AI uses mathematical models to train computers using massive amounts of data to learn and solve problems in ways that can seem human, including to perform complex tasks like image recognition.
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.
MS is a condition where the immune system attacks the brain and spinal cord. This causes problems in how a person moves, feels or thinks. In the UK, 130,000 people live with MS, costing the NHS more than £2.9 billion a year.
Magnetic Resonance Imaging (MRI) markers are crucial for studying and testing treatments for MS. However, measuring these markers needs different types of specialised MRI scans, limiting the effectiveness of many routine hospital scans.
As part of a new study, published in Nature Communications, researchers tested the effectiveness of MindGlide on over 14,000 images from more than 1,000 patients with MS.
This task had previously required expert neuro-radiologists to interpret years of complex scans manually – and the turnaround time for reporting these images is often weeks due to the NHS workload.
However, for the first time, 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 - SAMSEG (a tool used to identify and outline different parts of the brain in MRI scans) and WMH-SynthSeg (a tool that detects and measures bright spots seen on certain MRI scans, that can be important for diagnosing and monitoring conditions like MS) - when compared to expert clinical analysis.
MindGlide was 60% better than SAMSEG and 20% better than WMH-SynthSeg for locating brain abnormalities known as plaques (or lesions) or for monitoring treatment effect.
First author, Dr Philipp Goebl (UCL Queen Square Institute of Neurology and UCL Hawkes Institute), said: “Using MindGlide will enable us to use existing brain images in hospital archives to better understand multiple sclerosis and how treatment affects the brain.
"We hope that the tool will unlock valuable information from millions of untapped brain images that were previously difficult or impossible to understand, immediately leading to valuable insights into multiple sclerosis for researchers and, in the near future, to better understand a patient’s condition through AI in the clinic. We hope this will be possible in the next five to 10 years."
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 - such as T2-weighted MRI without FLAIR (a type of scan that highlights fluids in the body but still contains bright signals - making it harder to see plaques).
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 (i.e. at annual scans attended by patients).
Additionally, MindGlide was able to corroborate previous high-quality research regarding which treatments were most effective.
The researchers now hope that MindGlide can be used to evaluate MS treatments in real-world settings, overcoming previous limitations of relying solely on high-quality clinical trial data, which often did not capture the full diversity of people with MS.
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.”
The current implementation of MindGlide is limited to brain scans and does not include spinal cord imaging, which is important for assessing disability in people with MS. Future research will need to develop a more comprehensive assessment of the whole neural system to encompass both the brain and the spinal cord.
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 to validate MindGlide, against three separate databases of 14,952 images from 1,001 patients.
Goebl P, Wingrove J, Abdelmannan O, Brito Vega B, Stutters J, Ramos SDG, Kenway O, Rossor T, Wassmer E, Arnold DL, Collins DL, Hemingway C, Narayanan S, Chataway J, Chard D, Iglesias JE, Barkhof F, Parker GJM, Oxtoby NP, Hacohen Y, Thompson A, Alexander DC, Ciccarelli O, Eshaghi A.
Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research.
Nat Commun. 2025 Apr 7;16(1):3149. doi: 10.1038/s41467-025-58274-8