Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI.

Details

Serval ID
serval:BIB_2D7A7725F6B5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI.
Journal
Psychiatry Research
Author(s)
Dukart J., Mueller K., Barthel H., Villringer A., Sabri O., Schroeter M.L.
Working group(s)
Alzheimer's Disease Neuroimaging Initiative
ISSN
1872-7123 (Electronic)
ISSN-L
0165-1781
Publication state
Published
Issued date
2013
Peer-reviewed
Oui
Volume
212
Number
3
Pages
230-236
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Abstract
The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.
Pubmed
Web of science
Create date
26/07/2013 16:25
Last modification date
20/08/2019 13:12
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