Researchers from The University of Texas at Austin's Oden Institute for Computational Engineering and Sciences shed new light on heart health by focusing on the complexities of the mitral valve and heart valve function as well as high speed computational models of heart function
Biomedical engineering professor Michael Sacks is the director of the Willerson Center for Cardiovascular Modeling and Simulation. He led a team of researchers to create a detailed computer model of the heart.
The mitral valve plays a crucial role in the heart's function, acting as a gatekeeper to ensure blood flows in one direction from the left atrium into the left ventricle, similar to traffic moving smoothly along a one-way street. When the heart contracts, the mitral valve opens, allowing blood to move forward into the left ventricle. Once the left ventricle is filled, the mitral valve closes swiftly, preventing blood from flowing backward.
However, after a heart attack, also known as a myocardial infarction (MI), the mitral valve may not close properly, causing blood to leak back into the left atrium. This problem, called ischemic mitral regurgitation (IMR), can reduce the heart’s efficiency, increase pressure, lead to lung congestion, stroke, raise the risk of irregular heartbeats, and potentially causing heart failure.
Sacks said that this latest discovery is a culmination of decades of research.
This is a result of about thirty years worth of work by myself and my collaborators on trying to understand how myocardial infarction and mitral valve regurgitation happen together.
The researchers started by using data from experiments of left heart failure to build this model and then used the model to simulate what happens when someone has a heart attack, focusing on how the size and location of the heart dead tissue that results in mitral valve regurgitation.
Their simulations revealed how infarcted (and dead) heart tissue in different regions of the heart affect the mitral valve function. They found that particular locations of damage can change much worse regurgitation compared to other locations. Importantly, these findings were completely performed computationally and alligned well with experimental findings.
Understanding the finding can help physicians better diagnose and treat IMR, potentially improving outcomes for patients with this condition. Yet this potential solution to the problem is not easy due to how fast clinical decisions must be made following a heart attack.
“The fundamental problem with all these advanced simulations is that while they are great scientific tools, they are very slow. We need new computational tools to make this work. Otherwise, it's great science, but it's never going to be used in the clinic," said Sacks.
Sacks and his team addressed this in their second paper by introducing a faster method for running simulations of the heart. Their innovation lies in rethinking how researchers solve the complex equations that describe heart function. Traditional models use the finite element method, which requires complex and time-consuming calculations for each scenario.
Instead, the team turned to machine learning. But rather than using the typical approach, which involves training an AI on massive datasets, they trained their AI on the physics of the heart. This new approach focuses on how factors like volume and pressure change as the heart beats.
By using these physics-based models, the researchers were able to maintain accuracy while drastically reducing computation time. They trained neural networks to predict heart behavior based on fundamental physical principles used in the current heart models. This allowed the machine learning models to simulate an entire heartbeat in less than a second rather than what would traditionally take hours.
The physics-based model is also highly adaptable with the capability to adjust to various heart conditions and provide a detailed understanding of heart function specific to a given patient. This new approach by Oden Institute researchers holds promise for heart attack patients suffering from IMR and with the potential to usher in a new era of cardiac care.
Article adapted from Oden Institute for Computational Sciences Website
