MedElevator
Войти в исследования Studies in health technology and informatics · 2026
Survival Prediction of TAVI Patients Using End-to-End Deep Image Regression on Whole-Body CT.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Müller Dominik, Rizoudis Alexia, Lind Charlotte, Kramer Frank, Voran Jakob, Ulrich Hannes
Аннотация
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 can effectively identify high-risk TAVI patients, offering potential as a clinical decision support tool for pre-operative assessment without manual feature engineering.
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.