Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

ABSTRACT:

Background

Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation.

Methods

Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmenta

SUBMITTER: Razeghi O 

PROVIDER: S-EPMC10857794 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature