Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram
Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.
Authors
- Yeongbong Jin (ORCID: https://orcid.org/0000-0002-9611-3100)
- Ki Hong Lee (ORCID: https://orcid.org/0000-0002-9938-3464)
- Bonggyun Ko (ORCID: https://orcid.org/0000-0002-1544-6377)
- Kang‐Ho Choi (ORCID: https://orcid.org/0000-0001-8851-2104)
- Woo-Jin Chang (ORCID: https://orcid.org/0000-0002-9010-0755)
Institutions
- Chonnam National University (KR)
- Seoul National University (KR)
- Chonnam National University Hospital (KR)
- Gwangju University (KR)
Publication Details
- Journal
- The Korean Journal of Internal Medicine
- Published
- 2025-02-21
- DOI
- https://doi.org/10.3904/kjim.2024.130
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78
Funders
- National Research Foundation
- Chonnam National University
- National Research Foundation of Korea
- Ministry of Education, India
- Ministry of Science and ICT, South Korea