Artificial Intelligence‐Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices
Fine-tuning on homogeneous data improved performance and generalizability, whereas heterogeneous data led to reduced performance. ECG system format differences had minimal impact on model accuracy.
Authors
- Atsuhiko Yagishita (ORCID: https://orcid.org/0000-0001-7774-7477)
- Shinya Suzuki (ORCID: https://orcid.org/0000-0002-4769-5925)
- Nagata Eiichiro
- Keiichi Satoh (ORCID: https://orcid.org/0000-0003-3316-301X)
- Naomi Hirota (ORCID: https://orcid.org/0000-0003-1184-4228)
- Jun Motogi
- Mari Amino
- Taira Nakayama (ORCID: https://orcid.org/0000-0001-7641-8065)
- Yoshifumi SUMIOKA
- Yoichi Ohnuki
- Tsuneo Takayanagi
- Yuji Ikari
- Koichiro Yoshioka
- Takeshi Yamashita
- Yota Sekiguchi
- Akira Hyodo
- Nobumoto Moriai
Institutions
- Tokai University (JP)
- Cardiovascular Institute Hospital (JP)
- Nihon Kohden (Japan) (JP)
Publication Details
- Journal
- Annals of Noninvasive Electrocardiology
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1111/anec.70159
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00