Predictive value of artificial intelligence and radiomics for atrial fibrillation recurrence after catheter ablation for pulmonary vein isolation.

Background: Atrial fibrillation (AF) is a common arrhythmia disorder with a high recurrence rate after catheter ablation for pulmonary vein isolation (PVI). Improved preoperative evaluation strategies are needed to enhance prediction accuracy and optimize patient selection for ablation. Materials and Methods: This study included 311 AF patients who underwent catheter ablation for PVI, stratified into recurrence ( Results: A feature selection process was applied to determine the most predictive features, resulting in a set of 50 radiomics features and 33 clinical features. The average dice value of the deep learning heart segmentation model was 88.95%, and the area under the curve (AUC) value of the radiomics model for predicting the risk of AF recurrence after PVI was 0.74(95% confidence interval (CI) 0.54, 0.79). The AUC value of the fusion model integrating clinical laboratory indicators and radiomic features was 0.79(95% CI 0.69-0.84). According to the results of the interpretability analysis of the model, multiple radiomics features were determined to be significantly associated with AF recurrence. Conclusion: This study presents a non-invasive model for predicting post-PVI recurrence and quantifies the contribution of specific cardiac structures to AF recurrence risk.