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World Health Organization invites the nations to progress towards universal health care coverage. This study evaluated preventive and curative dental services utilization among children aged 12 years and younger in Tehran, Iran, based on the Andersen behavioral model using a generalized structural equation modeling. A phone-based cross-sectional study was conducted in Tehran, Iran, on 886 children in 2023. Information on curative and preventive/consultation dental service utilization and associated factors was collected by a standard questionnaire. We used a generalized structural equation model (GSEM) to build a model based on Andersen's behavioral model. Of 886 children, 22.1% used curative dental services, and 19.9% used preventive/consultation services in the past year. Among children aged 6 years and younger, age (OR = 1.87, p-value <0.001) and parent-perceived oral health need (OR = 54.77, p-value <0.001) predicted curative services utilization and age (OR = 1.45, p-value <0.001), knowledge (OR = 1.36, p-value <0.001), dental visit before the age of one (OR = 6.05, p-value = 0.04), and socioeconomic status (OR = 1.65, p-value = 0.03) predicted preventive/consultation services utilization. Regarding children aged 7 to 12, knowledge (OR = 1.28, p-value = 0.03), dental visit before the age of one (OR = 11.12, p-value = 0.02), socioeconomic status (OR = 2.53, p-value = 0.01), dental insurance (OR = 4.17, p-value <0.001), and parent-perceived oral health need (OR = 19.48, p-value <0.001) associated with curative dental services utilization, and dental visit before the age of one (OR = 10.05, p-value = 0.02), oral health behavior (OR = 1.25, p-value = 0.04), socioeconomic status (OR = 3.74, p-value <0.001), and parent-perceived oral health need (OR = 4.62, p-value <0.001) related to preventive/consultation services utilization. The findings of this study underscore the significant influence of predisposing, enabling, and need factors on dental services utilization among children aged 12 years and younger. These results provide a valuable perspective for policymakers, highlighting the modifiable determinants that could be targeted to improve dental services utilization in this age group. [ABSTRACT FROM AUTHOR]

Objective: Oral health is often overlooked among the elderly due to the numerous comorbidities prevalent in this population. However, oral health significantly influences quality of life by affecting both general health and psychological well-being. The present study aimed to assess dental caries in elderly individuals using the DMFT index (decayed, missing, and filled teeth) and to explore its relationship with various factors based on data from the Azar cohort study in Iran. Materials and methods: In this cross-sectional study, data from the initial phase of the Azar cohort study, which involved 2629 elderly individuals (aged 60 years and older), were statistically analyzed. The Azar cohort study evaluated demographic factors, the history of chronic diseases, and behavioral habits using a well-designed questionnaire administered through face- to -face interviews. The DMFT index was evaluated through oral examination. A negative binomial regression analysis with a log link function was employed to investigate the relationship between the DMFT and related variables, including gender, age, marital status, level of education, socioeconomic status, chronic diseases, smoking, alcohol consumption, and body mass index. Results: The mean (SD) age of elderly individuals was 64.15±2.91 years. The mean (SD) DMFT was 28.42±6, and the mean (SD) number of missing teeth was 26.58±8.36. Approximately 70.8% of elderly individuals were edentulous. Women exhibited a higher mean DMFT score and a greater number of missing teeth compared to men. The mean DMFT score and its components significantly differed based on the level of education and socioeconomic status. However, no significant relationship was found between the DMFT index and the variables in the multiple regression analysis. Conclusion: The high prevalence of edentulism and the elevated DMFT scores in the elderly population of the Azar cohort indicate a poor oral health status among older individuals. Providing the dental services within the primary health care system for the adults, can be beneficial in improving oral health in old age. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) has gained significant attention in dentistry due to its potential to revolutionize practice and improve patient outcomes. However, dentists' views and attitudes toward technology can affect the application of AI. This perception and attitude can be affected by the personality traits of individuals. This study aims to evaluate the perceptions and attitudes of dentistry students toward AI. Methods: This cross-sectional study was conducted on dental students at Ordu University Faculty of Dentistry, involving a sample of 83 students. The study utilized the Big Five 50 Test to evaluate personality traits and a 5-point Likert scale to gather data on 20 statements regarding AI in dentistry. Data were analyzed using IBM SPSS Statistics software, and a chi-square test was employed to assess the relationship between the personality traits of dental students and their attitudes towards artificial intelligence, as well as the relationship between the gender of dental students and their attitudes towards artificial intelligence. Statistical significance was set at P < 0.05. Results: The study involved 83 participants, with 29 male and 54 female participants. The most common personality traits were Openness and Agreeableness, whereas the least common was Extraversion. Participants found AI useful and believed it could help dentists evaluate radiographs. However, the least agreed statement was that they would trust AI more than a dentist in evaluating radiograph results. A statistically significant difference was found between personal traits of dental students and in expressions comparing dentists and AI. Males were more familiar with AI than females. Conclusion: This study found that attitudes towards AI in dentistry vary based on personality traits. Developing educational strategies tailored to these traits can help foster more positive attitudes and improve AI integration into dental practice. [ABSTRACT FROM AUTHOR]

