Melek Volkan-Yazici

and 5 more

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 randomized into MT or KTMT groups. Evaluations were performed at baseline and following four weeks of physiotherapy. Muscle thickness and stiffness were assessed via shearwave ultrasonography; 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 improvements were found in muscle stiffness, pain threshold, sleep quality, quality of life (p<0.05) in both MT and KTMT group. Pain in bilateral temporalis and right occipital region of the trapezius muscle decreased more in the KTMT group compared to the MT group (p<0.05). No significant differences in muscle thickness (p>0.05) were found in either of the groups. Conclusion: Both MT and KTMT methods were effective in the treatment of bruxism. KT used in conjunction with MT has additionally decreased jaw pain and temporal region pain when compared to MT intervention only. Therefore, if jaw pain is the primary complaint of a patient, our results recommend including KT application in the physiotherapeutic treatment program.


and 2 more

Melike Başaran

and 6 more

Aims of the Study: A radiographic examination is a significant part of the clinical routine for the diagnosis, management, and follow-up of the disease. Artificial intelligence in dentistry shows that the deep learning technique high enough quality and effective to diagnose and interpret the images in the dental practice. For this purpose, it is aimed to evaluate diagnostic charting on panoramic radiography using a deep-learning AI system in this study. Methods: 1084 anonymized dental panoramic radiographs were labeled for 10 different dental situations including crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model (Craniocatch, Eskişehir, Turkey) based on a deep CNN method was proposed. A Faster R-CNN Inception v2 (COCO) model implemented with Tensorflow library was used for model development. The training and validation data sets were used to predict and generate optimal CNN algorithm weight factors. Results: The proposed artificial intelligence model has promising results for detecting dental conditions in panoramic radiographs except for caries and dental calculus. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown as 0,9433, 0,9122, 0,8947, respectively. Conclusion: Thanks to the improvement of the success rate of AI models in all areas of dentistry radiology, it is predicted that they will help physicians especially in panoramic diagnosis and treatment planning, as well as digital-based student education, especially in this pandemic period when online training is on our agenda.