Coronavirus Disease 2019 (COVID-19) diagnostic technologies: A country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic

被引:29
作者
Fields, Brandon K. K. [1 ]
Demirjian, Natalie L. [1 ,2 ]
Gholamrezanezhad, Ali [1 ,3 ]
机构
[1] Univ Southern Calif, Keck Sch Med, Los Angeles, CA 90033 USA
[2] Univ Southern Calif, Dept Integrat Anat Sci, Los Angeles, CA 90033 USA
[3] Univ Southern Calif, Dept Radiol, Los Angeles, CA 90033 USA
关键词
COVID-19; SARS-CoV-2; Chest CT; RT-PCR; Machine-learning; Coronavirus; Pandemic; Radiology; Pneumonia; ACUTE RESPIRATORY SYNDROME; CT;
D O I
10.1016/j.clinimag.2020.08.014
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Since first report of a novel coronavirus in December of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has crippled healthcare systems around the world. While many initial screening protocols centered around laboratory detection of the virus, early testing assays were thought to be poorly sensitive in comparison to chest computed tomography, especially in asymptomatic disease. Coupled with shortages of reverse transcription polymerase chain reaction (RT-PCR) testing kits in many parts of the world, these regions instead turned to the use of advanced imaging as a first-line screening modality. However, in contrast to previous Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome coronavirus epidemics, chest X-ray has not demonstrated optimal sensitivity to be of much utility in first-line screening protocols. Though current national and international guidelines recommend for the use of RT-PCR as the primary screening tool for suspected cases of COVID-19, institutional and regional protocols must consider local availability of resources when issuing universal recommendations. Successful containment and social mitigation strategies worldwide have been thus far predicated on unified governmental responses, though the underlying ideologies of these practices may not be widely applicable in many Western nations. As the strain on the radiology workforce continues to mount, early results indicate a promising role for the use of machine-learning algorithms as risk stratification schema in the months to come.
引用
收藏
页码:219 / 225
页数:7
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