Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population.

Journal: Heart (British Cardiac Society)
Published Date: May 16, 2025

Abstract

BACKGROUND: Coronary artery disease (CAD) is linked to an increased risk of mild cognitive impairment (MCI). Effective and convenient screening methods for identifying MCI from the CAD population are still lacking. This study aims to develop a deep learning model using fundus images to optimise MCI diagnosis in the CAD population, achieving early intervention and improving prognosis.

Authors

  • Yi Ye
    Department of Gastroenterology, Wenzhou People's Hospital, The Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, China.
  • Wei Feng
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You'anmenwai, Xitoutiao No.10, Beijing, P. R. China.
  • Yaodong Ding
    Department of Cardiology, Center for Coronary Artery Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Qing Chen
    Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Li Lin
    Department of Cardiology, Lishui Central Hospital and the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
  • Peng Xia
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Tong Ma
    Monash University, Melbourne, Victoria, Australia.
  • Lie Ju
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Xiangang Chang
    Beijing Airdoc Technology Co., Ltd., Beijing, China.
  • Xiaoyi Wang
    Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Longjun Cai
    Beijing Wispirit Technology Co Ltd, Beijing, China.
  • Zongyuan Ge
    AIM for Health Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia; Monash-Airdoc Research Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia.
  • Yong Zeng
    a College of Pharmacy , Chengdu University of Traditional Chinese Medicine , Chengdu , P.R. China.

Keywords

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