ArticleBrain communications2025
Brain age in multiple sclerosis: a study with deep learning and traditional machine learning.
Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Accelerated Brain Aging Identifies Functional Vulnerability Beyond Chronological Age in Multiple Sclerosis.Sensors (Basel, Switzerland) · 2026Trial
- Brain Regional Predictors of Surgical Response Are Dynamically Associated with Post-Surgical Pain Relief in Trigeminal Neuralgia.Brain sciences · 2026Article
- Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.Journal of internal medicine · 2026Review
- An automated quantitative report for multiple sclerosis using only 3D T2-fluid-attenuated inversion recovery MRI.Neuroradiology · 2026Article
- Brain age gap in multiple sclerosis: associated with disability but independent of serum biomarkers.Therapeutic advances in neurological disorders · 2026Article
- Quantitative susceptibility mapping of paramagnetic rim lesions in early multiple sclerosis: A cross-sectional study of brain age and disability.Neuroimage. Reports · 2025Article
- Mapping neurodegeneration with diffusion MRI: biomarkers, mechanisms, and clinical translation.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
'Brain age' is a numerical estimate of the biological age of the brain and an overall effort to measure neurodegeneration, regardless of disease type. In multiple sclerosis, accelerated brain ageing has been linked to disability accrual. Artificial intelligence has emerged as a promising tool for the assessment and quantification of the impact of neurodegenerative diseases. Despite the existence of numerous AI models, there is a noticeable lack of comparative imaging data for traditional machine learning versus deep learning in conditions such as multiple sclerosis. A retrospective observational study was initiated to analyse clinical and MRI data (4584 MRIs) from various scanners in a large longitudinal cohort (
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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.