European radiology
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Multicenter Study Comparative Study
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.
To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. ⋯ • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.
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Sonoelastography has been increasingly used to investigate musculoskeletal disorders. The aim of this meta-analysis was to investigate the utility of sonoelastography in diagnosing rotator cuff tendon pathology and pertinent disorders. ⋯ • Supraspinatus and infraspinatus tendons are likely to have decreased elasticity in shoulders with adhesive capsulitis, as assessed by shear wave sonoelastography. • There was no significant difference in tendon elasticity between shoulders with and without rotator cuff tendinopathy or tendon tears when evaluated by strain and shear wave sonoelastography.
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To compare the chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) to other non-COVID viral pneumonia. ⋯ • Most common CT findings of coronavirus disease 2019 (COVID-19) were a predominant pattern of ground-glass opacity (GGO), followed by a mixed pattern of GGO and consolidation, bilateral disease, peripheral distribution, and lower lobe involvement. • Most frequent CT findings of non-COVID viral pneumonia were a predominantly mixed pattern of GGO and consolidation, followed by a predominant pattern of GGO, bilateral disease, random or diffuse distribution, and lower lobe involvement. • COVID-19 pneumonia presented a higher prevalence of peripheral distribution, and involvement of upper and middle lobes compared with non-COVID viral pneumonia.
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The global pandemic of coronavirus disease 2019 (COVID-19) has upended the world with over 6.6 million infections and over 391,000 deaths worldwide. Reverse-transcription polymerase chain reaction (RT-PCR) assay is the preferred method of diagnosis of COVID-19 infection. ⋯ We review important aspects of CT in COVID-19 infection from the justification of its use to specific scan protocols for optimizing radiation dose and diagnostic information. Key Points• Chest CT provides useful information in patients with moderate to severe COVID-19 pneumonia.• When indicated, chest CT in most patients with COVID-19 pneumonia must be performed with non-contrast, low-dose protocol.• Although chest CT has high sensitivity for diagnosis of COVID-19 pneumonia, CT findings are non-specific and overlap with other viral infections including influenza and H1N1.
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Observational Study
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.
Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. ⋯ • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.