BACKGROUND: Supervised machine learning has been proposed as a revolutionary 
approach for identifying sensitive medical image biomarkers (or combination of 
them) allowing for automatic diagnosis of individual subjects. The aim of this 
work was to assess the feasibility of a supervised machine learning algorithm 
for the assisted diagnosis of patients with clinically diagnosed Parkinson's 
disease (PD) and Progressive Supranuclear Palsy (PSP).
METHOD: Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD 
patients (28), PSP patients (28) and healthy control subjects (28) were used by 
a supervised machine learning algorithm based on the combination of Principal 
Components Analysis as feature extraction technique and on Support Vector 
Machines as classification algorithm. The algorithm was able to obtain 
voxel-based morphological biomarkers of PD and PSP.
RESULTS: The algorithm allowed individual diagnosis of PD versus controls, PSP 
versus controls and PSP versus PD with an Accuracy, Specificity and 
Sensitivity>90%. Voxels influencing classification between PD and PSP patients 
involved midbrain, pons, corpus callosum and thalamus, four critical regions 
known to be strongly involved in the pathophysiological mechanisms of PSP.
COMPARISON WITH EXISTING METHODS: Classification accuracy of individual PSP 
patients was consistent with previous manual morphological metrics and with 
other supervised machine learning application to MRI data, whereas accuracy in 
the detection of individual PD patients was significantly higher with our 
classification method.
CONCLUSIONS: The algorithm provides excellent discrimination of PD patients from 
PSP patients at an individual level, thus encouraging the application of 
computer-based diagnosis in clinical practice.