Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis
Abstract
Background: Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement disorders. However, existing studies vary widely in design, sample size, imaging pipelines, and analytic rigor, resulting in high methodological heterogeneity that limits quantitative comparability.
Objectives: This exploratory meta-analysis and narrative synthesis aimed to characterize performance trends, methodological diversity, and sources of variability among ML models trained on DTI data for classifying movement disorders, rather than to infer a single pooled diagnostic effect. This was designated exploratory because extreme heterogeneity prevented confirmatory pooled effect inference, so the analysis focused on describing performance distributions and methodological patterns rather than estimating a unified diagnostic effect.
Methods: A systematic search of PubMed, Web of Science, and Scopus identified human studies applying ML algorithms to DTI for diagnostic or classification purposes. Accuracy, sensitivity, specificity, and the area under the curve (AUC) were extracted, with multiple imputation used for incomplete metrics with missingness rates below 40%. Random-effects modeling was employed to provide descriptive summaries, and subgroup analyses were conducted to explore trends across disorders, model architectures, and imaging modalities. Study qualities were assessed with JBI tools.
Results:…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 139 participants or records.
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