Precision Treatment Strategies in Multiple Sclerosis with AI
Research type
Research Study
Full title
Precision Treatment Strategies in Multiple Sclerosis Using Next-generation Machine Learning
IRAS ID
321053
Contact name
Arman Eshaghi
Contact email
Sponsor organisation
University College London
Duration of Study in the UK
4 years, 11 months, 30 days
Research summary
In multiple sclerosis (MS), the immune system attacks the brain and spinal cord. This causes problems in how we move, feel or think. MS types are based on when and how symptoms appear. This classification does not reflect the underlying tissue changes (pathology), potential response to treatment or future disability. More than 15 treatments currently exist, each with different risks and strengths. Brain images, such as magnetic resonance imaging (MRI) and blood tests (serum neurofilament levels [sNfL]), help identify the underlying tissue changes. If types of MS were redefined based on the underlying tissue changes, doctors could recommend the most appropriate treatment with the fewest side effects.
Artificial intelligence (AI) uses computers for tasks that otherwise rely on human intelligence. I want to develop tools for real-world use from diverse patient populations so that my tools can benefit anyone with MS.Aims
1) Use MRI and sNfL to define MS types that reflect underlying abnormalities.
2) Build AI tools that measure and predict whether a treatment works at an individual level.
3) Build AI tools based on objectives (1) and (2) that can learn from each other without sharing patients’ data across centres to optimise and refine the predictions. This ensures I can include more patients from diverse backgrounds for truly personalised predictions.
I will measure how new MS types complement the existing MS types based on disability outcomes and calculate how my digital tools may save costs in future healthcare by recommending appropriate treatments earlier and
delaying disability.The result will be research tools that predict whether a treatment works using widely available NHS data. It will give researchers information on the probability of treatment response and disease worsening. In the future, this could evolve into software that will be used in hospitals to help doctors recommend treatments, minimise disability accrual, reduce side effects and save money.
REC name
South West - Cornwall & Plymouth Research Ethics Committee
REC reference
23/SW/0032
Date of REC Opinion
27 Mar 2023
REC opinion
Favourable Opinion