ArticleNature communications2021
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 134 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
134 citing papers in PubMed, 2 syntheses or guidelines pooled it, 250 citations in OpenAlex.
- Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.Neurosciences (Riyadh, Saudi Arabia) · 2024Pooled it
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- Towards a biologically informed description of multiple sclerosis disease course - a roadmap for transition.Nature reviews. Neurology · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Comprehensive Characterization of Clinical Phenotypes and CTG Repeat-Associated Manifestations in Spinocerebellar Ataxia Type 8.Cerebellum (London, England) · 2026Article
- Phenotypic Diversity in Multiple Sclerosis Can Be Represented by Four Additive Symptom Modules.Brain sciences · 2026Article
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.Biological psychiatry global open science · 2026Review
- Brain atrophy rates vary with age in relapsing-remitting multiple sclerosis.Journal of neurology, neurosurgery, and psychiatry · 2026Article
- Glial cells in chronic inflammation: diversity, dysfunction and therapeutic targeting.Nature reviews. Immunology · 2026Review
- 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
- Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture.PLOS digital health · 2026Article
- Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence.PLOS digital health · 2026Article
- Adaptive multi-stage domain unlearning for white-matter lesion segmentation.Frontiers in medicine · 2026Article
- Multiple sclerosis as an asynchronous neuroinflammatory system.Frontiers in immunology · 2026Review
- Progression of fragile X-associated tremor/ataxia syndrome revealed by subtype and stage inference.Brain communications · 2026Article
- 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
- Article
- Disability Worsening Phenotypes in Multiple Sclerosis and Impact of Disease-Modifying Treatments.Neurology · 2025Article
- Combined magnetic resonance imaging and serum analysis reveals distinct multiple sclerosis types.Brain : a journal of neurology · 2025Article
74 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 6 institutions in 5 countries.
Funding
Abstract
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.
Indexed as
Identifiers
What OpenQuestion holds
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.