This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model’s performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.

Background and Objectives: We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence. Materials and Methods: The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis. Results: The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes. Conclusions: This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction.

To compare the immediate effects of a single session of manual therapy (MT) or Kinesio taping following MT (KTMT) in patients with bruxism using shear wave ultrasonography. Twenty patients were inc...

Spheno-occipital synchondrosis (SOS) is an important growth area in the craniofacial skeleton. It has been explored in research relate to age assessment and forensic medicine due to its closure in the postnatal period. The aim of this study is to evaluate the growth and development period of SOS on cone-beam computed tomography (CBCT) images with pseudo-color imaging depend on fusion stages. In this cross-sectional retrospective study, 280 CBCT sagittal sections’ images (163 women, 117 men) were used to evaluate the SOS fusion stages by dividing them into five categories. ImageJ version 1.3 software was used to analyze. SOS stages and histogram analyzes were evaluated. The significance level was set at p = 0.05. In the evaluation of synchondrosis stages according to gender and age, the incidence of stages 4 and 5 in individuals aged 15–25 years was statistically significantly higher (p < 0.01) compared to stages 1 and 2 in individuals aged 5–14 years. The mean minimum, maximum and open histogram values of synchondroses in the same age group were also statistically significantly higher (p < 0.05). In the assessment of synchondrosis maturation using three-way ROC analysis, histogram analyses indicated Stage 1 for data below 68.33, Stage 3 for data above 104.5, and Stage 2 for values in between. ImageJ histogram analysis can numerically reveal radiographic differences between SOS fusion stages and can perform staging on pseudo-colored cross-sectional CBCT images. Different image processing software can reveal differences between phases through false coloring.