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Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3D\u00a0CNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (\u0394z). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Slice extraction using RL was slightly more precise (\u0394z: RL 1.8\u2009mm (95% CI: [1.6, 2.0]), 2D\u00a0CNN 2.0\u2009mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1\u2009cm<jats:sup>2<\/jats:sup> (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Critical relevance statement<\/jats:title>\n                <jats:p>Since inflammatory changes in epicardial adipose tissue (EAT)\u00a0are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:list list-type=\"bullet\">\n                  <jats:list-item>\n                    <jats:p>Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential.<\/jats:p>\n                  <\/jats:list-item>\n                  <jats:list-item>\n                    <jats:p>A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT.<\/jats:p>\n                  <\/jats:list-item>\n                  <jats:list-item>\n                    <jats:p>End-to-end automation enables large-scale research on the value of EAT for outcome analysis.<\/jats:p>\n                  <\/jats:list-item>\n                <\/jats:list><\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s13244-024-01875-6","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T11:22:18Z","timestamp":1734607338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2666-9928","authenticated-orcid":false,"given":"Maike","family":"Theis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Garajov\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babak","family":"Salam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Nowak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Block","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrike I.","family":"Attenberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"K\u00fctting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian A.","family":"Luetkens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alois M.","family":"Sprinkart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"1875_CR1","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1093\/ehjci\/jex314","volume":"19","author":"J Mancio","year":"2018","unstructured":"Mancio J, Azevedo D, Saraiva F et al (2018) Epicardial adipose tissue volume assessed by computed tomography and coronary artery disease: a systematic review and meta-analysis. 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