1st Supervisor: Thomas Booth, King’s College London
2nd Supervisor: Marc Modat, King’s College London
Tertiary Supervisor: Jorge Cardoso
Aim of the PhD Project:
- Patients often have non-specific symptoms e.g. headache or memory loss, which might or might not represent neurological disease
- Because MRI is central to detecting brain abnormalities, patients often undergo work up
- Aim: automatically label brain MRI as normal/abnormal (e.g. tumour/atrophy)
- How: recurrent neural network or deep learning transformer approaches
The objective of this project is to develop a decision-making tool that identifies abnormalities on magnetic resonance imaging (MRI) brain scans using deep learning.
This will be applied to patient groups where there are non-specific symptoms such as headache or memory loss.
A clinically validated decision-making tool that identifies abnormalities on brain MRI scans does not exist. This project will meet this clinical need by providing a deep learning model that can automate the identification of abnormalities immediately in ‘real-world’ conditions.
This is important as over 330,000 patients are waiting more than 30 days for their MRI reports in the UK. This number is forecast to increase, as there is a greater demand for MRI than there is availability of radiologists to report these scans. UK-specific workforce shortages in clinical radiology are negatively impacting patient care by delaying diagnosis; a similar picture is seen globally.
Immediate triage of a brain MRI into normal or abnormal potentially allows early intervention to improve short- and long-term clinical outcomes. In general, detecting an abnormality or illness early would result in lower costs for the healthcare system because less specialist medicine and fewer hours of treatment are needed for the patient to recover.
The objectives are:
- Build a classification model to determine normal and abnormal brain scans
- Clean datasets for patient groups where there are non-specific symptoms such as headache or memory loss.
- Validation on non-research grade data of the classification model identifying abnormalities on brain MRI scans to assess possible clinical use for prioritisation and/or triage of true negatives (i.e. normal scans).
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