Evidence map›Paper›PMID 33087340›Full record

ArticleBMJ open diabetes research & care2020

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.

Yifei Zhang, Juan Shi, Ying Peng, Zhiyun Zhao, Qidong Zheng, Zilong Wang, Kun Liu, Shengyin Jiao, Kexin Qiu, Ziheng Zhou and 20 more

Registry-linked trialOpen access · goldAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open diabetes research & care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04240652 (The Diabetic Retinopathy Screening, Prevention and Control Program), which is not on this map. Cited by 70 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 3 pooled it
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04240652 recruitingnot on this map

The Diabetic Retinopathy Screening, Prevention and Control Program

Typeobservational_patient_registrySponsorShanghai Jiao Tong University School of MedicineRan2018 to 2040Enrolled500,000ConditionsDiabetic Retinopathy
3 · Its place in the literature

Who cites it

70 citing papers in PubMed, 3 syntheses or guidelines pooled it, 100 citations in OpenAlex.

  1. Pooled it
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  15. Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2025
    Article
  16. Ten years later: how is AI impacting retina care today?Current opinion in ophthalmology · 2025
    Article
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors at 14 institutions in 2 countries.

Yifei ZhangDepartment 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.
Juan ShiDepartment 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.
Ying PengDepartment 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.
Zhiyun ZhaoDepartment 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.
Qidong ZhengDepartment of Internal Medicine, The Second People's Hospital of Yuhuan, Yuhuan, China.
Zilong WangDepartment of Research, VoxelCloud, Shanghai, China.
Kun LiuDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shengyin JiaoDepartment of Research, VoxelCloud, Shanghai, China.
Kexin QiuDepartment of Research, VoxelCloud, Shanghai, China.
Ziheng ZhouDepartment of Research, VoxelCloud, Shanghai, China.
Li YanDepartment of Ophthalmology, The Third People's Hospital of Datong, Datong, China.
Dong ZhaoCenter for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Hongwei JiangDepartment 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.
Yuancheng DaiDepartment of Internal Medicine of Traditional Chinese Medicine, Sheyang Diabetes Hospital, Yancheng, China.
Benli SuDepartment of Endocrinology, The Second Affiliated Hospital Dalian Medical University, Dalian, China.
Pei GuDepartment of Endocrinology, Datong Coal Group Ltd. General Hospital, Datong, China.
Heng SuDepartment of Endocrine and Metabolic Diseases, The First People's Hospital of Yunnan Province, Kunming, China.
Qin WanDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Yongde PengDepartment of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jianjun LiuDepartment of Endocrinology, Longkou People's Hospital, Yantai, China.
Ling HuDepartment of Endocrinology, The Third Affiliated Hospital of Nanchang University, Nanchang, China.
Tingyu KeDepartment of Endocrinology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
Lei ChenDepartment of Endocrinology and Metabolism, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.
Fengmei XuDepartment of Endocrinology and Metabolism, Hebi Coal (group) Ltd. General Hospital, Hebi, China.
Qijuan DongDepartment of Endocrinology and Metabolism, People's Hospital of Zhengzhou, Zhengzhou, China.
Demetri TerzopoulosDepartment of Computer Science, Computer Graphics & Vision Laboratory, University of California Los Angeles, Los Angeles, California, USA.
Guang NingDepartment 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.
Xun XuDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaowei DingDepartment of Research, VoxelCloud, Shanghai, China wqingw61@163.com dingxiaowei@sjtu.edu.cn.
Weiqing WangDepartment 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 wqingw61@163.com dingxiaowei@sjtu.edu.cn.ORCID 0000-0001-6027-3084
Shanghai Jiao Tong University · CNAffiliated Hospital of Southwest Medical University · CNAnyang Hospital of Traditional Chinese Medicine · CNBeijing Luhe Hospital Affiliated to Capital Medical University · CNDalian Medical University · CNFirst Affiliated Hospital of Henan University of Science and Technology · CNFirst People's Hospital of Yunnan Province · CNHaikou City People's Hospital · CNKunming Medical University · CNNanchang University · CNNanjing Medical University · CNUniversity of California, Los Angeles · USYuxian People's Hospital · CNZhengzhou People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly 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

methodsThe 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.

resultsIn 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.

conclusionThis 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.

Indexed as

Diabetes MellitusDiabetic RetinopathyAdultAgedArtificial IntelligenceChinaFemaleHumansMass ScreeningMiddle AgedProspective Studiesclinical studydiabetic retinopathydiagnostic techniques and proceduresepidemiology

Identifiers

PMID33087340
PMCPMC7580048
OpenAlexW3094410826

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.