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

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.

Methods: This study conducted a retrospective analysis of PeAF patients who underwent radiofrequency catheter ablation treatment between 2016 and 2019. A multimodal fusion framework based on a residual block network structure was proposed, integrating preprocedural AF rhythm 12-lead ECG signals, clinical scores, and baseline characteristics of the patients to construct a deep learning model for predicting the risk of postablation recurrence in PeAF patients. A fivefold cross-validation method was used to partition the data set for model training and testing.

Results: The fusion model was evaluated on a cohort of 77 PeAF patients, achieving good predictive performance with an average AUC of 0.74, and a maximum of 0.82. It significantly outperformed traditional clinical scoring systems and single-modal models based solely on ECG signals. Additionally, the model demonstrated lower variance (0.08), reflecting its robustness and stability with small sample sizes.

Conclusion: 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.