Ergin Kalkan

Automated cephalometric analysis requires accurate landmark localization and reliable transformation of radiographic data into clinically interpretable measurements. This study developed and validated a privacy-preserving deep learning pipeline for automated detection of Sella, Nasion, A-point, and B-point landmarks and ANB angle estimation on radiographs. Specifically, lateral cephalometric radiographs were used for ANB landmark detection and estimation. In addition, anatomical reference points used for Cobb angle measurement in scoliosis assessment were defined to ensure consistency in auxiliary spinal evaluations. Initially, 1286 raw DICOM images underwent technical quality control, protected health information audit, anonymization, DICOM-to-PNG fidelity validation, and strict landmark quality control, yielding 1282 final images. Each image was evaluated by three orthodontists to establish the reference landmark standard. Ground-truth ANB angles were geometrically computed from the annotated landmarks after y-up coordinate conversion. A UNet-based heatmap regression model was trained for four-landmark localization and evaluated using stable 5-fold cross-validation and an independent locked test set. In cross-validation, the model achieved a mean radial error of 1.03 ± 0.45 mm, ANB mean absolute error of 0.97 ± 0.61°, and class accuracy of 86.85 ± 4.90%. On the locked test set, mean radial error was 0.76 mm, ANB mean absolute error was 0.57°, class accuracy was 92.68%, and SDR@2.0 mm was 96.34%. This audited pipeline provides a geometrically verified and de-identification-compliant framework for automated ANB estimation from radiographs. As an exploratory proof-of-concept extension, the shared architecture achieved a Cobb angle mean absolute error of 0.78° on a small locked test subset ( n = 36), suggesting potential for auxiliary spinal evaluation that warrants dedicated validation.

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.

The relationship between the stomatognathic system and postural control, including gait, is a growing area of interdisciplinary research. While some studies suggest a connection between malocclusion and postural imbalances, the effect of occlusal correction on plantar pressure remains underexplored. This study investigates the changes in static and dynamic plantar pressure distribution following the correction of Class II malocclusion. A clinical study was conducted on 100 participants (50 women and 50 men, mean age 24.5 ± 3.2 years) with diagnosed anteroposterior malocclusion (Angle Class II ). Plantar pressure distribution was assessed using a baropodometric platform under static conditions (bipedal stance) and dynamic conditions (gait) both before and after achieving Angle Class I occlusion. Parameters analyzed included the centre of pressure (CoP) path, forefoot/hindfoot weight distribution (%), and pressure symmetry. Following malocclusion correction, a statistically significant improvement in plantar pressure balance was observed. The CoP path length and sway area decreased significantly during static posturography (p < 0.01). The forefoot/hindfoot load distribution became more symmetrical, approaching a physiologically optimal ratio. During gait, pressure analysis revealed a more harmonious and symmetrical pressure distribution across both feet, with a reduction in asymmetrical loading patterns that were present pre-treatment. The correction of Class II malocclusion has a significant positive effect on normalizing both static and dynamic plantar pressure distribution. These findings underscore the existence of a stomatognathic-podal relationship and highlight the importance of a multidisciplinary approach involving dentists and physiotherapists for the comprehensive management of patients with malocclusion.

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.

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.

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.