PhD Studentship - Disease Classification and Survival Analysis in Patients with Aortic Stenosis Using Transformer-Based Multi-Modal Artificial Intelligence Techniques

This is a full-time, funded PhD opportunity, open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

PROJECT CONTACT

Dr Wenqi Lu

PROJECT ADVERT

Aortic valve stenosis (AS) causes significant morbidity and mortality, with severe cases increasing the risk of heart failure, syncope, and sudden cardiac death. Echocardiography and CT are key for assessing AS severity, but conventional methods are time-consuming and susceptible to intra- and inter-observer variations. Current AI models use single modality inputs, failing to effectively integrate data from multiple sources due to their heterogeneity.

This project aims to develop a novel solution for accurate AS diagnosis and prognostication by employing transformer-based AI techniques as the disease classifier and survival predictor using data from multiple clinical domains. We partner with clinicians to provide clinical advice and relevance of our model development and validation.

Our outputs will enhance workflow processes and reduce diagnostic variability. Multi-modal AI can improve patient outcomes by enabling earlier detection of AS and better risk stratification for valvular intervention, leading to more personalised treatments to enhance human capabilities and improve quality of life.

PROJECT AIMS AND OBJECTIVES

The project aims to develop a novel solution for accurate AS diagnosis and prognostication by employing multi-modal AI techniques. The research objectives are to:

• automate the analysis and reporting of electrocardiography, echocardiography, and CT images to derive biomarkers in AS patients
• autonomously classify AS severity and subtype
• improve patient outcomes by facilitating risk stratification for interventions such as valve replacement surgery.

The project focuses on developing technologies that enhance human capabilities and improve quality of life. This project addresses a critical healthcare need while exemplifying Human-centred Computing principles by enhancing patient care through advanced, user-friendly, and personalised AI technologies.

SPECIFIC REQUIREMENTS OF THE PROJECT

Successful candidates would have a strong background in Computer Science, Engineering, Maths or Physics, and preference would be given to those with a good understanding of computer vision and deep learning.

It is essential to have a good background knowledge of machine learning and computer programming and a proactive approach to their work.

HOW TO APPLY

Interested applicants should contact Dr Wenqi Lu for an informal discussion.

To apply, please complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, please click on the ‘Apply’ button above and upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Expected start: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-CS-2024-aortic-valve-stenosis

Find out more

Email address: Dr Wenqi Lu

Advert information