Artificial Intelligence Analysis of Diabetic Retinopathy in Retinal Screening Events

Purpose: Patients with Type II Diabetes are at increased risk Diabetic Retinopathy (DR). Early detection and treatment of DR is critical for saving vision and preventing blindness. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR. Methods: : Patient images were obtained using a 45-degree Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an on-site grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern. Results: : The EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR. The kappa value for concordance on a diagnosis was 0.69 (95% CI: 0.61-0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement. The kappa value for concordance on “whether to refer” was 0.70 (95% CI: 0.60-0.80), indicating substantial agreement. Conclusions: : This retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment.