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
Paediatrics
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
Paediatrics

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

Circ Arrhythm Electrophysiol. 2024 Dec 3:e012679. doi: 10.1161/CIRCEP.123.012679. Online ahead of print.

ABSTRACT

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 (D1, n=218) and internal testing (D2, n=511). An independent validation set (D3, 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 (Mp), secondary branch PV model (Ms), and combined primary and secondary branch PV model (Mc) were built.

RESULTS: Features relating to primary PVs were found to be associated with AF+. The Mp 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 Ms 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.

PMID:39624901 | DOI:10.1161/CIRCEP.123.012679