Ocular Factors Affecting AI Diagnosis in Diabetic Retinopathy Screening for Resource-Limited Regions: Cross-Sectional Study

Abstract Background Diabetic retinopathy (DR) is the leading cause of vision loss among working-age adults worldwide. AI-assisted automated image reading has effectively alleviated the human resource challenges in large-scale remote screenings, yet there is limited analysis on the impact of complex ocular factors on the diagnostic efficacy of AI. Objective This study aimed to systematically analyze the impact of multiple ocular factors on the diagnostic efficacy of the EVisionAI system in detecting DR and vision-threatening diabetic retinopathy (VTDR) during large-scale screening of residents with diabetes in resource-limited regions. Methods This cross-sectional study used a multistage stratified random sampling method to screen residents with type 2 diabetes at primary health centers in resource-limited regions. Data collection involved structured questionnaires (for basic and disease information), hemoglobin A 1c testing, and comprehensive ophthalmic examinations (visual acuity, intraocular pressure, slit-lamp examination, axial length measurement, and fundus photography). Following data collection, 2 ophthalmologists independently graded fundus photographs according to the American Academy of Ophthalmology standards. The influence of various ocular factors on the diagnostic performance of EVisionAI was subsequently evaluated. Results Between October 21 and November 12, 2024, 1847 participants with type 2 diabetes (aged 32‐91 years) were enrolled, of whom 1748 (94.6%) completed the screening process and 3392 eyes were eligible for DR analysis. Participants had a mean age of 67.17 (SD 8.52) years and mean diabetes duration of 9.13 (SD 6.97) years, with 19.2% (335/1748) having DR and 7.4% (129/1748) having VTDR. Although EVisionAI’s diagnostic efficacy was comparable to that of ophthalmologists (sensitivity: 90.61%, 95% CI 87.89%-92.79%; specificity: 98.99%, 95% CI 98.52%-99.31%), some ocular factors—including pupil size, refractive media opacity, and tessellated fundus (TF)—significantly impaired its efficiency. Severe refractive media opacity and TF reduced its sensitivity to 80.95% and 82.86%, respectively, and these factors interfered more with early-stage DR detection than VTDR (97.45% detected), particularly in eyes with severe TF changes (sensitivity decreased to 60.71%). Most notably, severe vitreous degeneration-induced opacity almost invariably led to VTDR misdiagnosis. Additionally, pupil dilation improved the sensitivity of EVisionAI for diagnosing DR (excluding early-stage DR) but had minimal impact on specificity. Conclusions EVisionAI achieved high diagnostic accuracy for large-scale DR screening in resource-limited regions, yet its performance for early-stage disease was diminished by severe ocular factors. Optimizing for these factors is therefore essential to maximize its clinical utility in primary care settings with limited specialist access.