ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2021
Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.
Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
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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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.Neurosciences (Riyadh, Saudi Arabia) · 2024Pooled it
- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.Frontiers in neuroinformatics · 2020Pooled it
- Random Forest Algorithm-Based Ultrasonic Image in the Diagnosis of Patients with Dry Eye Syndrome and Its Relationship with Tear Osmotic Pressure.Computational and mathematical methods in medicine · 2022Trial
- Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation.EMBO molecular medicine · 2026Review
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.Scientific reports · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.Neuroradiology · 2024Article
- Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort.Bioengineering (Basel, Switzerland) · 2024Article
- Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning.Scientific reports · 2024Article
- Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- 2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.Quantitative imaging in medicine and surgery · 2024Article
- Review
- AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.Insights into imaging · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.International journal of molecular sciences · 2022Article
- Nonlesional Sources of Contrast Enhancement on Postgadolinium "Black-Blood" 3D T1-SPACE Images in Patients with Multiple Sclerosis.AJNR. American journal of neuroradiology · 2022Article
- Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks.European radiology · 2022Article
- Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.Neurology · 2021Review
- Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).Experimental and therapeutic medicine · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.
methodsA three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume.
resultsThe DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm
conclusionExcellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm
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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.