Transfer learning for alzheimer’s disease through neuroimaging biomarkers: a systematic review

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dc.contributor.authorAgarwal, Deevyankar
dc.contributor.authorMarques, Gonçalo
dc.contributor.authorTorre Díez, Isabel de la
dc.contributor.authorFranco Martín, Manuel Ángel
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorMartín Rodríguez, Francisco
dc.date.accessioned2025-06-05T08:42:50Z
dc.date.available2025-06-05T08:42:50Z
dc.date.issued2021-10-31
dc.date.updated2025-06-05T08:42:50Z
dc.description.abstractAlzheimer’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 learn-ing, 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.en
dc.description.sponsorshipThis research has been partially supported by European Commission and the Ministry of Industry, Energy and Tourism under the project AAL-20125036 named BWetake Care: ICTbased Solution for (Self-) Management of Daily Living.en
dc.identifier.citationAgarwal, D., Marques, G., de la Torre-Díez, I., Franco Martin, M. A., García Zapiraín, B., & Martín Rodríguez, F. (2021). Transfer learning for alzheimer’s disease through neuroimaging biomarkers: a systematic review [Review of Transfer learning for alzheimer’s disease through neuroimaging biomarkers: a systematic review]. Sensors, 21(21). MDPI. https://doi.org/10.3390/S21217259
dc.identifier.doi10.3390/S21217259
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/2942
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.subject.otherAlzheimer’s disease
dc.subject.otherNeuroimaging biomarkers
dc.subject.otherMagnetic resonance imaging
dc.subject.otherPositron emission tomography
dc.subject.otherTransfer learning
dc.titleTransfer learning for alzheimer’s disease through neuroimaging biomarkers: a systematic reviewen
dc.typereview article
dcterms.accessRightsopen access
oaire.citation.issue21
oaire.citation.titleSensors
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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