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(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. The collected chest radiography (CXRs) data were divided into training (80%) and testing (20%) datasets. The training dataset was trained by four different convolutional neural network (CNN) models. (3) Results: A total of 1625 patients were enrolled in this study. The InceptionV3 model achieved the highest F1 score of 0.76. (4) Conclusions: Analysis of CXRs using a CNN-based model provides a novel tool for clinicians to interpret ED patients with chest pain and suspected AAS and TAA. The integration of such imaging tools into ED could be considered in the future to enhance the diagnostic workflow for clinically fatal diseases.

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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. This study aimed to develop an artificial intelligence model using CNN-based transfer learning to identify AE in lateral neck radiographs. All cases in this study are from two hospitals, a medical center, and a local teaching hospital in Taiwan. In this retrospective study, we collected 251 lateral neck radiographs of patients with AE and 936 individuals without AE. Neck radiographs obtained from patients without and with AE were used as the input for model transfer learning in a pre-trained CNN including Inception V3, Densenet201, Resnet101, VGG19, and Inception V2 to select the optimal model. We used five-fold cross-validation to estimate the performance of the selected model. The confusion matrix of the final model was analyzed. We found that Inception V3 yielded the best results as the optimal model among all pre-train models. Based on the average value of the fivefold cross-validation, the confusion metrics were obtained: accuracy = 0.92, precision = 0.94, recall = 0.90, and area under the curve (AUC) = 0.96. Using the Inception V3-based model can provide an excellent performance to identify AE based on radiographic images. We suggest using the CNN-based model which can offer a non-invasive, accurate, and fast diagnostic method for AE in the future.

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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. We used a large clear plastic bag, cut an opening that covered the patient's head, and created a negative pressure environment inside the plastic bag using the hospital's medical gas pipeline system; thus reducing the amount of virus-containing aerosols leaked out and the risk of infection in the operators performing intubation. The video (http://www.caregiver.com.tw/Article.asp?ID=1258#article) about the detailed preparation of the plastic bag intubation kit (PBIK) has been posted on the website. This technique for safe endotracheal intubation in patients with COVID-19 is being used not only by EPs in Taiwan, but also by physicians and paramedics from other countries. Regarding designing the PBIK, our original intention was to use readily available materials to make tools that can improve the safety of the operators performing the intubations in situations where medical resources are exhausted. However, due to limited time and patients, further research is needed for validation.

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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. We employed a container house near the entrance of an ED to create an outdoor screening station, which separates suspected patients of COVID-19 from regular emergency patients to prevent cross infections. In our experience, a container house station can not only provide additional screen area but also reduce the consumption of personal protective equipment. Container houses are sturdier than tents and can be fully assembled rapidly. Appropriate protective equipment can be installed with them to fulfi ll demands for COVID-19 screening.