Jonathan.firstname.lastname@example.org Nuclear Medicine, Sheffield Background: Semi-quantification methods are well established in the clinic for assisted Teaching Hospitals NHS Foundation reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent Trust, I-floor, Royal Hallamshire Hospital, Glossop road, Sheffield S10 research has demonstrated the potential for improved classification performance offered 2JF, UK by machine learning algorithms. A direct comparison between methods is required to Full list of author information is establish whether a move towards widespread clinical adoption of machine learning available at the end of the article 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. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression
EJNMMI Physics – Springer Journals
Published: Nov 29, 2017
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