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

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

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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Crossref · 10.1016/j.artmed.2022.102332

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  • Deep learning models have demonstrated strong performance in AD classification and risk prediction and show growing potential for inferring cellular characteristics from imaging phenotypes.

    “Deep learning models have demonstrated strong performance in AD classification and risk prediction and show growing potential for inferring cellular characteristics from imaging phenotypes.”

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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)
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2022-08
Volume
130
Pages
102332
ISSN
0933-3657 (ISSN-L)
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
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
  • Health Sciences › Medicine › Physiology › 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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