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Deep learning for Alzheimer's disease diagnosis: A survey
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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“Deep learning models have demonstrated strong performance in AD classification and risk prediction and show growing potential for inferring cellular characteristics from imaging phenotypes.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [35]
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Metadata
- 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
- Licence
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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