Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research.

ABSTRACT: Magnetic resonance imaging (MRI) biomarkers are vital for multiple sclerosis (MS) clinical research and trials but quantifying them requires multi-contrast protocols and limits the use of abundant single-contrast hospital archives. We developed MindGlide, a deep learning model to extract brain region and white matter lesion volumes from any single MRI contrast. We trained MindGlide on 4247 brain MRI scans from 2934 MS patients across 592 scanners, and externally validated it using 14,952 scans from 1,001 patients in two clinical trials (primary-progressive MS and secondary-progressive MS trials) and a routine-care MS dataset. The model outperformed two state-of-the-art models when tested against expert-labelled lesion volumes. In clinical trials, MindGlide detected treatment effects on T2-

SUBMITTER: Goebl P 

PROVIDER: S-EPMC11976987 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature