ArticleNeuroradiology2025
Prospective clinical evaluation of automatic lesion assessment in patients with multiple sclerosis.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort.Frontiers in radiology · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
purposeTo perform a real-world clinical validation of a commercial AI tool for automatic MRI assessment in multiple sclerosis (MS) patients, evaluating its impact on assessment time, workflow, and accuracy in detecting new and enlarging lesions.
methodsWe prospectively enrolled MS patients undergoing routine follow-up MRI from September-December 2024. Current and prior MRI examinations were anonymized and assessed independently by four neuroradiologists with and without AI assistance (mdbrain v.4.11.0). Assessment times were recorded, and radiologists completed utility questionnaires. Lesion quantification was compared between radiologist alone, radiologist with AI, and AI alone. Performance metrics including sensitivity, specificity, and predictive values were calculated case-level for detecting new and enlarging lesions.
resultsThe cohort included 112 MS patients scanned on 8 different MRI scanner models with varying protocols. Mean assessment time was reduced by 27 s when using AI versus without (p = 0.317). Radiologists found AI helpful in 87% of cases and reported difficulties in 11%. AI obtained negative predictive values of 0.89 for detecting new lesions when comparing to assessment without AI. Positive predictive values were low (0.35-0.65) due to false positive tendencies.
conclusionWe prospectively validated an AI tool for MS MRI follow-up in a real-world setting. It showed modest, non-significant time savings and low positive predictive value, limiting research use. High negative predictive value supports triaging potential. Radiologists found the AI tool helpful for lesion counting and detecting small new lesions. Findings highlight the need for thorough clinical evaluation, especially in areas lacking definitive ground truth.
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