Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence.

ABSTRACT:

Background

Structural remodeling of the left atrial appendage (LAA) is characteristic of atrial fibrillation (AF), and LAA morphology impacts radiofrequency catheter ablation (RFCA) outcomes. In this study, we aimed to develop and validate a predictive model for AF ablation outcomes using LAA morphological features, deep learning (DL) radiomics, and clinical variables.

Methods

In this multicenter retrospective study, 480 consecutive patients who underwent RFCA for AF at three tertiary hospitals between January 2016 and December 2022 were analyzed, with follow-up through December 2023. Preprocedural CT angiography (CTA) images and laboratory data were systematically collected. LAA segmentation was performed using an nnUNet-based model, followed by radiomic feature extraction.

SUBMITTER: Yin Y 

PROVIDER: S-EPMC12105238 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature