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Interpretative and Predictive Modeling in Cardiovascular Medicine

Manuel Rausch, University of Texas, Austin

Alberto Figueroa, University of Michigan

Colleen Witzenburg, University of Wisconsin

Heart and vascular diseases continue to be a significant source of morbidity and mortality. Fundamentally understanding and learning to predict disease progression would aid clinical decision making and improve human health. However, many of the diseases that fall under this umbrella arise from the multiphysical interplay of biological, chemical, and mechanical factors on a wide range of spatial and temporal scales and are thus difficult to describe and understand without advanced tools. Additionally, complex geometries and boundary conditions add to the challenges of understanding and predicting heart and vascular diseases. Computational modeling can overcome these obstacles and provide insight into disease where measurements are not possible because of their invasive nature or the limited spatial and temporal resolution of available imaging modalities. Additionally, computational models hold the promise of becoming predictive, precision tools that can forecast disease progression and inform the optimal treatment course. This minisymposia will bring together scientists modeling the cardiovascular system with a specific focus on understanding and predicting its diseases. Fluid models, solid models, and fluid-structure interaction models will all be included. We will stimulate discussion surrounding new computational methods, integration of experiments and imaging into the modeling pipeline, and novel findings.