Cited by 203Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsArtificial Intelligence in Healthcare

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Deep learning for Alzheimer's disease diagnosis: A survey

Khojaste-Sarakhsi, M., Haghighi, Seyedhamidreza Shahabi, Ghomi, S.M.T. Fatemi, Marchiori, Elena·2022·Artificial Intelligence in Medicine

DOI 10.1016/j.artmed.2022.102332

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Abstract

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle changes in biomarkers mainly detectable in different neuroimaging modalities. Developing computer-aided diagnostic models based on deep learning can provide excellent opportunities for the analysis of different neuroimage modalities along with other non-image biomarkers. In this survey, we perform a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis. Moreover, about 60 papers that have applied a trending topic or architecture for AD are investigated. Explainable models, normalizing flows, graph-based deep architectures, self-supervised learning, and attention mechanisms are considered. The main challenges in this body of literature have been categorized and explained from data-related, methodology-related, and clinical adoption aspects. We conclude our paper by addressing some future perspectives and providing recommendations to conduct further studies for AD diagnosis.

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Authors: Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E.
Affiliations: Radboud University Nijmegen (NL)
Amirkabir University of Technology (IR)
ORCID: Haghighi, Seyedhamidreza Shahabi
Ghomi, S.M.T. Fatemi
Marchiori, Elena
Venue: Artificial Intelligence in Medicine
Publisher: Elsevier BV
Published: 2022-08
Volume: 130
Pages: 102332
ISSN: 0933-3657 (ISSN-L)
DOI: 10.1016/j.artmed.2022.102332
OpenAlex: W4281730963
Cited by: 194
References: 304
Citations by year: 2026: 26 · 2025: 75 · 2024: 51 · 2023: 39 · 2022: 3
FWCI: 7.23
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Artificial Intelligence in Medicine (publishedVersion)
PubMed (publishedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: Alzheimer's disease, computer-aided diagnosis, deep learning, neuroimaging, convolutional neural networks, recurrent neural networks, generative models, normalizing flows, graph neural networks, self-supervised learning, attention mechanism, explainable models, biomarkers, early detection, clinical adoption
MeSH terms: Deep Learning, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Neurodegenerative Diseases

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Deep learning for Alzheimer's disease diagnosis: A survey

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