The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society

Rubin, Geoffrey D.; Ryerson, Christopher J.; Haramati, Linda B.; Sverzellati, Nicola; Kanne, Jeffrey P.; Raoof, Suhail; Schluger, Neil W.; Volpi, Annalisa; Yim, Jae Joon; Martin, Ian B.K.; Anderson, Deverick J.; Kong, Christina S.; Altes, Talissa A.; Bush, Andrew; Desai, Sujal R.; Goldin, onathan; Goo, Jin Mo; Humbert, Marc; Inoue, Yoshikazu; Kauczor, Hans‐Ulrich; Luo, Fengming; Mazzone, Peter J.; Prokop, Mathias; Rémy‐Jardin, Martine; Richeldi, Luca; Schaefer-Prokop, Cornelia; Tomiyama, Noriyuki; Wells, Adrian; Leung, Ann N.

doi:10.1148/radiol.2020201365

Cited by 1,139 publications

(1,544 citation statements)

References 25 publications

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“…Exposure to medical radiation is a concern in patients with suspected or diagnosed COVID-19 pneumonia, especially in infants, children, young adults, and pregnant patients. Therefore, it is necessary to prevent or reduce radiation exposure [6,7,20,54]. First, it should be distinguished appropriately in which patients need imaging and in which patient imaging is not needed.…”

Section: Radiation Concernmentioning

confidence: 99%

“…In a multinational recommendation that is newly defined and widely used by Fleischner Society, imaging is not recommended for cases with suspected of COVID-19 without risk factors (such as diabetes, older age, chronic obstructive pulmonary disease) and mild clinical findings. Moreover, imaging methods are indicated in a COVID-19 patient whose respiratory condition has worsened [6]. Second, chest CT should be obtained as low as possible radiation dose in cases with CT indication.…”

Section: Radiation Concernmentioning

confidence: 99%

“…Radiography machines and CT scanners are frequently used in patients with suspected COVID-19 during the pandemic. Therefore, vital precautions should be provided for COVID-19 in radiology units [6,55]. First, ensuring the health of radiology workers is essential for the best care of patients.…”

Section: Contamination Of Radiology Unitsmentioning

confidence: 99%

“…Third, to reduce the risk of contamination, the CT scanners and radiography devices and rooms must be deeply cleaned and disinfected according to standard protocols after each exam. If patient masking is available, active (mechanically) air exchange procedures are not required [6,55]. After each imaging procedure, the room Table 3.…”

Section: Contamination Of Radiology Unitsmentioning

confidence: 99%

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Chest CT features of the novel coronavirus disease (COVID-19)

Ufuk¹,

Savaş²

2020

Turk J Med Sci

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A new type of coronavirus (2019-nCoV) is rapidly spreading worldwide and causes pneumonia, respiratory distress, thromboembolic events, and death. Chest computed tomography (CT) plays an essential role in the diagnosis of viral pneumonia, monitoring disease progression, determination of disease severity, and evaluating therapeutic efficacy. Chest CT can show important clues of 2019-nCoV disease (also known as COVID-19) in patients with an appropriate clinic. Prompt diagnosis of COVID-19 is essential to prevent disease transmission and provides close clinical observation of patients with clinically severe disease. Therefore, radiologists and clinicians should be familiar with the CT imaging findings of COVID-19 pneumonia. Herein, we aimed to review the imaging findings of COVID-19 pneumonia and examine the critical points to be considered for imaging in cases with COVID-19 suspicion.

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Section: Radiation Concernmentioning

confidence: 99%

Section: Radiation Concernmentioning

confidence: 99%

Section: Contamination Of Radiology Unitsmentioning

confidence: 99%

Section: Contamination Of Radiology Unitsmentioning

confidence: 99%

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Chest CT features of the novel coronavirus disease (COVID-19)

Ufuk¹,

Savaş²

2020

Turk J Med Sci

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“…The diagnostic accuracy produced by manual interpretation of COVID-19 chest CT is good but needs to be improved to make resource allocation and disease management during the current pandemic I n P r e s s less strenuous on healthcare systems and economies worldwide. Current clinical algorithms for the management of COVID-19 patients are contingent on the amount of resources available and require definitive imaging results (20). While distinguishing COVID-19 from normal lung or other lung pathologies such as cancer on chest CT may be straightforward, differentiation between COVID-19 and other pneumonia can be particularly troublesome for physicians because of the radiographic similarities (21).…”

Section: Covid-19 Can Be Difficult To Distinguish From Other Types Ofmentioning

confidence: 99%

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

Bai¹,

Wang²,

Xiong³

et al. 2020

Radiology

354

379

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AI assistance improved radiologists' performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT. Key Results: An AI model had higher test accuracy (96% vs 85%, p<0.001), sensitivity (95% vs 79%, p<0.001), and specificity (96% vs 88%, p=0.002) than radiologists. In an independent test set, our AI model achieved an accuracy of 87%, sensitivity of 89% and specificity of 86%. With AI assistance, the radiologists achieved a higher average accuracy (90% vs 85%, p<0.001), sensitivity (88% vs 79%, p<0.001) and specificity (91% vs 88%, p=0.001). AbstractBackground: COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy.Purpose: To establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologist performance without and with AI assistance.Methods: 521 patients with positive RT-PCR for COVID-19 and abnormal chest CT findings were retrospectively identified from ten hospitals from January 2020 to April 2020. 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia on chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by two-layer fully-connected neural network to pool slices together.Our final cohort of 1,186 patients (132,583 CT slices) was divided into training, validation and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance on separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance.Results: Our final model achieved a test accuracy of 96% (95% CI: 90-98%), sensitivity 95% (95% CI: 83-100%) and specificity of 96% (95% CI: 88-99%) with Receiver Operating Characteristic (ROC) AUC of 0.95 and Precision-Recall (PR) AUC of 0.90. On independent testing, our model achieved an accuracy of 87% (95% CI: 82-90%), sensitivity of 89% (95% CI: 81-94%) and specificity of 86% (95% CI: 80-90%) with ROC AUC of 0.90 and PR AUC of 0.87. Assisted by the models' probabilities, the radiologists achieved a higher average test accuracy (90% vs. 85%, ∆=5, p<0.001), sensitivity (88% vs. 79%, ∆=9, p<0.001) and specificity (91% vs. 88%, ∆=3, p=0.001).

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Thoracic imaging tests for the diagnosis of COVID-19

Islam

Salameh

Leeflang

et al. 2020

Cochrane Database of Systematic Reviews

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society

Cited by 1,139 publications

References 25 publications

Chest CT features of the novel coronavirus disease (COVID-19)

Chest CT features of the novel coronavirus disease (COVID-19)

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

Thoracic imaging tests for the diagnosis of COVID-19

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