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

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 1,243 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. EAT quantification required approximately 30 seconds per scan. The final model was evaluated on an external testing cohort of 160 patients, including 90 non-Asian individuals. In this cohort, AI-predicted EAT volumes showed excellent agreement with expert annotations (r=0.975, P<0.0001). Bland-Altman analysis demonstrated a mean bias of -5.2 cm3, with 95% limits of agreement from -25.1 to 14.7 cm3. Among the non-Asian subgroup, model performance remained strong (r=0.970; bias: -3.2 cm3; limits of agreement: -25.1 to 18.7 cm3). AI-derived EAT volume was independently associated with obstructive CAD (odds ratio 1.11; 95% confidence interval: 1.04-1.19; P=0.004), after adjusting for confounders. The global chi-square statistic increased from 81.7 with coronary calcium score alone to 93.3 when EAT volume was added (P=0.001), indicating improved risk prediction. Conclusion We developed and validated a deep learning system for automated EAT volume quantification from NCCT scans. The model demonstrated high accuracy and generalizability across ethnically diverse populations, supporting its potential for routine EAT assessment and CAD risk stratification.