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

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收录情况： ◇ SCIE ◇ 自然指数

作者：
Li Fei[1] * ; Su Yuandong[2,3] * ; Lin Fengbin[1] * ; Li Zhihuan[3] * ; Song Yunhe[1] ; Nie Sheng[4] ; Xu Jie[5] ; Chen Linjiang[6] ; Chen Shiyan[7] ; Li Hao[8] ; Xue Kanmin[9] ; Che Huixin[10] ; Chen Zhengui[11] ; Yang Bin[12] ; Zhang Huiying[13] ; Ge Ming[14] ; Zhong Weihui[15] ; Yang Chunman[16] ; Chen Lina[17] ; Wang Fanyin[18] ; Jia Yunqin[19] ; Li Wanlin[20] ; Wu Yuqing[21] ; Li Yingjie[22] ; Gao Yuanxu[3,23] ; Zhou Yong[24] ; Zhang Kang[3,*1] ; Zhang Xiulan[1,*2] ;
机构： [1]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China. [2]State Key Laboratory of Biotherapy and Center for Translational Innovations, West China Hospital and Sichuan University, Chengdu, China. [3]PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, China. [4]State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease and Nanfang Hospital, Southern Medical University, Guangzhou, China. [5]Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China. [6]Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China. [7]Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China. [8]Department of Ophthalmology, Guizhou Provincial People’s Hospital, Guiyang, China. [9]Nuffield Laboratory of Ophthalmology, Department of Clinical Neurosciences, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom. [10]He Eye Specialist Hospital, Shenyang, Liaoning Province, China. [11]Jiangmen Xinhui Aier New Hope Eye Hospital, Jiangmen, Guangdong, China. [12]Department of Ophthalmology, Zigong Third People’s Hospital, Zigong, China. [13]Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou, China. [14]Department of Ophthalmology and Optometry, Guizhou Nursing Vocational College, Guiyang, China. [15]Department of Ophthalmology, Guangzhou Development District Hospital, Guangzhou, China. [16]Department of Ophthalmology, The Second Affiliated Hospital of Guizhou Medical University, Kaili, China. [17]Department of Ophthalmology, The Third People’s Hospital of Dalian, Dalian, Liaoning Province, China. [18]Department of Ophthalmology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China. [19]Department of Ophthalmology, Dali Bai Autonomous Prefecture People’s Hospital, Dali, China. [20]Department of Ophthalmology, Wuwei People’s Hospital, Wuwei, Gansu Province, China. [21]Department of Ophthalmology, Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, China. [22]Department of Ophthalmology, The First Hospital of Nanchang City, Nanchang, China. [23]State Key Laboratory of Lunar and Planetary Sciences, Macao University of Science and Technology, Taipa, Macau, China. [24]Clinical Research Institute, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
出处：
DOI： 10.1172/JCI157968
ISSN：

摘要：
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.

基金：
This work was supported by the National Natural Science Foundation of China (NSFC grants 82101117 and 82070955); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University (grant 303020104); the Science and Technology Program of Guangzhou, China (2021); the Science and Technology Development Fund (FDCT) of Macau (grant 0070/2020/A2); and FDCT-NSFC (grant 0007/2020/AFJ).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2021]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验
最新[2025]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验
JCR分区：
出版当年[2020]版：
Q1 MEDICINE, RESEARCH & EXPERIMENTAL
最新[2023]版：
Q1 MEDICINE, RESEARCH & EXPERIMENTAL

影响因子： 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

第一作者：
第一作者机构： [1]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
共同第一作者：
通讯作者：
通讯机构： [1]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China. [3]PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, China. [*1]Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau 999078. [*2]Zhongshan Ophthalmic Center, No. 7 Jinsui Road, Guangzhou 510060, China.
推荐引用方式(GB/T 7714)：
Li Fei,Su Yuandong,Lin Fengbin,et al.A deep-learning system predicts glaucoma incidence and progression using retinal photographs.[J].JOURNAL OF CLINICAL INVESTIGATION.2022,132(11):doi:10.1172/JCI157968.
APA：
Li Fei,Su Yuandong,Lin Fengbin,Li Zhihuan,Song Yunhe...&Zhang Xiulan.(2022).A deep-learning system predicts glaucoma incidence and progression using retinal photographs..JOURNAL OF CLINICAL INVESTIGATION,132,(11)
MLA：
Li Fei,et al."A deep-learning system predicts glaucoma incidence and progression using retinal photographs.".JOURNAL OF CLINICAL INVESTIGATION 132..11(2022)

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