Abstract
Introduction: Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac which surrounds the heart. The automated quantification of EAT volume is possible from CCTA scans via a deep-learning approach. The use of auto-EAT quantification for the assessment of atrial fibrillation (AF) risk in the post-operative period, and longer-term, has not been investigated. We applied a validated deep-learning approach for automated segmentation of EAT from routine CCTA scans to assess the immediate post-operative and long-term risk of AF conveyed by EAT.
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