Background: Although the oral health status has improved in developed countries in recent years, oral diseases are still unequally distributed across socio-economic groups. Research on the impact of socio-economic factors on oral health care among older adults in Europe, including Portugal, remains limited. The main aim of this study was to investigate the association between socio-economic factors and oral health indicators in Portuguese older adults. Methods: This retrospective cross-sectional study analyzed data from 915 participants (aged 65 years and older) from the third wave (2015–2016) of the Epidemiology of Chronic Diseases Cohort Study (EpiDoC), a population-based study. Socio-economic and demographic information, general health-related characteristics and oral health data (prosthetic need, oral hygiene frequency, and last dental procedure) were collected by questionnaire. Multivariate logistic and multinomial regression models analyzed the associations between socio-economic factors and oral health indicators. Results: Participants with lower education level, perceived lower income and a fewer number of private sector appointments were more likely to have poor oral health (need for prosthetic treatment, less frequent oral hygiene, and tooth extraction as last dental procedure). Other factors found to be associated with oral health were sex, age, geographical area, smoking habits, and body mass index. Conclusions: Socio-economic factors were associated with oral health in Portuguese older adults. These findings may contribute to future national public health strategies by expanding oral health services to ensure better access and coverage for at-risk groups. [ABSTRACT FROM AUTHOR]

Objective: To explore the evolution of orthodontics with the integration of Artificial Intelligence (AI), highlighting past achievements and future directions in diagnostics, treatment planning, and patient engagement. Methods: A review of the literature was conducted to examine the historical progression of orthodontics, with a specific focus on the transformative role of AI. Key areas of focus include diagnostic accuracy, virtual surgical planning, and teledentistry. The review also discusses the clinical and non-clinical applications of AI in orthodontics and the challenges faced in its widespread adoption. Results: AI has significantly improved diagnostic precision through automated cephalometric analyses. Personalized treatment planning and AI-driven virtual simulations have optimized orthognathic surgery. AI tools enhance patient communication and engagement through virtual assistants and telemedicine platforms. Challenges such as data security, ethical concerns, and financial barriers limit AI's seamless integration into orthodontics. Conclusion: AI is revolutionizing orthodontics, enhancing diagnostics, treatment planning, and patient experience. Despite significant advancements, the integration of AI requires careful consideration of ethical, financial, and data security issues to ensure responsible and sustainable use in the field. [ABSTRACT FROM AUTHOR]

Background: Dentistry 1.0, an era where the patient journey was embedded in fully analog realities gave way to a 2.0 era of select digitalization since the late 1990s. Now, we are arguably at another tipping point, where for the foreseeable future digital workflows and solutions are expected to influence every aspect of the patient journey and big data becomes the new healthcare currency. With this, the dental profession's perspective on how to best serve the entire patient journey must also expand to explore unprecedented opportunities to unobstruct another layer of critical choke points and unlock further value in global oral health. Consider some examples. Today, the global patient journey is already obstructed well before the point of care due to insufficient global coverage and imbalanced geographic distribution of dentists. While there is a growing and aging global population, the majority of dentists serve only a minority of the human population. Such coverage gaps fuel the already growing trend of patients who consume health insights online. Going forward, those who respond to the needs of more empowered and well-informed patients are poised to be the partners of choice for modern patients who increasingly expect to take ownership in their own health journey. In addition, the Age of Information has decisively accelerated toward the Age of Intelligence. This expands our imagination in new ways, not only on how to keep up with, but also how to influence the latest scientific, clinical, economic, social, and technological developments for the greater good. Meanwhile, there are real dichotomies worth noting. For instance, the relevance of dental education to dental practice is under increasing pressure. The norm in dental education remains relatively analog curricula, which struggles to adapt considering the rate of change far exceeds the average dental school reality and capabilities. This means newly graduated dentists increasingly face this rapid digitalization alone. Such pressures further fuel other trends, such as the rise of corporate dentistry and acceleration of non-traditional players in the field. In a world when in any given week the equivalent of half of America passes through a Walmart, it is not hard to see why such big retailers find it attractive to enter healthcare. In this article, the authors explore key mega trends influencing dentistry and reflect on the oral healthcare ecosystem beyond the clinic, where clinical knowledge and skills are relevant and needed. Description: Today's global realities offer dentists unprecedented opportunities to expand their professional impact and presence well beyond established norms of clinical practice, academics, and research. The authors offer readers insights into relevant trends and give specific examples, from first-hand experiences, of professional pathways suitable for dentists in medical device and technology settings, including in contexts of innovation, development, marketing, education, sales, and post-market surveillance. [ABSTRACT FROM AUTHOR]

Oral diseases affect nearly 3.5 billion people, and medical resources are limited, which makes access to oral health services nontrivial. Imaging-based machine learning technology is one of the most promising technologies to improve oral medical services and reduce patient costs. The development of machine learning technology requires publicly accessible datasets. However, previous public dental datasets have several limitations: a small volume of computed tomography (CT) images, a lack of multimodal data, and a lack of complexity and diversity of data. These issues are detrimental to the development of the field of dentistry. Thus, to solve these problems, this paper introduces a new dental dataset that contains 169 patients, three commonly used dental image modalities, and images of various health conditions of the oral cavity. The proposed dataset has good potential to facilitate research on oral medical services, such as reconstructing the 3D structure of assisting clinicians in diagnosis and treatment, image translation, and image segmentation. [ABSTRACT FROM AUTHOR]

