The University of Puthisastra (UP; Khmer: សាកលវិទ្យាល័យពុទ្ធិសាស្ត្រ) is a private university in Phnom Penh, Cambodia. UP was recognised by the Royal Government of Cambodia under a sub-decree signed by Prime Minister Hun Sen on 15 November 2007. UP has also been awarded full accreditation, for its Foundation Year Course, by the Accreditation Committee of Cambodia (ACC).UP celebrates more than a decade of excellence in health science and technology and aims to train and inspire the next generation of doctors, dentists, pharmacists, nurses, midwives, laboratory technicians, computer scientists and technology entrepreneurs. UP focuses on the health sciences and science and technology has eight departments (Medicine, Dentistry, Pharmacy, Nursing, Midwifery, Medical Laboratory, and ICT) and a Center for Health Counselling..
Background: Accurate identification of anatomical landmarks on panoramic radiographs is a foundational yet challenging skill in dentistry. Traditional didactic teaching often requires supplementation to achieve proficiency. This study evaluates and compares the efficacy of three supplementary learning modalities: self-directed learning (SDL), traditional manual tracing (MT), and an AI-driven approach using ChatGPT. Methods: In this prospective study, 63 third-year dental students were assigned to one of three groups (n = 21 each): SDL, MT, or ChatGPT-assisted learning. Following a theoretical lecture, students were assessed using a 30-item test immediately after the lecture (baseline) and again at a 4-week follow-up. Intra- and intergroup differences were analysed using Wilcoxon signed-rank and Kruskal–Wallis tests, respectively. Results: Intergroup analysis demonstrated that the MT group achieved significantly higher overall scores than both the SDL and ChatGPT groups (P < .05), correctly identifying the most landmarks (26/30). Within-group analysis revealed significant improvements from baseline in the MT group for 24 landmarks (P < .05 for key structures like the hard palate and hyoid bone) and in the ChatGPT group for 16 landmarks (P < .05 for the glossopharyngeal air space). The SDL group showed no significant improvement. Notably, the ChatGPT group outperformed MT in identifying four specific landmarks, including the zygomatic process and nasopharyngeal air space. Conclusion: For optimal learning in dental radiology, an integrated approach is recommended. MT proved most effective overall, while ChatGPT added value for specific landmarks. Combining both methods may further enhance student proficiency. Clinical Relevance: Identification of landmarks is essential for accurate diagnosis and treatment planning. This study demonstrates that MT significantly enhances landmark recognition, while ChatGPT provides supplementary value. Integrating traditional and AI-assisted methods may further strengthen dental radiology education.
Non-communicable diseases (NCDs) pose a major public health issue in low- and middle-income countries (LMICs), with Cambodia facing a substantial and increasing burden. This narrative review complies with national data and policy-relevant findings to address the epidemiology of significant NCD categories, identify underlying risk factors, and evaluate systematic challenges to provide effective prevention and care. In addition, it also assesses current national strategies and highlights the most important areas for intervention, including primary prevention, expanded screening, sustainable health financing and intersectoral action. Addressing the increase of NCDs requires coordinated effort among governments, healthcare providers, and communities through comprehensive intersectional strategies. Lessons from the Cambodian experience are intended for future research, public policy, and NCD interventions in similar LMICs that are undergoing rapid demographic and epidemiological transitions, thereby promoting more effective and equitable NCD control.
Accurate identification of intraoral radiographic landmarks is essential for diagnosis; however, complex jaw anatomy presents learning challenges for undergraduate dental students. The educational potential of AI-assisted tools and drawing-based learning for intraoral radiographic anatomy remains unexplored. This study evaluated the effectiveness of ChatGPT-5.0-assisted learning and drawing-based learning as supplementary tools to enhance students’ understanding of intraoral radiographic anatomy. This comparative study included third-year Bachelor of Dental Surgery students. After a standardized lecture, students were randomly assigned to three groups: ChatGPT, self-directed learning, and drawing (n = 26 each), and baseline knowledge was assessed. The ChatGPT group used structured prompts, the drawing-based learning group drew and labelled landmarks, and the self-directed learning group studied independently. A post-intervention assessment was conducted after 12 weeks. Intragroup changes were analysed using paired t-tests with Cohen’s d, and intergroup differences using one-way ANOVA with Eta-squared (η²). ChatGPT and drawing groups showed significantly greater improvement than the Self-directed learning group. The drawing group demonstrated the most consistent gains, with significant improvement in 12 landmarks and moderate-to-large effect sizes (Cohen’s d ≈ 0.45–0.77). The ChatGPT group showed significant improvements in several landmarks, particularly in the maxillary and mandibular anterior regions. Intergroup analysis showed higher post-instructional scores for the drawing group in 20 of 30 landmarks, with moderate-to-large η² values (≈ 0.08–0.25). ChatGPT and drawing-based learning are effective supplementary strategies for learning intraoral radiographic landmarks, outperforming self-directed learning alone. Their integration into dental radiology education may enhance anatomical understanding and long-term learning outcomes.
