Global epidemiology · 2025
Artificial intelligence for advancing eye care in resource-poor settings: Assessing the predictive accuracy of an AI-model for diabetic retinopathy screening in India.
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Chawla Rohan, Karkhanis Prachi, Shah Malay, Das Aritra, Sharma Rishabh, Almaula Dhwani, Venkatesh Pradeep, Singh Harsh Vardhan, Kumar Mukul, Samanta Ramanuj, Kumarl Vinod, Shah Amar, Vadera Bhavin, Jain Nakul, Sen Akanksha, Shreedhar Shyamsundar, Garg Vipin, Dhaval Soma, Ganesh Kowshik, Rana Srinivas, Tandon Radhika
Аннотация
BACKGROUND: Timely identification and treatment of Diabetic Retinopathy (DR) is critical in avoiding vision loss. DR screening is challenging, especially in resource-limited areas where trained ophthalmologists are scarce. AI solutions show promise in addressing this challenge. In this study, the performance metrics of an AI solution (MadhuNetrAI) developed in India was evaluated for referring and grading DR.
METHODS: MadhuNetrAI was developed de novo by the All India Institute of Medical Sciences (AIIMS) and Wadhwani AI (WIAI). It was tested on 1078 fundus images (from AIIMS Delhi and an unannotated subset of publicly available EyePACS images) against two ophthalmologists and an adjudicator serving as independent gold-standard annotators, wherein the disease status of the patients remained unknown.
FINDINGS: MadhuNetrAI demonstrated high sensitivity (93·2 %; CI: 89·5 %-95·6 %) and specificity (95·3 %; CI: 93·7 %-96·6 %) in detecting referable DR (moderate, severe, proliferative DR). The area-under-the-curve for referring DR against the gold standard was 0·97 (CI: 0·95-0·99) indicating excellent diagnostic performance. The agreement in grading DR severity was high (kappa = 0·89, CI: 0·86-0·91). The model performed comparably in detecting DR too.
INTERPRETATION: MadhuNetrAI's ability to grade DR severity and identify referrable cases could bring DR patients to care much earlier. Further research and clinical trials are needed to ensure its reliability and generalizability across diverse populations and image qualities.
FUNDING: MadhuNetrAI was developed by technical and programmatic teams at WIAI, with inputs and contributions by the clinical team at AIIMS, and funded by USAID. The authors have no financial or non-financial conflicts of interest to disclose.
METHODS: MadhuNetrAI was developed de novo by the All India Institute of Medical Sciences (AIIMS) and Wadhwani AI (WIAI). It was tested on 1078 fundus images (from AIIMS Delhi and an unannotated subset of publicly available EyePACS images) against two ophthalmologists and an adjudicator serving as independent gold-standard annotators, wherein the disease status of the patients remained unknown.
FINDINGS: MadhuNetrAI demonstrated high sensitivity (93·2 %; CI: 89·5 %-95·6 %) and specificity (95·3 %; CI: 93·7 %-96·6 %) in detecting referable DR (moderate, severe, proliferative DR). The area-under-the-curve for referring DR against the gold standard was 0·97 (CI: 0·95-0·99) indicating excellent diagnostic performance. The agreement in grading DR severity was high (kappa = 0·89, CI: 0·86-0·91). The model performed comparably in detecting DR too.
INTERPRETATION: MadhuNetrAI's ability to grade DR severity and identify referrable cases could bring DR patients to care much earlier. Further research and clinical trials are needed to ensure its reliability and generalizability across diverse populations and image qualities.
FUNDING: MadhuNetrAI was developed by technical and programmatic teams at WIAI, with inputs and contributions by the clinical team at AIIMS, and funded by USAID. The authors have no financial or non-financial conflicts of interest to disclose.