Purpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values. Methods: In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom. Results: Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47–3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased. Conclusion: These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues.

Objective: This study aimed to compare the Dental Discomfort Questionnaire (DDQ) scores in children with and without intellectual disability (ID) and to measure correlation between the total DDQ and the Decayed, Missing, and Filled Teeth (DMFT/dmft) scores, as well as the condition of the tooth causing pain. Method: This cross-sectional study included 81 children with normal intellectual development who attended the Departments of Pediatric Dentistry at two Turkish Universities and 80 children with different levels of intellectual disability who reported dental pain in special education centers. The 12-question DDQ (Turkish version) was applied to the parents of the patients with their consent. The relationship of the DDQ scores with tha of the DMFT/dmft, dental status, and demographic data was evaluated. Results: When the DDQ scores of children with intellectual disabilities were evaluated, it was found that the majority of the answers given to the questions were statistically similar (p < 0.05) to those of children with normal cognitive level. In the questions in which “pain when eating and brushing teeth” was evaluated, a higher score was obtained, which led to an increase in the total DDQ score (p < 0.001). There was a statistically significant difference between the groups in terms of the distribution of dental conditions (p < 0.001). When compared to the normal cognitive group, patients with mild and severe intellectual disabilities had more deep dentin caries, thoughy, frequent periapical abscess was less common in those groups (p < 0.001 and p = 0.022). There was no statistically significant relationship between DMFTscores. Conclusion: The DDQ was found to be a descriptive, functional, and easy-to-use questionnaire for children with intellectual disabilitiesin terms of detecting the presence of dental pain. No correlation was found between DMFT/dmft, dental status and DDQ scores.

BACKGROUND: Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases. OBJECTIVE: This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding. METHODS: The dataset of 116 patients who were previously treated by senior orthodontists and divided into two groups according to their treatment modalities were examined. Machine Learning algorithms including Multilayer Perceptron, Linear Logistic Regression, k-nearest Neighbors, Naïve Bayes, and Random Forest were trained on this dataset. Several metrics were used for the evaluation of accuracy, precision, recall, and kappa statistic. RESULTS: The most important 12 features were determined with the feature selection algorithm. While all algorithms achieved over 90% accuracy, Random Forest yielded 95% accuracy, with high reliability values (kappa = 0.90). CONCLUSION: The employment of machine learning methods for the treatment decision with or without extraction in the early treatment of patients in the mixed dentition can be particularly useful for pedodontists and general practitioners.

Büyüme gelişimi devam eden bireylerde diş eksikliği alveoler büyüme yetersizliği, fonksiyon ve fonasyonun olumsuz etkilenmesi ile birlikte psikolojik gelişimin de olumsuz etkilendiği istenmeyen estetik görünüme yol açmaktadır. Diş eksikliklerinin tedavisi için çocuklarda hareketli protezler ve geçici köprü restorasyonları kullanılmaktadır ancak bu seçeneklerin çocuklar tarafından kabul edilebilirliği çok zordur. Bunun yanında implant tedavisi erişkinlerde uzun yıllardır başarı ile uygulanmaktadır. Bu durum klinisyenleri çocuklarda da implant uygulamaları açısından cesaretlendirmiştir. Bu derlemede büyüme gelişimin değerlendirilmesi ve takibi ile çocuklarda implant uygulaması için ideal zamanlama prensiplerine odaklanılmıştır.

No abstract