The study aims to examine dental care utilization and the determinants contributing to the observed inequalities among Thai older adults during the demographic shift of Thailand to a complete-aged society, in which over 20% of the population are over the age of 60 years old, prior to the implementation of the national oral health plan for the older adults in 2015. This study analyzed data from the nationally representative Health and Welfare Surveys conducted in 2009 and 2015. Participants included individuals aged 60 years and older (N = 11,402 in 2009; N = 25,566 in 2015). Dental care utilization was measured by self-reported utilization within the past 12 months. Descriptive and logistic regression analyses were performed regarding the objective. Socioeconomic and geographic inequalities in dental care utilization persisted throughout the study period. Older adults with higher socioeconomic status were more likely to utilize dental care than their lower socioeconomic status peers.  Utilization was highest in Bangkok and lowest in the rural areas of each region. The beneficiaries of the Civil Servant Medical Scheme were twice as likely to use dental services compared to those under the Universal Coverage Scheme. The findings from the multivariate analysis identify several key factors that significantly influence the utilization of dental care. These factors provide valuable insights into how demographic, socioeconomic, geographic, and health-related factors can influence access to dental services. Demographic Factors: Women tend to use dental care services more than men. In 2009, their Adjusted Odds Ratio (OR_adj) was 1.19 (1.02, 1.38), which increased to 1.31 (1.18, 1.47) by 2015. Younger older adults (ages 65-74) also exhibit higher dental visit rates, with an OR_adj of 2.28 (1.76, 2.96) in 2009 and 1.93 (1.57, 2.35) in 2015. Middle-aged older adults (ages 75-84) experienced a significant increase in dental care utilization, with OR_adj values of 1.86 (1.42, 2.43) in 2009 and 1.61 (1.30, 1.98) in 2015. Socioeconomic Factors: Individuals in the highest income group (5th quintile) had an OR_adj of 1.96 (1.60, 2.41) in 2009, which increased to 2.25 (1.94, 2.60) in 2015. Higher education levels, particularly completing secondary school, are associated with more frequent dental visits, reflected by an OR_adj of 1.78 (1.30, 2.43) in 2009 and 2.68 (2.03, 3.54) in 2015. Geographic Factors: Residents of the Central region utilize dental services less frequently. Their OR_adj was 0.44 (0.33, 0.59) in 2009 and decreased to 0.34 (0.27, 0.41) in 2015. Those living in rural areas also reported lower utilization rates, with an OR_adj of 0.49 (0.32, 0.54) in 2009 and 0.34 (0.28, 0.41) in 2015. Health-Related Factors: Being enrolled in the Civil Servant Medical Benefit Scheme (CSMBS) is associated with more dental visits, with an OR_adj of 1.85 (1.59, 2.14) in 2009, increasing to 2.10 (1.88, 2.35) in 2015. Additionally, having a chronic illness is linked to increased dental care utilization, with an OR_adj of 1.25 (1.10, 1.43) in 2009 and 1.29 (1.16, 1.43) in 2015. During the transition of aged society, inequalities in dental care utilization among Thai older adults persist, particularly among those with low socioeconomic status and residents in rural areas. Targeted policy reforms, such as expanding preventive programs and integrating dental services into primary care, are essential to promote equitable oral health access among Thailand’s aging population.

This study aimed to compare knowledge, attitudes, practices in children oral healthcare, and caregiver satisfaction between two chatbots: 21-day and short-term. The study was a quasi-experimental design involving parents of children aged 2-5 years in urban areas of Songkla Province, divided into two groups: 21-day and short-term chatbot group. Google form surveys were used to gather research data before and after intervention. T-test and chi-square statistics were used to compare the knowledge, attitude, practice, and satisfaction scores. The two groups' differences before and after the intervention were compared in cases where the pre-intervention data showed a statistically significant difference. According to the study's findings, there were 75 participants in the 21-day chatbot group and 81 in the short-term group. There was a statistically significant difference between the knowledge and dietary behavior scores. The knowledge scores of the short-term and 21-day chatbot groups were 5.2 ± 1.8 and 6.8 ± 1.6, respectively (P-value < 0.001). The respective scores for dietary behavior were 2.5 ± 1.5 and 2.9 ± 1.2 (P-value = 0.004). The oral hygiene care and attitude scores were significantly different from the beginning of the study. However, when comparing the differences, the increased scores were not significantly different. The satisfaction scores showed significant difference, with values of 4.4 ± 0.9 and 4.1 ± 1.1, respectively (P-value = 0.033). In summary, 21-day chatbot showed different increases in knowledge, dietary behavior, and user satisfaction compared to the short-term chatbot. Meanwhile, the increases in oral hygiene care and attitude scores were not significantly different.

