Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.

ABSTRACT:

Aims

Epicardial adipose tissue (EAT), located within the pericardial sac, has emerged as a biomarker for coronary artery disease (CAD) progression. This study aimed to develop and validate a deep learning-based system for automated EAT volume quantification using non-contrast computed tomography (NCCT) scans from a large, multi-centre, pan-Asian cohort.

Methods and results

A total of 1243 NCCT patient scans from three centres were used to train and internally validate a deep learning model based on 3D UNet++ architecture for pericardium segmentation, followed by intensity thresholding to derive EAT volume. Epicardial adipose tissue quantification required ∼30 s per scan. The final model was evaluated on an external testing cohort of 160 patients, including 90 non-Asian indiv

SUBMITTER: Leng S 

PROVIDER: S-EPMC12629654 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature