Will AI Have a Positive Effect on Eye Care Specialists?

AI in Eye Care Industry: Current Status

Disease screening: DR, AMD, and rare pathologies & biomarkers

A 2022 study by the University of Illinois showed that eye care specialists mostly see AI helping with disease screening, monitoring, and patient triage tasks. Notably, a significant increase in willingness to incorporate AI in practice has emerged after the COVID-19 pandemic, presumably due to a need for remote consultations.

The growing interest in AI for disease screening and monitoring coincides with the development of sophisticated AI systems. Due to their significant causes of visual impairment, Diabetic Retinopathy and AMD are the primary targets for AI screenings.

With over 422 million people worldwide affected by diabetic retinopathy and an estimated 80 million suffering from age-related macular degeneration, the workload on eye care specialists is immense. Unsurprisingly, most AI-powered screening solutions focus on helping clinicians with these diagnoses.

AI algorithms are trained to recognize DR-related alterations on images: hemorrhages, exudates, and neovascularization. AI also offers significant advancements in Age-related Macular Degeneration screening. Algorithms accurately segment data in OCT scans, helping assess retinal structures and quantify fluids during treatment. Trained models predict disease progression risks and analyze treatment responses.

AI in eye care can segment retinal structures to distinguish between normal retina scans and pathology on OCT, detect atrophic changes, and follow all alterations over time. It can even highlight rare inherited retinal dystrophies. For example, Altris AI is trained to recognize Vitelliform dystrophy and Macular telangiectasia type 2.

More Efficient Patient Triage

The number of eye scans clinicians are performing is growing at a pace much faster than human experts can interpret them. This delays the diagnosis and treatment of sight-threatening diseases, sometimes with devastating results for patients.

Our recent survey showed that among more than 1000 participating eye care specialists, 40% have more than 10 OCT exams daily. Meanwhile, 35% of eye care specialists have 5-10 OCT daily examinations. Unfortunately, more patients per day mean an increased risk that specialists may miss some minor, rare, or early conditions.

AI systems can quickly triage scans based on severity. Prioritized urgent cases can be flagged for immediate attention. Healthy patients can be monitored without urgency.

This ensures patients with time-sensitive conditions get the care they need, while less urgent cases receive a timely but less immediate review.

Optometrists can use AI systems to specify the need to refer patients based on eye image analysis.

Another advantage of AI used as a “copilot” is its continuous improvement. Providers that create such systems usually integrate new data and research findings into algorithms, resulting in an ever-evolving resource for eye care specialists.

In other words, the accuracy of the patients’ triage will get better and better with the data.

Early Glaucoma Detection

Glaucoma is a leading cause of vision-related morbidity worldwide. Although blindness is the most feared outcome, even mild visual field loss may harm the quality of life.

In a way, glaucoma is one of the most challenging eye diseases that specialists must treat; with most eye problems, the patient comes when something is wrong. Glaucoma, however, has no symptoms until it is advanced, and the damage can not be reversed. One common reason glaucoma is not diagnosed early is the inability to recognize glaucomatous optic disc and RNFL damage. Ophthalmologists often rely primarily on intraocular pressure and visual fields and not on the appearance of the optic disc.

Combining optical coherence tomography imaging and artificial intelligence, Altris AI offers a solution to the problem. The platform performs Ganglion Cell Complex asymmetry analysis on OCT scan that categorizes the risk of developing glaucoma. Glaucoma Early Risk Assessment Module can help decrease the number of false-positive referrals and increase the standard of care by supporting early diagnosis to improve patients’ prognosis.

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