Background: There is an increasing occurrence of periodontitis and metabolic syndrome (MetS), which is resulting in a decline in the overall quality of life. Both disorders can occur together since they are both linked to insulin resistance and systemic inflammation. However, evidence for a role of interleukin (IL)-20 in this comorbidity is very limited. This cross-sectional study aimed to comprehensively investigate, for the first time, the levels of RANKL/OPG, MMP-8 and OSI as well as the role of IL-20 in patients with MetS and periodontitis. Methods: The study included a total of 80 individuals, divided into four groups: 20 individuals who were healthy both systemically and periodontally, 20 individuals who were systemically healthy but had periodontitis, 20 individuals who had MetS but were periodontally healthy, and 20 individuals who had both MetS and periodontitis. Periodontal clinical parameters (plaque index, gingival index, bleeding on probing, clinical attachment loss, probing pocket depth) were evaluated. Gingival crevicular fluid (GGF) and serum samples were collected and used for biochemical assays. Enzyme-linked immunosorbent assay was used to determine the levels of IL-20, receptor activator of nuclear factor kappa B ligand (RANKL)/osteoprotegerin (OPG), matrix metalloproteinase-8 (MMP-8) and oxidative stress index (OSI). Results: IL-20 levels measured in serum and GCF were statistically significantly highest in patients with MetS and periodontitis (p = 0.001). Significant positive correlation was observed between serum and GCF IL-20 values and periodontal parameters (p < 0.05). There was a positive correlation between RANKL and RANKL/OPG levels and IL-20 and clinical parameters (p < 0.05). OSI values were found to be increased in the presence of both periodontitis and MetS (p = 0.001) and were positively correlated with serum and GCF IL-20 (p < 0.05). Conclusions: These data from the study suggest a correlation between IL-20 and both MetS and periodontitis. IL-20 may potentially worsen the condition of periodontal tissue by increasing both the oxidative stress levels, periodontal collagen degredation and the ratio of RANKL to OPG. Trial registration: This study was registered on ClinicTrials.gov (NCT06092853), 2023-10-10, retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: Psychological distress has significantly impacted students in Egypt and Jordan. These countries have faced many challenges, including the COVID-19 pandemic, the fallout from the conflict in Syria, and the war in Ukraine. These crises have had far-reaching consequences, affecting the economy, food security, and energy supplies, particularly with the increased number of refugees in these countries. Amid these existing complexities, the ongoing war in Gaza further exacerbates the situation, compounding mental health challenges. Therefore, this study aimed to analyze how the war in Gaza impacted the mental health of students in Egypt and Jordan. Methods: We conducted a cross-sectional study involving students from Egypt and Jordan between December 2023 and January 2024. The questionnaire incorporated the Brief Symptom Inventory 18 (BSI-18) to assess the overall general distress score, as well as the domains of anxiety, somatization, and depression. Results: A total of 1509 Jordanian and Egyptian students were included in the study, of which 66% were female. Approximately 18% of the students had an elevation (≥ 50) in their BSI-18 total score. Females scored significantly higher in general distress [30 (18–44.25) vs. 24 (11–41), p < 0.001], and all three dimensions. The univariate predictors for elevated general distress (≥ 50) were being Egyptian (OR 1.49, 95% CI 1.08 to 2.08, p = 0.014), watching social media multiple times a day (OR 3.04, 95% CI 1.97 to 4.69, p < 0.001), and having a Palestinian connection (friend, neighbor, colleague, or relative) (OR 1.63, 95% CI 1.23 to 2.16, p < 0.001). These three predictors were retained in the backward stepwise multivariate regression analysis model. The univariate predictors for moderate and marked general distress (≥ 57) were watching social media multiple times a day (OR 3.26, 95% CI 1.78 to 5.99, p < 0.001) and having a Palestinian connection (OR 1.49, 95% CI 1.05 to 2.14, p = 0.026). Only the former was retained in the backward stepwise multivariate regression analysis. Conclusion: General distress has increased among students in Egypt and Jordan throughout the War in Gaza. Mental, psychological, and social support activities are necessary for these students, particularly those watching social media multiple times daily. The development of coping skills to manage the additional stressors of war and socioeconomic status necessitates further research within this group of students. [ABSTRACT FROM AUTHOR]

Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following machine learning algorithms were used to build the predictive models: Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron Classifier, Random Forest Classifier, and Support Vector Machine (SVM). A 5-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and ROC curves were constructed. All tested variables demonstrated statistical significance (p < 0.10) and were included in the construction of the predictive model. The Co-Gn variable stood out as the most important among the evaluated independent variables, showing greater relevance in three of the four algorithms used in assessing feature importance. In the analysis of the models' performance, the AUC ranged from 0.82 [95% CI = 0.72–0.93] to 0.66 [95% CI = 0.53–0.76] for the test data, and from 0.83 [95% CI = 0.80–0.87] to 0.71 [95% CI = 0.61–0.75] for cross-validation. The precision of the models ranged from 0.83 [95% CI = 0.75–0.91] to 0.68 [95% CI = 0.58–0.78] in the test phase, and from 0.78 [95% CI = 0.74–0.82] to 0.69 [95% CI = 0.65–0.75] in cross-validation. The SVM, KNN, and Gradient Boosting Classifier algorithms stood out with the highest AUC and precision values in both cross-validation and testing. The use of condyle, coronoid process, and sigmoid notch characteristics, in combination with supervised machine learning predictive models, shows potential for contributing to sex prediction based on morphometric bone characteristics, particularly regarding the distance between the condyle and gnathion. However, given the study's limitations, these findings should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Background: Prevalence of rising stress among medical students has been reported widely and may jeopardize patient interaction and care. Evolving medical education needs to demonstrate compassionate self-care practices to enhance mental well-being of students. Although numerous researches have highlighted the stressors and coping strategies but no structured educational intervention has been reported so far among medical students of Pakistan. Hence, we explored if a Personal Development Plan could stimulate meaningful self-reflection and self-care practices among dental students. Methods: All first-year dental students of Riphah International University participated in this mandatory lesson plan. This 1-h activity every fortnightly, consisted of a thought-provoking discussion with predetermined reading material followed by SMART goal settings & later follow ups. Students were divided into 4 groups of 18–20 students with a designated trained Mentor. At the end of academic year, students were asked to submit a 500-word reflection based on their experience of understanding self-care needs (intellectual, emotional, physical, spiritual aspects). A mixed-methods embedded design was used. First a quantitative analysis of the content of all 75 students' reflections was conducted using a scoring system based on the REFLECT rubric. Then for the qualitative manual thematic analysis, the researchers exclusively used reflections that scored levels 3 and 4 on the REFLECT rubric. Results: Approximately 60% of students demonstrated an in-depth thoughtful reflection according to the REFLECT rubric. Students reflected by acknowledging the importance of self-care for their personal and professional grooming, Some observed the positive impact of working on self-determined healthy lifestyle behaviours on their physical & emotional wellbeing. They stressed the importance of physical activity, meditation and social connections in their daily life. They also reflected on reduced stress after incorporating these self-care strategies. Conclusions: This study supports the value of incorporating self-care strategies in medical/dental curriculum to enhance personal and professional wellbeing of students and hence patient care. Students reflected on different themes of self-care related to physical, spiritual and social wellbeing. In addition, more than half of the students exhibited higher levels of reflection suggesting that these sessions contributed to action-oriented self-awareness towards holistic health. [ABSTRACT FROM AUTHOR]

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models. [ABSTRACT FROM AUTHOR]

Abstract: Aims: This study aimed to investigate the impact of online learning on the mental health and health behaviors of Thai dental students during the coronavirus disease 2019 (COVID-19) pandemic. Materials and Methods: A cross-sectional analytical study was conducted among Thai dental students from first to sixth year who had been engaged in online learning during the COVID-19 period. A total of 440 students participated. Data were collected from January to March 2023 using an online questionnaire comprising 36 items addressing demographic information, online learning experiences, and health behaviors, including exercise and sleep. Psychological health was assessed using the Depression, Anxiety, and Stress Scale-21 (DASS-21). Data analysis included descriptive statistics, chi-square tests, binary logistic regression, Pearson's correlation, and simple linear regression. Results: The study found high rates of mental health issues among the participants, with 58.2% experiencing depression, 57.3% experiencing anxiety, and 41.1% reporting stress. Personal factors influencing mental health included university type and household situation. Key factors associated with depression were the number of hours spent on assignments and the quality of the online learning environment (β = 1.096, 1.193). For anxiety, factors such as frequency of exercise, sleep quality, time spent on assignments, and familiarity with assignment submission platforms were significant (β = −0.773, −0.666, 0.801, 1.002). Stress was significantly correlated with the frequency of exercise, sleep quality, and assignment workload (β = −1.072, −0.497, 1.210). Conclusions: The findings highlight a significant association between online learning-related factors, such as the number of hours spent on assignments, and the mental health outcomes of dental students. Health behaviors, particularly exercise and sleep quality, were also linked to mental health status. These results suggest that interventions aimed at reducing assignment-related stress, improving sleep hygiene, and promoting physical activity may be crucial in mitigating mental health problems among dental students. Clinically, addressing these factors could enhance both psychological well-being and academic performance, ultimately contributing to more effective learning environments for dental professionals. [ABSTRACT FROM AUTHOR]

