Retinal screening examinations can prevent vision loss from diabetes, but are costly and highly underutilized. We hypothesized that artificial intelligence-assisted non-mydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes.
Prospective cohort study.
Adults ages 18 or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients.
All participants underwent non-mydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared to the historical adherence rate from the same clinic.
Rate of adherence to eye screening recommendations.
By automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% had an inconclusive result. The remaining 48.9% had negative screening results, confirmed by human over-read, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (CI 92.29% to 100%) in detecting an abnormal screening result, while its specificity was 65.67% (CI 56.98% to 73.65%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1-year compared to the historical adherence rate of 18.7% (P < 0.0001, Fisher's Exact Test).
Implementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.

Copyright © 2020. Published by Elsevier Inc.
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