Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms.  A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified.  This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation.  Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate.  The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively.


 Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms.  The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.
machine learning algorithms
Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images.  This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation.  Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. 
semi-quantification methods