Background: In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of dental caries (DC) using DL algorithms from 2D radiographs. Materials and Methods: This comprehensive umbrella review adhered to the "Reporting guideline for overviews of reviews of healthcare interventions" (PRIOR). Specific keywords were generated to assess the accuracy of AI and DL algorithms in detecting DC from radiographical images. To ensure the highest quality of research, thorough searches were performed on PubMed/Medline, Web of Science, Scopus, and Embase. Additionally, bias in the selected articles was rigorously assessed using the Joanna Briggs Institute (JBI) tool. Results: In this umbrella review, seven systematic reviews (SRs) were assessed from a total of 77 studies included. Various DL algorithms were used across these studies, with conventional neural networks and other techniques being the predominant methods for detecting DC. The SRs included in the study examined 24 original articles that used 2D radiographical images for caries detection. Accuracy rates varied between 0.733 and 0.986 across datasets ranging in size from 15 to 2,500 images. Conclusion: The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Utilization of oral health services is a global public health challenge. Low- and middle-income countries are disproportionately affected. Uganda is one of the low- and middle-income countries where only 4% of the population has access to oral health services. Northern Uganda is one of the regions in the country with limited information about oral health utilization. Objective: To assess factors associated with utilization of oral health services among adults aged 18–70 years in Lira district, Northern Uganda. Methods: This was a quantitative cross-sectional study conducted in Lira District. Multistage sampling was used to select 576 respondents. Data was collected using a researcher-administered structured questionnaire that was adopted, modified, and pretested. Data was analyzed at three levels, including univariate, bivariate, and multivariate at 0.05 significance level. Results: Of the 634 respondents, 576 responded, giving a 90.9% response rate. Results show that utilization of oral health services was 20.5% (118) among adults aged 18–70 years in 6 months. Predictors of utilization of oral services among this age group are: having not attended any formal education (AOR = 0.2, 95%CI 0.06–0.62), having poor attitudes towards oral health services (AOR = 0.55, 95%CI 0.34–0.89), having cultural influence on the utilization of the oral health services (AOR = 4.84, 95% CI 2.77–8.43), accessing the services from private not-for-profit facilities (AOR = 4.67, 95% CI 1.79–12.16), being un sure of the availability of the equipment used (AOR = 0.42, 95% CI 0.18–0.9), and availability of friendly services (AOR = 2.53, 95% CI 1.12–5.50). Conclusions and recommendations: Utilization of oral health services is low in Lira district, with only 2 in 10 of the adults aged 18-70years of age utilizing oral health services in 6 months. To improve utilization, targeted health education campaigns should address cultural barriers and attitudes, while ensuring that public health facilities are better equipped and provide more user-friendly services. [ABSTRACT FROM AUTHOR]

To overcome the limitation of a single classification model's inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to compare and evaluate three AI-assisted cephalometric analysis platforms—CephX, WeDoCeph, and WebCeph—with the traditional digital tracing method using NemoCeph software. Material and method: A total of 1500 lateral cephalometric films that met the inclusion criteria were classified as Class I, Class II, and Class III. Subsequently, 40 patients were randomly selected from each class. These selected films were uploaded to 3 AI-assisted cephalometric analysis platforms and analyzed without any manual intervention. The same films were also analyzed by an orthodontist using the NemoCeph program. Results: The results revealed significant differences in key angular measurements (ANB, FMA, IMPA, and NLA) across Class I, II, and III patients when comparing the four cephalometric analysis methods (WebCeph, WeDoCeph, CephX, and NemoCeph). Notably, ANB (p < 0.05), FMA (p < 0.001), IMPA (p < 0.001), and NLA (p < 0.001) varied significantly. Linear measurements also differed, with significant differences in U1-NA (p = 0.002) and Co-A (p = 0.002) in certain classes. Repeated measurement analysis revealed variation in SNA (p = 0.011) and FMA (p = 0.030), particularly in the Class II NemoCeph group, suggesting method-dependent variability. Conclusion: AI-assisted cephalometric analysis platforms such as WebCeph, WeDoCeph, and CephX give rise to notable variation in accuracy and reliability compared to traditional manual digital tracing, specifically in terms of angular and linear measurements. These results emphasize the importance of meticulous selection and assessment of analysis methods in orthodontic diagnostics and treatment planning. [ABSTRACT FROM AUTHOR]

Copyright of Turkiye Klinikleri Journal of Health Sciences / Türkiye Klinikleri Sağlık Bilimleri Dergisi is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

OBJECTIVES: To assess the content of YouTube videos on the management of orthodontic emergencies during the COVID-19 pandemic. MATERIALS AND METHODS: YouTube was systematically searched using the keywords "orthodontic emergency," "orthodontic homecare," "braces emergency," and "braces homecare." The first 100 videos for each keyword were assessed. The upload source, number of views, likes and dislikes, duration, and global quality score were extracted. Home and clinical advice reliability was evaluated using the reliability score (DISCERN) according to the COVID-19 orthodontic emergencies protocol published by the British Orthodontic Society. RESULTS: The majority of the videos were found to be poor quality. Home care advice showed higher reliability than clinical advice. Orthodontic emergency videos uploaded by healthcare professionals had higher reliability. CONCLUSION: Many types of orthodontic emergencies have received little attention on YouTube, and more useful and reliable educational videos should be uploaded in the event of future pandemics. [ABSTRACT FROM AUTHOR]

