Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.

ABSTRACT:

Background

Following atrial fibrillation ablation, it is challenging to distinguish patients who will remain arrhythmia-free from those at risk for recurrence. New explainable machine learning (xML) techniques allow for systematic assessment of arrhythmia recurrence risk following catheter ablation. We aim to develop an xML algorithm that predicts recurrence and reveals key risk factors to facilitate better follow-up strategy after an ablation procedure.

Methods

We reconstructed pre-and post-ablation models of the left atrium (LA) from late gadolinium enhanced magnetic resonance (LGE-MRI) for 67 patients. Patient-specific features (LGE-based measurements of pre/post-ablation arrhythmogenic substrate, LA geometry metrics, computational simulation results, and clinical risk fa

SUBMITTER: Bifulco SF 

PROVIDER: S-EPMC12521406 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature