Coronavirus Disease 2019 (COVID-19): A Perspective from China

By Z. Zu, M. Jiang, P. Xu, Wen Chen, Q. Ni, G. Lu, L. Zhang. Radiology, 2020

1,807 citations recorded by the source corpus.

Abstract In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection occurred in Wuhan, Hubei Province, China and spread across China and beyond. On February 12, 2020, WHO officially named the disease caused by the novel coronavirus as Coronavirus Disease 2019 (COVID-19). Since most COVID-19 infected patients were diagnosed with pneumonia and characteristic CT imaging patterns, radiological examinations have become vital in early diagnosis and assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19, while highlighting the role of chest CT in prevention and disease control. A full translation of this article in Chinese is available in the supplement. - 请见䃼充资料阅读文章中文版∘

Subfields: Infectious Diseases, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9f5e2d6911aceb662fcad172ffb1688613ac000d

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In Infectious Diseases

E. Sintema

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Psychological status of medical workforce during the COVID-19 pandemic: A cross-sectional study (2020, neutral): “With the global spread of COVID-19, the challenge for many countries who are now dealing with large clusters or community transmission is obvious.”
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020, methodology): “In clinics, real-time reverse-transcription–polymerase-chainreaction (RT-PCR) is the golden standard to make a definitive diagnosis of COVID-19 infection [6].”
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning (2020, background): “The main drawbacks of RT-PCR are time consuming and error-prone results (Pathak et al., 2020; Zu et al., 2020).”
Correlation between climate indicators and COVID-19 pandemic in New York, USA (2020, background): “Such statistics make New York an ideal epicenter for the spread of infectious diseases (Zu et al., 2020).”
Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT (2020, background): “For example, influenza and COVID-19 both demonstrate GGO and consolidation on chest CT (14).”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, background): “Similarly, Zu et al. [8] and Chung et al. [6] discovered that 33% of chest CTs can have rounded lung opacities.”
Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia (2020, neutral): “With population growth like this allows covid-19 to spread rapidly (Zu et al., 2020).”
Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms (2020, background): “In additional to its efficiency in diagnosing the infection, it can estimate the level of severity of pneumonia [9].”
Effect of COVID-19 on the Performance of Grade 12 Students: Implications for STEM Education (2020, background): “The only literature available is directly related to medical studies (Chinazzi et al., 2020; Hopman, Allegranzi, & Mehtar, 2020; Kraemer et al., 2020; Wu & McGoogan, 2020; Zu et al., 2020).”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Computed tomography, and X-ray images play an important ole in the early diagnosis and treatment of COVID-19 disease [7] .”
COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “Ever since the dawn of the outbreak, imaging has stood out as a promising avenue of research and care [Zu et al., 2020; Poggiali et al., 2020].”