Abstract
OBJECTIVES: To assess and compare the diagnostic accuracy of non-ophthalmologist-led diabetic retinopathy screening (DRS) at health and wellness centres (HWCs) and offline artificial intelligence (AI)-assisted community-based screening, using specialist grading as the reference standard in India. DESIGN, SETTINGS AND PARTICIPANTS: Pragmatic diagnostic accuracy study in primary healthcare settings. The settings included HWCs and community-based screening sites in rural Block Boothgarh, Mohali District, Punjab, India. A total of 600 people with diabetes aged ≥30 years were enrolled across three screening models: (1) non-ophthalmologist-led DRS at the HWC, (2) AI-assisted smartphone-based DRS in the community and (3) standard referral-based care. Retinal images were captured using non-mydriatic fundus cameras and independently graded by two masked human graders; a senior retina specialist resolved any disagreements. The AI was assessed for its ability to detect diabetic retinopathy (DR) and referable diabetic retinopathy (RDR). Diagnostic performance metrics were reported. RESULTS: The non-ophthalmologist-led model demonstrated 86.4% sensitivity (95% CI 65.1% to 97.1%) and 94.3% specificity (95% CI 88.5% to 97.7%) for DR detection, with an ungradability rate of 8%. For RDR, sensitivity reached 95.8% (95% CI 78.9% to 99.9%) and specificity was 93.1% (95% CI 88.0% to 96.5%). The offline AI-assisted model achieved 93.3% sensitivity (95% CI 68.1% to 99.8%) and 85.1% specificity (95% CI 76.9% to 91.2%) for RDR, but with a higher ungradability rate (38%), mainly due to cataracts and poor image quality. Both approaches effectively identified referable cases; however, the non-ophthalmologist-led model demonstrated greater accuracy and operational feasibility. CONCLUSIONS: This study demonstrates that non-ophthalmologist-led DRS at HWCs can enhance access to primary care. Offline AI-enabled screening demonstrates potential for community use but is currently limited by image quality and binary classification outputs. Integrating both approaches may strengthen DRS coverage in resource-limited settings. CLINICAL TRIALS REGISTRY OF INDIA: CTRI/2022/10/046283.