Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.

ML models using DTI demonstrate high internal performance across studies, although generalizability remains limited across multiple movement disorders; however, current evidence remains exploratory due to small sample sizes, methodological fragmentation, and a lack of standardized imaging pipelines. Rather than confirmatory inference, these findings provide a descriptive map of emerging trends in ML-DTI diagnostics. Future progress will depend on data harmonization initiatives, multicenter collaborations, and federated learning frameworks that can support reproducible, generalizable, and clinically interpretable models.