AI tool can track effectiveness of multiple sclerosis treatments

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A new artificial intelligence tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis has been developed by UCL researchers.

Artificial intelligence 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 patients with multiple sclerosis, such as measuring damaged areas of the brain and highlighting subtle changes such as brain shrinkage and plaques.

Multiple sclerosis 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 multiple sclerosis. 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 multiple sclerosis.

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 artificial intelligence 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 multiple sclerosis) – 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 artificial intelligence in the clinic. We hope this will be possible in the next 5 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 (ie, 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 multiple sclerosis 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 multiple sclerosis.

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 multiple sclerosis progression and how treatments work and affect the brain.”

Source: News Release
University College London
April 7, 2025