This research aims to develop and evaluate deep learning models for diagnosing Class III malocclusion in pediatric patients using lateral cephalometric radiographs. The study compared an artificial neural network (ANN) model and a convolutional neural network (CNN) model with image embedding and logistic regression. Radiographs from patients aged 3-12 years (2007-2023) were analyzed. Model performance was evaluated using classification accuracy, sensitivity, precision, F1 score, and area under the ROC curve (AUC). Contrary to expectations, the ANN model outperformed the CNN model. The ANN model achieved 90.3% classification accuracy, high sensitivity and precision, an F1 score of 0.902, and an AUC of 0.948, indicating excellent discrimination ability. In contrast, the CNN model showed a lower performance with 71.6% classification accuracy, an F1 score of 0.715, and an AUC of 0.750. Despite the underperformance of the CNN model, potential improvements include data augmentation, larger diverse datasets, and exploring advanced CNN architectures. The superior performance of the ANN model suggests its potential as a reliable diagnostic tool for general dentists in early screening of Class III malocclusion. This study demonstrates the promise of deep learning in orthodontic diagnosis, particularly the effectiveness of ANN models. Further research is needed to enhance CNN performance and validate findings with larger, diverse datasets. Developing such AI-based diagnostic tools could significantly impact early detection, timely referrals, and treatment planning for Class III malocclusion in pediatric patients.

To quantify Bifidobacterium longum and Fusobacterium nucleatum levels in the infected root canals of primary teeth and to analyse the association between these bacteria, clinical signs and symptoms, and radiographic findings. One hundred and twenty Thai children, aged 2 to 8 years old, were recruited from the Pediatric Dental Clinic, Maha Chakri Sirindhorn Dental Hospital, Mahidol University, Nakhon Pathom, Thailand. The treatment received was either pulpectomy or pulpotomy based on the diagnosis of the American Academy of Pediatric Dentistry (AAPD) guidelines. Clinical signs and symptoms and periapical radiographs of the infected primary teeth were recorded. A total of 120 samples were collected from primary molar teeth using aseptic techniques. DNA was extracted from samples and quantitative real-time PCR was performed using fluorescent dye (SYBR green). Participants included 62 boys (52%) and 58 girls (48%). Mean age±standard deviation was 5.62±1.22 years old. Eighty-three participants (69%) and 115 participants (96%) had clinical signs and symptoms and showed radiographic pathology of an infected root canal, respectively. There was a 100% (120/120) detection rate using the 16srRNA universal primers. B. longum and F. nucleatum were detected at 56% (67/120) and 57% (68/120), respectively. Range of total bacteria, B. longum and F. nucleatum detection were 1.63x10²-1.64x10⁷, 0-1.17x10⁶ and 0-2.02x10⁶ cells/ml, respectively. Range of the ratio of B. longum/total bacteria and F. nucleatum/total bacteria were 0-3.13x10^-1 and 0-7.13x10^-1, respectively. The ratio of B. longum/total bacteria correlated with clinical signs and symptoms in only the sensitivity to percussion (p=0.043) while the ratio of F. nucleatum/total bacteria were correlated with three clinical signs and symptoms which are sensitivity to percussion (p=0.027), sensitivity to palpation (p=0.001), and the presence of gingival abscess (p=0.001). The ratio of B. longum/total bacteria were not correlated with any of radiographic findings while the ratio of F. nucleatum/total bacteria were associated with the widening periodontal ligament (PDL) space (p=0.004), periapical lesion (p=0.028), furcation involvement (p=0.002), and root resorption (p=0.027). In conclusion, B. longum and F. nucleatum were detected higher than 50% in the infected root canal of primary teeth. F. nucleatum levels showed positive correlation with many clinical symptoms and radiograph pathology, while B. longum levels showed positive correlation with sensitivity to percussion.

