Journal of thoracic imaging
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An expert consensus recently proposed a standardized coronavirus disease 2019 (COVID-19) reporting language for computed tomography (CT) findings of COVID-19 pneumonia. ⋯ The standardized chest CT classification demonstrated high specificity and positive predictive value in differentiating COVID-19 from other viral infections when presenting a "typical" appearance in a high pretest probability environment. Good to excellent inter-rater agreement was found regarding the CT standardized categories between the readers.
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The COVID-19 global pandemic has emerged as an unprecedented health care crisis. To reduce risks of severe acute respiratory syndrome coronavirus 2 transmission in the Radiology Department, this article describes measures to increase the preparedness of Radiology Department, such as careful screening of staff and patients, thorough disinfection of equipments and rooms, appropriate use of personal protection equipment, and early isolation of patients with incidentally detected computed tomography findings suspicious for COVID-19. The familiarity of radiologists with clinical and imaging manifestations of COVID-19 pneumonia and their prognostic implications is essential to provide optimal care to patients.
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Observational Study
Diagnostic Performance of Chest X-Ray for COVID-19 Pneumonia During the SARS-CoV-2 Pandemic in Lombardy, Italy.
Chest x-ray (CXR) can play a role in diagnosing patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but only few small-scale studies are available. We assessed the diagnostic performance of CXR in consecutive patients presenting at the emergency room at the Policlinico San Donato, Milan, Italy from February 24 to April 8, 2020 for suspected SARS-CoV-2 infection. The results of CXR were classified as positive or negative according to the original prospective radiologic reports. ⋯ Thus, we included 535 patients with concomitant CXR and RT-PCR on admission (aged 65±17 y; 340 males, 195 females), resulting in 408 RT-PCR positive and 127 negative patients at the composite reference standard. Original CXR reports showed an 89.0% sensitivity (95% confidence intervals [CI], 85.5%-91.8%), 60.6% specificity (95% CI, 51.6%-69.2%), 87.9% positive predictive value (95% CI, 84.4%-90.9%), and 63.1% negative predictive value (95% CI, 53.9%-71.7%). The adoption of CXR alongside RT-PCR to triage patients with suspected SARS-CoV-2 infection could foster a safe and efficient workflow, counteracting possible false negative RT-PCR results.
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Routine screening CT for the identification of COVID-19 pneumonia is currently not recommended by most radiology societies. However, the number of CTs performed in persons under investigation (PUI) for COVID-19 has increased. We also anticipate that some patients will have incidentally detected findings that could be attributable to COVID-19 pneumonia, requiring radiologists to decide whether or not to mention COVID-19 specifically as a differential diagnostic possibility. ⋯ In addition, practice patterns may vary, and this document is meant to serve as a guide. Consultation with clinical colleagues at each institution is suggested to establish a consensus reporting approach. The goal of this expert consensus is to help radiologists recognize findings of COVID-19 pneumonia and aid their communication with other healthcare providers, assisting management of patients during this pandemic.
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Comparative Study
Thoracic Radiologists' Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology.
There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists' perspectives with those of computer science (CS) experts working in the AI development. ⋯ Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts' perceptions were closer to eventual reality.