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Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study(Open Access)

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机构: [1]Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [2]Department of Internal Medicine, The Second People’s Hospital of Yuhuan, Yuhuan, China [3]Department of Research, VoxelCloud, Shanghai, China [4]Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [5]Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China [6]Department of Ophthalmology, The Third People’s Hospital of Datong, Datong, China [7]Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China [8]Department of Endocrinology and Metabolism, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology Luoyang City Clinical Research Center for Endocrinology and Metabolism, Luoyang, China [9]Department of Internal Medicine of Traditional Chinese Medicine, Sheyang Diabetes Hospital, Yancheng, China [10]Department of Endocrinology, The Second Affiliated Hospital Dalian Medical University, Dalian, China [11]Department of Endocrinology, Datong Coal Group Ltd. General Hospital, Datong, China [12]Department of Endocrine and Metabolic Diseases, The First People’s Hospital of Yunnan Province, Kunming, China [13]Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China [14]Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [15]Department of Endocrinology, Longkou People’s Hospital, Yantai, China [16]Department of Endocrinology, The Third Affiliated Hospital of Nanchang University, Nanchang, China [17]Department of Endocrinology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China [18]Department of Endocrinology and Metabolism, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China [19]Department of Endocrinology and Metabolism, Hebi Coal (group) Ltd. General Hospital, Hebi, China [20]Department of Endocrinology and Metabolism, People’s Hospital of Zhengzhou, Zhengzhou, China [21]Department of Computer Science, Computer Graphics & Vision Laboratory, University of California Los Angeles, Los Angeles, California, USA [22]Department of Research, VoxelCloud, Los Angeles, California, USA
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关键词: clinical study diabetic retinopathy diagnostic techniques and procedures epidemiology

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INTRODUCTION: Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China. RESEARCH DESIGN AND METHODS: The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation. RESULTS: In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups. CONCLUSION: This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers. TRIAL REGISTRATION NUMBER: NCT04240652. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 2 区 内分泌学与代谢
最新[2023]版:
大类 | 2 区 医学
小类 | 3 区 内分泌学与代谢
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出版当年[2019]版:
Q3 ENDOCRINOLOGY & METABOLISM
最新[2023]版:
Q2 ENDOCRINOLOGY & METABOLISM

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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通讯机构: [3]Department of Research, VoxelCloud, Shanghai, China [5]Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
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