ArticleJournal of neurology2024
Predicting disease severity in multiple sclerosis using multimodal data and machine learning.
Article in Journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Article
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- Multiple sclerosis as an asynchronous neuroinflammatory system.Frontiers in immunology · 2026Review
- Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening.Frontiers in immunology · 2026Article
- Effectiveness of Rituximab in rElapsiNg Multiple SclErosis previously treated with hiGhly-Active Disease modifying thErapies (RENEGADE study).Journal of neurology · 2025Observational
- Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients.European journal of neurology · 2025Article
- Multimodal predictors of disability progression and processing speed decline in relapsing-remitting multiple sclerosis.Scientific reports · 2025Article
- Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights.International journal of molecular sciences · 2025Review
- Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data.NPJ digital medicine · 2025Article
- Neurophysiological biomarkers of networks impairment in amyotrophic lateral sclerosis.Journal of neurology · 2025Review
- Semi-Supervised Learning for Predicting Multiple Sclerosis.Journal of personalized medicine · 2025Article
- First-year treatment response predicts the following 5-year disease course in patients with relapsing-remitting multiple sclerosis.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Article
- Multiple sclerosis: 2024 update.Free neuropathology · 2025Review
- Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis.Frontiers in immunology · 2025Article
- A multimodal approach to distinguish multiple sclerosis phenotypes at diagnosis using biomarker profiles.Therapeutic advances in neurological disorders · 2025Article
- System-level hypothesis of dopamine imbalance in early multiple sclerosis.Frontiers in neurology · 2025Article
- Current challenges in secondary progressive multiple sclerosis: diagnosis, activity detection and treatment.Frontiers in immunology · 2025Review
- Assessing the role of volumetric brain information in multiple sclerosis progression.Computational and structural biotechnology journal · 2025Article
- Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data.Multiple sclerosis and related disorders · 2024Article
Corrections and comments
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Authors and funding
40 authors.
Funding
Abstract
backgroundMultiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.
methodsWe have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.
resultsWe found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.
conclusionCombining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.