Background Teleorthodontics is a transformative approach that enhances access and treatment efficiency in orthodontic care through telecommunications, digital imaging, and AI-assisted planning. While it offers benefits such as reduced in-office visits and improved patient compliance through real-time remote monitoring, its rapid adoption raises important ethical concerns that require careful consideration. Objectives This scoping review aims to map and synthesize the current literature on ethical considerations related to teleorthodontics, including patient privacy, informed consent, regulatory challenges, malpractice risks, and the integrity of the patient-doctor relationship. Eligibility criteria Peer-reviewed articles published between 2010 and 2025, focusing on ethical issues in teleorthodontics and teledentistry, were included. Studies not addressing ethical dimensions or lacking relevance to orthodontic practice were excluded. Sources of evidence A comprehensive search was conducted in PubMed, Scopus, Web of Science, and Google Scholar. Additional sources were identified by screening reference lists of eligible studies. Charting methods Following the Arksey and O’Malley framework, enhanced by Levac et al., data were extracted on publication year, country, study type, and ethical themes. Thematic analysis was performed to categorize the concerns identified across the studies. Results This scoping review identified 20 studies published between 2017 and 2024, focusing on ethical considerations in teleorthodontics. The findings highlight major ethical concerns, including data security vulnerabilities, lack of standardized informed consent protocols, limitations of remote monitoring, increased malpractice risks, and challenges related to maintaining the quality of the patient-doctor relationship in virtual care settings. Conclusion This scoping review identifies key ethical concerns in teleorthodontics, including data privacy, informed consent, malpractice risks, and the evolving patient-doctor relationship. While teleorthodontics enhances accessibility, challenges such as limited clinical oversight and regulatory ambiguity persist. Emerging technologies like AI and blockchain may help mitigate these risks.
Objectives: Palatal mini-implants have become essential tools in orthodontic anchorage; however, their success depends on adequate palatal hard tissue thickness and acceptable soft tissue morphology. The objective of this study is to evaluate the thickness and distribution of suitable sites for palatal orthodontic mini-implant placement using cone-beam computed tomography (CBCT). Material and Methods: This retrospective analysis of CBCT images from forty-nine subjects (24 males, 25 females; age range 14–30 years) was conducted. Subjects were categorized into adolescents ( n = 24) and adults ( n = 25). Palatal hard tissue, soft tissue, and combined thickness were measured at four coronal planes corresponding to the first premolar (PM1), second premolar (PM2), first molar (M1), and second molar (M2) regions. Measurements were obtained bilaterally at 1-mm intervals from the midpalatal suture up to 10 mm laterally. Intra-examiner reliability was assessed using the intraclass correlation coefficient (ICC). A post hoc power analysis was performed to evaluate the adequacy of the sample size for detecting sex-related differences. Statistical analysis included two-way analysis of variance (ANOVA) to evaluate the effects of coronal plane and distance from the midline, along with one-way ANOVA and Mann–Whitney U tests to assess age and sex-related differences. Statistical significance was set at p < 0.05. Results: Intra-examiner reliability demonstrated good reproducibility (ICC = 0.766; 95% confidence interval: 0.537–0.890; p < 0.001). Palatal hard tissue thickness showed a consistent V-shaped distribution across all coronal planes, with maximum thickness at the midpalatal suture, minimum at 2–3 mm lateral to the midline, and gradual increase toward 10 mm. Mean hard tissue thickness differed significantly among planes ( p < 0.001), being greatest in the PM1 region (5.2 ± 1.4 mm), followed by the PM2 (4.8 ± 1.3 mm), M1 (4.1 ± 1.2 mm), and M2 regions (3.7 ± 1.1 mm). Combined hard and soft tissue thickness ≥7 mm was consistently observed in the PM1 and PM2 regions at 1–5 mm from the midpalatal suture in both adolescents and adults. Two-way ANOVA showed significant effects of coronal plane and distance from the midline ( p < 0.05). No statistically significant effects of age and sex were observed ( p > 0.05); however, post hoc power analysis revealed low statistical power (7.4%) to detect small differences between sexes, with observed effect sizes ranging from r = 0.022 to 0.305, indicating that the study was underpowered for detecting subtle sex-related variations. Conclusion: Palatal hard tissue thickness follows a consistent V-pattern with the PM1 and PM2 regions, particularly at positions 1–5 mm lateral to the midpalatal suture, which provide the most favorable anatomical conditions for safe and predictable palatal miniscrew placement.
