Fully automated deep learning quantification of epicardial adipose tissue from calcium-scoring CT scans showed high agreement with expert manual quantification (R = 0.974; P < .001).
Observational (n=850)
Yes
Does a deep learning algorithm accurately and rapidly quantify epicardial adipose tissue from calcium-scoring CT scans compared to expert manual readers?
A deep learning algorithm can fully automate the quantification of epicardial adipose tissue from routine calcium-scoring CT scans in under 2 seconds with expert-level accuracy.
Effect estimate: R = 0.974
p-value: p=<.001
Purpose To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data. Materials and Methods In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non–contrast material–enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans. Results Automated quantification was performed in a mean (± standard deviation) time of 1.57 seconds ± 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P < .001), with no significant bias (0.53 cm3; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P < .001) but with a bias of 4.35 cm3 (P < .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P < .001), with significant bias for reader 1 (5.11 cm3; P < .001) but not for reader 2 (0.88 cm3; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P < .001) in 70 patients, with no significant bias (0.64 cm3; P = .43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P = .026). Conclusion Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment. Keywords: Adults, Arteriosclerosis, CT, CT-Quantitative, Computer Applications-General, (Informatics), Heart, Quantification, Segmentation, Supervised learning © RSNA, 2019 See also the commentary by Schoepf and Abadia in this issue.
Commandeur et al. (2019) conducted an observational in Epicardial adipose tissue quantification (n=850). Deep learning algorithm vs. Expert manual quantification was evaluated on Agreement with expert manual quantification (R = 0.974, p=<.001). Fully automated deep learning quantification of epicardial adipose tissue from calcium-scoring CT scans showed high agreement with expert manual quantification (R = 0.974; P < .001).