ArticleAJNR. American journal of neuroradiology2025
Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.
Article in AJNR. American journal of 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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1 citing paper in PubMed.
- A lightweight method for regression-based normalization of the cross-sectional area for cervical spinal cord atrophy assessment.Medical & biological engineering & computing · 2026Article
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10 authors.
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Abstract
background and purposeMRI is widely used to assess disease burden in MS. This study aimed to evaluate the effectiveness of a commercially available k-nearest neighbors (kNN) network software in quantifying white matter lesion (WML) burden in MS. We compared the software's WML quantification to expert radiologists' assessments. MATERIALS AND
methodsWe retrospectively reviewed brain MRI examinations of adult patients with MS and of adult patients without MS and with a normal brain MRI referred from the neurology clinic. MR images were processed by using an AI-powered, cloud-based kNN software, which generated a DICOM lesion distribution map and a report of WML count and volume in 4 brain regions (periventricular, deep, juxtacortical, and infratentorial white matter). Two blinded radiologists performed semiquantitative assessments of WM lesion load and lesion segmentation accuracy. Additionally, 4 blinded neuroradiologists independently reviewed the data to determine if MRI findings supported an MS diagnosis. The associations between radiologist-rated WML load and kNN model WML volume and count were evaluated with Spearman rank order correlation coefficient (rho) because these variables were not normally distributed. Results were considered significant when
resultsThe study included 32 patients with MS (35.4 years ±9.1) and 19 patients without MS (33.5 years ±12.1). The kNN software demonstrated 94.1% and 84.3% accuracy in differentiating MS from non-MS subjects based respectively on WML count and WML volume, compared with radiologists' accuracy of 90.2% to 94.1%. Lesion segmentation was more accurate for the deep WM and infratentorial regions than for the juxtacortical region (both
conclusionskNN-derived WML volume and WML count provide valuable quantitative metrics of disease burden in MS. AI-powered postprocessing software may enhance the interpretation of brain MRIs in MS patients.
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