Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

Journal: Aging
PMID: 38761181

Abstract

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation.

Authors

  • Yu-Ting Lin
    From the Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Chin-Sheng Lin
    Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan.
  • Chien-Sung Tsai
    Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center;
  • Dung-Jang Tsai
    Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Yu-Sheng Lou
    School of Public Health, National Defense Medical Center, Taipei, Taiwan.
  • Wen-Hui Fang
    Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan.
  • Yung-Tsai Lee
    Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei, Taiwan.
  • Chin Lin
    School of Public Health, National Defense Medical Center, Taipei, Taiwan.

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