Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT... Jonathan.taylor@sth.nhs.uk 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EJNMMI Physics Springer Journals

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s).
Subject
Medicine & Public Health; Nuclear Medicine; Imaging / Radiology; Applied and Technical Physics; Computational Mathematics and Numerical Analysis; Engineering, general
eISSN
2197-7364
D.O.I.
10.1186/s40658-017-0196-1
Publisher site
See Article on Publisher Site

Abstract

Jonathan.taylor@sth.nhs.uk 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

Journal

EJNMMI PhysicsSpringer Journals

Published: Nov 29, 2017

References

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