Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions.

Journal: Translational vision science & technology
Published Date:

Abstract

PURPOSE: Fundus images are typically used as the sole training input for automated diabetic retinopathy (DR) classification. In this study, we considered several well-known DR risk factors and attempted to improve the accuracy of DR screening.

Authors

  • Min-Yen Hsu
    Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Jeng-Yuan Chiou
    Department of Health Policy and Management, Chung Shan Medical University, Taichung, Taiwan.
  • Jung-Tzu Liu
    Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
  • Chee-Ming Lee
    Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Ya-Wen Lee
    Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
  • Chien-Chih Chou
    Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Shih-Chang Lo
    Department of Internal Medicine, Division of Endocrinology and Metabolism, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Edy Kornelius
    Department of Internal Medicine, Division of Endocrinology and Metabolism, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Yi-Sun Yang
    Department of Internal Medicine, Division of Endocrinology and Metabolism, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Sung-Yen Chang
    Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
  • Yu-Cheng Liu
    Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA.
  • Chien-Ning Huang
    Department of Internal Medicine, Division of Endocrinology and Metabolism, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Vincent S Tseng
    Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan.