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?

Loading next page...
 
/lp/springer_journal/comparison-of-machine-learning-and-semi-quantification-algorithms-for-b0IE20RKQ1
Publisher
Springer International Publishing
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off