A Deep Learning-Based Multimodal Fusion Model for Recurrence Prediction in Persistent Atrial Fibrillation Patients.

Journal: Journal of cardiovascular electrophysiology
Published Date: May 23, 2025

Abstract

BACKGROUND: The long-term success rate of atrial fibrillation (AF) ablation remains a significant clinical challenge, particularly in patients with persistent atrial fibrillation (Persistent AF, PeAF). The recurrence risk in PeAF patients is influenced by various factors, which complicates the prediction of ablation outcomes. While clinical characteristics provide important references for risk assessment, the predictive accuracy of existing methods is limited and they fail to fully leverage the rich information contained in electrocardiogram (ECG) signals. Integrating clinical features with ECG signals holds promise for enhancing recurrence prediction accuracy and supporting personalized management.

Authors

  • Li Chen
    Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
  • Xujian Feng
  • Haonan Chen
    College of Computer Science and Software Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong 518060, P. R. China.
  • Biqi Tang
    Department of Biomedical Engineering, Fudan University, Shanghai, China.
  • Quan Fang
    Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Taibo Chen
    Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Cuiwei Yang

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