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This study aimed to present a complete overview of the trends, difficulties, and improvements in dental treatment for children diagnosed with autism spectrum disorder through rigorous bibliometric analysis. The dimensional database field was chosen to enable the inclusion and recall of the greatest number of relevant entries. All peer-reviewed international journals published between 2004 and 2023 were included in this study. The sophisticated search keywords' Autistic Disorder" OR "Autism Spectrum Disorder" AND "Dental management" were used. Studies that examined patterns and difficulties in the dental care of children with ASD were included in the inclusion and exclusion criteria for this analysis. Among the listed countries, The United States had the highest number of documents and citations. Barbara Sheller is the most cited author, followed closely by Arthur H.Friedlander, Michael E.Mahler, and John A.Yagiela. This study also revealed that most articles involved multinational collaborations, highlighting the global scope of dental research. The United States leads in terms of research output and citations, followed by India and China. Research on autism spectrum disorder has grown substantially over the past few years, and the authors recommend that future research concentrate on creating effective behavioral techniques that will assist children with autism spectrum disorder in coping with dental treatment. [ABSTRACT FROM AUTHOR]

This systematic review investigates the probable effect of proton pump inhibitor (PPI) use on the severity of periodontal disease and peri-implantitis and implant survival. We conducted a literature search in PubMed, Scopus, and Cochrane Central Library up to April 2024. Two review authors independently screened the title and abstracts and then the full texts of retrieved studies. Observational and clinical trial studies that assessed the association between PPI use and periodontal disease severity and peri-implantitis or implant survival were included. Data extraction from the included studies was done by 2 reviewers independently. Of 940 studies initially retrieved from online searching, 7 met the inclusion criteria. Three studies examined periodontitis, whereas 4 focused on peri-implantitis and implant longevity. On the contrary, evidence regarding the impact of PPI use on peri-implantitis and implant survival is conflicting. Therefore, more well-designed randomized controlled trials are warranted to come to a definite conclusion. Because PPIs alter the gut microbiome and affect bone, plus that the pathogenesis and etiology of periodontal disease are affected by bacteria within the periodontal pocket, it is hypothesized that they may affect periodontal pathogenesis. [ABSTRACT FROM AUTHOR]

Purpose: Virtual reality (VR) simulations have been increasingly employed to train dental students prior to clinical practice. According to the literature, blended learning designs in the form of VR simulations can be utilized by both dental students and instructors to provide quality education. They can also save time and improve motor skills before students enter clinical stages. Therefore, this study was designed to review the importance of available VR simulators and their impact on student learning and outcomes. Method: The Preferred Reporting Items for Systematic Reviews and Meta‐Analysis guidelines were followed to review the literature systematically, and different databases such as PubMed, ScienceDirect, Cochrane Library, Scopus, and Google Scholar were searched (up to December 2023) for relevant articles using keywords: "virtual reality," "virtual reality simulators," "virtual reality simulation," and "dental education." The Mixed Methods Appraisal Tool was used to assess the study quality. Results: After a comprehensive literature search, 1477 research articles were identified, of which 16 were included in the present study. In terms of students' learning outcomes, engagement, and optimal approach, a significant improvement was observed compared to conventional training methods, specifically in their knowledge, performance, confidence, and psychomotor skills. Conclusion: The findings suggest that VR simulators enhance the overall learning abilities of dental students and should be regarded as an integral component of the current curriculum. However, it is important to recognize that VR simulators cannot fully substitute traditional training methods; rather, they can effectively complement them. [ABSTRACT FROM AUTHOR]

Introduction: Gingival darkening, known as smokers' gingival melanosis or smoker's melanosis, is a benign symptom attributed to excessive deposition of melanin pigment. Melanin, responsible for skin, hair, and eye coloration, accumulates excessively, resulting in darkened gums. While not typically a severe health concern, smokers and former smokers may worry about this aesthetic change. This study aims to differentiate and categorize gingival pigmentation in smokers compared to non‐smokers using a deep learning‐based SqueezeNet. This innovative diagnostic tool holds significant promise in the medical and dental fields for assessing the presence and prognosis of such gingival pigmentations. Methods: The research utilized intraoral images obtained from patients at Saveetha Dental College. Two hundred patients exhibiting oral and gingival pigmentation were enrolled, specifically targeting those with a smoking history and excluding cases of amalgam, metallic, physiological pigmentation, intestinal polyposis, and pigmentation from food allergies. After applying inclusion and exclusion criteria, 102 subjects were selected from the initial pool for further analysis. These subjects were classified into healthy and pigmented gingiva, displaying brownish‐black pigmentation. Intraoral photographs were captured and subjected to segmentation and annotation using a SqueezeNet for subsequent analysis. Machine learning techniques were employed for further evaluation, including random forest, logistic Regression, and naive Bayes. Results: The accuracy results indicate the performance of three different machine learning algorithms, namely random forest, logistic regression, and Naive Bayes, on predicting the presence of pigmented gingiva in smokers and non‐smokers. Using random forest, logistic regression, and Naive Bayes, respectively, it was discovered that the accuracy of pigmented gingiva in smokers and non‐smokers was 88.8% and 92.6%, 93.3% and 95.2%, 95.1%, and 95.6%. A confusion matrix and a Roc graph were produced. Conclusions: The results suggest that machine learning algorithms can be effective in predicting the presence of pigmented gingiva in different populations. The confusion matrix and ROC graph can provide further insights into the model's performance and can be used to optimize the classifier's threshold for prediction. Thus, it allows patients to self‐diagnose and doctors to diagnose in their hectic clinical routines. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this in vitro investigation was to assess how implant depth could affect the three‐dimensional positional accuracy of digital impressions made from angulated implants. Materials and Methods: Four modified maxillary models were printed and divided into four study groups. In each model, two angulated implant analogs were placed at the sites of the first premolar and first molar at four different depths of 1 (G1), 2 (G2), 3 (G3), and 4 (G4) mm from the models' edentate area. Scan bodies were connected to the analogs, and one operator made 10 full‐arch scans for each master model using an intraoral scanner. Afterward, the marginal gingival part of all models was removed, and digital scans were performed for each model using a laboratory scanner to achieve a reference STL file as the control group. One‐way ANOVA and Leven's tests were used to measure and compare the 3D distance deviations across research groups after the superimposing test and control scans. Results: A significant difference between research groups was revealed by trueness and precision analysis (p < 0.001). The trueness and precision results obtained for G1 and G4 were significantly better than those for G2 and G3 (p < 0.05). Conclusion: This study demonstrated that implant depth could affect the digital implant impressions' 3D positional accuracy. [ABSTRACT FROM AUTHOR]