Diagnostic Accuracy of the EyeArt Artificial Intelligence System for Diabetic Retinopathy: A Systematic Review and Meta-Analysis

Abstract

Introduction: Diabetic retinopathy (DR) persists as a predominant cause of preventable vision loss globally, with its prevalence escalating in conjunction with the diabetes epidemic. Efficient, automated screening is needed to enable earlier detection of DR at scale. Artificial intelligence-driven platforms, such as EyeArt® (Eyenuk Inc.), offer a scalable solution with potential to alleviate the burden on healthcare systems. Methods: A systematic review and meta-analysis were conducted following PRISMA and MOOSE guidelines. This review was prospectively registered in PROSPERO (CRD42024571137). Observational studies published between 2016 and 2024 assessing the diagnostic performance of the EyeArt® system for DR detection were retrieved from PubMed, Scopus, and Embase. Data on sensitivity, specificity, and diagnostic odds ratio (DOR) were extracted, and pooled estimates were calculated using a random-effects model. Study quality was assessed using QUADAS-2 and GRADE frameworks. Results: Seventeen studies, met the inclusion criteria. The pooled log DOR was 3.96 (95% CI: 3.54–4.39), and the area under the summary receiver operating characteristic curve was 0.932 (95% CI: 0.885–0.985), indicating high overall diagnostic accuracy. No significant heterogeneity was observed in the pooled diagnostic OR, although sensitivity and specificity varied across studies. Conclusions: EyeArt® demonstrates high diagnostic accuracy for detecting any-grade and referable DR across diverse clinical and geographical settings. Its integration into DR screening programs could improve early detection, optimize healthcare resource allocation, and expand access to ophthalmic care, particularly in resource-limited environments.