Song WH, Tse G, Chen KY, Liu T. Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease. World J Cardiol 2025; 17(7): 108510 [DOI: 10.4330/wjc.v17.i7.108510]
Corresponding Author of This Article
Tong Liu, Professor, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi District, Tianjin 300211, China. liutongdoc@126.com
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Cardiol. Jul 26, 2025; 17(7): 108510 Published online Jul 26, 2025. doi: 10.4330/wjc.v17.i7.108510
Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease
Wen-Hua Song, Gary Tse, Kang-Yin Chen, Tong Liu
Wen-Hua Song, Gary Tse, Kang-Yin Chen, Tong Liu, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
Gary Tse, School of Nursing and Health Sciences, Hong Kong Metropolitan University, Hong Kong 999077, China
Co-corresponding authors: Kang-Yin Chen and Tong Liu.
Author contributions: Song WH conceived the study and wrote the paper; Tse G edited and revised the manuscript; Chen KY reviewed the manuscript; Liu T revised the manuscript; All authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 82170327 and No. 82370332; and Tianjin Key Medical Discipline (Specialty) Construction Project, No. TJYXZDXK-029A.
Conflict-of-interest statement: The authors declare that they have no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tong Liu, Professor, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi District, Tianjin 300211, China. liutongdoc@126.com
Received: April 17, 2025 Revised: May 6, 2025 Accepted: June 23, 2025 Published online: July 26, 2025 Processing time: 97 Days and 5.9 Hours
Abstract
With the rapid advancement and widespread adoption of new artificial intelligence (AI) technologies, personalized medicine and more accurate diagnosis using medical imaging are now possible. Among its many applications, AI has shown remarkable potential in the analysis of electrocardiograms (ECGs), which provide essential insights into the electrical activity of the heart and allowing early detection of ischemic heart disease (IHD). Notably, single-lead ECG (SLECG) analysis has emerged as a key focus in recent research due to its potential for widespread and efficient screening. This editorial focuses on the latest research progress of AI-enabled SLECG utilized in the diagnosis of IHD.
Core Tip: Wearable devices equipped with electrocardiogram (ECG) capabilities offer single-lead ECG (SLECG) data and clinically acceptable performance for ischemic heart disease (IHD) detection by artificial intelligence (AI) algorithms. A key innovation is the development of AI-enabled SLECG in detecting IHD during resting conditions and stress test. The focus on portable, single-lead devices aligns with global health priorities, particularly in underserved regions. By integrating machine learning into wearable technology, this research paves the way for scalable, cost-effective IHD screening, democratizing access to early detection.
