RESUMEN
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.
RESUMEN
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.