AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report
Wearable ECG-based AI modeling is feasible for predicting trends in HF biomarkers in persistent AF. These results provide early evidence that ECG-derived digital biomarkers may offer a scalable, non-invasive approach for longitudinal HF monitoring.
Authors
- Yun Kwan Kim (ORCID: https://orcid.org/0000-0002-6397-4043)
- Y HWANG
- Chae-Bin Song
Institutions
- Korea University (KR)
- Technology Management Company (United States) (US)
- The Catholic University of Korea St. Vincent's Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2026-02-05
- DOI
- https://doi.org/10.3389/fcvm.2025.1748673
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Catholic University of Korea
- Daewoong Pharmaceutical Company
- College of Medicine, Catholic University of Korea