Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Agarwal, Deevyankar, Marques, Gonçalo, de la Torre-Díez, Isabel, Franco Martin, Manuel A., García Zapiraín, Begoña, Martín Rodríguez, Francisco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587338/
https://www.ncbi.nlm.nih.gov/pubmed/34770565
http://dx.doi.org/10.3390/s21217259
_version_ 1784598120372371456
author Agarwal, Deevyankar
Marques, Gonçalo
de la Torre-Díez, Isabel
Franco Martin, Manuel A.
García Zapiraín, Begoña
Martín Rodríguez, Francisco
author_facet Agarwal, Deevyankar
Marques, Gonçalo
de la Torre-Díez, Isabel
Franco Martin, Manuel A.
García Zapiraín, Begoña
Martín Rodríguez, Francisco
author_sort Agarwal, Deevyankar
collection PubMed
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 Online
Article
Text
id pubmed-8587338
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85873382021-11-13 Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review Agarwal, Deevyankar Marques, Gonçalo de la Torre-Díez, Isabel Franco Martin, Manuel A. García Zapiraín, Begoña Martín Rodríguez, Francisco Sensors (Basel) 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 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. MDPI 2021-10-31 /pmc/articles/PMC8587338/ /pubmed/34770565 http://dx.doi.org/10.3390/s21217259 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Agarwal, Deevyankar
Marques, Gonçalo
de la Torre-Díez, Isabel
Franco Martin, Manuel A.
García Zapiraín, Begoña
Martín Rodríguez, Francisco
Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
title 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_short 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
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587338/
https://www.ncbi.nlm.nih.gov/pubmed/34770565
http://dx.doi.org/10.3390/s21217259
work_keys_str_mv AT agarwaldeevyankar transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview
AT marquesgoncalo transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview
AT delatorrediezisabel transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview
AT francomartinmanuela transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview
AT garciazapirainbegona transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview
AT martinrodriguezfrancisco transferlearningforalzheimersdiseasethroughneuroimagingbiomarkersasystematicreview