The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society
Geoffrey D. Rubin,Christopher J. Ryerson,Linda B. Haramati,Nicola Sverzellati,Jeffrey P. Kanne,Suhail Raoof,Neil W. Schluger,Annalisa Volpi,Jae-Joon Yim,Ian B.K. Martin,Deverick J. Anderson,Christina S. Kong,Talissa A. Altes,Andrew Bush,Sujal R. Desai,Jonathan G. Goldin,Jin Mo Goo,Marc Humbert,Yoshikazu Inoue,Hans-Ulrich Kauczor,Fengming Luo,Peter J. Mazzone,Mathias Prokop,Martine Remy-Jardin,Luca Richeldi,Cornelia M. Schaefer-Prokop,Noriyuki Tomiyama,Athol U. Wells,Ann N. Leung +28 more
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TLDR
A multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing COVID-19 patients across a spectrum of healthcare environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of CXR and CT in the management of COIDs.About:
This article is published in Chest.The article was published on 2020-04-07 and is currently open access. It has received 1232 citations till now. The article focuses on the topics: Health care.read more
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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.
TL;DR: COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
Journal ArticleDOI
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.
Journal ArticleDOI
CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.
Mathias Prokop,Wouter M. van Everdingen,Tjalco van Rees Vellinga,Henriette M. E. Quarles van Ufford,Lauran Stöger,Ludo F. M. Beenen,Bram Geurts,Hester A. Gietema,Jasenko Krdzalic,Cornelia M. Schaefer-Prokop,Bram van Ginneken,Monique Brink +11 more
TL;DR: CO-RADS is a categorical assessment scheme for pulmonary involvement of CO VID-19 on non-enhanced chest CT providing very good performance for predicting COVID-19 in patients with moderate to severe symptoms and has a substantial interobserver agreement, especially for categories 1 and 5.
Journal ArticleDOI
Predictors of COVID-19 severity: A literature review.
Benjamin Gallo Marin,Ghazal Aghagoli,Katya Lavine,Lanbo Yang,Emily J. Siff,Silvia S. Chiang,Silvia S. Chiang,Thais P. Salazar-Mather,Luba Dumenco,Michael C Savaria,Su Aung,Timothy P. Flanigan,Ian C. Michelow,Ian C. Michelow +13 more
TL;DR: A synthesis of the current literature pertaining to factors predictive of COVID‐19 clinical course and outcomes shows findings associated with increased disease severity and/or mortality include age, multiple pre‐existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end‐organ dysfunction.
Journal ArticleDOI
Acute Pulmonary Embolism Associated with COVID-19 Pneumonia Detected with Pulmonary CT Angiography.
TL;DR: In patients with severe clinical features of coronavirus disease 2019 infection, the proportion of patients with acute pulmonary embolus was 23% (95% confidence interval: 15%, 33%) at pulmonary CT ...
References
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Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention
Zunyou Wu,Jennifer M. McGoogan +1 more
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TL;DR: Results of an analysis of nasal and throat swabs from 17 patients in Zhuhai, China, who had received a diagnosis of Covid-19 and found SARS-CoV-2 Viral Load in Upper Respiratory Specimens positive.
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