This study aims to investigate the relationship between malocclusion, cervical lordosis, and atlanto-occipital instability (AOI). By analyzing cephalometric and cervical spine measurements, this research seeks to provide insights into how malocclusion may influence cervical posture and to emphasize the importance of considering postural alignment in orthodontic diagnosis and treatment planning. This study aimed to investigate the relationship between malocclusion (ANB angle) and loss of cervical lordosis (cervical Cobb angle). A total of 155 patients (77 female, 78 male) who applied to Kastamonu Training and Research Hospital were included in the study. SNA, SNB, and ANB angles were measured using full-head tomography, and skeletal classification was performed. Cervical lordosis was evaluated using the Cobb method. Additionally, to analyze atlanto-occipital instability, measurements of the basion-dental apex distance, Atlas Anterior Tubercle-Posterior Dens (ADI) distance, lateral mass steps in the rotational plane, Powers Ratio, and clivus canal angle were performed. Statistical analyses revealed a moderate negative correlation between skeletal class and cervical Cobb angle (Pearson correlation coefficient $$r=-0.32$$, $$p=0.001$$; Spearman correlation coefficient $$r=-0.29$$, $$p=0.002$$). Similarly, a negative correlation was found between the ANB angle and cervical Cobb angle (Pearson correlation coefficient $$r=-0.28$$, $$p=0.003$$; Spearman correlation coefficient $$r=-0.26$$, $$p=0.004$$). These findings suggest that malocclusion may not only affect dental and maxillofacial structures but also influence cervical spine posture. The study found that cervical lordosis angle increased in skeletal Class 2 individuals, and this condition may be associated with postural disorders. Additionally, the prevalence of skeletal Class 3 malocclusion has increased by approximately 50% since 2004. These findings highlight the importance of considering cervical spine posture in orthodontic treatment planning and the need for a multidisciplinary approach between orthodontists and orthopedists. This study demonstrates a relationship between skeletal class, ANB angle, and cervical lordosis. Future studies with larger sample sizes will provide a more detailed understanding of this relationship.

Objectives: This study aims to analyze the evolution of orthodontic anchorage techniques before and after the year 2000 by utilizing the Latent Dirichlet Allocation (LDA) algorithm. The study investigates changes in treatment approaches, technological advancements, and material preferences in orthodontics. Material and Methods: A comparative analysis was conducted using the LDA algorithm to examine orthodontic literature before and after 2000. The study categorized key topics and terms related to orthodontic anchorage, focusing on treatment methods, material choices, and biomechanical principles. The data were analyzed to identify shifts in treatment trends and technological developments. Results: The findings indicate a transition from conventional anchorage methods, such as stainless steel wires and basic biomechanics, to more advanced skeletal anchorage systems, including mini-screws and cortical bone-supported techniques. Post-2000, the use of finite element analysis and computer-assisted treatment planning has significantly increased. Additionally, treatment approaches have shifted towards more controlled and stable mechanics, emphasizing precision in orthodontic movements. Conclusion: The study highlights a significant shift in orthodontic anchorage techniques over the past decades, demonstrating the impact of technological innovations and engineering principles on treatment methodologies. The increasing reliance on skeletal anchorage and digital planning tools suggests that future advancements will continue to optimize orthodontic outcomes, improving both efficiency and patient comfort.

This study aimed to investigate the association between idiopathic scoliosis (IS) and facial asymmetry in a gender-balanced case-control design. Based on power analysis, 100 participants were recruited (50 IS patients: 25 male, 25 female; 50 controls: 25 male, 25 female). Facial asymmetry was evaluated through clinical examination and frontal cephalometric analysis. Statistical analysis was performed using chi-square tests and logistic regression, with post-hoc power calculation. Facial asymmetry was present in 41 (82%) of IS patients compared to 18 (36%) of controls ( p < 0.001). The achieved power was 99% for detecting this difference. Among IS patients, 21 males (84%) and 20 females (80%) exhibited facial asymmetry. In controls, facial asymmetry was observed in 10 males (40%) and 8 females (32%). The odds ratio for facial asymmetry in IS patients was 7.64 (95% CI 3.02–19.32) compared to controls. Idiopathic scoliosis is significantly associated with facial asymmetry, with no substantial gender difference in this association. The high statistical power confirms the reliability of these findings.

