New Delhi, April 7 : UK researchers have developed a new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS).

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.

The AI tool, called MindGlide, developed by researchers from the University College London (UCL) uses mathematical models to train computers by using massive amounts of data to learn. It solves problems in ways that can seem human, including how to perform complex tasks like image recognition.

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.

While MRI markers are crucial for studying and testing treatments for MS, measuring them needs specialised scans, limiting the effectiveness of many routine hospital scans.

"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," said Dr. Philipp Goebl from UCL's Queen Square Institute of Neurology.

In the new study, published in the journal Nature Communications, 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.

"Using MindGlide will enable us to use existing brain images in hospital archives to better understand multiple sclerosis and how treatment affects the brain,' Goebl added.

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 (that is., 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.

"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," said Dr. Arman Eshaghi, from the institute.

/IANS