Why the study?
Manual quantification of epicardial adipose tissue volume is labor-intensive and error-prone, while existing deep learning methods are mostly uninterpretable and fail to harness complete anatomical characteristics.
Does an enhanced deep learning method accurately quantify epicardial adipose tissue on CCTA compared to expert manual quantification?
Population
108 patients who underwent routine CCTA examinations
Comparison
Enhanced deep learning method vs expert manual quantification
Key result
An enhanced deep learning method for EAT quantification showed strong agreement with expert manual quantification, yielding a median 3D volume Dice score of 0.896 and correlation of 0.980 (p<0.001).
Authors
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Supports automated EAT quantification in research; leaves open clinical adoption pending prospective validation.
Does an enhanced deep learning method accurately quantify epicardial adipose tissue on CCTA compared to expert manual quantification?
Effect estimate: correlation 0.980
p-value: p=<0.001
An enhanced deep learning method incorporating pericardial anatomical structures provides accurate automatic quantification of epicardial adipose tissue on CCTA scans.
Tang et al. (2024) studied Patients undergoing routine CCTA examinations (n=108). Enhanced deep learning method vs. Expert manual quantification was evaluated on Agreement with expert manual quantification (3D volume Dice score coefficient and correlation) (correlation 0.980, p=<0.001). An enhanced deep learning method for EAT quantification showed strong agreement with expert manual quantification, yielding a median 3D volume Dice score of 0.896 and correlation of 0.980 (p<0.001).