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: = 151) groups based on postoperative outcome. The deep learning model SwinUNETR was used to segment the heart region and extract the radiomics features of different areas of the heart. A risk prediction model was built using six machine learning methods. Finally, an interpretable analysis method based on radiomics was provided to capture the relationship between the radiological features of different heart structures and the recurrence of AF. 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.