机构：[1]Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of ComputerScience and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinologyand Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, ShanghaiClinical Center for Diabetes, Shanghai, China[2]MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China[3]Department of Ophthalmology, Huadong Sanatorium, Wuxi, China[4]Department of Ophthalmology, Shanghai SixthPeople’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China[5]Department of Ophthalmology and Visual Sciences,The Chinese University of Hong Kong, Hong Kong, China[6]Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore[7]Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China[8]Department of Chemicaland Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China[9]State Key Laboratory of Ophthalmology,Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China[10]Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing,China[11]Medical Records and Statistics Office, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,China[12]Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China华中科技大学同济医学院附属同济医院[13]NationalEngineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab,School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China[14]Shri Bhagwan Mahavir VitreoretinalServices, Medical Research Foundation, Sankara Nethralaya, Chennai, India[15]Department of Ophthalmology, Shanghai General Hospital, ShanghaiJiao Tong University School of Medicine, Shanghai, China[16]Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital,Capital Medical University, Beijing Ophthalmology and Visual Science Key Laboratory, 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[17]Center for Excellence in Molecular Science,Chinese Academy of Sciences, Shanghai, China[18]School of Biomedical Engineering, Shanghai Tech University, Shanghai, China[19]Shanghai UnitedImaging Intelligence, Shanghai, China[20]Shanghai Clinical Research and Trial Center, Shanghai, China[21]Ophthalmology and Visual Sciences AcademicClinical Program, Duke-NUS Medical School, Singapore, Singapore[22]Centre for Innovation and Precision Eye Health and Department of Ophthalmology,Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore[23]Tsinghua Medicine, Beijing Tsinghua Changgung Hospital,Tsinghua University, Beijing, China
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals. A deep learning algorithm shows promising performance in predicting progression to diabetic retinopathy in patients, up to 5 years in advance, potentially providing support for medical treatment decisions and indications for personalized screening frequency in a real-world cohort.
基金：
National Key Research and Development Program of China [2022YFA1004804]; Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases [2022ZZ01002]; Chinese Academy of Engineering [2022-XY-08]; National Key R & D Program of China [2022YFC2502800]; National Natural Science Fund of China [8238810007, ynlc201909, YG2022QN089]; Excellent Young Scientists Fund of NSFC [82100879]; General Fund of NSFC [62077037]; National Key Research and Development Program of China [82022012]; Interdisciplinary Program of Shanghai Jiao Tong University [62272298, 2022YFC2407000]; College-level Project Fund of Shanghai Jiao Tong University Affiliated Sixth People's Hospital [YG2023LC11]; Medical-industrial Cross-fund of Shanghai Jiao Tong University [YG2022ZD007]; Clinical Special Program of Shanghai Municipal Health Commission [20224044]; Three-year action plan to strengthen the construction of public health system in Shanghai [GWVI-11.1-28]
语种：
外文
高被引：
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类|1 区医学
小类|1 区生化与分子生物学1 区细胞生物学1 区医学：研究与实验
最新[2023]版：
大类|1 区医学
小类|1 区生化与分子生物学1 区细胞生物学1 区医学：研究与实验
JCR分区：
出版当年[2022]版：
Q1BIOCHEMISTRY & MOLECULAR BIOLOGYQ1CELL BIOLOGYQ1MEDICINE, RESEARCH & EXPERIMENTAL
最新[2023]版：
Q1BIOCHEMISTRY & MOLECULAR BIOLOGYQ1CELL BIOLOGYQ1MEDICINE, RESEARCH & EXPERIMENTAL
第一作者机构：[1]Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of ComputerScience and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinologyand Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, ShanghaiClinical Center for Diabetes, Shanghai, China[2]MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China
共同第一作者：
通讯作者：
通讯机构：[1]Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of ComputerScience and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinologyand Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, ShanghaiClinical Center for Diabetes, Shanghai, China[2]MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China[6]Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore[23]Tsinghua Medicine, Beijing Tsinghua Changgung Hospital,Tsinghua University, Beijing, China
推荐引用方式(GB/T 7714)：
Ling Dai,Bin Sheng,Tingli Chen,et al.A deep learning system for predicting time to progression of diabetic retinopathy[J].NATURE MEDICINE.2024,doi:10.1038/s41591-023-02702-z.
APA：
Ling Dai,Bin Sheng,Tingli Chen,Qiang Wu,Ruhan Liu...&Weiping Jia.(2024).A deep learning system for predicting time to progression of diabetic retinopathy.NATURE MEDICINE,,
MLA：
Ling Dai,et al."A deep learning system for predicting time to progression of diabetic retinopathy".NATURE MEDICINE .(2024)
医学