… hemodynamic optimization to reduce or prevent renal injury. … to confirm feasibility and validate
model predictions before … are not directly interpretable by humans or end users. Although …

… , we focus on methods for obtaining accurate hemodynamic … have been performed to
assess the predictive power of different … the deep learning model using a supervised learning …

… predictive models via novel algorithmic strategies, machine … Known coronary artery disease
(CAD), hemodynamic … This study introduces the feasibility of a deep-learning model as a “…

… The machine learning method proposed in this study leverages … in assessing the hemodynamic
footprint of this condition. … in this study demonstrates superior accuracy to predict positive …

… (DL) models to classify tissue stiffness and to predict nonlinear … Finally, material and
hemodynamic parameters of models … phenomena may still not be feasible due to ethical reasons, …

… of the present study was to develop a deep learning model to predict the … A deep learning
model was also developed for predicting the hemodynamics of the human thoracic aorta [21]. …

… of Andersen and colleagues [15] in an animal model, a human … We know that the predicted
mortality for the operable … that if we had even any minor hemodynamic problems we would put …

… of blood flow in stenosed arteries with a hybrid framework of machine learning and … flows
and the deep neural network (DNN) for its high efficiency in artificial learning. Specifically, the …

… A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta.
J Biomech.2020;99:109544. and by anomaly detection algorithms (used widely in ECG …

… to predict mortality after CTA 18 or early revascularization after SPECT imaging. For the
prediction of hemodynamic … in this study correctly excluded hemodynamic significance in the …