Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supra-nuclear palsy (PSP) are neurodegenerative disorders which have parkinsonism as a core clinical feature. In the early stages PD and atypical parkinsonian syndrome (APS) (MSA and PSP) may often be indistinguishable and differential diagnosis is therefore crucial. Our work employs radiomics based features extracted from standard T1 weighted MRI images that are used in a machine learning framework to differentiate PD from APS. Results demonstrate a superior test accuracy of 92% that support our underlying hypothesis that radiomics on T1-weighted images can provide a discriminatory feature space between PD and APS.
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