Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disea...

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Vydané v:Frontiers in aging neuroscience Ročník 13; s. 708932
Hlavní autori: Díaz-Álvarez, Josefa, Matias-Guiu, Jordi A., Cabrera-Martín, María Nieves, Pytel, Vanesa, Segovia-Ríos, Ignacio, García-Gutiérrez, Fernando, Hernández-Lorenzo, Laura, Matias-Guiu, Jorge, Carreras, José Luis, Ayala, José L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 03.02.2022
Predmet:
Aging Neuroscience
Alzheimer’s disease
frontotemporal dementia
machine learning
positron emission tomography
primary progressive aphasia
unsupervised algorithm
genetic algorithm
Alzheimer’s disease
frontotemporal dementia
unsupervised algorithm
machine learning
positron emission tomography
evolutionary algorithm
primary progressive aphasia
ISSN:1663-4365, 1663-4365
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Shrnutí:Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.
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This article was submitted to Alzheimer’s Disease and Related Dementias, a section of the journal Frontiers in Aging Neuroscience
Edited by: Fermín Segovia, University of Granada, Spain
Reviewed by: Akira Masuda, Doshisha University, Japan; Elena Rodriguez-Vieitez, Karolinska Institutet (KI), Sweden
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
ISSN:1663-4365
1663-4365
DOI:10.3389/fnagi.2021.708932