ArticlePloS one2024
Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Plasma Biomarkers for Alzheimer's Disease Across the Continuum: A Systematic Review of SIMOA-Based Studies.Cellular and molecular neurobiology · 2026Pooled it
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Advances in nanotechnology for the diagnosis and management of autoimmune diseases.Asian journal of pharmaceutical sciences · 2026Review
- Explainable machine learning with bayesian hyper-optimization for predicting cognitive impairment from longitudinal nursing home data.Scientific reports · 2026Article
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- Multiple sclerosis as an asynchronous neuroinflammatory system.Frontiers in immunology · 2026Review
- From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Multimodal predictors of disability progression and processing speed decline in relapsing-remitting multiple sclerosis.Scientific reports · 2025Article
- Conventional and Advanced Magnetic Resonance Imaging Biomarkers of Multiple Sclerosis in the Brain.Cureus · 2025Review
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.
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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 Socratic graph.