Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review

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 revie...

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Autores principales:	Deevyankar Agarwal, Gonçalo Marques, Isabel de la Torre-Díez, Manuel A. Franco Martin, Begoña García Zapiraín, Francisco Martín Rodríguez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Alzheimer’s disease neuroimaging biomarkers magnetic resonance imaging positron emission tomography transfer learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e15e4398a09c4a6aa4937f3d47427ed6
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spelling	oai:doaj.org-article:e15e4398a09c4a6aa4937f3d47427ed62021-11-11T19:13:24ZTransfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review10.3390/s212172591424-8220https://doaj.org/article/e15e4398a09c4a6aa4937f3d47427ed62021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7259https://doaj.org/toc/1424-8220Alzheimer’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.Deevyankar AgarwalGonçalo MarquesIsabel de la Torre-DíezManuel A. Franco MartinBegoña García ZapiraínFrancisco Martín RodríguezMDPI AGarticleAlzheimer’s diseaseneuroimaging biomarkersmagnetic resonance imagingpositron emission tomographytransfer learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7259, p 7259 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alzheimer’s disease neuroimaging biomarkers magnetic resonance imaging positron emission tomography transfer learning Chemical technology TP1-1185
spellingShingle	Alzheimer’s disease neuroimaging biomarkers magnetic resonance imaging positron emission tomography transfer learning Chemical technology TP1-1185 Deevyankar Agarwal Gonçalo Marques Isabel de la Torre-Díez Manuel A. Franco Martin Begoña García Zapiraín Francisco Martín Rodríguez Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
description	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.
format	article
author	Deevyankar Agarwal Gonçalo Marques Isabel de la Torre-Díez Manuel A. Franco Martin Begoña García Zapiraín Francisco Martín Rodríguez
author_facet	Deevyankar Agarwal Gonçalo Marques Isabel de la Torre-Díez Manuel A. Franco Martin Begoña García Zapiraín Francisco Martín Rodríguez
author_sort	Deevyankar Agarwal
title	Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title_short	Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title_full	Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title_fullStr	Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title_full_unstemmed	Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title_sort	transfer learning for alzheimer’s disease through neuroimaging biomarkers: a systematic review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e15e4398a09c4a6aa4937f3d47427ed6
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Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review

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