Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel
By J. Kanne, B. Little, Jonathan H. Chung, B. Elicker, L. Ketai. Radiology, 2020
746 citations recorded by the source corpus.
Subfields: Critical Care and Intensive Care Medicine, Oncology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e9c980c62aece803f0984dbed551093af58ca39e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Critical Care and Intensive Care Medicine
In Radiology, Nuclear Medicine and Imaging
- Marzyeh Ghassemi
- Joseph Paul Cohen
- Ioannis D. Apostolopoulos
- Alexander Wong
- Linda Wang
- Deniz Korkmaz
- Ferhat Ucar
- Abolfazl Attar
Citations in the corpus
- Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review (2020, background): “Occurring with pleural effusion and extensive tiny lung nodules may suggest bacterial superinfection [41].”
- Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Manual examination of X-ray images provides accurate diagnois of the disease in 60–70% [11,12] .”
- Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT (2020, background): “By contrast, chest CT has demonstrated about 56-98% sensitivity in detecting COVID-19 at initial presentation and can be helpful in rectifying false negatives obtained from RT-PCR during early stages of disease development (7, 8).”
- Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “As such, there is growing interest in other diagnostic methodologies that use medical imaging for the screening and diagnosis of COVID-19 cases (Kanne et al., 2020).”
- COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “Particularly in the beginning of the outbreak, computed tomography (CT) scans captured the attention of both the medical [Ng et al., 2020; Kanne et al., 2020] and the ML [McCall, 2020] communities.”
- Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, positive): “Due to the low RT-PCR sensitivity of 60% – 70%, even if negative results are obtained, symptoms can be detected by examining radiological images of patients [9,10].”