Effects of the COVID-19 pandemic on senior dental students in Korea: Examining stress, burnout, and depression.

Background/purpose: The COVID-19 pandemic has had a profound and enduring impact on various aspects of society, including medical education and the training of dental students. The field of dentistry, given its nature, is particularly susceptible to the challenges posed by a pandemic. Prolonged exposure to the pandemic is believed to have increased stress and burnout among medical and dental students. This study aimed to investigate and analyze the relationship between COVID-19 and stress, burnout, and depression in Korean dental students. Materials and methods: A cross-sectional survey was conducted among 162 third and fourth-grade students from the School of Dentistry at Seoul National University. The survey comprised four main sections: general information, the Maslach Burnout Inventory (MBI), the Patient Health Questionnaire-9 (PHQ-9), and the Impact of Event Scale-Revised (IES-R). Results: The results indicated significant differences in age, study time, career satisfaction, and counseling needs between third and fourth-grade students. The fourth-grade students exhibited higher scores in the IES-R survey, PHQ-9 total score, emotional exhaustion, and depersonalization subscale items of the MBI. Furthermore, the group with abnormal responses to COVID-19 demonstrated lower levels of career satisfaction. Conclusion: Fourth-grade dental students experienced higher levels of depression, vulnerability to the effects of COVID-19, and burnout. These findings highlight the need for addressing the mental health challenges faced by dental students during the COVID-19 pandemic.

Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic.

BACKGROUND: Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. METHODS AND FINDINGS: Population and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences. INTERPRETATION: Compared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs. METHODS: We used data from the Medical Information Mart for Intensive Care III database and the eICU Collaborative Research Database to develop a vasopressor prediction model. We performed systematic data preprocessing using matching of cohorts, oversampling, and imputation to control for bias, class imbalance, and missing data. Bidirectional long short-term memory (Bi-LSTM), a multivariate time series model, was used to predict the need for vasopressor therapy using serial physiological data collected 21 h prior to prediction time. RESULTS: Using data from 10,941 critically ill patients from 209 ICUs, our model achieved an initial area under the curve of 0.96 (95% CI 0.96-0.96) to predict the need for vasopressor therapy in 2 h within the first day of ICU admission. After matching to control class imbalance, the Bi-LSTM model had area under the curve of 0.83 (95% CI 0.82-0.83). Heart rate, respiratory rate, and mean arterial pressure contributed most to the model. CONCLUSIONS: We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24 h of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.

Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within 24 h using commonly available bedside and laboratory parameters taken at critical care admission. We extracted data from 2 large critical care databases (MIMIC-III and eICU-CRD). Missing variables were imputed using autoencoder. Machine learning classifiers using logistic regression and random forest were trained using 60% of the data and tested using the remaining 40% of the data. We compared the performance of logistic regression and random forest models to predict intubation in critically ill patients. After excluding patients with limitations of therapy and missing data, we included 17,616 critically ill patients in this retrospective cohort. Within 24 h of admission, 2,292 patients required intubation, whilst 15,324 patients were not intubated. Blood gas parameters (PaO2, PaCO2, HCO3-), Glasgow Coma Score, respiratory variables (respiratory rate, SpO2), temperature, age, and oxygen therapy were used to predict intubation. Random forest had AUC 0.86 (95% CI 0.85-0.87) and logistic regression had AUC 0.77 (95% CI 0.76-0.78) for intubation prediction performance. Random forest model had sensitivity of 0.88 (95% CI 0.86-0.90) and specificity of 0.66 (95% CI 0.63-0.69), with good calibration throughout the range of intubation risks. The results showed that machine learning could predict the need for intubation in critically ill patients using commonly collected bedside clinical parameters and laboratory results. It may be used in real-time to help clinicians predict the need for intubation within 24 h of intensive care unit admission.

Automatic mandibular canal detection using a deep convolutional neural network.

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients' discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.