PhD Studentship: Diabetic Foot Ulcer

Project summary

The management of chronic wounds poses a considerable burden on healthcare systems, with approximately 2.2 million patients currently afflicted, resulting in annual costs of £5.3 billion for the NHS to address wound care and its associated comorbidities, including amputations.

The logistical challenges of transporting vulnerable patients to and from hospitals incur additional costs and elevate the risk of infections, leading to a significant rise in patient mortalities. Addressing this complex scenario necessitates an accurate and automated computerised solution for measuring and characterising wound areas, which is currently non-existent.

This research project aims to develop an innovative digital technology solution to enhance clinicians’ confidence in monitoring wounds and facilitating remote assessment and monitoring. By enabling at-home tracking, the system aims to encourage more regular checks and prompt responses to declines in recovery, ultimately fostering early intervention. We partner with industrial partners and clinicians to help provide a real-world solution to facilitate the effective treatment of DFU.

This approach contributes to reducing healthcare costs and engenders greater trust in digital technology among end-users, thereby enhancing overall patient care- the collaborative development of this system with renowned researchers in the field of AI for wound monitoring and a chance to work with clinical and industrial partners.

Aims and objectives

The proposed research project aims to design innovative multimodal intelligent techniques to measure diabetic foot ulcers and wounds accurately. The research objectives are to:

• Create a world-leading multi-modal digital wound repository.
• Design an innovative 2.5D modelling tool for wound assessment.
• Create a novel algorithm using a multimodal dataset to improve the accuracy of predicting ulcer/wound healing.

Specific requirements of the project

The successful candidate 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 for them to have a good background knowledge of machine learning and computer programming and a proactive approach to their work.

A self-motivated, driven, and creative individual will push the bounds of existing research by our world-leading team: Yap, M.H., Kendrick, C. and Cassidy, B. eds., 2023. Diabetic Foot Ulcers Grand Challenge: Third Challenge, DFUC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Vol. 13797). Springer Nature.

How to apply

Interested applicants should contact Dr Connah Kendrick (connah.kendrick@mmu.ac.uk) for an informal discussion. To apply, you must:

• Complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).
• Complete the PGR thesis proposal form addressing the project’s aims and objectives, demonstrating how your skills relate to the area of research and why you see this area as important and interesting.
• Applicants should ensure their submitted CV clearly demonstrates any experience and work in ML and AI

If applying online, you must upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk. The closing date is 1 July 2024. The expected start date is October 2024.

Please quote the reference: SciEng-CK-2024-diabetic-foot-ulcer.

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