J Med Internet Res. 2026 Sep 30;28:e85181. doi: 10.2196/85181.
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 A1c 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.
PMID:42814672 | DOI:10.2196/85181