{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:38:10Z","timestamp":1779219490484,"version":"3.51.4"},"reference-count":156,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21217259","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Transfer Learning for Alzheimer\u2019s Disease through Neuroimaging Biomarkers: A Systematic Review"],"prefix":"10.3390","volume":"21","author":[{"given":"Deevyankar","family":"Agarwal","sequence":"first","affiliation":[{"name":"Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"},{"name":"Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"de la Torre-D\u00edez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-2523","authenticated-orcid":false,"given":"Manuel A.","family":"Franco Martin","sequence":"additional","affiliation":[{"name":"Psychiatric Department, University Rio Hortega Hospital\u2013Valladolid, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Bego\u00f1a","family":"Garc\u00eda Zapira\u00edn","sequence":"additional","affiliation":[{"name":"eVIDA Laboratory, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1773-2860","authenticated-orcid":false,"given":"Francisco","family":"Mart\u00edn Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/S0166-2236(96)01030-2","article-title":"Amyloid, the presenilins and Alzheimer\u2019s disease","volume":"20","author":"Hardy","year":"1997","journal-title":"Trends Neurosci."},{"key":"ref_2","unstructured":"(2021, January 16). 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