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March 3, 2026
Automated differentiation of parkinsonian disorders: an ROI-based analysis of subcortical shape and cortical surface features
YD
Yousef Dehghan
YS
Yashar Sarbaz
University of Tabriz
FI
for the 4-Repeat Tauopathy Neuroimaging Initiative
Key Points
Automated differentiation improves accuracy in identifying parkinsonian disorders, enhancing clinical assessment.
Key metrics show 85% accuracy in classifying various parkinsonian disorders based on subcortical shape features.
ROI-based analysis of MRI data aids in distinguishing between different types of parkinsonism.
This analysis highlights the potential for automated methods in clinical diagnostics, eliminating manual assessment bias.
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Cite This Study
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Dehghan et al. (Tue,) studied this question.
synapsesocial.com/papers/69a76055c6e9836116a2cf63
https://doi.org/https://doi.org/10.1007/s11571-025-10402-2
Automated differentiation of parkinsonian disorders: an ROI-based analysis of subcortical shape and cortical surface features | Synapse