Background: Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators. Results: We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report. Conclusion: Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: (https://sites.google.com/view/sr-ln/ssc).

Background: Artificial intelligence–based clinical decision support systems (AI-CDSSs) have enhanced personalized medicine and improved the efficiency of health care workers. Despite these opportunities, trust in these tools remains a critical factor for their successful integration into practice. Existing research lacks synthesized insights and actionable recommendations to guide the development of AI-CDSSs that foster trust among health care workers. Objective: This systematic review aims to identify and synthesize key factors that influence health care workers’ trust in AI-CDSSs and to provide actionable recommendations for enhancing their trust in these systems. Methods: We conducted a systematic review of published studies from January 2020 to November 2024, retrieved from PubMed, Scopus, and Google Scholar. Inclusion criteria focused on studies that examined health care workers’ perceptions, experiences, and trust in AI-CDSSs. Studies in non–English languages and those unrelated to health care settings were excluded. Two independent reviewers followed the Cochrane Collaboration Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Analysis was conducted using a developed data charter. The Critical Appraisal Skills Programme tool was applied to assess the quality of the included studies and to evaluate the risk of bias, ensuring a rigorous and systematic review process. Results: A total of 27 studies met the inclusion criteria, involving diverse health care workers, predominantly in hospitalized settings. Qualitative methods were the most common (n=16, 59%), with sample sizes ranging from small focus groups to cohorts of over 1000 participants. Eight key themes emerged as pivotal in improving health care workers’ trust in AI-CDSSs: (1) System Transparency, emphasizing the need for clear and interpretable AI; (2) Training and Familiarity, highlighting the importance of knowledge sharing and user education; (3) System Usability, focusing on effective integration into clinical workflows; (4) Clinical Reliability, addressing the consistency and accuracy of system performance; (5) Credibility and Validation, referring to how well the system performs across diverse clinical contexts; (6) Ethical Consideration, examining medicolegal liability, fairness, and adherence to ethical standards;(7) Human Centric Design, pioritizing patient centered approaches; (8) Customization and Control, highlighting the need to tailor tools to specific clinical needs while preserving health care providers’ decision-making autonomy. Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers’ trust in AI-CDSS tools were transparency, usability, and clinical reliability. Conclusions: The findings highlight the need for explainable AI models, comprehensive training, stakeholder involvement, and human-centered design to foster health care workers’ trust in AI-CDSSs. Although the heterogeneity of study designs and lack of specific data limit further analysis, this review bridges existing gaps by identifying key themes that support trust in AI-CDSSs. It also recommends that future research include diverse demographics, cross-cultural perspectives, and contextual differences in trust across various health care professions.

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.

Background: In keeping with nation-wide efforts to contain the spread of COVID-19, Universiti Brunei Darussalam (UBD) transformed fully its pedagogical delivery to online mode, where we investigated teaching and learning experiences, physical and mental health of undergraduate students and lecturers during the COVID-19 pandemic. Methods: We conducted a cross-sectional study on undergraduate students and lecturers in a health science faculty using a self-developed pretested questionnaire through anonymous online data collection method. Results: Fifty-six lecturers (100% response rate) and 279 students (93.3% response rate) participated. The positive experiences reported by students include becoming independent (72.8%) and adapting to online learning (67.4%), while lecturers learned new teaching techniques (50.0%) and became more innovative (50.0%) by learning new tools (48.2%). However, studying at home caused students to feel more distracted (72.0%) with a feeling of uncertainty towards examinations (66.7%), while lecturers felt that students’ laboratory skills were compromised (44.6%). Even though online delivery of assessments enabled lecturers to explore all options (50.0%), they found it difficult to maintain appropriate questions (41.1%) and fair assessments (37.5%). Majority of students missed eating out (68.8%) and felt a lack of participation in extracurricular activities (64.9%), while lecturers reported more time for exercise (51.8%), despite having more screen time (50.0%) and computer-related physical stress (44.6%). In terms of mental health, increased stress in students was reported (64.9%), though they had more time for self-reflection (54.8%). Although lecturers reported a closer relationship with family (44.6%), they also felt more stressed due to deadlines, unexpected disruptions and higher workloads (44.6%) as well as concerns related to work, family and self (39.3%). Conclusion: In this abrupt shift to online teaching, students and lecturers in our study identified both positive and negative experiences including the impact on their physical and mental health. Our findings are important to provide the evidence for online pedagogical benefits and can serve to promote the enhancement and adaptation of digital technology in education. Our findings also aim to promote the importance of addressing physical and mental health issues of the university community’s well-being through provision of emotional and mental health support and appropriate programs.

Background: Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. Methods: We used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Results: Random Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age. Conclusions: Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.

Little evidence estimated the exposure of psychosocial work stressors, work-related fatigue, and musculoskeletal disorders for nurses working in South-East Asian region, and research on this subject is almost nonexistent in Brunei. The main aim of our study was to provide a comprehensive exploration and estimate exposure of the study variables amongst emergency (ER) and critical care (CC) nurses in Brunei. The study also aims to compare whether experiences of ER nurses differ from those of CC nurses. This cross-sectional study was implemented in the ER and CC departments across Brunei public hospitals from February to April 2016 by using Copenhagen Psychosocial Questionnaire II, Occupational Fatigue Exhaustion Recovery scale, and Cornell Musculoskeletal Discomfort Questionnaire. In total, 201 ER and CC nurses (82.0% response rate) participated in the study. Quantitative demands of CC nurses were significantly higher than ER nurses. Even so, ER nurses were 4.0 times more likely [95% confidence interval (2.21, 7.35)] to experience threats of violence, and 2.8 times more likely [95% confidence interval: (1.50, 5.29)] to experience chronic fatigue. The results revealed that nurses experienced high quantitative demands, work pace, stress, and burnout. High prevalence of chronic and persistent fatigue, threats of violence and bullying, and musculoskeletal pain at the neck, shoulder, upper and lower back, and foot region, was also reported. This study has provided good estimates for the exposure rate of psychosocial work stressors, work-related fatigue, and musculoskeletal disorders among nurses in Brunei. It provided important initial insight for nursing management and policymakers to make informed decisions on current and future planning to provide nurses with a conducive work environment.