JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) algorithms offer an effective solution to alleviate the burden of diabetic retinopathy (DR) screening in public health settings. However, there are challenges in translating diagnostic performance and its application when deployed in real-world conditions.

Objective:

The study aimed to assess the technical feasibility of integration and diagnostic performance of validated DR screening AI algorithms in real-world outpatient public health settings.

Methods:

Prior to integrating an AI algorithm for DR screening (DRS), the study involved several steps: 1) Five AI companies, including four from India and one international, were invited to evaluate their diagnostic performance using low-cost non-mydriatic fundus cameras in public health settings 2) The AI algorithms were prospectively validated on fundus images from 250 people with diabetes mellitus (PwDM) captured by a trained optometrist in public health settings in Chandigarh Tricity in North India. The performance evaluation used diagnostic metrics, including sensitivity, specificity, and accuracy, compared to human grader assessments 3) The AI algorithm with better diagnostic performance was integrated into a low-cost screening camera deployed at a community health centre (CHC) in the Moga district of Punjab. For AI algorithm analysis, a trained health system optometrist captured non-mydriatic images of 343 (PwDM) patients.

Results:

Three web-based AI-based screening companies agreed to participate, while one declined and one chose to drop due to low specificity identified during the interim analysis. The three AI algorithms demonstrated variable diagnostic performance, with sensitivity (60% to 80%) and specificity (14% to 96%). The better-performing algorithm AI-3 (sensitivity: 68%, specificity: 96, and accuracy: 88·43%) upon integration demonstrated high sensitivity of image gradability (99.5%), DR detection (99.6%), and referral DR (79%) at the CHC.

Conclusions:

This study highlights the importance of systematic AI validation for responsible clinical integration, demonstrating DRS potential to improve healthcare access in resource-limited public health settings. Clinical Trial: Clinical Trials Registry India (CTRI/2022/10/046185)