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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 dise...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851241/
https://www.ncbi.nlm.nih.gov/pubmed/35185510
http://dx.doi.org/10.3389/fnagi.2021.708932
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author 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.
author_facet 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.
author_sort Díaz-Álvarez, Josefa
collection PubMed
description 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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spelling pubmed-88512412022-02-18 Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging 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. Front Aging Neurosci Aging Neuroscience 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. Frontiers Media S.A. 2022-02-03 /pmc/articles/PMC8851241/ /pubmed/35185510 http://dx.doi.org/10.3389/fnagi.2021.708932 Text en Copyright © 2022 Díaz-Álvarez, Matias-Guiu, Cabrera-Martín, Pytel, Segovia-Ríos, García-Gutiérrez, Hernández-Lorenzo, Matias-Guiu, Carreras, Ayala and Alzheimer’s Disease Neuroimaging Initiative. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
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.
Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title_full Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title_fullStr Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title_full_unstemmed Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title_short Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging
title_sort genetic algorithms for optimized diagnosis of alzheimer’s disease and frontotemporal dementia using fluorodeoxyglucose positron emission tomography imaging
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851241/
https://www.ncbi.nlm.nih.gov/pubmed/35185510
http://dx.doi.org/10.3389/fnagi.2021.708932
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