Diabetic retinopathy (DR) is a leading cause of preventable blindness, and the growing global burden of diabetes is placing increasing pressure on ophthalmic services. Artificial intelligence (AI)-based retinal image analysis offers a promising strategy to scale up DR screening while reducing reliance on specialist graders. We assessed the performance of an AI-based DR screening system implemented in a real-world endocrinology clinic at the Erasmus Hospital, Belgium. Adult patients with diabetes underwent non-mydriatic fundus photography, and images were analyzed by the AI system for referable DR and diabetic macular edema. All images were independently graded by a retinal specialist using the Early Treatment Diabetic Retinopathy Study (ETDRS) classification as the reference standard. Of 405 patients screened, 353 (86.7%) were included in the primary analysis. The AI system achieved an area under the curve of 96.5%, sensitivity of 88.9%, specificity of 98.7%, and high predictive values for referable DR detection. Subgroup analyses showed consistently high accuracy across demographic and clinical strata. Multivariate analysis identified higher HbA1c at diagnosis and longer diabetes duration as significant predictors of referable DR for both AI and human grading. These findings support the robustness, generalizability and operational feasibility of this AI system for DR screening in routine clinical care.
Berrada et al. (Fri,) studied this question.