Enhancing diabetic retinopathy diagnosis and grading: a retrospective study on AI-assisted decision making and cost analysis

Background/Aims Diabetic retinopathy (DR) is a major ocular complication of diabetes mellitus. While artificial intelligence (AI)-based DR screening tools have gained widespread adoption, most research focuses on comparing AI performance with human, with limited attention to AI’s role as assistants. This study evaluates the impact of AI-assisted decision-making on DR diagnosis and grading based on colour fundus photographs (CFP) and ultra-widefield fundus (UWF) images. Methods A total of 224 retinal images were analysed by 21 ophthalmologists and primary care physicians (PCPs) in China. Participants independently diagnosed and graded DR based on CFP and UWF images. After a 1-week interval, they repeated the task with AI assistance. Diagnosis accuracy was compared with a gold standard before and after AI assistance. Incremental costs and accuracy improvements were assessed using generalized estimating equations (GEE) models. Results AI assistance significantly improved DR diagnosis accuracy for both CFP and UWF images. For CFP, accuracy increased from 79.90% to 85.68% for PCPs, 81.19% to 88.69% for ophthalmic residents and 81.41% to 88.05% for ophthalmic attendings. Similar improvements were observed for UWF, with accuracy rising from 83.62% to 89.66% for residents and from 81.31% to 88.98% for attendings. GEE analysis revealed an incremental cost of 4.79 units and an accuracy improvement of 0.35 units with AI assistance. Conclusion AI assistance shows potential in improving the accuracy of DR diagnosis and grading. Despite the associated costs, AI enables ophthalmologists to achieve superior diagnosis, facilitating earlier DR detection and treatment.