Abstract 11962: Applying a Deep-Learning Approach for Automated Quantification of Epicardial Adipose Tissue on Coronary Computed Tomography Angiography in Challenging Clinical Populations

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A11962-A11962
• Main Authors: West, Henry W, Siddique, Muhammad, Volpe, Lucrezia, Desai, Ria, Lyasheva, Maria, Dangas, Katerina, Shirodaria, Cheerag, Neubauer, Stefan, Channon, Keith M, Desai, Milind Y, Williams, Michelle C, Rodrigues, Jonathan C, Adlam, David, Ed, Nicol D, Antoniades, Charalambos
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.11962

IntroductionEpicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac. The automated quantification of EAT volume is possible from routine CCTA scans via a deep-learning approach. PurposeTo apply a deep-learning approach for automated segmentation of EAT from CCTA scans in challenging clinical populations to assess the real-world viability of automated segmentation. MethodsA deep-learning automated EAT segmentation tool using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created and trained on over 2500 consecutive CCTA scans performed as part of clinical care in patients with stable chest pain from 2015 onwards within the European arm of the Oxford Risk Factors And Non Invasive Imaging (ORFAN) Study. External validation was performed in 817 scans from USA ORFAN sites (Figure 1A demonstrates human vs machine segmented EAT volume for a single case). The network was then applied to sets of unseen CCTAs from the AdipoRedOx Study (UK, n=253) and the SCOTHEART trial (UK, n=1558) to test its ability to perform EAT segmentations in challenging but common patient populations1) recent cardiac surgery (