ECG‐Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction

Journal of the American Heart Association 2026 Open access

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Authors: İbrahi̇m Karabayir, Olivia Gilbert, Ali Asghar Valika, Turgay Celık, Arunkumar Krishnan, Lokesh K. Chinthala, Ambarish Pandey, Elsayed Z. Soliman, John L. Jefferies, Dalane W. Kitzman, David M. Herrington, Robert L. Davis
+1 more Oğuz Akbilgiç
DOI: 10.1161/jaha.124.041948
Cited by: 1
References: 34

Abstract

BACKGROUND: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequently overlooked or attributed to other conditions. This study proposes a novel, externally validated artificial intelligence (AI) tool using ECG data (ECG-AI) for the simultaneous detection of subtypes of LV dysfunction and HFpEF. METHODS: Two ECG-AI models, using 12-lead or single-lead ECG, were developed using data from Atrium Health Wake Forest Baptist and University of Tennessee Health Science Center (UTHSC) to classify ECGs into 4 categories: reduced LV ejection fraction (rEF; EF

Fields of Study & Subjects

ECG Monitoring and Analysis Heart Failure Treatment and Management Cardiovascular Function and Risk Factors

Publication details

Journal / source
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Volume
15
Issue
15
Pages
e041948–e041948
Publication date
28 July 2026
Language
EN