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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.
Highlights
Dementia is one of the most common neurodegenerative disorders in elderly and it is expected that its prevalence increases in the near future, mainly due to the aging population in developed nations [1]
We study the benefits of taking into account the information derived from neuropsychological tests in the development of computer systems to aid the diagnosis of dementia
Database A database collected during a recent longitudinal study was used to evaluate our proposed approach. It includes data from 46 subjects who were originally diagnosed with Mild Cognitive Impairment (MCI): one Positron Emission Tomography (PET) image and five neuropsychological scores were acquired per subject
Summary
Dementia is one of the most common neurodegenerative disorders in elderly and it is expected that its prevalence increases in the near future, mainly due to the aging population in developed nations [1]. The growth of the multivariate systems is mostly due to the recent advances on machine learning [17] which provide more reliable statistical classifiers, with a higher ability to address the small sample size problem [18] This problem can be addressed by means of a feature extraction technique that reduces the huge amount of data contained in a brain image into a relatively small unidimensional vector. In this case, the structure of the CAD systems based on neuroimaging and machine learning is as follows: After the preprocessing of the images (which involves the spatial registration and the intensity normalization), an algorithm is applied to select and summarize the relevant information. In terms of neuroimaging modalities, researches have used both structural [2,19] and functional data [9], including nuclear imaging modalities such as PET [5,20] and SPECT [3,7]
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