A Deep Learning‐Based Multimodal Fusion Model for Recurrence Prediction in Persistent Atrial Fibrillation Patients
This study innovatively combines AF rhythm ECG signals with clinical characteristics to construct a deep learning model for predicting the recurrence risk in PeAF patients after radiofrequency catheter ablation. The results show that this method effectively improves prediction performance and provides support for personalized clinical decision-making, with significant potential for clinical application.
Authors
- Xujian Feng (ORCID: https://orcid.org/0000-0002-8387-9748)
- Taibo Chen (ORCID: https://orcid.org/0000-0002-5978-8272)
- Cuiwei Yang (ORCID: https://orcid.org/0000-0003-3338-5835)
- Quan Fang (ORCID: https://orcid.org/0000-0003-0381-6796)
- Li Chen (ORCID: https://orcid.org/0000-0003-0666-3561)
- Haonan Chen (ORCID: https://orcid.org/0000-0002-9795-3064)
- Biqi Tang
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Fudan University (CN)
- Peking Union Medical College Hospital (CN)
Publication Details
- Journal
- Journal of Cardiovascular Electrophysiology
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1111/jce.16733
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78