Early diagnosis of oral potentially malignant disorders (OPMDs) can bring about considerable improvement in reducing the statistics related to morbidity and mortality associated with oral cancer. The objective of this review was to summarize advanced diagnostic aids used for OPMDs. Pubmed, Embase, and Cochrane Central Register of Controlled Trials electronic databases from 1969 to 2022 focus on advanced diagnostic techniques for potentially malignant disorders. An electronic database was searched for keywords: "Potentially malignant disorders", "Early diagnostic aids", advanced diagnostic aids in oral PMDs, precancerous lesions, precancerous conditions, diagnosis of potentially malignant disorders, and artificial intelligence in oral cancer. A total of 42 studies met inclusion and exclusion criteria. The mean standard deviation (σ) was calculated as 3.129 and specificity and sensitivity were obtained at a confidence interval (CI) of 95%. The pooled value for sensitivity and specificity of each modality of diagnostic aid was calculated. In conclusion, advanced diagnostic techniques would be very useful in the early detection of OPMDs. It is important for dentists and dental practitioners to be well versed in all available techniques of diagnosis. Implementation of early diagnostic aids at a larger screening platform could be beneficial in overcoming the cost factor involved and may improve the probability burden of oral cancer-related mortality. [ABSTRACT FROM AUTHOR]

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning. [ABSTRACT FROM AUTHOR]

ABSTRACT: Objective: To compare the performance of an AI model with that of experienced radiologists in detecting periapical periodontitis using radiographic images. Methods: Thirty radiographic images (CBCT, panoramic, and periapical) were analyzed by an AI model and two experienced radiologists. Diagnostic accuracy, sensitivity, specificity, and confidence levels were evaluated. Statistical analyses included Chi-square tests, independent samples t-tests, and Pearson correlation analysis. Results: The AI model achieved 89.6% accuracy, 86.5% sensitivity, and 88.1% specificity. Radiologist 1 showed the highest performance (accuracy 98.5%, sensitivity 93.8%, specificity 96.7%), while Radiologist 2 performed slightly lower than the AI model (accuracy 81.7%, sensitivity 83.3%, specificity 80%). The AI model demonstrated the highest mean confidence level (86.5% ± 9.18). Moderate positive correlations were observed between the AI's confidence and that of Radiologist 1 (0.383) and Radiologist 2 (0.347). Conclusions: The AI model demonstrated comparable performance to experienced radiologists in detecting periapical periodontitis. These findings suggest that AI could serve as a valuable tool in dental diagnostics, potentially improving efficiency and consistency. However, further research is needed to refine AI models and evaluate their performance across diverse clinical scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Revista ADM is the property of Asociacion Dental Mexicana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: During the Coronavirus Disease 2019 (COVID-19) pandemic, there has been concern about nosocomial infections acquired through dental practice, where machines -- such as air turbines -- that generate aerosols are used, and where there are many opportunities to come into contact with saliva and blood. Because there is no report to date on whether dental treatment is associated with a risk of SARS-CoV-2 infection in Japan, the aim of the present cross-sectional study was to examine the risk of SARS-CoV-2 infection associated with dental treatment. Materials and Methods: Cross-sectional data were gathered from the Japan COVID-19 and Society Internet Survey (JACSIS), a large-scale internet survey conducted in 2021 (n=28,175). From September 27, 2021, to October 30, 2021, the questionnaires were distributed to candidates selected from the panelists of a Japanese Internet research company to represent the Japanese population regarding age, sex, and residential prefecture using a simple random sampling procedure. The risk of SARS-CoV-2 infection related to dental treatment was examined and analysed. Results: Multivariable logistic regression analysis showed that younger age, male sex and living alone were statistically significant factors positively associated with SARS-CoV-2 infection, whereas the presence or absence of dental treatment was not statistically significantly correlated with SARS-CoV-2 infection. Conclusion: The present epidemiological study showed that dental treatment is not a positive risk factor for SARS-CoV-2 infection in Japan. [ABSTRACT FROM AUTHOR]

Background: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propose and evaluate the performance of a convolutional neural network (CNN) model for automatic tooth segmentation on IOS images. Methods: A dataset of 761 IOS images (380 upper jaws, 381 lower jaws) was acquired using an intraoral scanner. The inclusion criteria included a full set of permanent teeth, teeth with orthodontic brackets, and partially edentulous dentition. A multi-step 3D U-Net pipeline was designed for automated tooth segmentation on IOS images. The model's performance was assessed in terms of time and accuracy. Additionally, the model was deployed on an online cloud-based platform, where a separate subsample of 18 IOS images was used to test the clinical applicability of the model by comparing three modes of segmentation: automated artificial intelligence-driven (A-AI), refined (R-AI), and semi-automatic (SA) segmentation. Results: The average time for automated segmentation was 31.7 ± 8.1 s per jaw. The CNN model achieved an Intersection over Union (IoU) score of 91%, with the full set of teeth achieving the highest performance and the partially edentulous group scoring the lowest. In terms of clinical applicability, SA took an average of 860.4 s per case, whereas R-AI showed a 2.6-fold decrease in time (328.5 s). Furthermore, R-AI offered higher performance and reliability compared to SA, regardless of the dentition group. Conclusions: The 3D U-Net pipeline was accurate, efficient, and consistent for automatic tooth segmentation on IOS images. The online cloud-based platform could serve as a viable alternative for IOS segmentation. [ABSTRACT FROM AUTHOR]

