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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/59539

    Título
    Transfer learning for Alzheimer’s disease through neuroimaging biomarkers: A systematic review
    Autor
    Agarwal, Deevyankar
    Marques, Gonçalo
    Torre Díez, Isabel de laAutoridad UVA
    Franco Martín, Manuel Ángel
    Garcia Zapirain, Begonya
    Martín Rodríguez, FranciscoAutoridad UVA Orcid
    Año del Documento
    2021
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Sensors 2021, vol. 21, n. 21, 7259
    Zusammenfassung
    Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
    Materias (normalizadas)
    Tecnología médica
    Neurosciences
    Image processing
    Materias Unesco
    33 Ciencias Tecnológicas
    Palabras Clave
    Alzheimer’s disease
    Neuroimaging biomarkers
    Transfer learning
    Enfermedad de Alzheimer
    Biomarcadores de neuroimagen
    Transferir el aprendizaje
    Revisión por pares
    SI
    DOI
    10.3390/s21217259
    Patrocinador
    Ministerio de Industria, Energía y Turismo (AAL-20125036)
    Version del Editor
    https://www.mdpi.com/1424-8220/21/21/7259
    Propietario de los Derechos
    © 2021 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/59539
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Artículos de revista [362]
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    Dateien zu dieser Ressource
    Nombre:
    Transfer-Learning-for-Alzheimer’s-Disease.pdf
    Tamaño:
    2.419Mb
    Formato:
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