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BACKGROUND: The gingival biotype (GB) influences treatment planning and clinical outcomes in several dental specialties. This study aimed to investigate the associations between the GB and various clinical crown and periodontal parameters, such as probing depth (PD), papillary height (PH), keratinized tissue width (KTW), crown width/crown length ratio (CW/CL), and gingival thickness (GT). The secondary objective was to evaluate the optimal cutoff values for all parameters to determine the GB in both the maxillary and mandibular anterior teeth. METHODS: This cross-sectional study included 50 healthy individuals (26 men and 24 women) aged between 20 and 35 years. The GB was determined as a binary variable based on the transparency of a periodontal probe through the buccal gingival margin (TRAN). The clinical crown and periodontal parameters, such as PH, PD, KTW, GT (free gingival thickness [FGT] and attached gingival thickness [AGT]), and the CW/CL ratio were measured. The associations between different variables were evaluated by the chi-square test. Correlations between various clinical parameters and GB were assessed using point-biserial correlation analyses. Receiver operating characteristic (ROC) analysis and the Youden index were used to calculate the optimal cutoff values for the PH, PD, KTW, FGT, AGT, and CW/CL ratio to discriminate GB. The statistical significance level was set at p < 0.05. RESULTS: The mean age of the males was 28.23 ± 2.81 years, while that of the females was 27.08 ± 2.85 years. Thick GB was present in 56% of individuals, and thin GB was present in 44% of individuals. Compared with females, males had a predilection for thick GB compared with females. According to the ROC analysis, the cutoff values to discriminate GB for mandibular anterior teeth were 3.4 mm for PH, 1.96 mm for PD, 4.21 mm for KTW, 0.98 mm for FGT, 0.43 mm for AGT, and 0.91 for the CW/CL ratio. Similarly, the cutoff values for discriminating the GB for maxillary anterior teeth were 4.02 mm for PH, 1.92 mm for PD, 3.89 mm for KTW, 1.02 mm for FGT, 0.42 mm for AGT, and 0.83 for the CW/CL ratio. PH, PD, and FGT showed strong positive correlations with GB, whereas KTW, AGT, and the CW/CL ratio showed weak positive correlation with GB. CONCLUSION: Within the limitations of the present study, a significant association between all clinical crown and periodontal parameters with the GB has been confirmed. FGT for mandibular anterior teeth and PH for the mandibular anterior teeth have emerged as the most reliable measurements to differentiate between thick and thin GB based on ROC analysis. KEY POINTS: All the clinical parameters such as papillary height, probing depth, width of keratinized gingiva, gingival thickness, and crown width/height ratio were significantly associated with gingival biotype. Free gingival thickness for mandibular anterior teeth and papillary height for the maxillary anterior teeth have emerged as the most reliable measurement to differentiate between thick and thin gingival biotypes.
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INTRODUCTION: The utilization of artificial intelligence (AI) and machine learning (ML) models has brought about a significant transformation in the manner in which periodontists gather information, evaluate associated risks, develop diverse treatment alternatives, anticipate and diagnose dental conditions that compromise periodontal health. The principal objective of this prospective study was to examine periodontists' understanding and acceptance of the application of AI in the realm of periodontology. MATERIALS AND METHODS: This observational study was conducted on 275 participants based on questionnaire using Google Forms. These forms were pre-validated and subsequently circulated among periodontists in Maharashtra via various social media platforms. The study, in its entirety, comprised four open-ended questions on participants' demographics and 14 closed-ended questions, all of which were presented to the participants in English. These questions aimed to elicit participants' awareness, knowledge, attitudes, and perspectives regarding emerging applications of AI in the field of periodontology. To analyze the collected data, researchers employed the widely utilized Statistical Package for Social Sciences (SPSS) version 22.0. RESULT: A 75% response rate was achieved and 68% of the respondents were female. 62% periodontists were aware of AI; however, only 24% were aware of its working principles. Most respondents agreed with the use of AI in periodontal diagnosis; however, they disagreed with the use of AI in predicting clinical attachment loss (69%). 80-82% respondents felt that AI should be a part of postgraduate training and should be implemented in clinical practice. However, most periodontists do not use AI for diagnostic or research purposes. 49% periodontists felt that AI does not have better diagnostic accuracy than periodontists, and therefore cannot replace them in the future. CONCLUSION: Most periodontists possessed a reasonable level of understanding regarding the utilization of AI in the domain of periodontology and expressed a desire to incorporate it into their diagnostic and treatment planning processes for periodontal conditions. Additional endeavors must be undertaken to enhance periodontists' awareness concerning the effective implementation of AI within their professional practice, with the aim of facilitating personalized treatment planning for their respective patients. It is postulated that the integration of AI will augment the likelihood of achieving favorable outcomes within the realm of periodontology.
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BACKGROUND: Bacteremia frequently occurs after treatment procedures such as extractions, scaling, root planing, periodontal surgery. There is currently significant interest in the possibility that bacteremia with oral bacteria may play role in pathogenesis of atherosclerosis. There are well-conducted studies that have determined the frequency of passage of periodontal microorganisms to the bloodstream after periodontal treatment. There is scarce information related to the incidence of periodontopathic microorganisms during bacteremia induced by this procedure. AIM: The aim of this study was to establish the frequency of passage of periodontopathic microorganisms in peripheric blood after scaling and root planing in patients with periodontitis. MATERIALS AND METHODS: Forty subjects with chronic periodontitis were included in the study. Blood samples were drawn from each patient at following intervals pre-treatment i.e., before SRP (P1), immediately after SRP (P2), and 30 minutes after SRP (P3). Following SRP, blood samples were analyzed for following microorganisms: Porphyromonasgingivalis, Tannerella. forysthus, Eikenellanella. corrodens, Campylobacter species, Micromonas. micros, and Prevotella. intermedia. STATISTICAL ANALYSIS USED: Chi-square test. RESULTS: Bacteremia was found in 70% (28/40) immediately after SRP and after 30 min, it was reduced to 25% (10/40) and 7.5% (3/40) presented bacteremia before SRP. CONCLUSIONS: It was concluded that bacteremia frequently occurs immediately after SRP with P. gingivalis showing the highest frequency in blood.