Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia

Saha, S., Corben, L.A., Selvadurai, L.P. et al. Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia. Sci Rep 15, 17629 (2025). doi:10.1038/s41598-025-01047-6 

 This study pioneers the development of clinically relevant, multidomain, fully objective composite biomarkers of disease severity and progression, using multimodal neuroimaging and background data (i.e., demographic, disease history, genetics). Data from 31 individuals with FRDA and 31 controls from a longitudinal multimodal natural history study IMAGE-FRDA, were included.

Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia