Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10662/19822
Registro completo de Metadatos
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Díaz Álvarez, Josefa | - |
dc.contributor.author | Matias-Guiu Antem, Jordi A. | - |
dc.contributor.author | Cabrera Martín, María Nieves | - |
dc.contributor.author | Pytel Córdoba, Vanesa | - |
dc.contributor.author | Segovia, Ignacio | - |
dc.contributor.author | García Gutierrez, Fernando | - |
dc.contributor.author | Hernández Lorenzo, Laura | - |
dc.contributor.author | Matias-Guiu Guía, Jorge | - |
dc.contributor.author | Carreras Delgado, José Luis | - |
dc.contributor.author | Ayala Rodríguez, José Luis | - |
dc.date.accessioned | 2024-02-02T18:57:45Z | - |
dc.date.available | 2024-02-02T18:57:45Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1663-4365 | - |
dc.identifier.uri | http://hdl.handle.net/10662/19822 | - |
dc.description.abstract | 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 18F-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. | es_ES |
dc.description.sponsorship | We acknowledge support from the Spanish Ministry of Economy and Competitiveness, Ministry of Science and Innovation, and he European Regional Development Fund (FEDER) under project PID2019-110866RB-I00, part of the Grant PID2020- 115570GB-C21 funded by MCIN/AEI/10.13039/501100011033 and Junta de Extremadura, project GR15068. JAM-G was supported by the Instituto de Salud Carlos III through the project INT20/00079 (co-funded by European Regional Development Fund “A way to make Europe”). | es_ES |
dc.format.extent | 13 | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | positron emission tomography | es_ES |
dc.subject | Alzheimer’s disease | es_ES |
dc.subject | frontotemporal dementia | es_ES |
dc.subject | primary progressive aphasia | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | unsupervised algorithm | es_ES |
dc.subject | genetic algorithm | es_ES |
dc.subject | evolutionary algorithm | es_ES |
dc.title | Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 32 Ciencias Médicas | es_ES |
dc.subject.unesco | 3304 Tecnología de Los Ordenadores | es_ES |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | Díaz-Álvarez J, Matias-Guiu JA, Cabrera-Martín MN, Pytel V, Segovia- Ríos I, García-Gutiérrez F, Hernández-Lorenzo L, Matias-Guiu J, Carreras JL, Ayala JL and Alzheimer’s Disease Neuroimaging Initiative (2022) Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging Front. Aging Neurosci. 13:708932. doi: 10.3389/fnagi.2021.708932 | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | N/A | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicaciones | es_ES |
dc.relation.publisherversion | https://www.frontiersin.org/articles/10.3389/fnagi.2021.708932/full | es_ES |
dc.identifier.doi | 10.3389/fnagi.2021.708932 | - |
dc.identifier.publicationtitle | Frontiers in Aging Neuroscience | es_ES |
dc.identifier.publicationfirstpage | 983 | es_ES |
dc.identifier.publicationlastpage | 996 | es_ES |
dc.identifier.publicationvolume | 13 | es_ES |
dc.identifier.orcid | 0000-0003-2105-3905 | es_ES |
Colección: | DTCYC - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
fnagi-13-708932.pdf | 5,08 MB | Adobe PDF | Descargar |
Este elemento está sujeto a una licencia Licencia Creative Commons