ArticleEuropean radiology2025
Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation.EMBO molecular medicine · 2026Review
- Spinal cord imaging in multiple sclerosis: from diagnosis to disease progression.Journal of neurology · 2026Review
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Accurate Segmentation and Three-dimensional Reconstruction Algorithm of Spinal Cord Injury Lesions Based on Multimodal Magnetic Resonance Imaging.Current medical imaging · 2026Article
- LesionSCynth: A simple parametric lesion synthesis method to improve spinal cord lesion segmentation in low-data scenarios.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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31 authors.
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Abstract
objectiveTo develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the performance of clinicians in detecting SC lesions. MATERIALS AND
methodsA DL tool was developed based on SC sagittal T2 and STIR acquisitions from the imaging database of the French MS registry (OFSEP), including retrospective data from 40 different scanners. A multi-reader study based on retrospective data was performed between December 2023 and June 2024 to compare the performance of 20 clinicians in interpreting upper and lower SC acquisitions with and without the use of the tool. A ground truth was established by three experts. Sensitivity, precision, and inter-reader variability were evaluated.
resultsWe included 50 patients (39 females, median age: 41 years [range: 15-67]) with SC MRI acquired between February 2017 and December 2022. When reading with the tool, the clinicians' mean sensitivity to detect SC lesions improved (from 74.3% [95% CI = 67.8-80.6%] to 79.2% [95% CI: 73.5-85.0%]; p < 0.0001), with no evidence of difference in the mean precision: (69.0% [95% CI: 62.8-75.2%] vs 70.1% [95% CI: 64.3-75.9%]; p = 0.08). Inter-reader variability in lesion detection was slightly improved with the tool (Light's kappa = 0.55 vs 0.60), but without statistical difference (p = 0.056).
conclusionThe use of an automatic tool can help clinicians detect SC lesions in pwMS by increasing their sensitivity. KEY POINTS: Question No tool to help detect MS SC lesions is used in clinical practice despite their frequency and prognostic value. Findings This DL-based tool led to improvement in clinicians' sensitivity in detecting SC lesions from both sagittal T2 and STIR sequences, without decreasing precision. Clinical relevance Our study indicated the potential of a DL-based tool to assist clinicians in the challenging task of detecting SC lesions in people with MS on a combination of sequences commonly acquired in clinical practice.
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