Survival prediction of TAVI patients using end-to-end deep image regression on whole-body CT
- 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 (<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 canIntroduction: 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 (<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.…
| Author: | Dominik MüllerORCiDGND, Alexia RizoudisORCiD, Charlotte Lind, Frank KramerORCiDGND, Jakob Voran, Hannes Ulrich |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1334597 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/133459 |
| ISBN: | 9781643686875OPAC |
| ISSN: | 0926-9630OPAC |
| ISSN: | 1879-8365OPAC |
| Parent Title (English): | German Medical Data Sciences 2026: BIG DATA, small data, your Data; Transitions in the age of biomedical AI, proceedings of the 71st Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (GMDS), Freiburg, Germany, 27 September - 1 October 2026 |
| Publisher: | IOS Press |
| Place of publication: | Amsterdam |
| Editor: | Ulrich Sax, Thomas Ganslandt, Rüdiger Breitschwerdt, Klaus Jung, Jochem König, Anika Großhennig |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2026 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2026/10/05 |
| First Page: | 217 |
| Last Page: | 224 |
| Series: | Studies in Health Technology and Informatics ; 340 |
| DOI: | https://doi.org/10.3233/SHTI261005 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell |
