Automated differentiation of parkinsonian disorders: an ROI-based analysis of subcortical shape and cortical surface features

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.