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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/31024

Title: Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer's Disease
Authors: Segovia Román, Fermín
Bastin, Christine
Salmon, Eric
Górriz Sáez, Juan Manuel
Ramírez Pérez de Inestrosa, Javier
Phillips, Christophe
Issue Date: 2014
Abstract: In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning algorithms in order to find the patterns that characterize the disorder, and a few combine several imaging modalities to improve the diagnostic accuracy. However, they usually do not use neuropsychological testing data in that analysis. The purpose of this work is to measure the advantages of using not only neuroimages as data source in CAD systems for dementia but also neuropsychological scores. To this aim, we compared the accuracy rates achieved by systems that use neuropsychological scores beside the imaging data in the classification step and systems that use only one of these data sources. In order to address the small sample size problem and facilitate the data combination, a dimensionality reduction step (implemented using three different algorithms) was also applied on the imaging data. After each image is summarized in a reduced set of image features, the data sources were combined and classified using three different data combination approaches and a Support Vector Machine classifier. That way, by testing different dimensionality reduction methods and several data combination approaches, we aim not only highlighting the advantages of using neuropsychological scores in the classification, but also implementing the most accurate computer system for early dementia detention. The accuracy of the CAD systems were estimated using a database with records from 46 subjects, diagnosed with MCI or AD. A peak accuracy rate of 89% was obtained. In all cases the accuracy achieved using both, neuropsychological scores and imaging data, was substantially higher than the one obtained using only the imaging data.
Sponsorship: This work was supported by the University of Liege, the “Fonds de la Recherche Scientifique” (FRS-FNRS), the “Stichting Alzheimer Onderzoek/Fondation Recherche Alzheimer” (SAO-FRA), and the Inter University Attraction Pole P7/11.
Publisher: Public Library of Science (PLOS)
Keywords: Alzheimer disease
Data reduction
Diagnostic medicine
Neuropsychological testing
Positron emission tomography
URI: http://hdl.handle.net/10481/31024
ISSN: 1932-6203
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Segovia, F.; et al. Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer's Disease. Plos One, 9(2): e88687 (2014). [http://hdl.handle.net/10481/31024]
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