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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study aims to develop and assess an optimized three-dimensional convolutional neural network model (3D CNN) for predicting major cardiac events from coronary computed tomography angiography (CCTA) images in patients with suspected coronary artery disease. Patients undergoing CCTA with suspected coronary artery disease (CAD) were retrospectively included in this single-center study and split into training and test sets. The endpoint was defined as a composite of all-cause death, myocardial infarction, unstable angina, or revascularization events. Cardiovascular risk assessment relied on Morise score and the extent of CAD (eoCAD). An optimized 3D CNN mimicking the DenseNet architecture was trained on CCTA images to predict the clinical endpoints. The data was unannotated for presence of coronary plaque. A total of 5562 patients were assigned to the training group (66.4% male, median age 61.1\u2009\u00b1\u200911.2); 714 to the test group (69.3% male, 61.5\u2009\u00b1\u200911.4). Over a 7.2-year follow-up, the composite endpoint occurred in 760 training group and 83 test group patients. In the test cohort, the CNN achieved an AUC of 0.872\u2009\u00b1\u20090.020 for predicting the composite endpoint. The predictive performance improved in a stepwise manner: from an AUC of 0.652\u2009\u00b1\u20090.031 while using Morise score alone to 0.901\u2009\u00b1\u20090.016 when adding eoCAD and finally to 0.920\u2009\u00b1\u20090.015 when combining Morise score, eoCAD, and CNN (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001 and\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.012, respectively). Deep learning\u2013based analysis of CCTA images improves prognostic risk stratification when combined with clinical and imaging risk factors in patients with suspected CAD.\u00a0\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01667-4","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T17:26:35Z","timestamp":1759253195000},"page":"2695-2705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D Convolutional Neural Network for Predicting Clinical Outcome from Coronary Computed Tomography Angiography in Patients with Suspected Coronary Artery Disease"],"prefix":"10.1007","volume":"39","author":[{"given":"Era","family":"Stambollxhiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonard","family":"Frei\u00dfmuth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas Jakob","family":"Moser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Adolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albrecht","family":"Will","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Hendrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keno","family":"Bressem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6267-1692","authenticated-orcid":false,"given":"Martin","family":"Hadamitzky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"1667_CR1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1093\/eurheartj\/ehz425","volume":"41","author":"J Knuuti","year":"2020","unstructured":"Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al.: 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. 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Approval was granted by by the local institutional review board of Technical\u00a0University Munich (Trial Number 485\/S and 20\/20 S). Informed consent was obtained from\u00a0all individual participants included in the study. Consent to publish does not apply in this case,\u00a0as the images used are aggregated coronary segments\u00a0and consent for publication was not obtained explicitely. Data may be used in the context of a cooperation study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Prof. Dr. Martin Hadamitzky reports receiving research grant from Cleerly, Inc. Dr. Keno Bressem reports receiving grants from the European Union (101079894), Bayern Innovativ GmbH, German Federal Ministry of Education and Research, Max Kade Foundation, and Wilhelm-Sander Foundation. He also reports receiving speaker. fees from Canon Medical Systems Corporation and GE HealthCare. Furthermore, he is an advisor for the EU Horizon 2020 LifeChamps project (875329) and the EU IHI Project. IMAGIO (101112053). The other authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}