ArticleNature communications2025
Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Development and validation of an automated MRI-based pipeline for temporal classification of intracerebral hemorrhage using U-Net segmentation and machine learning.Radiological physics and technology · 2026Article
- [XVIII Post-ECTRIMS Meeting: Review of the New Developments Presented at the 2025 ECTRIMS Congress (Part I)].Revista de neurologia · 2026Article
- Moving artificial intelligence from research to real-world clinical use in neurology.Nature reviews. Neurology · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- 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
- Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions.World journal of gastrointestinal oncology · 2026Article
- Benchmark White Matter Hyperintensity Segmentation Methods Fail on Heterogeneous Clinical MRI: A New Dataset and Deep Learning-Based Solutions.Journal of imaging informatics in medicine · 2026Article
- Microstructure-informed deep learning improves thalamic atrophy segmentation and clinical associations in multiple sclerosis and related neuroimmunological diseases.NeuroImage. Clinical · 2026Article
- Paramagnetic Rim Lesions in Pediatric Multiple Sclerosis and Their Association With Brain Tissue Atrophy.Neurology(R) neuroimmunology & neuroinflammation · 2026Article
- Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Magnetic resonance imaging (MRI) biomarkers are vital for multiple sclerosis (MS) clinical research and trials but quantifying them requires multi-contrast protocols and limits the use of abundant single-contrast hospital archives. We developed MindGlide, a deep learning model to extract brain region and white matter lesion volumes from any single MRI contrast. We trained MindGlide on 4247 brain MRI scans from 2934 MS patients across 592 scanners, and externally validated it using 14,952 scans from 1,001 patients in two clinical trials (primary-progressive MS and secondary-progressive MS trials) and a routine-care MS dataset. The model outperformed two state-of-the-art models when tested against expert-labelled lesion volumes. In clinical trials, MindGlide detected treatment effects on T2-lesion accrual and cortical and deep grey matter volume loss. In routine-care data, T2-lesion volume increased with moderate-efficacy treatment but remained stable with high-efficacy treatment. MindGlide uniquely enables quantitative analysis of archival single-contrast MRIs, unlocking insights from untapped hospital datasets.
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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.