Background To examine the influence of voxel sizes to detect of peri-implant fenestration defects on cone beam computed tomography (CBCT) images. Materials and methods This study performed with three sheep heads both maxilla and mandible and two types of dental implant type 1 zirconium implant (Zr40) (n = 6) and type 2 titanium implant (Ti22) (n = 10). A total of 14 peri-implant fenestrations (8 buccal surfaces, 6 palatal/lingual surface) were created while 18 surfaces (8 buccal, 10 palatal/lingual) were free of fenestrations. Three observers have evaluated the images of fenestration at each site. Images obtained with 0.75 mm3, 0.100 mm3, 0.150 mm3, 0.200 mm3, and 0.400 mm3 voxel sizes. For intra- and inter-observer agreements for each voxel size, Kappa coefficients were calculated. Results Intra- and inter-observer kappa values were the highest for 0.150 mm3, and the lowest in 0.75 mm3 and 0.400 mm3 voxel sizes for all types of implants. The highest area under the curve (AUC) values were found higher for the scan mode of 0.150 mm3, whereas lower AUC values were found for the voxel size for 0.400 mm3. Titanium implants had higher AUC values than zirconium with the statistical significance for all voxel sizes (p ≤ 0.05). Conclusion A voxel size of 0.150 mm3 can be used to detect peri-implant fenestration bone defects. CBCT is the most reliable diagnostic tool for peri-implant fenestration bone defects.

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 Bruxism is significantly associated with craniofacial pain, feeling of stiffness or fatigue of the jaw and neck pain. Various physiotherapeutic strategies are used in the treatment of bruxism; however, it is not clear which method leads to greater decrease in pain. Objective The aim of this study is to compare the effects of two physiotherapy methods (manual therapy [MT] and Kinesio taping with manual therapy [KTMT]) in patients with bruxism. Methods Patients were randomised into MT or KTMT groups. Evaluations were performed at baseline and following 4 weeks of physiotherapy. Muscle thickness and stiffness were assessed via shear wave elastography; pain thresholds were evaluated using algometer. Sleep quality was assessed using Pittsburgh Sleep Quality Index, and quality of life was assessed with Likert scales regarding the associated symptoms. Results Significant decreases were found in muscle stiffness, pain threshold, sleep quality and quality of life (P < .05) in both MT and KTMT groups. Pain in bilateral temporalis and right occipital region of the trapezius muscle decreased more in the KTMT group compared with the MT group (P < .05). No significant differences in muscle thickness (P > .05) were found in either of the groups. Conclusion Both MT and KTMT methods were effective in the treatment of bruxism. Kinesio Tape used in conjunction with MT has additionally decreased jaw pain and temporal region pain compared with MT intervention only. Therefore, if jaw pain is the primary complaint of a patient, our results recommend including Kinesio Tape application in the physiotherapeutic treatment program.

No abstract

No abstract