In a heartbeat

• bbeach

Adarsh Krishnamurthy (pictured, left) and Ming-Chen Hsu (center) got to talking about their research interests – Krishnamurthy works in simulating cardiac mechanics and Hsu in simulating valve dynamics – when they had an idea.

“He had the heart without the valve, and I had the valve without the heart,” Hsu said. “We thought ‘why don’t we just put them together?’”

Modeling heart-valve replacements
Modeling heart valves and the mechanics of the fluid moving through the heart chambers can be used to predict deformations and diseases that affect the valves. Simulations can also add a virtual prosthetic replacement valve to examine its effectiveness.

Replacing valves can be a very risky procedure for patients with valvular heart disease, so knowing as much as possible ahead of time about how a prosthetic valve will help a specific patient with specific circumstances will save money and lives.

But one simulation of heart-valve movement (dynamic closure and opening) takes about four-and-a-half hours to complete, and one simulation of fluid movement (with fluid-structure interaction) through a valve takes about one week. In addition, development of a coupled fluid-structure interaction model with accurate movement of the ventricles is complicated, and this can take years.

From years to minutes
So Krishnamurthy, assistant professor of mechanical engineering, and Hsu, associate professor of mechanical engineering, knew who they needed to add to their team: Soumik Sarkar (pictured, right), associate professor of mechanical engineering, and an expert in machine learning. They also enlisted the help of Aditya Balu, graduate research assistant in mechanical engineering.

The team now hopes to teach a machine to predict heart valve simulations and, eventually, fluid-structure interaction simulations of heart valves by providing enough data and simulations to learn from. Their initial tests of machine learning have produced predictions that are 95% accurate according to the actual physics simulations that are run.

The potential with machine learning is simulations that normally take hours or days could be predicted by a machine within a matter of seconds. The long-term goal is to design custom-created heart valves unique to a patient’s anatomy and health problems. The team recently published their work in Scientific Reports.