ReviewFrontiers in immunology2021
Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.
Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells.BMC neurology · 2025Article
- Semi-Supervised Learning for Predicting Multiple Sclerosis.Journal of personalized medicine · 2025Article
- Assessing the role of volumetric brain information in multiple sclerosis progression.Computational and structural biotechnology journal · 2025Article
- Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables.Frontiers in neurology · 2025Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.Zeitschrift fur medizinische Physik · 2024Article
- Review
- Effects of formalin fixation on polarimetric properties of brain tissue: fresh or fixed?Neurophotonics · 2023Article
- Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.Brain sciences · 2023Review
- Review
- Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.Journal of personalized medicine · 2021Review
- Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.
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.