Publication Name	World Journal of Cardiology
Manuscript ID	108510
Country	China

Category	Cardiac & Cardiovascular Systems
Manuscript Type	Editorial
Article Title	Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease
Manuscript Source	Invited Manuscript
All Author List	Wen-Hua Song, Gary Tse, Kang-Yin Chen and Tong Liu
Funding Agency and Grant Number	Funding Agency Grant Number National Natural Science Foundation of China No. 82170327 National Natural Science Foundation of China No. 82370332 Tianjin Key Medical Discipline (Specialty) Construction Project No. TJYXZDXK-029A
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
Key Words	Artificial intelligence; Machine learning; Ischemic heart disease; Electrocardiogram; Diagnosis
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.
Citation	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; In press

Received	2025-04-17 11:23
Peer-Review Started	2025-04-17 11:24
To Make the First Decision
Return for Revision	2025-04-23 11:48
Revised	2025-05-06 05:35
Second Decision	2025-06-23 02:49
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-06-23 06:20
Articles in Press	2025-06-23 06:20
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8462 (online)
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