Diagnostic Accuracy of EyeArt for Fundus-Based Detection of Diabetic Retinopathy: A Systematic Review and Meta-analysis.

Abstract

BACKGROUND Diabetic retinopathy (DR) is a leading cause of preventable blindness globally. Although early detection via routine retinal screening significantly reduces vision loss, screening rates remain suboptimal due to workforce shortages and limited accessibility. Autonomous artificial intelligence (AI) systems such as EyeArt offer an FDA-authorized solution for point-of-care DR screening without ophthalmologist oversight METHODS: We conducted a systematic review and meta-analysis following PRISMA-DTA guidelines to assess the diagnostic accuracy of EyeArt in detecting referable diabetic retinopathy (rDR) from color fundus photographs. Searches of PubMed, Embase, and ClinicalTrials.gov through April 2025 identified eligible studies involving adult populations screened with EyeArt. Sensitivity and specificity were pooled using bivariate random-effects models. Subgroup and applicability analyses were conducted to evaluate heterogeneity and clinical relevance. RESULTS Seventeen studies comprising 162,695 examinations were included. EyeArt demonstrated a pooled sensitivity of 95% (95% CI: 92-97%) and specificity of 81% (95% CI: 74-87%). Subgroup analyses indicated consistent accuracy across study designs, economic settings, healthcare contexts, device types, external validation and image gradability. Specificity varied slightly with vendor involvement. CONCLUSION Across 17 real-world studies (162,695 examinations), EyeArt exhibits high diagnostic accuracy for detecting referable diabetic retinopathy (pooled sensitivity 95%, specificity 81%), with high certainty for sensitivity and moderate certainty for specificity. Its consistently strong sensitivity supports autonomous screening in primary care. However, variability in specificity-along with inconsistent reporting/handling of ungradable images-warrants attention and standardized quality-assurance. Successful deployment will depend on workflow/EHR integration, sustainable reimbursement, and targeted implementation in underserved populations to maximize public-health impact.