In recent years, artificial intelligence (AI), particularly large language models (LLM), has advanced rapidly and been applied across various sectors, including healthcare and dentistry. A prominent application is the development of chatbots that simulate human-like conversations using natural language processing (NLP). These tools can assist in patient education, especially when providing accessible explanations of complex medical procedures. However, the quality of information they provide in non-English languages, such as Thai, remains underexplored. This study aimed to assess the quality of Thai-language responses generated by LLM-based chatbots regarding orthognathic surgery combined with orthodontic treatment (OGS). To evaluate the quality of information provided by Thai-language LLM-powered chatbots in response to frequently asked patient questions about orthognathic surgery with orthodontic treatment. Two board-certified oral and maxillofacial surgeons created a set of 10 frequently asked questions in Thai about OGS. These were submitted to four major AI chatbot platforms: ChatGPT (GPT-4o), Google Gemini (Gemini 2.0 Flash), Anthropic Claude (Claude 3.5 Sonnet), and Microsoft Copilot (standard version with o1 reasoning), using only their free or basic versions. Chatbot responses were anonymized and coded to blind the evaluators. Two experienced OGS specialists independently assessed each answer using the Global Quality Score (GQS), which evaluates five domains: accuracy, completeness, clarity, relevance, and consistency. Scores were recorded in Microsoft Excel and analyzed using descriptive statistics. Inter-rater reliability was measured using the intraclass correlation coefficient. Gemini 2.0 Flash scored highest in accuracy (3.90 ± 1.66), followed by Copilot (3.40 ± 1.58), GPT-4o (2.80 ± 1.23), and Claude 3.5 Sonnet (2.40 ± 1.07). For completeness, Gemini led again (4.70 ± 0.95), while Claude had the lowest score (3.90 ± 1.10). In clarity, Copilot ranked highest (4.50 ± 0.85), and Claude lowest (2.80 ± 0.79). In relevance, Gemini scored highest (4.40 ± 0.84), while Claude trailed (3.30 ± 0.67). Copilot achieved the highest consistency (4.70 ± 0.48), and Claude the lowest (3.80 ± 0.92). The overall inter-rater reliability for GQS was good. The quality of responses generated by Thai-language LLM chatbots on OGS-related topics varied between platforms. Google Gemini 2.0 Flash demonstrated the highest overall performance across multiple evaluation domains. While GPT-4o produced understandable content for general users. To enhance chatbot reliability, Thai healthcare professionals are encouraged to contribute high-quality, accessible Thai-language content for model training. Chatbots may serve as supplementary tools for patient education but should not replace professional medical consultation.

The transfer of Subdistrict Health Promoting Hospitals (SHPHs) to Local Administrative Organizations (LAOs) in Tak Province is part of Thailand’s decentralization strategy. In this process, five SHPHs, including their oral health services, moved under the jurisdiction of local municipalities and Subdistrict Administrative Organizations. This change brought a shift in command structure and required personnel to adapt to new organizational cultures and methods. To understand these changes, researchers conducted qualitative interviews with 26 stakeholders, including LAO executives, SHPH leaders, dental therapists, and district hospital dental heads. Through content analysis, three central themes emerged. Firstly, resistance surfaced during the transfer due to differing interpretations of readiness between national public health authorities and local bodies. Secondly, personnel were compelled to adapt and negotiate in their roles, carefully balancing official regulations, professional standards, and the welfare of local residents. These negotiations reflected a shift in power dynamics and information asymmetry between professionals and the community after the transfer. Lastly, the identity of oral health services evolved as local support increased, giving dental therapists to engage more in their profession, though oversight mechanisms remained somewhat unclear. In summary, while the transfer process faced friction, it highlighted the determination of both local authorities and SHPHs to succeed in the new arrangement. The differing notions of readiness underscored fundamental contrasts between public health and local administrative perspectives. For long-term success, personnel must be fluent in regulations, know their professional boundaries, and build strong negotiation skills. Importantly, as local organizations bolster their support for oral health services, clear and consistent oversight frameworks are needed to ensure quality and accountability in the system.

This study aimed to examine the level of sweet preference and the influencing factors, including social and cultural conditions, among pre-elderly Thai-Malay Muslims. A mixed methods design was employed. In the quantitative phase, 263 participants aged 40–59 years residing in Saiburi District, Pattani, were assessed for sweet preference using the free-choice method with six sucrose concentrations. Interviews were conducted to collect data on sex, education, income, occupation, residence (coastal/inland), urbanicity (urban/rural), body mass index (BMI), age, and self-perceived sweet preference. Data were analyzed using Chi-square tests, odds ratios and logistic regression. The qualitative study was conducted followed the quantitative study by selecting participants from each extreme group (highest and lowest) for semi-structured interviews on social and cultural influences. Among the 263 participants, most were female with low socioeconomic status. High sweet preference (>10 °Bx) was found in 37.3%. Logistic regression analysis showed that occupation and self-perceived sweet preference were significantly associated with the level of sweet preference. Labor-intensive workers were twice as likely to prefer higher sweetness level than non-labor workers (OR = 2.0, 95% CI: 1.1–3.5). Those perceiving themselves as sweet likers were 2.2 times more likely to prefer higher sweetness than those who were neutral or dislike (OR = 2.2, 95% CI: 1.3–3.9). In the qualitative phase, 10 participants from each group (highest and lowest sweet preference group were include, content analysis revealed that participants with high sweet preference perceived sweetness as refreshing and energizing during heavy work, sweet perception reinforced by family and environmental contexts. In contrast those with low sweet preference often mentioned health concerns or family influences discouraging sugar use. Cultural practices including sweet food consumption during Ramadan, serving sweet dishes to guests, and offering sugar in religious ceremonies were identified as additional factors shaping sweet preference. In conclusion, strategies to reduce sugar consumption should integrate social and cultural contexts to ensure sustainable behavior change.