Artificial Intelligence Approaches to Diabetic Foot Ulcer Risk Identification and Early Intervention

About the project

About the Project This PhD will design, develop, and evaluate AI-based approaches to diabetes foot ulcer (DFU) prevention, from risk identification, clinical decision support up to the data quality challenges that underpin reliable model development. The student will work closely with the supervising team's established NHS primary care clinical network and contribute to a translational research programme in AI-driven diabetes complications prevention. Project Highlights: The work on this project will: Investigate AI and machine learning architectures for early DFU risk identification using routinely collected health data. Compare agentic AI labelling pipelines against ground truth annotation, examining reliability, and fitness for purpose in clinical prediction tasks. Evaluate the trustworthiness and fairness of developed models for health equity in AI-driven prevention. Design and assess explainable AI (XAI) outputs that are meaningful to clinicians and patients, supporting shared decision-making in primary care settings. Engage with NHS-connected clinical partners to ensure prevention-focused outputs are grounded in real care pathway needs. The project benefits from working closely with BCS Health and Care and the European Federation of Medical Informatics (EFMI). Project description: Diabetic foot ulcers affect millions of people globally and represent a significant, largely preventable burden on both patients and health services. This PhD will investigate how AI can meaningfully advance DFU prevention. You will start by conducting a systematic evaluation of machine learning and deep learning approaches to DFU risk identification from heterogeneous health data, assessing performance, generalisability and explainability of the models. Then you will move into data labelling, comparing agenting AI approaches to automated label generation against clinician-validated ground truth annotation. Finally you will design and evaluate decision support systems that connect your model predictions to actionable prevention steps. General admissions criteria You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. Specific candidate requirements You will benefit from a familiarity with NLP, transformer architectures, or structured clinical data Particularly welcome: candidates holding or undertaking a healthcare qualification. How to Apply We’d encourage you to contact Dr Elisavet Andrikopoulou ( elisavet.andrikopoulou@port.ac.uk ) to discuss your interest before you apply, quoting the project code. When you are ready to apply, please follow the ' Apply now ' link on the Computing PhD subject area page and select the link for the relevant intake.. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘ How to Apply ’ page offers further guidance on the PhD application process. When applying please quote project code CMP10590529 .

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