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, Li Yan, Dong Zhao, Hongwei Jiang, Yuancheng Dai, Benli Su, Pei Gu, Heng Su, Qin Wan, Yongde Peng, Jianjun Liu, Ling Hu, Tingyu Ke, Lei Chen, Fengmei Xu, Qijuan Dong, Demetri Terzopoulos, Guang Ning, Xun Xu, Xiaowei Ding, Weiqing Wang, Yifei Zhang, Juan Shi, Ying Peng, Zhiyun Zhao, Qidong Zheng, Zilong Wang, Kun Liu, Shengyin Jiao, Kexin Qiu, Ziheng Zhou, Li Yan, Dong Zhao, Hongwei Jiang, Yuancheng Dai, Benli Su, Pei Gu, Heng Su, Qin Wan, Yongde Peng, Jianjun Liu, Ling Hu, Tingyu Ke, Lei Chen, Fengmei Xu, Qijuan Dong, Demetri Terzopoulos, Guang Ning, Xun Xu, Xiaowei Ding, Weiqing Wang

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.

Conflict of interest statement

Competing interests: None declared.

© 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.

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.

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Source: PubMed

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