Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.

Objective: To evaluate the role of visual and quantitative chest CT parameters in assessing treatment response in patients with severe asthma. Materials and Methods: Korean participants enrolled in a prospective multicenter study, named the Precision Medicine Intervention in Severe Asthma study, from May 2020 to August 2021, underwent baseline and follow-up chest CT scans (inspiration/expiration) 10–12 months apart, before and after biologic treatment. Two radiologists scored bronchiectasis severity and mucus plugging extent. Quantitative parameters were obtained from each CT scan as follows: normal lung area (normal), air trapping without emphysema (AT without emph), air trapping with emphysema (AT with emph), and airway (total branch count, Pi10). Clinical parameters, including pulmonary function tests (forced expiratory volume in 1 s [FEV1] and FEV1/ forced vital capacity [FVC]), sputum and blood eosinophil count, were assessed at initial and follow-up stages. Changes in CT parameters were correlated with changes in clinical parameters using Pearson or Spearman correlation. Results: Thirty-four participants (female:male, 20:14; median age, 50.5 years) diagnosed with severe asthma from three centers were included. Changes in the bronchiectasis and mucus plugging extent scores were negatively correlated with changes in FEV1 and FEV1/FVC (ρ = from -0.544 to -0.368, all P < 0.05). Changes in quantitative CT parameters were correlated with changes in FEV1 (normal, r = 0.373 [P = 0.030], AT without emph, r = -0.351 [P = 0.042]), FEV1/FVC (normal, r = 0.390 [P = 0.022], AT without emph, r = -0.370 [P = 0.031]). Changes in total branch count were positively correlated with changes in FEV1 (r = 0.349 [P = 0.043]). There was no correlation between changes in Pi10 and the clinical parameters (P > 0.05). Conclusion Visual and quantitative CT parameters of normal, AT without emph, and total branch count may be effective for evaluating treatment response in patients with severe asthma.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, there was a shortage of medical resources and the need for proper treatment guidelines for brain tumor patients became more pressing. Thus, the Korean Society for Neuro-Oncology (KSNO), a multidisciplinary academic society, has undertaken efforts to develop a guideline that is tailored to the domestic situation and that can be used in similar crisis situations in the future. As part II of the guideline, this consensus survey is to suggest management options in specific clinical scenarios during the crisis period. Methods: The KSNO Guideline Working Group consisted of 22 multidisciplinary experts on neuro-oncology in Korea. In order to confirm a consensus reached by the experts, opinions on 5 specific clinical scenarios about the management of brain tumor patients during the crisis period were devised and asked. To build-up the consensus process, Delphi method was employed. Results: The summary of the final consensus from each scenario are as follows. For patients with newly diagnosed astrocytoma with isocitrate dehydrogenase (IDH)-mutant and oligodendroglioma with IDH-mutant/1p19q codeleted, observation was preferred for patients with low-risk, World Health Organization (WHO) grade 2, and Karnofsky Performance Scale (KPS) ≥60, while adjuvant radiotherapy alone was preferred for patients with high-risk, WHO grade 2, and KPS ≥60. For newly diagnosed patients with glioblastoma, the most preferred adjuvant treatment strategy after surgery was radiotherapy plus temozolomide except for patients aged ≥70 years with KPS of 60 and unmethylated MGMT promoters. In patients with symptomatic brain metastasis, the preferred treatment differed according to the number of brain metastasis and performance status. For patients with newly diagnosed atypical meningioma, adjuvant radiation was deferred in patients with older age, poor performance status, complete resection, or low mitotic count. Conclusion It is imperative that proper medical care for brain tumor patients be sustained and provided, even during the crisis period. The findings of this consensus survey will be a useful reference in determining appropriate treatment options for brain tumor patients in the specific clinical scenarios covered by the survey during the future crisis.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the need for appropriate treatment guidelines for patients with brain tumors was indispensable due to the lack and limitations of medical resources. Thus, the Korean Society for Neuro-Oncology (KSNO), a multidisciplinary academic society, has undertaken efforts to develop a guideline that is tailored to the domestic situation and that can be used in similar crisis situations in the future. Methods: The KSNO Guideline Working Group was composed of 22 multidisciplinary experts on neuro-oncology in Korea. In order to reach consensus among the experts, the Delphi method was used to build up the final recommendations. Results: All participating experts completed the series of surveys, and the results of final survey were used to draft the current consensus recommendations. Priority levels of surgery and radiotherapy during crises were proposed using appropriate time window-based criteria for management outcome. The highest priority for surgery is assigned to patients who are life-threatening or have a risk of significant impact on a patient’s prognosis unless immediate intervention is given within 24–48 hours. As for the radiotherapy, patients who are at risk of compromising their overall survival or neurological status within 4–6 weeks are assigned to the highest priority. Curative-intent chemotherapy has the highest priority, followed by neoadjuvant/adjuvant and palliative chemotherapy during a crisis period. Telemedicine should be actively considered as a management tool for brain tumor patients during the mass infection crises such as the COVID-19 pandemic. Conclusion It is crucial that adequate medical care for patients with brain tumors is maintained and provided, even during times of crisis. This guideline will serve as a valuable resource, assisting in the delivery of treatment to brain tumor patients in the event of any future crisis.

