Artificial Intelligence–Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction
In this first-in-kind independent validation and head-to-head comparison study, AI-ECG for LVSD detection demonstrated strong performance despite training on disparate populations. However, the limited-availability of models hinders independent validation.
Authors
- Cornelis P. Allaart (ORCID: https://orcid.org/0000-0002-0506-9512)
- Thomas G. Mast
- Machteld Boonstra (ORCID: https://orcid.org/0000-0001-7550-0489)
- Yu-Chang Huang
- Rohan Khera
- Philip M. Croon
- Evangelos K. Oikonomou
- Folkert W. Asselbergs
- Bauke K.O. Arends
- Zhi-Yong Liu
- Min Sung Lee
- Joon-Myoung Kwon
- Lovedeep S. Dhingra
- Chang-Fu Kuo
- Michiel M. Winter
- Jasper L. Selder
- Rutger R. van de Leur
- Hak Seung Lee
Institutions
- Yale New Haven Hospital (US)
- Radboud University Nijmegen (NL)
- Chang Gung University (TW)
- Catharina Ziekenhuis (NL)
- University Medical Center Utrecht (NL)
- Chang Gung Memorial Hospital (TW)
- Yale University (US)
- Yale New Haven Health System (US)
- Sejong Institute (KR)
- Amsterdam University Medical Centers (NL)
- University College London (GB)
- University of Amsterdam (NL)
Publication Details
- Journal
- JACC Advances
- Published
- 2026-01-20
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102572
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 30.07