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Cited by 724Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareAlzheimer's disease research and treatments

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

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Jo, Taeho, Nho, Kwangsik, Saykin, Andrew J.·2019·Frontiers in Aging Neuroscience
DOI 10.3389/fnagi.2019.00220PMC PMC6710444
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Abstract

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

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  • AI-based neuroimaging analysis, particularly through deep learning and pattern recognition techniques, has significantly advanced the interpretation and quantification of complex imaging data.

    “AI-based neuroimaging analysis, particularly through the application of deep learning and pattern recognition techniques, has significantly advanced the interpretation and quantification of complex imaging data83,84.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [83]

    The Discussion (page not provided) says deep learning automatically identifies features in neuroimaging data and has improved image-recognition performance; the Results report strong Alzheimer's classification performance. The paper does not establish improved quantification of imaging data, and its Transparency discussion cautions that the features behind predictions can be difficult to interpret. (LLM, conf=0.83)

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Authors
Jo, T., Nho, K., & Saykin, A. J.
Affiliations
Indiana University Bloomington (US)
Corresponding author
Jo, Taeho
ORCID
Venue
Frontiers in Aging Neuroscience
Publisher
Frontiers Media SA
Published
2019-08-20
Volume
11
Pages
220-220
ISSN
1663-4365 (ISSN-L)
DOI
10.3389/fnagi.2019.00220
PMC
PMC6710444
OpenAlex
W2964629181
Cited by
724
References
18
Citations by year
2026: 81 · 2025: 113 · 2024: 125 · 2023: 144 · 2022: 104 · 2021: 98 · 2020: 52 · 2019: 7
FWCI
11.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Aging Neuroscience (publishedVersion, cc-by)
  • arXiv (Cornell University) (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Purdue University Indianapolis (Indiana University) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • National Institutes of Health (P30 AG10133, R01 AG19771, R01 AG057739, R01 CA129769)
  • U.S. National Library of Medicine (R01 LM012535)
  • National Institute on Aging (R03 AG054936)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, deep learning, neuroimaging, diagnostic classification, outcome prediction, mild cognitive impairment, conversion prediction, multimodal neuroimaging, fluid biomarkers, convolutional neural networks, recurrent neural networks, stacked autoencoder, feature selection, early detection

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