{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:09:51Z","timestamp":1789657791311,"version":"4.0.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686875","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,17]]},"abstract":"<jats:p>Introduction: Transcatheter aortic valve implantation (TAVI) is a standard treatment for patients with severe aortic stenosis. However, predicting long-term survival outcomes remains challenging, as traditional risk scores such as EuroSCORE II and STS-PROM exhibit poor calibration for TAVI cohorts. Methods: In this work, we developed an end-to-end deep learning pipeline to predict overall survival of TAVI patients directly from pre-procedural whole-body CT scans. The pipeline uses a 3D DenseNet121 architecture within the AUCMEDI framework, trained as a regression model to predict survival in days, using 5-fold cross-validation on 317 TAVI patients. Results: The model achieved an AUC of 0.787, a specificity of 72.1%, and a sensitivity of 85.3% for identifying short-term survival patients (&lt;12 months) in a binary evaluation, comparable to state-of-the-art multimodal ML models requiring extensive clinical data. Conclusion: End-to-end deep learning on routinely acquired CT scans can effectively identify high-risk TAVI patients, offering potential as a clinical decision support tool for pre-operative assessment without manual feature engineering.<\/jats:p>","DOI":"10.3233\/shti261005","type":"book-chapter","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:48:09Z","timestamp":1789656489000},"source":"Crossref","is-referenced-by-count":0,"title":["Survival Prediction of TAVI Patients Using End-to-End Deep Image Regression on Whole-Body CT"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-9885","authenticated-orcid":false,"given":"Dominik","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"University of Augsburg, IT-Infrastructure for Translational Medical Research, Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6536-3122","authenticated-orcid":false,"given":"Alexia","family":"Rizoudis","sequence":"additional","affiliation":[{"name":"University of Augsburg, IT-Infrastructure for Translational Medical Research, Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3100-8095","authenticated-orcid":false,"given":"Charlotte","family":"Lind","sequence":"additional","affiliation":[{"name":"University Hospital Schleswig-Holstein, Department of Internal Medicine III, Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2857-7122","authenticated-orcid":false,"given":"Frank","family":"Kramer","sequence":"additional","affiliation":[{"name":"University of Augsburg, IT-Infrastructure for Translational Medical Research, Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4547-9260","authenticated-orcid":false,"given":"Jakob","family":"Voran","sequence":"additional","affiliation":[{"name":"University Hospital Schleswig-Holstein, Department of Internal Medicine III, Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-6798","authenticated-orcid":false,"given":"Hannes","family":"Ulrich","sequence":"additional","affiliation":[{"name":"Universit\u00e4tsmedizin Greifswald, Department of Medical Informatics, Institute for Community Medicine, Greifswald, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2026: BIG DATA, small data, Your Data; Transitions in the age of biomedical AI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI261005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:48:10Z","timestamp":1789656490000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI261005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"ISBN":["9781643686875"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti261005","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]}}}