2020
DOI: 10.25259/jcis_76_2020
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Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients

Abstract: Objectives: The purpose of this study is to assess the performance of radiologists using a new software called “COVID-19 score” when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. Chest radiography (or chest X-ray, CXR) and CT are important for the imaging diagnosis of the coronavirus pneumonia (COVID-19). CXR mobile devices are efficient during epidemies, because allow to reduce the risk of contagion and are easy to sanitize. Material and Meth… Show more

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Cited by 5 publications
(6 citation statements)
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“…These findings demonstrate that chest imaging modalities are sensitive in identifying COVID-19 with lower respiratory tract involvement, while their specificity is limited [80]. Interestingly, inter-rater agreement on the interpretation of chest x-rays is moderate [81,82].…”
Section: Imagingmentioning
confidence: 78%
“…These findings demonstrate that chest imaging modalities are sensitive in identifying COVID-19 with lower respiratory tract involvement, while their specificity is limited [80]. Interestingly, inter-rater agreement on the interpretation of chest x-rays is moderate [81,82].…”
Section: Imagingmentioning
confidence: 78%
“…Conversely, Mehta 8 proposed a CBMIR system for sub-images in high-resolution digital pathology images, utilizing scale-invariant feature extraction. Lowe 9 utilized Scale-Invariant Feature Transform (SIFT) to index sub-images and reported an 80% accuracy rate for the top 5 retrieved images. Lowe’s experiments were conducted on 50 ImmunohHistoChemistry (IHC) stained pathology images at eight different resolutions.…”
Section: Related Workmentioning
confidence: 99%
“…Therefore, training is more difficult and time-consuming. Oktay et al [ 66 ] proposed a U-Net that can capture fine structures in medical images, which is suitable for the segmentation of lesions and lung nodules in COVID-19 applications. Training a segmentation network requires sufficient labeled data.…”
Section: Application Of Artificial Intelligence In Computed Tomography Segmentationmentioning
confidence: 99%