UCL Researchers Develop AI Tool to Track Effectiveness of Multiple Sclerosis Treatment - Video

A recent study highlights a new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS). The findings are published in Nature Communications. The tool, called MindGlide, can extract key information from brain images (MRI scans) acquired during the care of MS patients, such as measuring damaged areas of the brain and highlighting subtle changes such as brain shrinkage and plaques.

MindGlide is a deep learning (AI) model developed by UCL researchers to assess MRI images of the brain and identify damage and changes caused by MS. In developing MindGlide, scientists used an initial dataset of 4,247 brain MRI scans from 2,934 MS patients across 592 MRI scanners. During this process, MindGlide trains itself to identify markers of the disease. This new study was carried out to validate MindGlide against three separate databases of 14,952 images from 1,001 patients.

Researchers tested the effectiveness of MindGlide on over 14,000 images from more than 1,000 patients with MS. MindGlide was able to successfully use AI to detect how different treatments affected disease progression in clinical trials and routine care, using images that could not previously be analysed and routine MRI scan images. The process took just five to 10 seconds per image.

MindGlide also performed better than two other AI tools when compared to expert clinical analysis. The results from the study show that it is possible to use MindGlide to accurately identify and measure important brain tissues and lesions even with limited MRI data and single types of scans that aren’t usually used for this purpose.

As well as performing better at detecting changes in the brain’s outer layer, MindGlide also performed well in deeper brain areas. The findings were valid and reliable both at one point in time and over longer periods.

Dr Arman Eshaghi (UCL Queen Square Institute of Neurology and UCL Hawkes Institute), the project’s principal investigator and lead of the MS-PINPOINT group, said: “We were not previously analysing the bulk of clinical brain images due to their lower quality. AI will unlock the untapped potential of the treasure trove of hospital information to provide unprecedented insights into MS progression and how treatments work and affect the brain.”

Reference: Goebl, P., Wingrove, J., Abdelmannan, O. et al. Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research. Nat Commun 16, 3149 (2025). https://doi.org/10.1038/s41467-025-58274-8