Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Bradley Feiger, John Gounley, Dale Adler, Jane A. Leopold, Erik W. Draeger, Rafeed Chaudhury, Justin Ryan, Girish Pathangey, Kevin Winarta, David Frakes, Franziska Michor, Amanda Randles

Research output: Contribution to journalArticle

Abstract

Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient’s hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient’s vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics – pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.

Original languageEnglish (US)
Article number9508
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks'. Together they form a unique fingerprint.

  • Cite this

    Feiger, B., Gounley, J., Adler, D., Leopold, J. A., Draeger, E. W., Chaudhury, R., Ryan, J., Pathangey, G., Winarta, K., Frakes, D., Michor, F., & Randles, A. (2020). Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks. Scientific reports, 10(1), [9508]. https://doi.org/10.1038/s41598-020-66225-0