Applications:
Engineering, Healthcare Research, Informatics
Methodologies:
Artificial Intelligence, Computing, Machine Learning, Mathematical and Statistical Modeling

Yasser Aboelkassem

Assistant Professor

College of Innovation and Technology

Assistant Professor of Digital Manufacturing Technology, College of Innovation and Technology, University of Michigan-Flint

In this project, we use multi-scale models coupled with machine learning algorithms to study cardiac electromechanic coupling. The approach spans out the molecular, Brownian, and Langevin dynamics of the contractile (sarcomeric proteins) mechanism of cardiac cells and up-to-the finite element analysis of the tissue and organ levels. In this work, a novel surrogate machine learning algorithm for the sarcomere contraction is developed. The model is trained and established using in-silico data-driven dynamic sampling procedures implemented using our previously derived myofilament mathematical models.

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling