Collaborative Research: SCH: Machine-learning Enhanced Computational Models of Cardiac Pathophysiology

Abstract Fibrosis plays a significant role in the adverse remodeling of many organs, including the heart. Computational models could become a useful tool for addressing the challenges associated with this pathology. However, most models of the heart lack the ability to efficiently simulate the fibrotic process. This diminishes the potential for using these models to gain a fundamental understanding of the development and progression of fibrosis, as well as evaluate potential treatment strategies. Therefore, this collaborative project seeks to combine concepts from engineering, computer science, applied mathematics, and physiology to develop advanced computational models of the heart. The long-term goal is to develop a computationally efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict the structural and functional changes that occur in the presence of ischemic and non- ischemic heart disease. Once the model has been validated, it can be deployed to predict the outcomes for different treatments.