Artificial Intelligence–Based Feature Analysis of Pulmonary Vein Morphology on Computed Tomography Scans and Risk of Atrial Fibrillation Recurrence After Catheter Ablation: A Multi-Site Study

BACKGROUND: Atrial fibrillation (AF) recurrence (AF+) is common after catheter ablation. Pulmonary vein (PV) isolation is the cornerstone of AF ablation, but PV remodeling has been associated with the risk of AF+. We aimed to evaluate whether artificial intelligence–based morphological features of primary and secondary PV branches on computed tomography images are associated with AF+ post-ablation. METHODS: Two artificial intelligence models were trained for the segmentation of computed tomography images, enabling the isolation of PV branches. Patients from Cleveland Clinic (n=135) and Vanderbilt University (n=594) were combined and divided into 2 sets for training and cross-validation (D 1 , n=218) and internal testing (D 2 , n=511). An independent validation set (D 3 , n=80) was obtained from University Hospitals of Cleveland. We extracted 48 fractal-based and 12 shape-based radiomic features from primary and secondary PV branches of patients with AF+ and without recurrence after catheter ablation of AF. To predict AF+, 3 Gradient Boosting classification models based on significant features from primary branch PV model (M p ), secondary branch PV model (M s ), and combined primary and secondary branch PV model (M c ) were built. RESULTS: Features relating to primary PVs were found to be associated with AF+. The M p classifier achieved area under the curve values of 0.73, 0.71, and 0.70 across the 3 datasets. AF+ cases exhibited greater surface complexity in their primary PV area, as evidenced by higher fractal dimension values compared with AF nonrecurrence cases. The M s classifier results revealed a weaker association with AF+, suggesting higher relevance to AF+ post-ablation from primary PV branch morphology. CONCLUSIONS: This largest multi-institutional study to date revealed associations between artificial intelligence–extracted morphological features of the primary PV branches with AF+ in 809 patients from 3 sites. Future work will focus on enhancing the predictive ability of the classifier by integrating clinical, structural, and morphological features, including left atrial appendage and left atrium–related characteristics.