Principal Investigator: Dr Arman Eshaghi
Institution: University College London (UCL)Tags: 47233, Machine Learning, MRI, neurodegeneration, neuroimaging, norm development, spinal cord
Patients with multiple sclerosis have a relentless shrinkage of the brain and spinal cord. Researchers rely on measurements from healthy volunteers to precisely detect when patients show abnormality. However, even after precisely measuring the change in brain and spinal cord scans of patients with multiple sclerosis, it is still a challenge to provide personalised prediction of the current and future progression of a patient. This is in part due to the lack of reference measures that can provide a normative sample of the change in the brain and spinal cord. We are in the process of processing more than 25,000 MRI scans from patients with MS. Here, I aim to leverage the large dataset available as part of the UK Biobank to construct normative samples which will later be used to stage and predict future progression of MS in patients.