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Introduction Pulmonary opacities in COVID-19 increase throughout the illness and peak after ten days. The radiological literature mainly focuses on CT findings. The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation. Methods We retrospectively identified consecutive reverse transcription polymerase reaction-confirmed COVID-19 patients (n = 104, 75% men) and patients (n = 75, 51% men) with repeated negative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests. Two radiologists blindly and independently reviewed the CXRs, documented findings, assigned radiographic assessment of lung edema (RALE) scores, and predicted the patients’ COVID-19 status. We calculated interobserver reliability. The score use for diagnosis and prognosis of COVID-19 was evaluated with the area under the receiver operating characteristic curve. Results The overall RALE score failed to identify COVID-19 patients at presentation. However, the score was inversely correlated with a COVID-19 diagnosis within ≤2 days, and a positive correlation was found six days after symptom onset.Interobserver agreement with regard to separating normal from abnormal CXRs was moderate (k = 0.408) with low specificity (25% and 27%). Definite pleural effusion had almost perfect agreement (k = 0.833) and substantially reduced the odds of a COVID-19 diagnosis. Disease distribution and experts’ opinion on COVID-19 status had only fair interobserver agreement. The RALE score interobserver reliability was moderate to good (intraclass correlation coefficient = 0.745). A high RALE score predicted a poor outcome (intensive care unit hospitalization, intubation, or death) in COVID-19 patients; a score of ≥5 substantially increased the odds of having a poor outcome. Conclusion Chest radiography was found not to be a valid diagnostic tool for COVID-19, as normal or near-normal CXRs are more likely early in the disease course. Pleural effusions at presentation suggest a diagnosis other than COVID-19. More extensive lung opacities at presentation are associated with poor outcome in COVID-19 patients. Thus, patients with more than minimal opacities should be monitored closely for clinical deterioration. This clinical application of CXR is its greatest strength in COVID-19 as it impacts patient care.

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Objectives: To assess the effect of a commercial Artificial Intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single Level 1 Trauma Center. Methods: A retrospective cohort study for two time periods – Pre-AI (1.1.2017-1.1.2018) and Post-AI (1.1.2019-1.1.2020), in a Level 1 Trauma Center was performed. Participants older than 18 years with a confirmed diagnosis of ICH on head CT upon admission to the emergency department were collected. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke –IS; and myocardial infarction - MI) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. Secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. Results: 587 participants (289 Pre-AI – age 71 ± 1, 169 men; 298 Post-AI – age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality weresignificantly reduced in the Post-AI group when compared to the Pre-AI group (27.7% vs 17.5%; p=0.004 and 31.8% vs 21.7%; p=0.017 respectively).Modified Rankin Scale (mRS) at discharge was significantly reduced Post-AI implementation (3.2 vs 2.8; p=0.044). Conclusion:Implementation of an AI based computer aided triage and prioritization solution for flagging participants with ICH in an emergent care setting coincided with significant reductions of 30- and 120-day all-cause mortality and morbidity.