Interactions between spinal posture and craniofacial morphology have been suggested, yet the biomechanical relationship between thoracic sagittal alignment and skeletal malocclusion remains insufficiently quantified. This observational cross-sectional study investigated the association between thoracic hyperkyphosis and skeletal malocclusion patterns and evaluated whether thoracic curvature is statistically associated with sagittal craniofacial relationships. Lateral cephalometric and spinal radiographic measurements were obtained from 166 individuals. Thoracic kyphosis angles and craniofacial skeletal parameters determining by Python (version 3.13) were analyzed using multinomial regression models adjusted for demographic variables. Thoracic hyperkyphosis was significantly associated with sagittal skeletal discrepancies (p < 0.05). In addition to the ANB-based analysis, individual assessments of SNA and SNB were performed to evaluate maxillary and mandibular contributions. No statistically significant differences were found in SNA (p = 0.245), whereas SNB showed a small but significant difference (p = 0.033) between hyperkyphotic and control groups. Path-based correlation decomposition showed that Cobb angle statistically accounted for approximately 33% of the age-ANB bivariate correlation (path coefficient β = 0.08, 95% bootstrap CI: 0.03–0.15). This is a descriptive decomposition of cross-sectional correlations and does not imply causation or temporal precedence. Receiver operating characteristic (ROC) analysis showed moderate discriminatory performance for identifying skeletal malocclusion groups (area under the curve [AUC] = 0.74). These findings demonstrate a cross-sectional statistical association between thoracic spinal alignment and craniofacial skeletal patterns. Due to the observational nature of the study design, causal relationships cannot be inferred. Postural assessment may be considered as a complementary component in orthodontic evaluations, but this recommendation requires confirmation in prospective longitudinal studies.

Aims: Accurate revenue forecasting is essential for effective financial planning in dental healthcare services; however, increasing variability in multidisciplinary clinical workload limits the adequacy of traditional linear approaches. This study aimed to develop and compare explainable machine learning–based models for forecasting monthly revenue in a multidisciplinary public dental clinic and to evaluate the specific contribution of orthodontic production within this framework.Methods: Monthly aggregate data covering 48 months (2021–2024) from dental clinics affiliated with the Kastamonu Provincial Health Directorate were analyzed. Independent variables consisted of monthly procedure volumes in endodontics, restorative treatment, prosthodontics, periodontology, orthodontics, pedodontics, and oral surgery, while total monthly sales constituted the dependent variable. Six regression models were developed using the scikit-learn library: ExtraTreesRegressor, HistGradientBoostingRegressor, ElasticNet, KNeighborsRegressor, MLPRegressor, and a Stacking Regressor combining ExtraTrees and HistGradientBoosting with a Ridge meta-learner. A preprocessing pipeline including RobustScaler and log1p transformation was applied where appropriate. Model performance was evaluated using R², mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Model interpretability was assessed using SHapley Additive exPlanations (SHAP).Results: All models achieved acceptable predictive performance; however, the Stacking model provided the best overall fit (R² ≈ 0.95) and the lowest MAE, RMSE, and MAPE (≈18%). Tree-based ensemble methods consistently outperformed distance-based and neural network approaches. ElasticNet demonstrated competitive performance (R² ≈ 0.91), indicating a strong linear component in revenue dynamics. SHAP analysis revealed that prosthodontic and restorative procedure volumes were the dominant drivers of monthly revenue, whereas orthodontic production had a comparatively modest short-term contribution.Conclusion: Ensemble-based machine learning models, particularly stacking approaches, can accurately forecast monthly revenue in multidisciplinary dental clinics using clinical production indicators. Prosthodontic and restorative services represent the primary drivers of short-term revenue fluctuations, while orthodontic activity contributes more modestly within a monthly time frame. These findings support the integration of explainable machine learning models into financial planning and operational decision-making processes in dental healthcare institutions.

Effective medical waste planning relies on the reliable estimation of waste volumes. As operational factors diversify, traditional linear regressions often fail to capture the underlying structure, whereas latent variable–based and ensemble approaches can better represent this complexity. In this study, fine-tuned Partial Least Squares (PLS), scikit-learn–based Gradient Boosting regression (GBR), and a baseline Ordinary Least Squares (OLS) model were compared for estimating medical waste generation using 48 months (2021–2024) of approximate data from Dental Clinics affiliated with the Provincial Health Directorate in Kastamonu. The model inputs were the monthly procedure counts for endodontics, treatment, prosthetics, periodontology, orthodontics, pedodontics, and surgery. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R²). All models produced accurate predictions; however, PLS provided the strongest fit (R² = 0.979; MAE = 30.488; RMSE = 37.043), outperforming GBR (R² = 0.962; MAE = 36.544; RMSE = 48.990) and the OLS baseline (R² = 0.927; MAE = 41.762; RMSE = 59.013). The findings demonstrate that modern, data-driven waste-management planning is feasible in healthcare institutions and highlight PLS as a robust option, particularly under conditions of small sample size and collinearity.