A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study

Na Li, Mingming Ma, Mengyu Lai, Liping Gu, Mei Kang, Zilong Wang, Shengyin Jiao, Kang Dang, Junxiao Deng, Xiaowei Ding, Qin Zhen, Aifang Zhang, Tingting Shen, Zhi Zheng*, Yufan Wang*, Yongde Peng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Background: The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China. Methods: A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years of experience. The performance of DLA across different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, HbA1c, diabetes duration, UACR, and eGFR was evaluated. Results: For all 1674 gradable images, the area under the receiver operating curve, sensitivity, and specificity of the DLA for referable DR were 0.942 (95% CI, 0.920-0.964), 85.1% (95% CI, 83.4%-86.8%), and 95.6% (95% CI, 94.6%-96.6%), respectively. The DLA showed consistent performance across most subgroups, while it showed superior performance in the subgroups of patients with type 1 diabetes, UACR ≥ 30 mg/g, and eGFR < 90 mL/min/1.73m2. Conclusions: This study showed that the DLA was a reliable alternative method for the detection of referable DR and performed superior in patients with type 1 diabetes and diabetic nephropathy who were prone to DR.

Original languageEnglish
Pages (from-to)111-120
Number of pages10
JournalJournal of Diabetes
Volume14
Issue number2
DOIs
Publication statusPublished - Feb 2022
Externally publishedYes

Keywords

  • deep learning algorithm
  • diabetic retinopathy
  • referable DR
  • retinal fundus images

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