eye hospital management software. However, each system has its unique features, so let’s look at the top 8 eye clinic management systems. 

Altris AI System

Altris AI is a unique ophthalmic image 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. 

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

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. 

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

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).

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

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.

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

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.

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

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

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

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.

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

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.

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.

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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.

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.

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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.

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. 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. 

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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. 

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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%

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.

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.

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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There are a lot of similar examples of AI misconceptions when famous professors and specialists in the field of ophthalmology made predictions that artificial intelligence is rapidly gaining strength in the eyecare industry. This gives rise to many myths and fears around the introduction of AI in clinical practice. More and more eye care professionals have faced the question: will artificial intelligence replace ophthalmologists and optometrists in the near future?

The increased attention to the issue of optometrists and ophthalmologists replaced by AI was also provoked by a World Economic Forum (WEF) report. According to this report, people can lose 85 million jobs by 2025 due to the shifting division of labor between people and machines.

In this post, we will discuss the top 5 AI misconceptions that are most often faced by the owners of ophthalmological clinics and optometry centers in order to dispel them once and for all.

Do AI algorithms work exactly like a human brain?

The concept of optometrists and ophthalmologists replaced by robots is gaining popularity. Nowadays, eye care specialists often discuss the potential of AI training in human cognitive skills. It is no longer just about the ability of AI to detect Diabetic retinopathy or interpret OCT scans with greater accuracy. The question is, will AI ever be able to replicate human consciousness? And can AI replicate how the human brain works?

What do we know about such models in different areas? AI systems are already demonstrating the work of some human cognitive functions. For example, AI models successfully compete with humans in computer games by gradually learning successful strategies. There is also an AI ​​model which creates enjoyable melodic music.

However, optometrists and ophthalmologists replaced by AI still seem unrealistic. Even with the above examples mimicking some aspects of human behavior, an AI algorithm still needs to learn what empathy is. Artificial intelligence does not understand and cannot make sense of its surrounding, nor can it learn from its surrounding as humans do. The most famous example that confirms this inability of AI is Siri or Alexa. Voice assistants can set up appointments but give strange answers when the conversation goes differently than their scenario.

While the human brain inspires modern AI techniques such as neural networks (NNs), the structure of NNs architectures is not biologically realistic. 

First of all, there is a set of qualities that ophthalmologists and optometrists use every day. It is empathy for the patient, as well as creativity, teamwork, and adaptability. These qualities help doctors provide effective care to their patients. It is unlikely that the machines will ever be able to work with children, older adults, or patients with specific disabilities on par with humans. In addition, any patient would like to hear the diagnosis or discuss a treatment plan with a doctor, not a machine. 

Therefore AI algorithm can’t work like a human brain, and the scenario where artificial intelligence replace ophthalmologists and optometrists will never happen. Nowadays, there are no developments that would make us think that AI image interpretation will ever be able at least to repeat important qualities of eye care specialists.

Is today’s state of AI dangerous for humans?

Today, AI algorithms can interpret retinal images and distinguish pathological from non-pathological scans. However, not all attempts at AI implementation have succeeded as well. One of the most popular non-medical examples is Facebook. Some time ago, Facebook tried to identify relevant news for certain groups of users. But the automated process could not detect the difference between real and fake news. Russian hackers managed to trick the system and bypass automatic filters. They posted fake news, forcing the Facebook team to come back to human editors.

This is just one example of how security lags behind performance when humans rely on AI too much. Artificial intelligence is a great tool, but in most cases, its abilities only give reliable and the most accurate results in collaboration with eye care professionals. Although machines are designed by humans, they often can’t predict human behavior and don’t know how to cope with situations or clinical cases that go beyond the scope of the algorithm.

Therefore AI is not dangerous for humans when ophthalmologists and optometrists periodically control the work of algorithms and review how the machine works. This is the number-two reason why artificial intelligence replace ophthalmologists and optometrists is unrealistic.

Will AI ever be 100% objective?

