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Research Paper|Volume 16, Issue 10|pp 8717—8731

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

Yu-Ting Lin1, Chin-Sheng Lin2, Chien-Sung Tsai1, Dung-Jang Tsai3,4,7,9, Yu-Sheng Lou3,4, Wen-Hui Fang3,5, Yung-Tsai Lee6,10, Chin Lin3,4,7,8
  • 1Department of Surgery, Division of Cardiovascular Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • 2Department of Internal Medicine, Division of Cardiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • 3Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • 4Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
  • 5Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • 6Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei, Taiwan
  • 7Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
  • 8Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan
  • 9Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
  • 10Department of Exercise and Healthy Science, National Taipei University of Nursing and Healthy Science, Taipei, Taiwan
Received: December 26, 2023Accepted: April 16, 2024Published: May 16, 2024
https://doi.org/10.18632/aging.205835

Copyright: © 2024 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.

Methods: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases.

Results: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5–51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs.

Conclusion: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.