To provide the right treatment for multiple sclerosis (MS), it is important to know when the disease changes from relapsing-remitting to secondary progressive, a transition that is currently recognized on average three years too late. Researchers at Uppsala University have now developed an AI model that can determine with 90% certainty which variant the patient has. The model increases the chances of starting the right treatment in time and thus slowing the progression of the disease.
MS is a chronic, inflammatory disease of the central nervous system. In Sweden, there are approximately 22,000 people living with MS. Most patients start with the relapsing-remitting form (RRMS), which is characterized by episodes of deterioration with intervening periods of stability. Over time, many people transition to secondary progressive MS (SPMS), where their symptoms instead get steadily worse, without obvious breaks.
Identifying this transition is important because the two different forms of MS require different treatments. Currently, the diagnosis is made on average three years after the transition begins, which can lead to patients receiving medicines that are no longer effective.
The new AI model summarizes clinical data from more than 22,000 patients in the Swedish MS Registry. The model is based on data already collected during regular health care visits, such as neurological tests, magnetic resonance imaging (MRI) scans and ongoing treatments.
"By recognizing patterns from previous patients, the model can determine whether a patient has the relapsing-remitting form or whether the disease has transitioned to secondary progressive MS. What is unique about the model is that it also indicates how confident it is in each individual assessment. This means that the doctor will know how reliable the conclusion is and how confident the AI is in its assessment," says Kim Kultima, who led the study.
In the study, now published in the journal npj Digital Medicine, the model identified the transition to secondary progressive MS correctly or earlier than documented in the patient's medical records in almost 87% of cases, with an overall accuracy of around 90%.
"For patients, this means that the diagnosis can be made earlier, which makes it possible to adjust the patient's treatment in time and slow down the progression of the disease. This also reduces the risk of patients receiving medicines that are no longer effective.
"In the long term, the model could also be used to identify suitable participants for clinical trials—which could contribute to more effective and individualized treatment strategies," Kultima concludes.
An open, anonymized version of the model is now available to researchers via the web service: https://msp-tracker.serve.scilifelab.se
More information: Akshai Parakkal Sreenivasan et al, Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis, npj Digital Medicine (2025). DOI: 10.1038/s41746-025-01616-z
Citation: AI analyzes patient data to detect multiple sclerosis progression, improving early treatment decisions (2025, April 28) retrieved 28 April 2025 from https://medicalxpress.com/news/2025-04-ai-patient-multiple-sclerosis-early.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
