To demonstrate the accuracy and reproducibility of low-dose submillisievert chest CT for the diagnosis of coronavirus disease 2019 infection in patients in the emergency department. Materials and Methods:This was a Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective study. From March 14 to 24, 2020, 192 patients in the emergency department with symptoms suggestive of COVID-19 infection were studied by using low-dose chest CT and real-time reverse transcription polymerase chain reaction (RT-PCR). Image analysis included the likelihood of COVID-19 infection and the semiquantitative extent of lung involvement. CT images were analyzed by two radiologists blinded to the RT-PCR results. Reproducibility was assessed using the McNemar test and intraclass correlation coefficient. Time between CT acquisition and report was measured.Results: When compared with RT-PCR, low-dose submillisievert chest CT demonstrated excellent sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis of COVID-19 (86.7%, 93.6%, 91.1%, 90.3%, and 90.2%, respectively), in particular in patients with clinical symptoms for more than 48 hours (95.6%, 93.2%, 91.5%, 96.5%, and 94.4%, respectively). In patients with a positive CT result, the likelihood of disease increased from 43.2% (pretest probability) to 91.1% or 91.4% (posttest probability), while in patients with a negative CT result, the likelihood of disease decreased to 9.6% or 3.7% for all patients or those with clinical symptoms for .48 hours. The prevalence of alternative diagnoses based on chest CT in patients without COVID-19 infection was 17.6%. The mean effective radiation dose was 0.56 mSv 6 0.25 (standard deviation). Median time between CT acquisition and report was 25 minutes (interquartile range: 13-49 minutes). Intra-and interreader reproducibility of CT was excellent (all intraclass correlation coefficients  0.95) without significant bias in the Bland-Altman analysis. Conclusion:Low-dose submillisievert chest CT allows for rapid, accurate, and reproducible assessment of COVID-19 infection in patients in the emergency department, in particular in patients with symptoms lasting longer than 48 hours. Chest CT has the additional advantage of offering alternative diagnoses in a significant subset of patients.

et al. with the exception of a pre-symptomatic phase. 7 CT findings begin as single or multifocal ground-glass opacities, pulmonary nodules, or air bronchograms, which progress with development of interlobular septal thickening and crazy paving, before regression in both size and density at the end of the second week of infection. Opacities often have extensive distribution, typically bilaterally, but also seen unilaterally, with occasional round morphology or reversed-halo or atoll sign. 5 In the dissipation phase, there may be continued patchy consolidative opacities in addition to reticular "strip-like" opacities, bronchial wall thickening, and interlobular septal thickening. 1,8 The characteristic ultrasound findings (bilateral and multilobar B-lines, subpleural consolidates, irregular pleural line, and decreased blood flow 3,4,9 ) have been shown to be highly consistent with CT findings 3,4 and can be expected to develop over a similar timeline. During the first few days of symptom presentation, scattered unilateral or bilateral multilobar B-lines can be visualised. 3,9 As the disease progresses from the end of week 1 through week 2, development of alveolar interstitial syndrome with diffuse, bilateral B-lines can occur in addition to an irregular pleural line with punctate defects and formation of subpleural consolidations with visible air bronchograms. Lastly, after the end of week 2 during convalescence, there can be an expected regression of prior findings with re-emergence of A-lines. 9 A summary of findings is listed in Table 1.Although the literature remains limited, there is still a clear benefit for clinicians to be familiar with ultrasound findings and their progression in COVID-19 patients. It may be particularly useful in helping emergency personnel to triage and diagnose suspected patients, 4 but also for monitoring progression of the disease throughout hospitalisation. Additionally, it offers substantial benefits in comparison to CT imaging, including portability, lower cost, reduced radiation, and ease of sterilisation. Physicians are encouraged to be familiar with and to utilise lung ultrasound in the management of COVID-19 patients.

To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients. Materials and Methods: This was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland-Altman analysis was used to assess intra-and interreader reproducibility. Results: Adverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semiquantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC 0.960 versus 0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an  error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis. Conclusion: AI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while I n p r e s s 3 reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.

Highlights RT-PCR and chest CT are imperfect tests for COVID-19 diagnosis. Diagnostic studies using RT-PCR or chest CT as reference standard are inherently biased. Latent class analysis can estimate the true accuracy of diagnostic tests for COVID-19.