Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr 21:14:841696.
doi: 10.3389/fnagi.2022.841696. eCollection 2022.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Changxing Qu  1   2 , Yinxi Zou  1   3 , Yingqiao Ma  1 , Qin Chen  4 , Jiawei Luo  5 , Huiyong Fan  6 , Zhiyun Jia  1 , Qiyong Gong  1   7 , Taolin Chen  1   8   9
Affiliations

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Changxing Qu et al. Front Aging Neurosci. .

Abstract

Alzheimer's disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150-1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878-1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Keywords: Alzheimer’s disease; diagnosis; generative adversarial networks (GANs); meta-analysis; mild cognitive impairment (MCI); psychoradiology; systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the study selection process (PRISMA flow chart).
FIGURE 2
FIGURE 2
Characteristics of the included studies: (A) Publication year, (B) data source, (C) modality of data, (D) classification task, (E) type of GAN, and (F) quality assessment.
FIGURE 3
FIGURE 3
The structure of the GAN and some improvements reported in the included studies.
FIGURE 4
FIGURE 4
Forest plot of the accuracy in the task of AD vs. CN classification.
FIGURE 5
FIGURE 5
Forest plots showing the pooled sensitivity and specificity in the task of AD vs. CN classification. (A) The pooled sensitivity and specificity in the GAN group; (B) the pooled sensitivity and specificity in the non-GAN group.
FIGURE 6
FIGURE 6
SROC curve for the task of AD vs. CN classification: (A) SROC curve for the GAN group and (B) SROC curve for the non-GAN group.
FIGURE 7
FIGURE 7
Forest plot of the accuracy in the pMCI vs. sMCI classification task.
FIGURE 8
FIGURE 8
Forest plots showing the pooled sensitivity and specificity in the task of pMCI vs. sMCI classification. (A) The pooled sensitivity in the GAN group and (B) the non-GAN group. (C) The pooled specificity in the GAN group and (D) the non-GAN group.
FIGURE 9
FIGURE 9
SROC curves for the task of pMCI vs. sMCI classification: (A) SROC curve for the GAN group and (B) SROC curve for the non-GAN group.
FIGURE 10
FIGURE 10
Schematic diagram of the function of image processing using GAN.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Ansart M., Epelbaum S., Bassignana G., Bône A., Bottani S., Cattai T., et al. (2021). Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review. Med. Image Anal. 67:101848. 10.1016/j.media.2020.101848 - DOI - PubMed
    1. Arbizu J., Festari C., Altomare D., Walker Z., Bouwman F., Rivolta J., et al. (2018). Clinical utility of FDG-PET for the clinical diagnosis in MCI. Eur. J. Nuclear Med. Mol. Imaging 45 1497–1508. 10.1007/s00259-018-4039-7 - DOI - PubMed
    1. Atri A. (2019). The Alzheimer’s disease clinical spectrum diagnosis and management. Med. Clin. North Am. 103 263–293. 10.1016/j.mcna.2018.10.009 - DOI - PubMed
    1. Baydargil H. B., Park J.-S., Kang D.-Y. (2021). Anomaly analysis of Alzheimer’s disease in PET images using an unsupervised adversarial deep learning model. Appl. Sci. 11:2187. 10.3390/app11052187 - DOI
    1. Beach T. G., Monsell S. E., Phillips L. E., Kukull W. (2012). Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J. Neuropathol. Exp. Neurol. 71 266–273. 10.1097/NEN.0b013e31824b211b - DOI - PMC - PubMed

Publication types