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PhD Studentship: Using AI to predict and prevent future stroke using routine investigations

University of Plymouth - Brain Research & Imaging Centre (BRIC)

Qualification Type: PhD
Location: Plymouth
Funding for: UK Students
Funding amount: From £17,668 full Home tuition fees plus a stipend, per annum (2022/23 rate)
Hours: Full Time
Placed On: 2nd February 2023
Closes: 27th March 2023

Project Title: Using AI to predict and prevent future stroke using routine investigations

DoS Dr Stephen Mullin (stephen.mullin@plymouth.ac.uk)

2nd Supervisor Professor Stephen Hall (stephen.d.hall@plymouth.ac.uk)

3rd Supervisor Professor Emmanuel Ifeachor (E.Ifeachor@plymouth.ac.uk)

4th Supervisor Dr Mark Thurston (mark.thurston@plymouth.ac.uk)

Applications are invited for a three-year PhD studentship. The studentship will start on 1 October 2023

Project Description

The advanced bioinformatics in imaging group is pleased to announce an exciting research opportunity to use routinely collected clinical data to help prevent future stroke. There are more than 100,000 strokes in the UK each year causing 38,000 deaths, making it a leading cause of death and disability. The first year of stroke aftercare costs between £12,000- £24,000 3per patient. Many evidence-based interventions are available to reduce stroke risk and ninety-five percent of those who had a stroke had at least one untreated risk factor for it, with up to an estimated 13.7% of these strokes deemed to be preventable. There is significant potential to reduce morbidity/mortality and associated costs.

The proposed project will draw on a dataset of routine investigations undertaken in patients who subsequently developed a stroke and matched controls. It comprises MRI and CT brain, ambulatory ECG and echocardiogram data. The aim of the project will be, using this unique dataset comprising some 10,000 stroke cases and 30,000 controls, to build and optimise a machine learning model to predict future stroke risk based on investigations undertaken prior to the stroke occurring.

It will entail learning a range of bioinformatic techniques such as deep learning, image segmentation and natural language processing. It will interface with de-identified clinical data extracted from the systems of University Hospitals Plymouth NHS Trust, particularly Digital Imaging and Communications in Medicine (DICOM) standard. Candidates will learn about the strict governance requirements for the handling of such data and be involved in processes to demonstrate compliance with it. A particular focus of the PhD will be the use of explainable AI to evaluate and demonstrate the validity of findings.

The successful candidate will sit within a multidisciplinary group, part of the Brain Research & Imaging Centre (BRIC) of the University of Plymouth. They will have access to scheduled teaching aligned to the MSc Human Neuroscience and a taught module programme, which allows development of generic research skills including academic writing, statistics, research design and governance. 

Eligibility

Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. A background in health data science is desirable but not essential. The successful candidate must however be able to demonstrate a track record of computer programming and the use of advanced bioinformatics techniques.

This vacancy will involve working with children and/or vulnerable adults and any appointment will be subject a Disclosure and Barring Service check. 

For Funding details and To Apply for this position, please view here.

If you wish to discuss this project further informally, please contact Dr Stephen Mullin, stephen.mullin@plymouth.ac.uk

For more information on the admissions process generally, please contact research.degree.admissions@plymouth.ac.uk

The closing date for applications is 12 noon on 27 March 2023. Shortlisted candidates will be invited for interview shortly after the deadline.  We regret that we may not be able to respond to all applications.  Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful.

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