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In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. The COVID-19 pandemic has rapidly propagated due to widespread person-to-person transmission 1-6. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific RT-PCR, but the test can take up to 2 d to complete. Chest CT is a valuable component of evaluation and diagnosis in symptomatic patients with

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Background: Chest radiography (CXR) has not been validated for its prognostic utility in evaluating patients with coronavirus disease 2019 .Purpose: The purpose of this study was to analyze the prognostic value of a CXR severity scoring system for younger (non-elderly) patients with COVID-19 upon initial presentation to the emergency department (ED). Outcomes of interest included hospitalization, intubation, prolonged stay, sepsis, and death. Materials & Methods:In this retrospective study, patients between the ages of 21 and 50 years who presented to EDs of an urban multicenter health system from March 10 -26, 2020 with COVID-19 confirmation on real-time reverse transcriptase polymerase chain reaction (RT-PCR) were identified.Each patient's ED CXR was divided into 6 zones and examined for opacities by two cardiothoracic radiologists with scores collated into a total concordant lung zone severity score. Clinical and laboratory variables were collected. Multivariable logistic regression was utilized to evaluate the relationship between clinical parameters, CXR scores, and patient outcomes. Results:The study included 338 patients: 210 males (62%), median age 39 [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. After adjustment for demographics and co-morbidities, independent predictors of hospital admission (n=145, 43%) were CXR severity score ≥ 2 (OR: 6.2, 95% CI 3.5-11, p<0.001) and obesity (OR 2.4 (1.1-5.4) or morbid obesity. Of patients who were admitted, a CXR score ≥3 was an independent predictor of intubation (n=28) (OR: 4.7, 95% CI 1.8-13, p=0.002) as was hospital site. We found no significant difference in primary outcomes across race/ethnicity, those with a history of tobacco use, asthma or diabetes mellitus type II. Conclusion:For patients aged 21-50 with COVID-19 presenting to the emergency department, a chest xray severity score was predictive of risk for hospital admission and intubation.