PhD Studentship - Developing machine learning algorithms for personalising multiple sclerosis care

PhD Studentship - Developing machine learning algorithms for personalising multiple sclerosis care.

Primary Supervisor: Dr Arman Eshaghi 

Secondary Supervisor: Prof Frederik Barkhof and Dirk Smeets (Icometrix)

A four-year funded PhD studentship is available at the Centre for Medical Image Computing (CMIC) and UCL Queen Square Institute of Neurology in collaboration with an industrial partner in MedTech (Icometrix). Funding will be in line with UCL policy for PhD stipend which can be found here . 

The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the unique multidisciplinary activities and events organised by the centre.

Background

Multiple sclerosis is a chronic disease for which more than 20 disease-modifying treatments (DMTs) are available to slow down the disease. However, research has indicated that about 25% of patients start on a treatment that is working suboptimally, and, on average it takes almost 4 years before a treatment switch happens.

The objective of this project is to develop predictive machine learning models to help identify the best treatment for each patient. We know that brain and spinal cord images (MRI) contain valuable predictive information. For example, it has been shown that MRI measures can predict long term disability. Currently, selecting the right treatment for the right patients is subjective. An artificial intelligence model combining MRI and non-imaging data would allow making more evidence-based treatment decisions when choosing the right DMT. In this project, the candidate will develop advanced cutting edge deep learning models that can use the widely available MRI data, electronic health records and combine to predict how MS may worsen and who may benefit from specific treatments. The outputs of this PhD project will be (a) precision medicine AI models that prepare real-world data for downstream modelling, and (b) biomarker measures from real-world hospital data and (c) information that can help recommend best treatment for individual patients.

Research aims

Developing multi-model fusion methods integrating neuroimaging biomarkers with clinical data in real-world MS populations using (a) deep neural network architecture that can prepare routine-care quality data for downstream processing, (b) deep reinforcement learning models that can provide predictions of future course of MS and best treatments. The model will be trained on existing longitudinal MRI and clinical data, as well as patient-reported outcomes and be incorporated in Icometrix’ ePRO tool (icompanion)

Person Specification

Candidates must have:

• A master’s in computer science, Artificial Intelligence of similar.
• Interest in Neuroscience and Brain Imaging.
• Knowledge of Python (Pytorch and MONAI), R, Computer Vision in general.

This studentship is only open to students that are eligible for Home Fee status, please see here for more details.

Application Deadline - 31^st May 2023

How to Apply:

Please complete the following steps to apply:

• Make a formal application to via the UCL application portal . Please select the programme code MRes Medical ImagingTMRMEISING01 and enter Developing reinforcement learning models for precision medicine in multiple sclerosis 23002 under ‘Name of Award 1’.
• Send an expression of interest and current CV to a.eshaghi@ucl.ac.uk, cdtadmin@ucl.ac.ukand dirk.smeets@icometrix.com. Please use the subject title: Project Code 23002 and quote your UCL Application ID.

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