ABSTRACT Background AI‐based dentistry has advanced significantly in recent years. AI models like deep learning (DL) and machine learning (ML) have paved the way for new approaches to image diagnostics and early risk prediction, making patient treatment plans more personalized. Aim The objective of this study was to assess the explainability, bias, and generalizability of AI models used in dentistry and evaluate the correlation between AI models. Methods Four databases were searched to retrieve relevant research records. The protocol was registered with PROSPERO. The data extraction sheet was designed according to PRISMA guidelines, and the data were managed in MS Excel. Also, a correlation analysis was performed to determine the nature of the relationship between the variables using SPSS. All tests were performed at a 95% confidence interval. Additionally, a critical appraisal of the included studies was also performed using the PROBAST tool. Results Eleven studies were included in this review. Overall, the assessment indicated variability in correlation strength between AI model accuracy and attributes of trustworthiness (r = 0.367–0.987). Analysis demonstrated the good performance of DL models (3D U‐Net; accuracy = 95.10%) relative to others (73%–98.20%). However, the heterogeneous nature of included studies (n = 11) focused on different dental domains like diagnosis, dental service use, and disease risk prediction, which limits its generalizability. Conclusion Findings from this review indicated the importance of methodological rigor while using AI models in dentistry. Results suggest that the incorporation of trustworthiness attributes can improve dental treatment planning and early disease diagnosis.
ABSTRACT Purpose Early diagnosis and treatment are essential in managing congenital cleft conditions. Use of artificial intelligence (AI) in routine diagnosis can substantially improve management of chronic conditions; however, in the context of CL/P, evidence remains fragmented. Thus, an umbrella review was planned to explore the applications of AI‐based diagnostic systems in managing orofacial clefts. Methods Priori protocol was registered with PROSPERO. Five electronic databases were thoroughly searched (PubMed/MEDLINE, Scopus, Embase, Google Scholar, ScienceDirect), based on the pre‐defined PICO framework. The data extraction form was designed in accordance with the Joanna Briggs Institute (JBI) guidelines and analyzed. Results were presented in the form of tables, supported by narrative summaries. Overlap assessment was conducted to avoid overemphasis and duplication of the overall results. Methodological robustness of the included studies was assessed using AMSTAR 2.0 tool. Results Of 395 initially retrieved articles, only three systematic reviews were included for this study. Overlap assessment indicated a high percentage of overlap among studies, with the corrected covered area to be 13.89%. Overall analysis revealed that AI models—DCNN (97% to 96%), RF (99% to 96%), and SVM (94% to 93%) demonstrated high accuracy in diagnosing orofacial clefts. Deep learning models were extensively used in diagnosing and predicting the orofacial cleft with accuracies across models ranging over 90%. Machine learning models also demonstrated good performance in identifying genetic risk. However, the lowest accuracies were demonstrated by the DesNet model (nearly 73% accuracy). Also, methodological robustness indicated a moderate level of confidence, suggesting limitations in the generalizability of the findings. Conclusions Findings present the potential of AI models in diagnosing and detecting orofacial clefts. AI models can support the early diagnosis of orofacial clefts; however, this study highlights the need for more comprehensive research to determine how different AI models can improve early diagnosis and treatment outcomes. PROSPERO Registration Number: CRD420251125934.
Background : Social media significantly influences both parents and children, affecting daily routines and various social sectors. Nutrition, crucial for growth and oral health, involves understanding nutrients and their impact on diet, health, and disease. Children's dietary habits are shaped by their parents' food choices and knowledge. A balanced diet, including vegetables and fruits is essential for health and disease prevention. Parents with better nutritional knowledge provide healthier food for their children. However, there's a lack of studies on how social media affects parents dental nutritional knowledge, children's eating behavior, and dental caries progression. Objective: The present study is formulated to assess influence of social media on dental nutritional knowledge of parents , eating behaviour and dental caries progression in their children. The study aimed to assess the extent and pattern of parents’ exposure to oral health related content on social media platforms, evaluate their level of dental nutritional awareness and consistently analyze children’s dietary behaviour and clinically record dental caries status using standardized indices. Methodology: A total no of 350 (parent and child pair) participants will be selected for this research project.A modified questionnaire from previous studies evaluating the influence of social media on parental perception of nutritional knowledge and eating behavior of children was developed in English and subsequently translated into Hindi and Marathi languages respectively. The questionnaire was validated and pilot tested. The primary data will be collected from this self-administered closed-ended questionnaire. Following this the child’s DMFT/DEFT scores wasrecorded simultaneously to access the caries incidence and to calculate the cumulative caries score. Results: Statistical analysis was done using Open Episoftware . Data was analysed statistically after sample collection. Descriptive statistics and Chi-Square test was used for statistical analysis.P>0.001 was found to be statistically significant. Conclusion: The study confirmed the fact that parental utilization of social media had significantly influenced their dental nutritional knowledge and was seen to have an impeding impact on eating behaviour in their children subsequently leading to increased dental caries incidence in them.
PURPOSE:Effective wound healing and postoperative pain management are critical in oral and maxillofacial surgery, particularly in alveoloplasty. This study evaluates the efficacy of a novel biomaterial in promoting wound healing and reducing postoperative pain following alveoloplasty. By comparing this biomaterial with conventional treatment methods, the study aims to determine its impact on wound healing, pain relief, and rescue medications required post operatively. MATERIALS AND METHODS:A bilateral comparison study was conducted with 14 patients undergoing bilateral alveoloplasty. Standard Alveoloplasty procedure was carried out. On one side, novel biomaterial was placed and sutured. After 21 days, alveoplasty was done on the opposite side and novel biomaterial was not used. Post operative wound healing was assessed using Southampton wound healing scale on post-operative day 1 (POD 1), POD 3, POD 7. Pain score and rescue medications required were noted and compared. RESULTS:Results indicate that the novel biomaterial significantly enhances wound healing, demonstrating reduced inflammation, accelerated tissue regeneration, and improved epithelialization. Additionally, patients treated with the biomaterial reported lower postoperative pain levels compared to the control group. It was also noticed that the rescue medications required were less when novel biomaterial was used. CONCLUSION:This study provides valuable insights into the role of advanced biomaterials in surgical recovery, emphasizing their potential to improve postoperative outcomes in alveoloplasty. The findings support the integration of this biomaterial into clinical practice, offering a promising alternative for enhanced healing and pain control.
