Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks.

ABSTRACT: Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient's hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-prin

SUBMITTER: Feiger B 

PROVIDER: S-EPMC7289812 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature