Subclinical Atrial Fibrillation Prediction in Patients with CIED by a Novel Deep Learning Framework
The ResKAN-Attention model demonstrated promising preliminary results for SCAF prediction with enhanced interpretability. The distilled risk score provided a potential method for early risk stratification in clinical settings, demonstrating that advanced artificial intelligence (AI) can effectively predict complex cardiovascular events using readily available data.
Authors
- Qing Cao (ORCID: https://orcid.org/0000-0003-2517-2231)
- K Chen
- Liqun Wu (ORCID: https://orcid.org/0009-0006-1594-6423)
- Qingzhi Luo
- Changjian Lin
- Qi Jin
- Yun Xie
- Ning Zhang
- Yue Wei
- Wenqi Pan
- Yongying Lan
- Chengze Lin
- Yangyang Bao
Institutions
- Ruijin Hospital (CN)
- Beijing Academy of Artificial Intelligence (CN)
Publication Details
- Journal
- Journal of Cardiovascular Development and Disease
- Published
- 2025-12-30
- DOI
- https://doi.org/10.3390/jcdd13010018
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University
- Ruijin Hospital
- School of Medicine, Shanghai Jiao Tong University