机构：[1]Center for Clinical Translational Innovations and Biomedical Big Data Center, West China Hospital and Sichuan University, Chengdu, China四川大学华西医院[2]Center forBiomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China[3]Department ofComputer Science and Technology, Tsinghua University, Beijing, China[4]Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing TongrenHospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China研究所眼科研究所首都医科大学附属北京同仁医院首都医科大学附属同仁医院[5]State Key Laboratory of Networking and Switching Technology, BeijingUniversity of Posts and Telecommunications, Beijing, China[6]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-senUniversity, Guangzhou, China[7]State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease and Nanfang Hospital,Southern Medical University, Guangzhou, China[8]Nuffield Laboratory of Ophthalmology, Department of Clinical Neurosciences, University of Oxfordand Oxford University Hospitals NHS Foundation Trust, Oxford, UK[9]Kidney Research Institute, Nephrology Division, West China Hospital and SichuanUniversity, Chengdu, China四川大学华西医院[10]Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China[11]GuangzhouWomen and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China[12]Department of Endocrinology, Kunshan Hospital Affiliated toJiangsu University, Kunshan, China[13]The Big Data Research Center, Chongqing Renji affiliated Hospital to the University of Chinese Academy of Sciences,Chongqing, China[14]Ophthalmic Center, Kiang Wu Hospital, Macau, China[15]Peking University First Affiliated Hospital, Beijing, China[16]Peking UniversityThird Affiliated Hospital, Beijing, China[17]Biotherapy Center, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China中山大学附属第三医院[18]Department of AppliedBiology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China[19]C-MER Dennis Lam and Partners Eye Center, C-MER InternationalEye Care Group, Hong Kong, China[20]Ophthalmic Center of the First People’s Hospital of Kashi Prefecture, Kashi Prefecture, Xinjiang, China[21]MedicalResearch Institute, Wuhan University, Wuhan, China[22]Clinical Research Institue, Shanghai General Hospital, Shanghai Jiaotong University School ofMedicine, Shanghai, China
Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min-1 per 1.73 m2 and 0.65-1.1 mmol l-1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.
基金：
National Natural Science Foundation of China (61906105,
61872218 and 61721003), National Key Research and Development Program of China
(2019YFB1404804, 2017YFC1104600, 2017YFC0112402), 1.3.5 project for disciplines
of excellence, West China Hospital, Sichuan University (ZYJC20001, ZYJC18010),
Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Macau
University of Science and Technology, Beijing National Research Center for Information
Science and Technology (BNRist), Tsinghua University Initiative Scientific Research
Program, and Guoqiang Institute, Tsinghua University, Wellcome Trust (216593/Z/19/Z).
第一作者机构：[1]Center for Clinical Translational Innovations and Biomedical Big Data Center, West China Hospital and Sichuan University, Chengdu, China[2]Center forBiomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
共同第一作者：
通讯作者：
通讯机构：[1]Center for Clinical Translational Innovations and Biomedical Big Data Center, West China Hospital and Sichuan University, Chengdu, China[2]Center forBiomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
推荐引用方式(GB/T 7714)：
Kang Zhang,Xiaohong Liu,Jie Xu,et al.Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images.[J].NATURE BIOMEDICAL ENGINEERING.2021,5(6):533-+.doi:10.1038/s41551-021-00745-6.
APA：
Kang Zhang,Xiaohong Liu,Jie Xu,Jin Yuan,Wenjia Cai...&Guangyu Wang.(2021).Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images..NATURE BIOMEDICAL ENGINEERING,5,(6)
MLA：
Kang Zhang,et al."Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images.".NATURE BIOMEDICAL ENGINEERING 5..6(2021):533-+
医学