Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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 i...

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

“…Predominantly, these applications in radiology are based on deductive AI techniques [ 1 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. However, generative AI, especially the generative adversarial network (GAN) which focuses on the creation of new and original content, has started attracting the attention of radiology researchers and clinicians as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 <...…”

“…This capability is highly relevant to medical imaging and therefore radiology [ 64 , 65 ]. Its current applications in radiology include image synthesis and data augmentation [ 1 , 55 , 56 , 57 , 59 , 60 , 61 , 62 ], image translation (e.g., from one modality to another one [ 1 , 55 , 56 , 58 , 59 , 60 , 61 , 62 ], from normal to abnormal [ 1 , 55 , 62 ], etc. ), image reconstruction (e.g., denoising [ 1 , 55 , 59 , 60 , 61 ], artifact removal [ 1 , 56 , 58 , 61 ], super-resolution (image spatial resolution improvement) [ 1 , 55 , 56 , 57 , 59 , 61 , 64 , 65 ], motion unsharpness correction [ 61 ], etc.…”

“…Its current applications in radiology include image synthesis and data augmentation [ 1 , 55 , 56 , 57 , 59 , 60 , 61 , 62 ], image translation (e.g., from one modality to another one [ 1 , 55 , 56 , 58 , 59 , 60 , 61 , 62 ], from normal to abnormal [ 1 , 55 , 62 ], etc. ), image reconstruction (e.g., denoising [ 1 , 55 , 59 , 60 , 61 ], artifact removal [ 1 , 56 , 58 , 61 ], super-resolution (image spatial resolution improvement) [ 1 , 55 , 56 , 57 , 59 , 61 , 64 , 65 ], motion unsharpness correction [ 61 ], etc. ), image feature extraction [ 55 , 57 , 60 , 61 ], image segmentation [ 1 , 55 , 56 , 57 , 60 , 61 , 62 ], anomaly detection [ 55 , 56 , 60 ], disease diagnosis [ …”

“…), image reconstruction (e.g., denoising [ 1 , 55 , 59 , 60 , 61 ], artifact removal [ 1 , 56 , 58 , 61 ], super-resolution (image spatial resolution improvement) [ 1 , 55 , 56 , 57 , 59 , 61 , 64 , 65 ], motion unsharpness correction [ 61 ], etc. ), image feature extraction [ 55 , 57 , 60 , 61 ], image segmentation [ 1 , 55 , 56 , 57 , 60 , 61 , 62 ], anomaly detection [ 55 , 56 , 60 ], disease diagnosis [ 55 , 57 , 60 ], prediction [ 55 , 56 , 61 ] and prognosis [ 55 , 57 , 60 , 61 ], and image registration [ 1 , 55 , 60 , 61 ].…”

“…Pediatric radiology is a subset of radiology [ 26 , 28 , 29 , 66 , 67 ]. The aforementioned review findings may not be applicable to pediatric radiology [ 28 , 29 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 67 ]. For example, the application of GAN for prostate cancer segmentation appears not relevant to children [ 60 , 68 ].…”