Using artificial intelligence as an initial triage strategy in diabetic retinopathy screening program in China

Abstract

Objective: To investigate the diagnostic accuracy and efficiency of an artificial intelligence (AI) triaging model in a diabetic retinopathy (DR) screening program. Methods: A DR screening program was conducted in Kashi City and Kizilsu Kirghiz Autonomous Prefecture of the Xinjiang Uyur Autonomous Region from May to July 2018, and 8 005 patients with diabetes mellitus were included. Fundus images, one centered at optic disc and one centered at macula, were taken for both eyes. A previously validated AI algorithm was applied as the first step to identify the patients with all 4 images. If the images were classified as gradable and negative DR, an AI-generated report was immediately provided without sending to manual grading, and 1/3 of these patients were randomly sampled for manual grading and quality control (group A). For the patients with at least one image classified as ungradable or positive for any DR, all images were sent for manual grading (group B). Finally, 300 patients were randomly selected from group A and group B respectively for accuracy assessment, where the patients and their images were classified by a specialist panel for referral DR (pre-proliferative DR, or proliferative DR, and/or diabetic macular edema). Results: Among 8 005 patients for DR screening [including 3 220 males and 4 785 females, aged (58.3±10.6) years], after AI triaging, 5 267 (65.8%) potentially received reports from AI system and 2 738 (34.2%) required manual grading. In group A, the accuracy and specificity of AI classification and manual grading on referral DR were all 100%. In group B, the accuracy of AI and manual grading were 75.8% and 90.3%, respectively, while the sensitivity of AI and manual grading was 100% and 79.1%, respectively. Conclusion: AI alleviates 60% of the workload of manual grading without missing any referral patients with the aid of the current AI triaging model.