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

Journal: Journal of diabetes
Published Date: Dec 9, 2021

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.

Authors

  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Mingming Ma
    Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, People's Republic of China.
  • Mengyu Lai
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Liping Gu
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Mei Kang
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Zilong Wang
    VoxelCloud, Shanghai, China.
  • Shengyin Jiao
    VoxelCloud, Shanghai, China.
  • Kang Dang
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Junxiao Deng
    VoxelCloud, Shanghai, China.
  • Xiaowei Ding
    VoxelCloud, Los Angeles, CA, USA.
  • Qin Zhen
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Aifang Zhang
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Tingting Shen
    College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China. shentingtingstt@163.com.
  • Zhi Zheng
    Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Nanjing University.
  • Yufan Wang
    Engineering Research Center for Digital Medicine of the Ministry of Education, Shanghai, China.
  • Yongde Peng
    Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.

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