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Multicenter Study
. 2020 Oct;8(1):e001596.
doi: 10.1136/bmjdrc-2020-001596.

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

Yifei Zhang  1 , Juan Shi  1 , Ying Peng  1 , Zhiyun Zhao  1 , Qidong Zheng  2 , Zilong Wang  3 , Kun Liu  4 , Shengyin Jiao  3 , Kexin Qiu  3 , Ziheng Zhou  3   5 , Li Yan  6 , Dong Zhao  7 , Hongwei Jiang  8 , Yuancheng Dai  9 , Benli Su  10 , Pei Gu  11 , Heng Su  12 , Qin Wan  13 , Yongde Peng  14 , Jianjun Liu  15 , Ling Hu  16 , Tingyu Ke  17 , Lei Chen  18 , Fengmei Xu  19 , Qijuan Dong  20 , Demetri Terzopoulos  21   22 , Guang Ning  1 , Xun Xu  4 , Xiaowei Ding  23   5 , Weiqing Wang  24
Affiliations
Multicenter Study

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

Yifei Zhang et al. BMJ Open Diabetes Res Care. 2020 Oct.

Abstract

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.

Keywords: clinical study; diabetic retinopathy; diagnostic techniques and procedures; epidemiology.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Fundus image grading work flow and adjudication. DL, deep learning; DR, diabetic retinopathy.
Figure 2
Figure 2
Geographic distribution of the 155 metabolic management centers in China involved in this study.
See this image and copyright information in PMC

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