Diagnostic performance of multiple artificial intelligence (AI) algorithms for diabetic retinopathy screening in primary care: Evidence from real-world settings in India

Abstract

Purpose: The diagnostic performance of artificial intelligence (AI) in real-world settings remains uncertain, particularly across different fundus camera systems. This study evaluates the diagnostic accuracy of three AI algorithms for diabetic retinopathy (DR) detection using two nonmydriatic fundus cameras, assessing image gradability and DR severity.

Methods: A prospective diagnostic accuracy study was conducted at a primary health center, Khijrabad, Punjab, India (March-July 2021). The study evaluated three commercially available artificial intelligence algorithms for DR detection using two nonmydriatic fundus cameras. Participants underwent two-field, nonmydriatic fundus imaging with both cameras. Image quality and DR presence were independently assessed by masked human graders, including optometrists and a retina specialist. Diagnostic performance was measured using sensitivity, specificity, and positive and negative predictive values.

Results: A total of 272 images from 136 participants (mean age 67.7 years; 62% female) were analyzed. Human graders classified more than 97% of images as gradable, with DR detected in 47% of the images. AI-1 demonstrated the highest sensitivity (Forus: 97.5% (0.956-0.994); Intuvision: 81.7% (0.768-0.867)) but comparatively low specificity: 62.7% (58.6-66.8) and 53.8% (0.474-0.602). AI-2 displayed a balanced performance (sensitivity 80.0% and 77.0%; specificity 95.7% and 92.0%). AI-3 had a moderate sensitivity (73-80%) with the specificity ranging from 82% to 86%.

Conclusion: AI performance varied across camera platforms, highlighting the need for context-specific validation to ensure safe integration into primary care and guide DR screening guidelines.