Thromboembolism and stroke are a leading cause of morbidity and mortality, accounting for over 30% of all deaths in recent decades. Mechanisms of such diseases depend on multiple inter-related factors, often connected to cardiac arrhythmias such as atrial fibrillation. Biophysical computer models have been developed to understand these mechanisms and predict risks but their complexity means they are generally incompatible with the clinical timescale and impractical to use in the clinic. Statistical deep learning (DL) models have been used as an efficient way to overcome the drawbacks of traditional mechanistic models and numerical methods. In particular, Physics-Informed Neural Networks (PINNs) can combine precise formalisms of biophysical equations with computational efficiency of DL.
This project’s overarching goal is develop PINNs as a novel DL tool for simulating thrombus formation in the cardiovascular system. The project aims to: 1) develop a PINN framework that combine advantages of the mechanistic and statistical models to simulate blood coagulation risks efficiently, 2) train and test the PINN using in-silico patient cohorts. 3) evaluate these novel PINNs on patients data and predict the associated risks of stroke.
The candidate will learn coding skills in python and Matlab, image processing skills, and gain a detail understanding of different DL architectures. They will also understand how new technology can be designed to be compatible with, and address, clinical needs. Finally they will achieve in-depth knowledge of the physiological mechanisms of blood coagulation and thrombus formation.
