The researchers then investigated the effects of varying the degree of stenosis, blood flow rate and viscosity, using the models to predict two diagnostic metrics — pressure gradient across the stenosis and wall shear stress on the aorta — to reflect the real-world impact of a person’s lifestyle choices on CoA.

“We were looking at how different physiological characteristics can change the flow profile,” Randles said. “If the person is running, if they’re running at altitude, if they’re pregnant — how would that change things like the pressure gradient across the narrowing of the vessel? That can influence when doctors are going to take action. You can’t capture the full state of that patient in just one simulation.”


Randles said the simulations indicated a synergy of viscosity and velocity of the blood at different points of the aorta, which also was influenced by the specific geometry of a particular patient. The relationships among the various physiological factors weren’t intuitive or linear, she added, requiring a large supercomputer like Vulcan combined with machine learning to fully understand the complex interplay among them.

To create a framework for building a predictive model with a minimal amount of simulations necessary to capture all the physiological factors, the team implemented machine learning models trained on data gathered from all 136 blood flow simulations performed on Vulcan. Machine learning enabled the team to reduce the number of viscosity/velocity pairing simulations needed from hundreds down to nine, making it feasible to someday develop patient-specific risk profiles, Randles said.

“The ideal is that in the future, when a new patient comes in you wouldn’t have to run 70 million compute hours, you would only have to do enough to get those few simulations,” Randles said. “It’s the first step to not requiring a supercomputer in the hospital. We want to be able to give enough training data and a machine learning framework they can employ to do just a few simulations that maybe would fit on a local cluster or something much more accessible, while also leveraging results from the large-scale supercomputing.”

To validate the models, researchers at Arizona State University 3D-printed aortas and completed benchtop experiments to simulate blood flow for comparison with the simulation results. 3D printing allowed the team to generate profiles of the aorta and extract data on wall sheer stress, velocity and other factors important to understanding flow, Randles said.

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