Five ways AI is transforming MS research and care
MS affects everyone differently. And people can experience a lot of changes in their MS over their lifetime. Artificial intelligence (AI) is increasingly being used as a powerful tool to make sense of this variation. It can find patterns in vast amounts of data and turn them into ways to improve the lives of people with MS.
What is AI?
AI is the field of computer science focused on building systems that can simulate human intelligence, such as:
- Recognising patterns in data
- Solving problems
- Making decisions and predictions.
Here, we explore five ways AI is already advancing MS research and care.
Better, faster MRI analysis
MRI scans play an essential role in diagnosing and monitoring MS. They help doctors see and measure changes in the brain caused by MS. And track signs that may predict how a person’s MS could progress. Researchers are developing and using AI tools to analyse scans faster and more accurately than the human eye can.
We’re funding researchers from the University of Cambridge to test an AI tool for this. It can quickly analyse MRI scans, highlight subtle changes and track how a person’s MS changes over time. The team is testing whether this information helps predict future relapses or disability and if the quicker analysis reduces waiting times.
Precision medicine
MS affects people in very different ways. So finding the right treatment is often a process of trial and error. AI can change this by analysing huge amounts of data to find patterns and predict which treatments are most likely to work for each person’s specific biology. We’re supporting researchers at the University of Cambridge to see if AI could help match people with the best first drug for their MS.
This personalised approach could help doctors and people with MS make more informed treatment decisions earlier. It could help doctors give effective treatments while reducing unnecessary side effects.
Redefining types of MS
MS is currently described as three main types: relapsing remitting, primary progressive, or secondary progressive. These are based on how someone’s MS symptoms behave. The lines between these MS types can be blurry, but treatment decisions are often based on these labels.
Dr Arman Eshaghi from University College London has used AI to look at the big picture. His research shows that these labels don’t always reflect what’s happening inside the body. The team used an AI tool to analyse thousands of MRI scans alongside blood tests measuring ‘serum neurofilament light chain’ (sNfL). This is a protein that’s released into the blood when nerves are damaged. The AI revealed two new biological patterns:
- An early high sNfL pattern. This version is aggressive from the start.
- A lower sNfL pattern. This version progresses more slowly.
Over time, understanding more about these broad patterns will help make sure people get the right treatment at the right time.
Understanding how other conditions impact MS
About 77% of people with MS live with other health conditions, like anxiety,depression, or high blood pressure. We’re supporting researchers at the University of Liverpool to use AI to study these links. By analysing data from the UK MS Register, their AI approach identifies which other conditions are linked to faster MS progression. This should help doctors understand who’s most at risk and guide their care.
Speeding up drug discovery
AI could speed up drug discovery in several ways. Researchers are using AI to quickly analyse lots of data and find drugs already approved for other conditions which might treat MS. They’re also looking into virtual models of someone’s MS – so treatments could be tested first on a computer version. These virtual versions of MS are called ‘digital twins’. They could help predict which options might work best.
A powerful sidekick for the MS community
The idea of AI can feel quite futuristic and scary. But it can be viewed as a helpful sidekick for healthcare and research professionals. The potential applications for MS are huge. But even the most sophisticated AI is only as smart as the information that’s fed in. By bringing together cutting-edge research with high-quality data from the MS community, we can make MS care and research more efficient, informed and personalised.
This blog came from an article in our MS Matters magazine.
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