Multimodal prediction of catheter ablation outcomes in patients with persistent atrial fibrillation.

The recurrence of atrial fibrillation (AF) following catheter ablation is a common complication in patients with persistent atrial fibrillation (psAF), increasing the risk of stroke and heart failure thereafter. Given the multifactorial nature of post-ablation AF, clinical predictions of successful ablation often suffer from poor accuracy and lack robustness. This paper proposes a multimodal prediction model for post-ablation AF, which extracts complex features from multidimensional data, including electrocardiogram (ECG) images, cellular characteristics, intraoperative and demographic information of patients. Specifically, a dual-module structure is proposed for ECG processing. It consists of an image module that extracts spatial features and a temporal module that captures sequential features, effectively capturing the spatiotemporal dynamics of ECG. A clinical-intraoperative data integration module is developed to combat the complex nature of cellular, demographic, and intraoperative data structures in clinical settings by leveraging sparse and dense feature integration, enabling effective representation and processing. Finally, a feature fusion module is introduced, composed of a dynamic weight mechanism and a multimodal Transformer model, enhancing feature interaction and facilitating effective information synchrony between different modalities. The experimental results demonstrate that the proposed model achieved an accuracy of 0.9079 and an Area Under the Curve (AUC) of 0.8690. These findings highlight significant effectiveness in post-ablation AF prediction, offering a comprehensive prediction framework that supports early intervention for patients with psAF at risk for AF recurrence.