Identifying Acute Aortic Syndrome and Thoracic Aortic Aneurysm from Chest Radiography in the Emergency Department Using Convolutional Neural Network Models.

(1) Background: Identifying acute aortic syndrome (AAS) and thoracic aortic aneurysm (TAA) in busy emergency departments (EDs) is crucial due to their life-threatening nature, necessitating timely and accurate diagnosis. (2) Methods: This retrospective case-control study was conducted in the ED of three hospitals. Adult patients visiting the ED between 1 January 2010 and 1 January 2020 with a chief complaint of chest or back pain were enrolled in the study.

Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis.

Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the analysis of medical images.

Using Container Houses to Reduce the Risks to Health-Care Workers While Performing the Throat Swab Test and Blood Sampling for SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) is still pandemic all over the world. Patients requesting screening in emergency departments (ED) have continually increased. Establishing additional screening stations outside of the ED to increase the number of patients tested and protect the safety of health care workers poses an urgent challenge.

Use of Plastic Bag to Reduce Risks in Operators During Endotracheal Intubation of Patients With Coronavirus Disease 2019.

The rapid spread of coronavirus disease 2019 (COVID-19) has led to a large number of patients being admitted to hospitals, resulting in a near collapse of the medical system. The shortage of negative pressure isolation rooms and personal protective equipment is a potential problem. It is a pressing challenge to prevent the risk of infection in emergency physicians (EPs) during the endotracheal intubation of patients with COVID-19.