Augmentation of literature review of COVID-19 radiology.

We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".

COVID-19 detection using machine learning: a large scale assessment of x-ray and CT image datasets

Millions of people are infected by the coronavirus disease 2019 (COVID-19) around the world. Within three months of its first report, it rapidly spread worldwide with thousands of deaths. Since that time, not only underdeveloped and developing countries, but also developed countries have suffered from insufficient medical resources and diagnoses. In this circumstance, researchers from medical and engineering fields have tried to develop automatic COVID-19 detection toolkits using machine learning (ML) techniques. The dataset is the fundamental element of any detection tool;therefore, most of the ML-based COVID-19 detection research was conducted used chest x-ray and computed tomography (CT) image datasets. In our study, we collected a series of publicly available unique COVID-19 x-ray and CT image datasets, then assessed and compared their performances using our proposed 22 layer convolutional neural network model along with ResNet-18 and VGG16. We investigated eight individual datasets known as Twitter, SIRM x-ray, COVID-19 Image Repository, EURORAD, BMICV, SIRM CT, COVID-CT, and SARS-CoV-2 CT. Our model obtained classification accuracy of 91%, 81%, 59%, 98%, 58%, 79%, and 97%, respectively. Our proposed model obtained the highest classification accuracy using four datasets (Twitter, COVID-19 Image Repository, COVID-CT, and SARS-CoV-2 CT). Similarly, ResNet-18 only utilized three (EURORAD, BMICV, and SIRM CT), whereas VGG16 only utilized the SIRM x-ray dataset. Results of this investigation indicate a significant comparison chart among the performance of the datasets. Indeed, our study is a large-scale assessment of existing COVID-19 x-ray and CT image datasets. And to the best of our knowledge, this is the first performance comparison study that includes all publicly available COVID-19 datasets.

Scientometric Analysis of Top 100 Most Cited Articles on Imaging in COVID-19: The Pandemic of Publications.

The coronavirus disease 2019 (COVID-19) pandemic in 2020 was paralleled by an equally overwhelming publication of scientific literature. This scientometric analysis was performed to evaluate the 100 most cited articles on COVID-19 imaging to highlight research trends and identify common characteristics of the most cited works. A search of the Web of Science database was performed using the keywords "COVID CT," "COVID Radiograph," and "COVID Imaging" on June 29, 2021. The 100 top cited articles found were arranged in descending order on the basis of citation counts and citations per year and relevant data were recorded. Our search revealed a total of 4,862 articles on COVID-19 imaging published in the years 2020 to 2021. The journal with maximum number of publications ( n = 22), citation count ( n = 8,788), and impact was Radiology . Citations for the top 100 articles ranged from 70 to 1,742 with the most cited article authored by A.I. Tao and published in Radiology . Two authors tied at first spot, having maximum impact, with both having 5 publications and a total of 3,638 citations among them. China was the leading country with both the maximum number of publications ( n = 49) and total citations ( n = 13,892), the United States coming second in both. This study evaluates publication and citation trends in literature and shows that the countries most affected by the pandemic early on have contributed to the majority of the literature. Furthermore, it will help radiologists to refer to the most popular and important article texts on which to base their unbiased and confident diagnoses.

Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials.

Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.