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1–7 according to acquisition conditions. CT images in groups 2–7 were converted into the target CT sty le (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2–7 improved after CT conversion (original vs. converted: 0.63vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists’ scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

Objective: This study evaluated the anxiety, work, and socioeconomic burden of emergency physicians (EP) during the coronavirus disease 2019 (COVID-19) pandemic in South Korea. Methods: A cross-sectional observational study was conducted using data from the 2020 Korean Emergency Physician Survey that included the following domains: hospital resources of suspected COVID-19 patients, availability of personal protective equipment, levels of home and workplace anxiety, the severity of work burnout, identification of stressors, changes in personal environments, and measures to decrease stress/anxiety. Results: Totally, 855 eligible participants were enrolled in the study. A statistically significant deterioration was observed in response to decreased working income or an unstable employment situation (0.46±0.88 points). The median number of patients with confirmed COVID-19 was 2.72. The risk of infection during emergency department (ED) work was found to be significantly increased (P<0.01). Of the 751 respondents (91.4%) who reported patient contact during their survey phase, 516 (62.8%) reported having taken the COVID-19 reverse transcription polymerase chain reaction test. The major factors that affected worsening of the work burden were determined to be unsuitable inter-hospital transfer arrangements for febrile patients (3.46±1.06 before vs. 1.99±1.16 after the pandemic, P<0.01) and delayed consultation for patients with fever or respiratory symptoms in the ED (3.20±1.06 before vs. 2.66±1.10 after the pandemic, P<0.01). Conclusion During the peak phase, the COVID-19 pandemic induced substantial workplace and socioeconomic burdens for Korean EPs. The sustained impact of the pandemic on the wellness of EPs deserves further investigation to guide targeted interventions.

Objective: Machine learning is emerging as a new alternative in various scientific fields and is potentially a new method of interpretation. Using the Light Gradient Boosting Machine (LightGBM), we analyzed the factors that influence the rechoice of emergency medicine responders. The survey is a cross-sectional study which provides an accurate understanding of a responder's current status. However, the results may vary depending on the composition, format, and question, and the relationship between the answers may be unclear. Methods: This study evaluated the modified 2020 Korean Emergency Physician Survey raw data. We applied the preferred model for random relationship check, random forest, support vector machine, and LightGBM models. The stacking ensemble model was used for the final decision process. Results: ‘It is fun working in an emergency room’was the most selected response factor for re-choice, followed by ‘interesting major’. The physical burden of age and lack of identity had a negative impact, whereas burnout and emotional stress factors had a lesser effect. Anxiety caused by the coronavirus disease 2019 (COVID-19) is thought to have a significant impact on this decision making. Conclusion Establishing the identity of emergency medicine and being faithful to its fundamental mission is a way to increase the rate of re-choice. Decreasing the burden of workload modified according to age is recommended to establish career longevity. The method of machine learning presents us with a new possibility of checking the relevance of survey results quickly and easily.

