AI for multiple sclerosis

Pablo Naval Baudín

Bellvitge Biomedical Research Institute (IDIBELL), l’Hospitalet de Llobregat, Spain

Multiple sclerosis is a debilitating neurodegenerative disease and one of the leading causes of disability in young adults, with a very high socio-economic impact. In recent years, the emergence of powerful new drugs has slowed the progression of the disease, which, combined with the various clinical trials with new molecules currently underway, is leading to a paradigm shift.

These new treatment options require the availability of biomarkers that can accurately predict the progression of disability in each patient. Using such markers, it would be possible to monitor disease progression and implement more aggressive treatment regimens earlier in patients with a higher risk of progression. In addition, this tool could accelerate the development of new treatments for multiple sclerosis. If accurate biomarkers were available, they could be used as surrogate indicators in clinical trials, potentially reducing their duration and cost and driving innovation in the field.

In this context, the aim of the present project is to develop a clinical support tool to help identify patients with multiple sclerosis who are at higher risk of progression towards disability. The tool will use machine learning models trained with data from clinical practice, laboratory tests and imaging (MRI) to develop a predictive algorithm that integrates various biomarkers. Collaboration between neurologists, neuroradiologists and data scientists will refine the algorithm to ensure its validity and the future integration of the tool into clinical workflows. This tool will enable accurate predictions of disease course and help clinicians implement early, individualised, more precise and effective multiple sclerosis treatment strategies.

MSPredict: MS Prognosis through Advanced Imaging Analysis

Stage 1