Background: Urinary concerns increase with age impacting health and quality of life. The aims of this study were to describe: (1) urinary concerns as an age-related change (ARC); (2) the challenges of urinary concerns; (3) adaptation strategies used to manage urinary concerns; and (4) the value of engaging with aging (EWA) as a framework to promote self-management of urinary concerns. Methods: Data was used from semi-structured interviews with 29 older adults (mean age 77 years). An iterative coding process was used. A codebook was developed based on a-priori themes derived from the EWA framework, our previous publication, and a line-by-line coding of one of the transcripts. As the analysis progressed, additional codes emerged, enriching the codebook. Results: Six themes emerged: (1) the participants' experiences; (2) responses to urinary concerns, (3) adaptation and management strategies; (4) knowledge and understanding of urinary concerns; (5) available capacities and resources; and (6) the impact of the COVID-19 pandemic on urinary concerns. Participants tended to address their urinary concerns by adjusting routines, medication schedules, or diet patterns. They tried to secure restroom locations or use tools or reminders to resolve their urinary concerns. COVID-19 led to increased inconvenience for older adults to engage in outdoor activities due to the closure of public restrooms. Conclusions: Our in-depth qualitative analysis found that participants developed personalized adjustments to address their needs and abilities to their urinary concerns. These findings offer insights into the individual aging experience, which will further enhance our understanding and advancement of person-centered care. [ABSTRACT FROM AUTHOR]

Background: Early childhood caries (ECC) is a global public health challenge that requires innovation, infrastructure, and health system influences to bolster initiatives for its management and control. The aim of this scoping review was to investigate the published evidence on the association between ECC and the targets of the Sustainable Development Goal 9 (SDG9) concerned with industry, innovation, and infrastructure development. Methods: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A search was conducted in PubMed, Web of Science, and Scopus between July and August 2023 using a search strategy related to the promotion of resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC. Only English language publications were included. Studies that solely examined ECC without reference to the SDG9 targets were excluded. Results: The search yielded 933 studies for review. After screening for the eligibility and removing duplicates, 916 unique articles remained for further screening. However, none of the identified studies provided data on the association between resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC. Conclusion: There were no primary studies that assessed the association between ECC and SDG9, even though the plausibility of a potential relationship exists. Future studies are needed to generate evidence on the link between ECC and SDG9 as this link may contribute to the reduction in the proportion of children with untreated ECC. [ABSTRACT FROM AUTHOR]

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. Results: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. Conclusions: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition. [ABSTRACT FROM AUTHOR]

Background and Aim: Artificial intelligence in dental science is a powerful tool for diagnosis, treatment and prevention. One of the most important applications of artificial intelligence in dental science is accurate diagnosis through radiological images Deep learning algorithms enable machines to analyze images better than even professional dentists. Another application of artificial intelligence in dentistry is personalization of treatments or Individual Dentistry. The scanners provide the best treatment by recording the patient's oral condition and artificial intelligence using the stored information. Artificial intelligence algorithms can identify hidden patterns and help dentists take preventive measures. Today, artificial intelligence has opened its place in dental education and research. The purpose of this review article is to investigate the application of artificial intelligence in the science and art of dentistry. Materials and methods: This review was conducted using the electronic versions of the articles available in Pubmed/Medline, Scopus (Elsevier), Web of Sciences (Claritive Analytics) from 2020 to 2023 using the keywords Artificial Intelligence, Dentistry, Education. . All the studies were in English and finally 123 articles were found in the search, of which 43 matched the desired titles, were selected and analyzed. Conclusion: Artificial intelligence is a reliable tool in diagnosing, increasing the quality of treatment and predicting the failures of dental treatments, and its limitations include the lack of familiarity of dentists, expensive equipment and the complexity of working with it, and until the limitations are resolved, it can be used as an auxiliary tool in dentistry. [ABSTRACT FROM AUTHOR]

Objective: To test the accuracy of an artificial intelligence (AI) algorithm in predicting the favorability of maxillary canine impaction as compared to the conventional manual tracing method using orthopantomograms. Materials and Methods: Orthopantomograms of 437 canine impactions were included in this study. Six parameters (sector classification, three angular, and two linear) on orthopantomograms were used to assess the severity of the maxillary canine impaction. The most advanced convolutional neural network model implemented using MATLAB program was used in assessing the favorability of maxillary canine impaction. The outcome of the parameters assessed by the implemented AI model and conventional manual tracing were compared. Receiver operating characteristic (ROC) cMaxillary canine impaction, diagnosis, accuracy, artificial intelligence urves, diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of AI model in comparison to manual tracing for the angular and sector parameters. Paired t-tests were used for linear measurements. Results: The overall clinical performance exceeded 90% for all the angular parameters including sector classification except for the angle between long axis of canine with lateral incisor which had a specificity score of 55%. The value and the area under the ROC curve were more than 0.9 for all the parameters. The distance from the canine cusp tip to the occlusal plane and midline was statistically significant between the groups (p = .000). Conclusion: The proposed AI algorithm had higher accuracy in predicting the favorability of eruption in maxillary canine impactions compared to conventional manual tracing [ABSTRACT FROM AUTHOR]