To honestly answer the questions of will artificial intelligence replace ophthalmologists and optometrists and whether it is 100% objective, you need to understand that an AI system will only be as good as its inputs. By loading unbiased training datasets, engineers can create an AI system that makes unbiased decisions. However, in the real world, AI is unlikely ever to be 100% objective. 

For example, many well-known companies, such as Amazon or Facebook, still struggle with the gender gap in hiring. Some time ago, Amazon used historical data from the past ten years to train its AI recruiting model. The algorithm was supposed to process data and candidates and free recruiters from the routine viewing of hundreds of CVs. However, soon Amazon team discovered that the data was biased against women. AI algorithm was trained by outdated information when the technology industry used to be dominated by men. Thus, the new recruitment system selected only male candidates. This forced Amazon to abandon the algorithm and re-open many recruiter positions.

In the field of ophthalmology, AI models can already accurately predict diabetes risk factors or potential vision loss from OCT images. So when will artificial intelligence replace ophthalmologists? In Altris, we are sure the algorithm will never achieve adequate objectivity, as it will always be limited by input data, whether demographics, gender, or age. 

Now we know that AI can’t be 100% objective. Indeed, ophthalmologists and optometrists can’t match the ability of algorithms to detect pixel-level patterns among the millions of pixels in the OCT scan. However, only the cooperation of eye care specialists and a quality AI model working together will allow for more accurate detection of diseases. The combined efforts of AI management systems and eye care specialists can help achieve the desired 100%.

Can AI make it without eye care specialists?

Various articles have speculated on whether artificial intelligence replace ophthalmologists and optometrists, raising concerns about unemployment. However, this never corresponded to the actual state of affairs. Carl Benedikt Frey, an Oxford Martin Citi Fellow at Oxford University, reported that while 47% of jobs are at risk of automation, the risk for doctors is estimated at only 0.4%.

In addition, in his book “Humans Are Underrated”, Geoff Colvin states that the most valuable skill for ophthalmologists is the ability to sense the thoughts and feelings of patients who are losing sight.

Many patients complain about the lack of contact with the doctor. They admit that the treatment would be more comfortable if doctors devoted more time to live communication. This mainly applies to children and the elderly, who need a lot of attention from eye care specialists. Empathy and similar human qualities are not only an understanding of the patient’s feelings but also an adequate response to them. Thus, a future in which optometrists and ophthalmologists are replaced by AI seems senseless.

Professor Tien Yin Wong, medical director of the Singapore National Eye Centre, claimed that AI holds great promise for retinal screening. And while AI for OCT interpretation will radically change clinical practice, the technology’s more significant impact will be to complement and enhance human capabilities rather than replace them. The field of ophthalmology demonstrates that the combined efforts of scientists and machines are more effective than either could achieve individually. 

Artificial intelligence for OCT interpretation is just a recommendation system for an eye care specialist. Often one pathological sign, for example, Cystoid macular edema (CME), or Intraretinal fluid, can indicate many diseases, like Wet AMD, DR, DME, CRVO, and others. That is why AI is only an assistant to a doctor, especially when it comes to rare pathologies.

All in all, AI for OCT interpretation is just a tiny part of clinical practice and can never work without humans. In order to detect the pathological signs and diagnose a disease correctly, an eye care specialist must perform different examination methods. Among these exams are visual acuity, intraocular pressure, ophthalmoscopy, and a basic patient examination, which includes anamnesis. Moreover, ophthalmologists and optometrists may also need to perform other visualization methods, like Fundus photography, FFA, or OCTA.

Will artificial intelligence replace ophthalmologists and optometrists?

This is probably one of the key AI misconceptions. Automation has led to a significant change in many industries, and ophthalmology is no exception. So when will AI replace eye care specialists? The answer is quite simple — AI will never replace them. It will eventually take over routine tasks, allowing the career of ophthalmologists and optometrists to advance in new and exciting directions.

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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.

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.

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 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: