AI-Driven Diagnostics vs. Clinician Assessment in Diabetic Retinopathy: A Comparative Analysis at a Secondary Eye Care Centre.

PURPOSE: To determine the diagnostic accuracy and reliability of artificial intelligence (AI) in identifying diabetic retinopathy (DR) and macular oedema (ME) compared to ophthalmologists. METHODS: This prospective study included 294 patients (576 eyes). Fundus images obtained using a non-mydriatic Topcon NW400 fundus camera were analyzed by an AI tool (Google ARDA (Automated Retinal Disease Assessment). Clinical grading was performed by a retina specialist using the International Clinical DR Severity Scale and considered the reference standard. Sensitivity, specificity, predictive values, and inter-grader agreement (κ statistics) were calculated. RESULTS: The AI tool identified 69.8% of the eyes as DR, compared to 75.2% by the retina specialist, with an 83.3% accuracy rate, specificity of 90.9%, sensitivity of 97.1%, and Kappa = 0.77. For DME, AI classified 15.3% of eyes, compared to 5.9% by ophthalmologists, with an 89.9% diagnostic efficiency and Kappa = 0.48. CONCLUSION: AI tools show high sensitivity and substantial agreement with ophthalmologists in diagnosing DR and DME, indicating their potential to enhance diagnostic accuracy and efficiency in retinal health screening.