Artificial intelligence techniques for illness detection by using MRI

About the Project

Supervisory Team: Dr. Sasan Mahmoodi and Professor Brigitte Vollmer

Hypoxic-ischaemic encephalopathy (HIE) affects babies' brains during the childbirth due to shortages of oxygen. Using Artificial Intelligence techniques, HIE disease is diagnosed much earlier than two years which is the current normal practice in hospitals. As a result of HIE early detection, then early interventions can be applied to improve the babies health.

Neonatal hypoxic-ischaemic encephalopathy (HIE) is a consequence of perinatal asphyxia and is a significant cause of perinatal death and neurodevelopmental impairments later in life. Approximately 0.2% of infants in high income countries suffer from HIE, and there is a mortality rate of 15%–25%. HIE carries a high risk for neuromotor, cognitive, and behavioural difficulties, epilepsy, visual and hearing impairment in survivors. Early diagnosis of the injury location and extent is important for counselling and identification of those who may benefit from early intervention. A range of techniques are used for diagnostic evaluation, including magnetic resonance imaging (MRI) with T1.

In this project, we are proposing to develop a method for HIE detection by analysing Susceptibility Weighted Images (SWIs). In the current clinical practice, the detection of HIE is performed 24 months after the birth by evaluating the child’s behaviour. We propose a framework to detect HIE after the birth for infants who may have suffered asphyxia during the birth by analysing the MRI images of their brains after the birth.

The early detection of HIE in new-borns helps health carers to intervene early to improve the prognosis of HIE and reduce the occurrence of sequelae. We also propose methods to find the regions where the brain injuries have occurred to enable the health-carers to predict what impacts these injuries might have on the patients’ behaviours and therefore to provide a more targeted intervention to aim for a better outcome at the age of 24 months.

Entry Requirements

You must have a UK 2:1 honours degree, or its international equivalent.

you must have knowledge of, or be interested in learning, some of the following topics:

• machine learning
• computer vision
• Python programming

Knowledge of deep learning is also desirable but not essential.

Fees and Funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

International candidates who are self-funded or sponsored are also encouraged to apply for this project.

For more information, please visit our postgraduate research funding pages.

How to Apply

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Computer Science (7089)
• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)

The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break.

The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.