Background: Depression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models to predict the risk of depression in college students and identify important family and individual factors.Methods: This study predicted college students at risk of depression and identified significant family and individual factors in 171 family data (171 fathers, mothers, and college students). The prediction accuracy of three ML models, sparse logistic regression (SLR), support vector machine (SVM), and random forest (RF), was compared.Results: The three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism.Discussion: These findings demonstrated the ability of ML models to accurately predict the risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.

Background: The incidence of depression among employees has gradually risen. Previous studies have focused on predicting the risk of depression, but most studies were conducted using basic statistical methods. This study used machine learning algorithms to build models that detect and identify the important factors associated with depression in the workplace.Methods: A total of 503 employees completed an online survey that included questionnaires on general characteristics, physical health, job-related factors, psychosocial protective, and risk factors in the workplace. The dataset contained 27 predictor variables and one dependent variable which referred to the status of employees (normal or at the risk of depression). The prediction accuracy of three machine learning models using sparse logistic regression, support vector machine, and random forest was compared with the accuracy, precision, sensitivity, specificity, and AUC. Additionally, the important factors identified via sparse logistic regression and random forest.Results: All machine learning models demonstrated similar results, with the lowest accuracy obtained from sparse logistic regression and support vector machine (86.8%) and the highest accuracy from random forest (88.7%). The important factors identified in this study were gender, physical health, job, psychosocial protective factors, and psychosocial risk and protective factors in the workplace.Discussion: The results of this study indicated the potential of machine learning models to accurately predict the risk of depression among employees. The identified factors that influence the risk of depression can contribute to the development of intelligent mental healthcare systems that can detect early signs of depressive symptoms in the workplace.

In cases where the bone presence is <4 mm, the lateral approach is typically the first approach considered. Nevertheless, for patients who prefer minimally invasive procedures or wish to reduce postoperative discomfort, the crestal approach is a viable option. The objective of this study was to investigate the potential of crestal sinus augmentation for achieving adequate bone in individuals with residual bone measuring <3 mm. The study comprised 11 participants (63.6 ± 10.9 years of age) who were treated at Seoul St. Mary’s Hospital between 2021 and 2023 and received crestal approach sinus augmentation due to insufficient bone density of <3 mm using the crestal approach kit. Pre-augmentation and post-augmentation cone-beam computed tomographic images were analyzed using the imaging software to measure vertical bone height, bucco–palatal width, and mesio–distal width. Before the intervention, the height of the vertical bone was 2.1 ± 0.5 mm, while the width from buccal to palatal was 10.6 ± 2.9 mm, and the width from mesial to distal was 9.9 ± 1.2 mm. Following the intervention, these measurements increased to 8.8 ± 0.9 mm, 12.2 ± 2.2 mm, and 10.2 ± 1.2 mm, respectively. The change in height of the vertical bone was 6.7 ± 1.0 mm, in bucco–palatal width was 1.6 ± 1.4 mm, and in mesio–distal width was 0.3 ± 0.4 mm. The results revealed a significant rise in the height of the vertical bone and bucco–palatal width. Crestal sinus augmentation has been demonstrated to be a highly effective approach for achieving an adequate quantity of bone in patients with residual bone <3 mm. Nonetheless, further observation is required to evaluate the long-term prognosis of the procedures.

Impact of a computed tomography-based artificial intelligence software on radiologists’ workflow for detecting acute intracranial hemorrhage - Diagnostic and Interventional Radiology

Background and Objectives: Maxillary sinus pathologic conditions may increase the risk of complications during posterior maxillary sinus augmentation surgery. The purpose of this study was to evaluate the changes in participants with preoperative maxillary sinus mucosal thickening and to assess this factor as a preoperative risk indicator for sinusitis after maxillary dental implantation. Materials and Methods: We compared the preoperative and postoperative maxillary sinus mucosal thickness (MSMT), the distance between the maxillary sinus ostium and sinus floor (MOD), and the MSMT/MOD ratio. The participants were divided into three groups (sinus augmentation, bone grafting, and no grafting). Results: The mean preoperative MSMT was 4.3 ± 2.0 mm, and the mean MSMT/MOD ratio was 0.13 ± 0.05. No postoperative sinusitis was observed in these patients, including cases caused by anatomical variations. The mean postoperative MSMT was 4.5 ± 2.3 mm, and the mean postoperative MSMT/MOD ratio was 0.15 ± 0.06. There was no statistically significant difference between the groups at each time point (p > 0.05). Conclusions: The study found no significant change in MSMT at post-treatment evaluation, even when considering different subgroups. It underscores the importance of preoperative maxillary sinus radiographic assessments and collaboration between dentists and otolaryngologists for better outcomes in patients with preoperative maxillary sinus mucosal thickening.

Background and Objectives: This study aims to bridge these gaps by utilizing data from the Korea National Health and Nutrition Examination Survey (2013–2015), examining the nuanced associations between milk consumption’s quantity, frequency, and type and the prevalence of dental caries. Materials and Methods: Utilizing data from the Korea National Health and Nutrition Examination Survey (2013–2015), this study explores the association between milk consumption and the prevalence of dental caries in a sample of 4843 subjects (weighted n = 15,581), including 2856 males and 1987 females; weighted sample sizes were 6656 and 8925 for men and women, respectively. The prevalence of dental caries was assessed by evaluating the number of decayed, filled, and missing teeth. Results: The analysis demonstrated a significant positive association between increased milk consumption and the risk of developing dental caries, with an overall odds ratio of 1.653 (95% CI: 1.153–2.370, p < 0.05). The association was more pronounced in females, exhibiting an odds ratio of 1.865 (95% CI: 1.157–3.006, p < 0.05), and age was identified as a significant variable, particularly among participants aged 50 and above. In contrast, the relationship among the male group, though positive (odds ratio: 1.613, 95% CI: 0.991–2.625), was not statistically significant (p = 0.054). Conclusion: These findings suggest that milk consumption may be a potential risk indicator for dental caries, particularly among women, emphasizing the need for targeted dietary recommendations in dental health practices.