Background The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation. Methods This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert. Results Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51–0.62), and 0.82 (CI 0.73–0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48–0.82). Conclusion After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.

Purpose of Review This review seeks to discuss the use of RA in the ED including benefits of administration, types of RA by anatomic location, complications and management, teaching methods currently in practice, and future applications of RA in the ED. Recent Findings The early use of RA in pain management may reduce the transition of acute to chronic pain. Multiple plane blocks have emerged as feasible and efficacious for ED pain complaints and are now being safely utilized. Summary Adverse effects of opioids and their potential for abuse have necessitated the exploration of substitute therapies. Regional anesthesia (RA) is a safe and effective alternative to opioid treatment for pain in the emergency department (ED). RA can manage pain for a wide variety of injuries while avoiding the risks of opioid use and decreasing length of stay when compared to other forms of analgesia and anesthesia, without compromising patient satisfaction.

Background: Severe acute respiratory syndrome coronavirus 2 induces a marked prothrombotic state with varied clinical presentations, including acute coronary artery occlusions leading to ST-elevation myocardial infarction (STEMI). However, while STEMI on electrocardiogram (ECG) is not always associated with acute coronary occlusion, this diagnostic uncertainty should not delay cardiac catheterization. Case Reports: We present 2 cases of patients with COVID-19 that presented with STEMI on ECG. While both patients underwent cardiac catheterization, a delay in time to intervention in the patient found to have acute coronary artery occlusion may have contributed to a poor outcome. Why Should an Emergency Physician Be Aware of This?: These cases highlight the fact that while not all COVID-19 patients with STEMI on ECG will have acute coronary artery occlusions, there is continued need for prompt percutaneous coronary intervention during the severe acute respiratory syndrome coronavirus 2 pandemic.