Background: To determine the prevalence and severity of periodontitis in an Iraqi population using the 2017 classification. Methods: Data on the periodontal status of patients were retrospectively collected from healthcare records. The information included demographics, clinical parameters including bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment loss (CAL). Descriptive and inferential statistical analyses were performed using SPSS (version 26, IBM, USA) software. Results: Out of 1578 records, the periodontal health, gingivitis, and periodontitis represented 26.4%, 37.1%, and 36.5%, respectively with males more affected by periodontal disease compared with females. Periodontitis cases were dominated by stage 3 and 4 which combined accounted for 77.3%, followed by stage 2 (21.3%), and stage 1 (1.4%). When looking at disease grade and levels of stability, localized pattern, Grade C, and unstable status were the most prevalent domains. Regression analysis suggested age, BOP, male, and positive family history as predictors for increasing extent and severity of CAL both in periodontal health and disease. Conclusion: The prevalence of periodontal disease (periodontitis, and gingivitis) was almost equally expressed. Severe periodontitis was the most dominant group whilst milder forms of disease were least prevalent. The data indicated that demographic variables and clinical parameters could predict severity of attachment loss.

Background: Periodontal diseases are inflammatory disorders caused by the accumulation of oral biofilm and the host response to this accumulation which characterized by exaggerated leukocytes and neutrophils attraction to the sites of inflammation by chemoattractants which are a very important part of the pathogenesis of periodontal diseases. This study aimed to determine and compare the clinical periodontal parameters and the leukocyte cell types in the peripheral blood between patients with gingivitis and periodontitis with different severities compared to healthy controls. Materials and methods: This study included 150 male subjects aged between 35-50 years. They were divided into three groups: gingivitis group (n=30), periodontitis patients (n=90) which subdivided into Mild =30 patients, Moderate =30 patients, Severe =30 patients and a control group (n=30) with clinically healthy periodontium. Clinical periodontal parameters were recorded ((plaque index (PLI), gingival index (GI), bleeding on probing (BOP), probing pocket depth (PPD) and clinical attachment level (CAL)). Blood samples were collected then an automated blood analyzer evaluated leukocyte cell types. Results: Significant differences in The counts of neutrophils and lymphocytes exhibited significant differences among the study groups and subgroups. On contrary, differences in monocytes, eosinophils, and basophils counts were not significant. Additionally, severity of periosontitis was significantly correlated with the mean counts of the various leukocyte cell types; however, clinical periodontal characteristics did not show such correlation with these inflammatory cells. Conclusion: This study demonstrated that periodontal disease with different severities is associated with possible episodes of bacteremia that originate from periodontal lesions which mediate inflammatory conditions that in turn causing changes in the systemic markers especially leukocytes cells types.

Background/Objectives: Conventional logistic regression is widely used in the field of dentistry, specifically for prediction purposes in longitudinal studies. This study aimed to compare the validity of different supervised machine learning (ML) models to the conventional logistic regression (LR) model to predict the outcomes of nonsurgical periodontal treatment (NSPT). Methods: Patients diagnosed with periodontitis received full periodontal charting, including bleeding on probing (BoP), probing pocket depth (PPD), and clinical attachment loss (CAL). Furthermore, the tooth type, tooth location, tooth surface, arch type, and gingival phenotype were also collected as site-specific predictors. Later, root surface debridement was provided and treatment outcomes were evaluated after 3 months. Site-specific predictors were used to train five ML models, including random forest (RF), decision tree (DT), support vector classifier (SVC), K-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB), to develop predictive models. Results: Site-specific predictors of 1108 examined sites were used, and the overall accuracy prediction of the conventional LR model was 70.4%, with PPD statistically significantly associated with the outcome of NSPT (odds ratio = 0.577, p = 0.001). Among the ML models examined, only GNB and SVC showed comparable prediction accuracy (71.0% and 70.4%, respectively) to the LR model, whereas the prediction accuracies of KNN, RF, and DT were 65.0%, 62.0%, and 61.0%, respectively. Similarly, baseline PPD was shown to be the most important featured predictor by both the RF and DT models. Conclusions: The evidence suggests that supervised ML models do not outperform the LR model in predicting the outcomes of NSPT. A larger sample size and more predictors of periodontitis are necessary to enhance the accuracy of ML models over the LR model in predicting the outcomes of NSPT.

Objectives: This study aimed to assess the familiarity and knowledge regarding photodynamic therapy (PDT) amongst the specialists, undergraduate and postgraduate students in the academic dental institutes. Subjects and Methods: A cross-sectional questionnaire-based survey was conducted among specialists, undergraduate and postgraduate students from three dental colleges. The three-sections questionnaire was distributed online to the targeted populations. The first section of the questionnaire aimed to collect the demographics variables of the respondents, while the second and third sections were dedicated to determining the familiarity and knowledge towards PDT, respectively. Results: Out of 700 respondents, only 403 (57.6%) responded to the questionnaire with an average age of 28.4 ±7.9 years, who were divided into 127 (31.5%) male and 276 (68.5%) female. Most respondents demonstrated an acceptable level of familiarity regarding PDT with postgraduates and specialists expressing significantly higher degree of familiarity than undergraduates. Additionally, the specialists showed a significantly higher degree of knowledge about PDT than postgraduates and undergraduates. Similarly, respondents with a longer professional career experience (>6 years) showed significantly higher knowledge than those with less years of experience. Overall, true familiarity of PDT was reported by only 25.1% of the respondents. Conclusion: Generally, respondents demonstrated average degrees of familiarity and knowledge about PDT. However, in-depth knowledge of the respondents was compromised suggesting integration of PDT topic in the dental curricula of undergraduates and postgraduates together with continuous education and training programs for dental practitioners.