Machine Learning Techniques for Predicting Blood Flow and Cancer Cell Trafficking in Microcirculation

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

The exchange of oxygen between blood and tissue primarily occurs in microcirculation, which is made of narrow capillary vessels. Microcirculation is also associated with many diseases, such as cardiac and cerebral disorders, diabetes, and cancer. Red blood cells act as oxygen carriers. Their distribution in the microcirculation is critical to healthy functioning and disease progression. Sophisticated computer models to predict red cell motion in capillary vessels have emerged over the past decades. However, such models often require extensive computing time and resources and are not feasible for organ-scale prediction over many cardiac cycles. To overcome such limitations, this proposal will consider the application of Artificial Intelligence or Machine Learning (ML) techniques for the prediction of blood flow and cell trafficking. The broader impact will be accomplished through the proposed research, training of graduate and undergraduate students, and K-12 outreach. Specific research objectives include the developments of (i) ML models to predict vessel cross-sectional variation of the blood velocity, and red cell concentration, (ii) ML models coupling the blood flow, cell volume fraction and a single-parameter measure of cell deformation, (iii) ML models to predict full 3D deformed cell shape, and (iv) models for high-accuracy long time-series prediction. Models will be trained using high-fidelity simulation data of cell flow in bifurcations and vessels of varying geometry and hemodynamic parameters, creating a model bank. The trained ML models will then be validated against high-fidelity simulations of red cell flow in physiologically realistic vascular geometry. Finally, the capability of the ML models will be demonstrated by considering large-scale cerebral vasculatures for which high-fidelity simulation is not feasible. The translational aspect of the proposal involves the development of similar ML models for predicting cancer cell trafficking. The proposed ML models may provide a significant improvement in patient-specific (precision) diagnosis. PhD and undergraduate students will be mentored. The outreach component will involve coding and computing for middle and high school female students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.