
A new *Nature Communications* paper introduces **MindGlide**, an AI model that enables robust quantification of key multiple sclerosis (MS) MRI biomarkers—**lesion burden and regional brain volumes—from a single routine MRI contrast**. By removing the usual dependency on multi-contrast research protocols, MindGlide is designed to turn “old” and heterogeneous hospital MRI archives into analysable datasets, helping bring quantitative MRI closer to real-world care and research. ([Nature](https://www.nature.com/articles/s41467-025-58274-8 "Enabling new insights from old scans by repurposing clinical MRI ..."))

MindGlide was trained on **4,247** MS scans from **2,934** patients across **592** scanners, then externally validated on **14,952** scans from **1,001** patients—including large progressive MS trial datasets and a routine-care cohort. The model **outperformed comparator tools** against expert-labelled lesion volumes and, crucially, could **detect treatment effects** on lesion accrual and grey-matter tissue loss in clinical trials—even using contrasts not typically used for those outcomes (e.g., T2-weighted scans). ([Nature](https://www.nature.com/articles/s41467-025-58274-8 "Enabling new insights from old scans by repurposing clinical MRI ..."))

MindGlide is **publicly released** with code, trained models, and a containerised environment, supporting reproducible deployment and community reuse. For MS-PINPOINT, this is a practical step toward scalable, clinic-compatible analytics: extracting meaningful biomarkers from routine scans can reduce barriers to real-world evidence studies and broaden the impact of existing MRI data collected over decades. ([GitHub](https://github.com/MS-PINPOINT/mindGlide "MS-PINPOINT/mindGlide: Brain Segmentation with MONAI ... - GitHub"))


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MindGlide unlocks routine MRI archives to accelerate MS research