Deep Learning-Based Diagnosis of Alzheimer's Disease

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

Key findings

• Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models.
• Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning.
• We summarize the most recent trends and findings using a thorough literature review.
• The study also explores the different biomarkers and datasets for AD diagnosis.
• Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

Why it matters

This review summarizes research on using deep learning for Alzheimer's disease diagnosis and notes that these techniques have been reported as more accurate than conventional machine learning models. It covers diagnosis only and states there is no proven cure, reporting no treatment or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.3390/jpm12050815
• PubMed: 35629237
• Original source →
• Free full text: Journal of Personalized Medicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{saleem2022-35629237,
  author  = {Saleem, T. J. and Zahra, SR. and Wu, F. and Alwakeel, A. and Alwakeel, M. and Jeribi, F. and Hijji, M.},
  title   = {{Deep Learning-Based Diagnosis of Alzheimer's Disease}},
  journal = {Journal of personalized medicine},
  year    = {2022},
  doi     = {10.3390/jpm12050815},
  pmid    = {35629237},
  url     = {https://doi.org/10.3390/jpm12050815}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Alzheimer's disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline Molecular psychiatry · 2021-10-01 · usefulness score 69/100 · Mild cognitive impairment
• Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization Neurology · 2009-12-30 · usefulness score 68/100 · Mild cognitive impairment
• Predicting Alzheimer's disease progression using multi-modal deep learning approach Scientific reports · 2019-02-13 · usefulness score 66/100 · Mild cognitive impairment
• A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease NeuroImage · 2019-01-14 · usefulness score 62/100 · Mild cognitive impairment
• MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change Neurology · 2009-07-01 · usefulness score 61/100 · Brain scans and imaging
• Phases of Aβ-deposition in the human brain and its relevance for the development of AD Neurology · 2002-06-25 · usefulness score 59/100 · Brain tissue and pathology
• Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study Archives of general psychiatry · 2004-01-01 · usefulness score 58/100 · Getting a diagnosis
• The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics Neuroepidemiology · 2008-02-07 · usefulness score 58/100 · Getting a diagnosis
• A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease Brain sciences · 2020-02-05 · usefulness score 49/100 · Brain scans and imaging

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• Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers International Journal of Molecular Sciences · 2025-03-08 · usefulness score 48/100 · Mild cognitive impairment
• Classifying mild cognitive impairment from normal cognition: fMRI complexity matches tau PET performance Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · 2025-07-01 · usefulness score 38/100 · Brain scans and imaging

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• Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease NeuroImage. Clinical · 2021-06-04 · usefulness score 47/100
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• A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18 F-FDG PET of the Brain Radiology · 2018-11-06 · usefulness score 48/100
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