Deep Learning-Based Diagnosis of Alzheimer's Disease

Overview

Journal J Pers Med

Date 2022 May 28

PMID 35629237

Authors

Tausifa Jan Saleem

Syed Rameem Zahra

Fan Wu

Ahmed Alwakeel

Mohammed Alwakeel

Fathe Jeribi

Mohammad Hijji

Affiliations

Soon will be listed here.

Abstract

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.

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References

Formichi P, Battisti C, Radi E, Federico A . Cerebrospinal fluid tau, A beta, and phosphorylated tau protein for the diagnosis of Alzheimer's disease. J Cell Physiol. 2006; 208(1):39-46. DOI: 10.1002/jcp.20602. View

Ramirez J, Gorriz J, Segovia F, Chaves R, Salas-Gonzalez D, Lopez M . Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neurosci Lett. 2010; 472(2):99-103. DOI: 10.1016/j.neulet.2010.01.056. View

Amoroso N, Diacono D, Fanizzi A, La Rocca M, Monaco A, Lombardi A . Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge. J Neurosci Methods. 2017; 302:3-9. DOI: 10.1016/j.jneumeth.2017.12.011. View

Oh K, Chung Y, Kim K, Kim W, Oh I . Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep. 2019; 9(1):18150. PMC: 6890708. DOI: 10.1038/s41598-019-54548-6. View

Gray K, Wolz R, Heckemann R, Aljabar P, Hammers A, Rueckert D . Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. Neuroimage. 2012; 60(1):221-9. PMC: 3303084. DOI: 10.1016/j.neuroimage.2011.12.071. View

Vemuri P, Wiste H, Weigand S, Shaw L, Trojanowski J, Weiner M . MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology. 2009; 73(4):294-301. PMC: 2715214. DOI: 10.1212/WNL.0b013e3181af79fb. View

Lee J, Burkett B, Min H, Senjem M, Lundt E, Botha H . Deep learning-based brain age prediction in normal aging and dementia. Nat Aging. 2023; 2(5):412-424. PMC: 10154042. DOI: 10.1038/s43587-022-00219-7. View

Raju M, Gopi V, Anitha V, Wahid K . Multi-class diagnosis of Alzheimer's disease using cascaded three dimensional-convolutional neural network. Phys Eng Sci Med. 2020; 43(4):1219-1228. DOI: 10.1007/s13246-020-00924-w. View

Thal D, Rub U, Orantes M, Braak H . Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002; 58(12):1791-800. DOI: 10.1212/wnl.58.12.1791. View

10.

Wang S, Phillips P, Sui Y, Liu B, Yang M, Cheng H . Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling. J Med Syst. 2018; 42(5):85. DOI: 10.1007/s10916-018-0932-7. View

11.

Sperling R . Potential of functional MRI as a biomarker in early Alzheimer's disease. Neurobiol Aging. 2011; 32 Suppl 1:S37-43. PMC: 3233699. DOI: 10.1016/j.neurobiolaging.2011.09.009. View

12.

Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R . Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin. 2018; 21:101645. PMC: 6413333. DOI: 10.1016/j.nicl.2018.101645. View

13.

Greve D, Billot B, Cordero D, Hoopes A, Hoffmann M, Dalca A . A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. Neuroimage. 2021; 244:118610. PMC: 8643077. DOI: 10.1016/j.neuroimage.2021.118610. View

14.

Alam S, Kwon G, Kim J, Park C . Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA. J Healthc Eng. 2017; 2017:8750506. PMC: 5576415. DOI: 10.1155/2017/8750506. View

15.

Lopez-Martin M, Nevado A, Carro B . Detection of early stages of Alzheimer's disease based on MEG activity with a randomized convolutional neural network. Artif Intell Med. 2020; 107:101924. DOI: 10.1016/j.artmed.2020.101924. View

16.

Bucci M, Chiotis K, Nordberg A . Alzheimer's disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry. 2021; 26(10):5888-5898. PMC: 8758489. DOI: 10.1038/s41380-021-01263-2. View

17.

Mandelkow E, Mandelkow E . Tau in Alzheimer's disease. Trends Cell Biol. 1998; 8(11):425-7. DOI: 10.1016/s0962-8924(98)01368-3. View

18.

Zhang X, Han L, Zhu W, Sun L, Zhang D . An Explainable 3D Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI. IEEE J Biomed Health Inform. 2021; 26(11):5289-5297. DOI: 10.1109/JBHI.2021.3066832. View

19.

Lu D, Popuri K, Ding G, Balachandar R, Beg M . Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep. 2018; 8(1):5697. PMC: 5890270. DOI: 10.1038/s41598-018-22871-z. View

20.

Koopman K, Le Bastard N, Martin J, Nagels G, De Deyn P, Engelborghs S . Improved discrimination of autopsy-confirmed Alzheimer's disease (AD) from non-AD dementias using CSF P-tau(181P). Neurochem Int. 2009; 55(4):214-8. DOI: 10.1016/j.neuint.2009.02.017. View