Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm.

Journal: Atherosclerosis
Published Date: Aug 12, 2023

Abstract

BACKGROUND AND AIMS: To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD.

Authors

  • Yin-Hao Lee
    Division of Cardiology, Department of Medicine, Taipei City Hospital, Yang Ming Branch, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Ming-Tsung Hsieh
    Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Chun-Chin Chang
    Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Yi-Lin Tsai
    Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Ruey-Hsing Chou
    Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Henry Hong-Shing Lu
    Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: henryhslu@nycu.edu.tw.
  • Po-Hsun Huang
    Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.

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