Strategies for integrating artificial intelligence and cognitive assessment to predict disability progression in relapsing-remitting multiple sclerosis
A study developed an AI model called MS-TranSurv to predict disability progression in relapsing-remitting multiple sclerosis (RRMS) using cognitive assessment data. It achieved a C-index of 0.61 and tAUROC of 0.74, indicating slightly better performance compared to traditional models, especially when using individual test-level data.
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- Neurologist, DataScientist
Why it matters
This research represents progress in using artificial intelligence and cognitive data for predicting disability in MS, potentially impacting clinical decision-making and patient monitoring strategies.
What this does not prove
The study does not establish any therapeutic efficacy or definitive patient outcomes, as the focus is strictly on prediction capabilities.
Next milestone
No next milestone was established from the available source.
Study facts
- Study design
- Not reported
- Participants / samples
- Not reported
- Randomised
- Not reported
- Controlled
- Not reported
- Primary endpoint met
- Not reported
- Relevant MS type
- Not reported
- Publication date
- 2026-09-25
- Evidence reviewed
- Abstract only
- Regulatory approval
- Not reported
- Research areas
- Not classified
Original sources
- Primary evidenceAI and Cognitive Assessment to Predict Disability in MS ↗
Supporting passages (7)
publication datePublication date: 2026-09-25
comparatorPerformance was compared with other machine learning models, including Dynamic DeepHit, Recurrent Deep Survival Machines, a Cox proportional hazards model in traditional statistics using clinical variables only, and a version of MS-TransSurv using mean test values.
primary endpointLongitudinal cognitive reaction-time data can be used for survival-based prediction of disability progression in RRMS.
follow upIn this cohort study, clinical data were obtained from the MSBase registry and cognitive data from the MSReactor computerised cognitive battery between February 2016 and September 2022, with a median follow-up of 3.2 years.
findingsMS-TranSurv showed slightly higher discrimination compared with benchmark models, with a C-index of 0.61 (95% CI 0.54-0.68) and tAUROC of 0.74 (95% CI 0.63-0.85), as well as comparable calibration, with an iBS of 0.24 (95% CI 0.16-0.32).
findingsUsing individual test-level data provided slightly better performance than using summary measures.
limitationsTitle: Strategies for integrating artificial intelligence and cognitive assessment to predict disability progression in relapsing-remitting multiple sclerosis: A model development study.
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