Abstract: Introduction: The Coronavirus disease 2019 (COVID-19) pandemic became a worldwide public health crisis. It leads to raised levels of burnout and anxiety among the people of the medical and dental fraternity, especially those involved in various duties related to COVID-19. The present research focused on evaluating the prevalence and predictors of burnout among medical officers and dental surgeons in Haryana's state dental health services. Materials and Methods: A cross-sectional study was conducted to assess burnout among medical officers and dental surgeons involved in COVID-19-related duties in state health services. Maslach's burnout scale was used to evaluate burnout. It is a prevalidated and self-administered questionnaire with three domains, that is, emotional exhaustion (EE), personal accomplishment (PA) assessment, and depersonalization (DP). The Chi square test, Student's t test (unpaired), and analysis of variance (ANOVA) with post hoc analysis were done wherever applicable. Pearson's correlation analysis was used to check the relationship among EE, DP, and PA. Results: A total of 200 study participants responded to the online survey. Nearly half of them (51%) were aged 25–34 years. The majority had service experience of up to 20 years (90%). Almost three-fourths (72%) of study participants performed duties for >8 h a day. Up to 86% reported that the second wave of COVID-19 caused increased burnout. Moderate-to-high degrees of occupational exhaustion (OE) and DP were reported among approximately 42% of study participants. Medical officers were reported to have statistically significantly higher levels of burnout compared to dental surgeons (P < 0.05). Conclusion: Moderate levels of burnout were reported among half of the study participants, suggestive of the need to address the issues related to mental health of healthcare providers. [ABSTRACT FROM AUTHOR]

Introduction: Despite strong and consistent epidemiological evidence linking cigarette smoking to several cardiovascular diseases (CVDs), the association between smoking intensity and CVD risk factors remains unclear. This study aimed to explore the possible effects of cigarette smoking on cardiometabolic risk in healthy individuals. Methods: This cross-sectional study was conducted between November 2022 and June 2023. Consecutive sampling was performed to include 160 healthy participants: 100 smokers with 60 males and 40 females; and 60 age- and sexmatched non-smokers with 36 males and 24 females. Blood samples were taken from each participant to assess their cardiometabolic function: lipid profile, von Willebrand factor (vWF), high-sensitivity cardiac troponin I (hs-cTnI), and fibrinogen levels; and liver function using an automated enzymatic method. In addition, blood sugar level, body mass index (BMI), and blood pressure were recorded. Results: Smokers had significantly higher vWF functional activity and hs-cTnI but significantly lower albumin and total bilirubin levels than non-smokers (65.87 ± 19.07 vs 56.45 ± 6.59, respectively, p<0.001; 0.0382 ± 0.0077 vs 0.0147 ± 0.0105, respectively, p<0.001; and 4.63 ± 0.32 vs 4.74 ± 0.28, respectively, p=0.026). The number of cigarettes consumed daily was associated positively and significantly with plasma levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, vWF functional activity, and hs-cTnI but were negatively associated with total bilirubin. Moreover, heavy smokers had a significantly higher BMI and waist-to-hip ratio among male smokers than non-smokers. Conclusions: Cigarette smoking was associated with increased dyslipidemia, BMI, and central obesity, in addition to higher vWF functional activity. Altogether, increased hs-cTnI levels in smokers indicate a higher susceptibility to CVD. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopadie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field. [ABSTRACT FROM AUTHOR]

Background: Dentistry students face many pressures associated with treatment methods, training, and Faculty-level administration. Although several research have assessed the anxiety, stress, and depression of undergraduate students studying dentistry globally, data is limited in Egypt. This work aimed to determine the levels of depression, anxiety, and stress in dental students at the Faculty of Dentistry, Kafr El Sheik University, Egypt. Methods: This questionnaire-based, cross-sectional study included 400 students at the Faculty of Dentistry, Kafr El Sheik University, Egypt. Participants were recruited using snowball sampling using a physical copy of the Depression, Anxiety, and Stress Scale (DASS 21) questionnaire. Results: Depression level was low in 90 (22.5%), moderate in 215 (53.8%), and high in 95 (23.8%) participants. Anxiety level was low in 134 (33.5%), moderate in 193 (48.3%), and high in 73 (18.3%) participants. Stress level was low in 43 (10.8%), moderate in 206 (51.5%), and high in 151 (37.8%) participants. There was a significant relation between overall DASS 21 and age greater than 20, higher year, female sex, below average financial status, and last year's regrade of students (P value < 0.001, 0.008, and 0.041, respectively) and an insignificant relation between overall DASS 21 and residence of students. A significant relation between year and (depression, anxiety, stress, and overall DASS 21) was observed. Conclusions: The significant depression, anxiety, and stress prevalence among Egyptian dentistry students is concerning. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)