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.