A deep learning model for screening type 2 diabetes from retinal photographs.

Journal: Nutrition, metabolism, and cardiovascular diseases : NMCD
Published Date: Jan 13, 2022

Abstract

BACKGROUND AND AIMS: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images.

Authors

  • Jae-Seung Yun
    Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Jaesik Kim
    Department of Computer Engineering, Ajou University, Suwon, South Korea.
  • Sang-Hyuk Jung
    Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Seon-Ah Cha
    Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Seung-Hyun Ko
    Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Yu-Bae Ahn
    Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Hong-Hee Won
    Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
  • Kyung-Ah Sohn
    Department of Artificial Intelligence, Ajou University, Suwon, South Korea.
  • Dokyoon Kim
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.

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