A deep-learning system predicts glaucoma incidence and progression using retinal photographs.

Journal: The Journal of clinical investigation
Published Date: Jun 1, 2022

Abstract

BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

Authors

  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Yuandong Su
    Center for Clinical Translational Innovations and Biomedical Big Data Center, West China Hospital and Sichuan University, Chengdu, China.
  • Fengbin Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China.
  • Zhihuan Li
    Center for Translational Innovations and Department of Respiratory and Critical Care Medicine, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China; Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.
  • Yunhe Song
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China.
  • Sheng Nie
    State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease and Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Jie Xu
    Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China.
  • Linjiang Chen
    Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Shiyan Chen
    Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Kanmin Xue
    Nuffield Department of Neuroscience, Oxford University, Oxford, UK.
  • Huixin Che
    He Eye Specialist Hospital, Shenyang, Liaoning Province, China.
  • Zhengui Chen
    Jiangmen Xinhui Aier New Hope Eye Hospital, Jiangmen, Guangdong, China.
  • Bin Yang
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
  • Huiying Zhang
    Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou, China.
  • Ming Ge
    College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; Key Laboratory of the Provincial Education, Department of Heilongjiang for Common Animal Disease Prevention and Treatment, Northeast Agricultural University, Harbin 150030, China. Electronic address: geming@neau.edu.cn.
  • Weihui Zhong
    Department of Ophthalmology, Guangzhou Development District Hospital, Guangzhou, China.
  • Chunman Yang
    Department of Ophthalmology, The Second Affiliated Hospital of Guizhou Medical University, Kaili, China.
  • Lina Chen
    Department of Ophthalmology, The Third People's Hospital of Dalian, Dalian, Liaoning Province, China.
  • Fanyin Wang
    Department of Ophthalmology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China.
  • Yunqin Jia
    Department of Ophthalmology, Dali Bai Autonomous Prefecture People's Hospital, Dali, China.
  • Wanlin Li
  • Yuqing Wu
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Yuanxu Gao
    Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China; State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; Department of Big Data and Biomedical AI, College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, China.
  • Yong Zhou
    National Institutes for Food and Drug Control, Beijing, 100050, China.
  • Kang Zhang
    Xifeng District People's Hospital, Qingyang, China.
  • Xiulan Zhang
    Zhongshan Ophthalmic Center, Sun Yat-sen University, China. Electronic address: zhangxl2@mail.sysu.edu.cn.

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