Objective: It has been reported that emergency physicians experience high levels of depression and psychological burnout due to night shifts and tend to consider early retirement. This study intended to check working hours and intensity, individual health, satisfaction, and overall welfare, given the increase in the numbers of emergency physicians. Methods: The study used the results of the ‘2020 Korean Emergency Physician Survey (KEPS)’ conducted from January 15 to February 26, 2021, which targeted 2,138 emergency physicians registered with the Korean Society of Emergency Medicine. The survey was conducted online through the web and mobile application. Results: One thousand three hundred seven out of the 2,167 emergency physicians (60.3%) registered at the society responded. The average age of the respondents was 42.6 years, and there were 1,148 male emergency physicians and 158 female emergency physicians. The average professional experience was 8.2 years, with 325 professors, 122 commissioned doctors, 648 public servants of which 90 had military service, and 45 medical practitioners. Conclusion In conclusion, the number of working hours of emergency physicians showed a decreasing trend compared to 2010 and 2015. However, emergency physicians often expected early retirement. This suggests that, while the absolute working hours are decreasing, the actual working intensity has not decreased. For the proper development of the emergency medical system, it is necessary to establish a stable working environment for emergency physicians. A systematic study is needed on the working hours and work intensity of emergency physicians.

Objective: As the history of emergency medicine grows longer, emergency medicine specialists are observed to comprise various age groups. There have been no prior studies on the generation shift analysis of emergency medicine specialists. Our study is designed to identify the characteristics of each generation and predict the generation shift in emergency medicine physicians. Methods: The analysis was based on the results of the 2020 survey of emergency medicine specialists. The study was conducted on 1,307 respondents and finally, the responses of 967 people were analyzed after excluding 199 who were not in charge of emergency room treatments and 141 who gave incomplete responses. The respondents were divided into four groups based on the following generations: first generation (1996-1999), second generation (2000-2008), third generation (2009-2014), and fourth generation (2015-2020), classified by the year in which they acquired their emergency medicine board license. The intergenerational difference analysis was done using the Kruskall-Wallis test. Results: The first generation was characterized by a high proportion of university hospital professors, large non-clinical activity hours, a few night shifts, higher social contributions, low satisfaction with the training and education environment, and a high retirement age compared to the later generations. In the second generation, the characteristics of the first, third, and fourth generations were generally mixed. The third and fourth generations showed similar characteristics overall, though there were differences in some categories. Conclusion There were significant differences by generation in the working environments, motivation for application, and retirement age. This is the first study that quantifies generational differences. Considering these trends, a discussion about the work environment needs to be initiated.

Objective: Emergency physicians are constantly exposed to night shifts and stress due to the nature of their work, resulting in health problems such as substance abuse, depression, and suicidal ideation. To date, depression in Korean emergency physicians has never been investigated. The present study endeavors to identify the prevalence and factors related to depression in emergency physicians in Korea. Methods: The present study retrospectively analyzed data from the 2020 Korean Emergency Physician Survey. The prevalence of depression was evaluated using the PHQ-9 (Patient Healthcare Questionnaire-9). Multivariable logistic regression analysis was used to identify the factors related to depression and other variables. Results: The overall response rate of the survey was 61.3% (1,307/2,138). A total of 1,102 respondents completed the PHQ- 9 and were analyzed. The prevalence of depression was 27.0% (95% confidence interval [CI], 27.8-33.6). Following factors were found to be associated with depression: military service as the type of position (odds ratio [OR], 0.316; 95% CI, 0.126- 0.794), schedule satisfaction (OR, 0.523; 95% CI, 0.356-0.769), health perspective (OR, 0.408; 95% CI, 0.252-0.662), weekly exercise (OR, 0.879; 95% CI, 0.774-0.998), sleep quality (OR, 4.728; 95% CI, 3.329-6.715), regular diet (OR, 0.380; 95% CI, 0.239-0.605), nighttime snacking (OR, 1.607; 95% CI, 1.124-2.298), and job satisfaction (OR, 0.456; 95% CI, 0.305-0.682). Conclusion More than a quarter of respondents had depression. Job satisfaction, schedule satisfaction, and a healthy lifestyle were negatively related to depression. However, poor sleep quality showed a significantly positive association, which suggests the necessity for a long-term follow-up.