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Machine Learning for Predictions of Haemodynamics in Cardiovascular Devices


Centre for Accountable, Responsible and Transparent AI

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Dr Katharine Fraser No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Bath United Kingdom Aerospace Engineering Applied Mathematics Artificial Intelligence Biomedical Engineering Data Analysis Fluid Mechanics Machine Learning Mechanical Engineering Medical Physics Statistics

About the Project

Cardiovascular diseases are the leading cause of death globally with 18 million deaths/year. Many cardiovascular diseases require treatment with blood contacting medical devices such as vascular stents, prosthetic valves and even artificial hearts. Computational Fluid Dynamics (CFD) is used in the design of these cardiovascular devices. However, blood flow is complicated by the non-Newtonian rheology of blood, the pulsatile and transitional flow characteristics, and the presence of cells and proteins. This complexity limits the accuracy of calculations, which are also time consuming and expensive. Inaccurate calculations lead to poor design choices, misunderstanding of the fundamental science, and lack of trust in simulation results. Improved methods for simulating both the fluid dynamics and biological interactions are required for CFD to fulfil its potential within the field.

This project will explore the use of artificial intelligence (AI) in CFD simulations of blood flow in artificial hearts, and in simpler geometries giving flow features characteristic of those found in cardiovascular devices. The project will focus on one or more of the following main ideas: the use of Fourier inspired methods to learn from oscillating flows with single frequencies in order to predict physiological pulsatile flows through rotating domains; refinement of the Reynolds Stress tensor by learning from Large Eddy Simulations (LES) to improve the use of unsteady Reynolds Averaged Navier Stokes (URANS) modelling in transitionally turbulent flows; use of data from turbulent eddies gained from LES for creating numerical models for haemolysis (damage to the red blood cells) implemented in URANS. Each of the ideas treats fluid dynamic simulations in a multiscale way, with the aim of achieving the accuracy of a high fidelity simulation, by using only a coarse simulation augmented with supplementary models learned from high fidelity simulations.

This project is built on the principles of Accountable, Responsible and Transparent (ART) AI:

• Accountable: Maximising the use of data to achieve increased understanding of the interactions between devices, fluid dynamics, and biological components, is important since we are accountable to funders, tax-payers, patients, clinicians and inventors.

• Responsible: Techniques for more accurate, and faster, simulations of blood flow in medical devices will be created. Increasing the speed of calculations will make design optimisation studies in full parameter space a reality, and open CFD to more users such as small medical device start-up companies, thus enabling the design of better cardiovascular devices. As a society we have a responsibility to care for people in the best way possible, so making optimised devices, rather than devices which simply accomplish the task, is our goal.

• Transparent: Proven improvements in accuracy using transparent simulation techniques will give both industry and regulators confidence and trust in the use of CFD for certification of cardiovascular devices, thereby reducing the need for in vivo experiments and speeding up the pipeline from idea to patient.

The student will become an expert on the use of AI in engineering, with a particular focus on the use of AI in numerical simulations and fluid dynamics. Through the PhD programme the student will learn state-of-the-art AI techniques and will apply them to this worthy problem. In addition to the technical aspects the social implications of the work will be considered, including the potential to reduce animal testing, make best use of publically funded research, and enhance efficiency in the medical device pipeline.

This project is associated with the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent AI (ART-AI).

Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Informal enquiries about the project should be directed to Dr Kate Fraser on email address [Email Address Removed].

Formal applications should be made via the University of Bath’s online application form and be accompanied by a research proposal. Enquiries about the application process should be sent to [Email Address Removed].

Start date: 4 October 2021.


Funding Notes

ART-AI CDT studentships are available on a competition basis and applicants are advised to apply early as offers are made from January onwards. We reserve the right to close the advert early if all funded places are filled. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£15,609 per annum in 2021/22, increased annually in line with the GDP deflator) for up to 4 years.

We also welcome applications from candidates who can source their own funding.
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