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A deep learning system for detecting diabetic retinopathy across the disease spectrum.

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, 200233, China. MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, 200240, China. Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China. Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200040, China. School of Biomedical Engineering, Shanghai Tech University, Shanghai, China. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, 200240, China. Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200040, China. zouhaidong@sjtu.edu.cn. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. shengbin@cs.sjtu.edu.cn. MoE Key Lab of Artificial Intelligence, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, 200240, China. shengbin@cs.sjtu.edu.cn. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, 200233, China. wpjia@sjtu.edu.cn.

Nature communications. 2021;(1):3242
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Abstract

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Methodological quality

Publication Type : Observational Study

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