New approaches to lesion assessment in multiple sclerosis DOI
Paolo Preziosa, Massimo Filippi, Maria A. Rocca

et al.

Current Opinion in Neurology, Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Purpose of review To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification characterization multiple sclerosis (MS) lesions, emphasizing their implications for clinical use research. Recent findings Artificial intelligence, particularly deep learning approaches, are revolutionizing MS assessment segmentation, improving accuracy, reproducibility, efficiency. intelligence-based tools now enable automated detection not only T2-hyperintense white matter but also specific subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, spinal cord which hold diagnostic prognostic value. Novel techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, soma neurite density imaging (SANDI), together with PET, providing deeper insights into pathology, better disentangling heterogeneities relevance. Summary intelligence-powered great potential fast, accurate reproducible lesional scenario, thus diagnosis, monitoring, treatment response assessment. Emerging may contribute to advance understanding pathophysiology, provide more markers disease progression, therapeutic targets.

Language: Английский

New approaches to lesion assessment in multiple sclerosis DOI
Paolo Preziosa, Massimo Filippi, Maria A. Rocca

et al.

Current Opinion in Neurology, Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Purpose of review To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification characterization multiple sclerosis (MS) lesions, emphasizing their implications for clinical use research. Recent findings Artificial intelligence, particularly deep learning approaches, are revolutionizing MS assessment segmentation, improving accuracy, reproducibility, efficiency. intelligence-based tools now enable automated detection not only T2-hyperintense white matter but also specific subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, spinal cord which hold diagnostic prognostic value. Novel techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, soma neurite density imaging (SANDI), together with PET, providing deeper insights into pathology, better disentangling heterogeneities relevance. Summary intelligence-powered great potential fast, accurate reproducible lesional scenario, thus diagnosis, monitoring, treatment response assessment. Emerging may contribute to advance understanding pathophysiology, provide more markers disease progression, therapeutic targets.

Language: Английский

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