ArticleNMR in biomedicine2022
Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- Cortical lesions in multiple sclerosis: 25 years of progress, remaining challenges and future directions.Nature reviews. Neurology · 2026Review
- A Robust Dual-Stage Learning-Based Pipeline for Multiclass Segmentation of Multiple Sclerosis Lesions in MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.NeuroImage. Clinical · 2026Article
- Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis.ArXiv · 2025Article
- MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour.Magma (New York, N.Y.) · 2025Review
- Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors.ArXiv · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort.Bioengineering (Basel, Switzerland) · 2024Article
- Decoding Gray Matter Involvement in Multiple Sclerosis via Imaging.Neuroimaging clinics of North America · 2024Review
- The use of 7T MRI in multiple sclerosis: review and consensus statement from the North American Imaging in Multiple Sclerosis Cooperative.Brain communications · 2024Review
- Review
- Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective.Journal of neurology · 2023Review
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.NMR in biomedicine · 2022Article
- Leptomeningeal enhancement in multiple sclerosis demonstrates posterior predilection and TMultiple 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
13 authors at 6 institutions in 4 countries.
Funding
Abstract
Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesion AI-Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T
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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.