Artificial Intelligence analysis of lesion dynamics and brain volume in patients with multiple sclerosis

BACKGROUND: Quantitative MRI markers increasingly complement conventional clinical assessment in multiple sclerosis (MS). Artificial intelligence (AI)-based volumetry enables standardized evaluation of lesion burden and brain atrophy in routine care. OBJECTIVE: To examine the association between AI-derived volumetric measures and disability, assess whether annual brain volume loss (ABVL) and lesion dynamics predict atrophy, and descriptively compare proxy radiological phenotype groups with established clinical phenotypes in a real-world MS cohort. MATERIAL AND METHODS: This retrospective study included 888 MRI examinations from 455 patients with MS (2016-2020). Longitudinal analyses were performed in 234 patients with ≥2 scans (667 MRIs), and an early MS cohort comprised 302 patients (580 scans). Automated segmentation (mdbrain® v3.4.0) provided lesion metrics and age/sex/skull-volume-adjusted brain volumes from routine 3D FLAIR and native T1-weighted sequences acquired under real-world clinical conditions. Pathological atrophy was defined as a normative z-score < -2. Generalized estimating equations (GEE) evaluated predictors of atrophy. RESULTS: Proxy radiological phenotype groups were defined as lesion-led (43.2%), cortex-led (35.4%), and NAWM-led (21.4%); clinical phenotypes included RRMS (82%), SPMS (14%), and PPMS (4%). EDSS correlated with lesion volume (ρ=0.28, p