Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.
Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.
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DOI：
10.2196/37365
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发表时间：
2022-06-29
影响因子：
3.2
通讯作者：
Shah, Zubair
中科院分区：
文献类型：
--
作者：
Ali, Hazrat;Shah, Zubair
关键词：
使用肺部图像诊断COVID-19的研究受到成像数据稀缺的限制。生成对抗网络（GAN）在合成和数据增强方面很受欢迎。GAN已被探索用于数据增强，以增强人工智能（AI）方法在肺部计算机断层扫描（CT）和X射线图像中诊断COVID-19的性能。然而，GAN在克服COVID-19数据稀缺方面的作用还没有得到很好的理解。这篇综述全面研究了GAN在应对与COVID-19数据稀缺和诊断相关的挑战方面的作用。这是总结COVID-19的不同GAN方法和肺部成像数据集的第一篇综述。它试图回答与GAN的应用，流行的GAN架构，常用的图像模态以及源代码的可用性相关的问题。对5个数据库进行了检索，即PubMed、IEEEEXplore、计算机协会（ACM）数字图书馆、Scopus和Google Scholar。检索于2021年10月11日至13日进行。搜索使用干预关键词进行，如“生成对抗网络”和“GAN”，以及应用关键词，如“COVID-19”和“冠状病毒”。本综述遵循系统性综述和范围界定综述的系统性综述和荟萃分析扩展的首选报告项目（PRISMA-ScR）指南进行。只有那些报告了基于GAN的方法来分析胸部X射线图像、胸部CT图像和胸部超声图像的研究才被纳入。任何使用深度学习方法但不使用GAN的研究都被排除在外。对发表国家、研究设计或结果没有限制。仅纳入2020年至2022年发表的英文研究。2020年以前的研究没有被纳入。该综述包括57项全文研究，这些研究报告了GAN在COVID-19肺部成像数据中的不同应用。大多数研究（n=42，74%）使用GAN进行数据增强，以提高AI技术在COVID-19诊断中的性能。GAN的其他流行应用是肺部分割和肺部图像的超分辨率。cycleGAN和条件GAN是最常用的架构，分别用于9项研究。此外，29项（51%）研究使用胸部X光图像，而21项（37%）研究使用CT图像进行GAN训练。对于大多数研究（n=47，82%），使用公开可用的数据进行实验并报告结果。仅2项（4%）研究报告了放射科医生/临床医生对结果的二次评价。研究表明，GAN在解决COVID-19肺部图像数据稀缺挑战方面具有巨大潜力。用GAN合成的数据有助于改进为诊断COVID-19而训练的卷积神经网络（CNN）模型的训练。此外，GAN还通过图像的超分辨率和分割来提高CNN的性能。该综述还确定了基于GAN的方法在临床应用中潜在转变的关键限制。
Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.
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