Objective: The goal was to perform a 10-year retrospective study on the correlation between sleep disordered breathing (SDB) and upper airway dimensions, as well as the relation between SDB status and TMJ position. The study also examined links between airway dimensions and TMJ position.
Methods: Patients were categorized into Control (n = 28) or SDB (n = 45). Cone beam (CBCT) data was analyzed using ™.
Results: Although there were distinctive features within the SDB group, these did not show a correlation of significance with airway dimensions. SDB patients were more likely to have their condyles posteriorly seated. Other factors, such as presence of TMD, limited mouth opening, and pain upon palpation of masticatory muscles also significantly affected the TMJ position. Volumetric airway analysis showed links to TMJ position.
Conclusion: SDB patients have a smaller airway volume and have a significant relationship between airway volume and TMJ position with their condyles seated more posteriorly.
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http://dx.doi.org/10.1080/08869634.2020.1853465 | DOI Listing |
Int J Dent
December 2024
School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
The aim of this comparative observational study is to evaluate and compare the size and position of the condyle among male and female patients with different skeletal patterns in the anterior-posterior dimension using cone beam computed tomography (CBCT) images. CBCT images of 120 patients, all prepared for other treatment purposes under the same conditions, were included in the study. The patients were classified into three groups-class I, class II, and class III-based on ANB angles and Wits analysis.
View Article and Find Full Text PDFJ Orthod Sci
November 2024
Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Chhattisgarh, India.
Objective: This retrospective study aimed to investigate the association between orthodontic treatment and development of temporomandibular disorders (TMDs) in pediatric patients.
Methods: This study analyzed 122 pediatric patients (age 10-18 years) who underwent orthodontic treatment. The inclusion criteria included comprehensive orthodontic records and substantial clinical documentation, while the exclusion criteria targeted preexisting TMDs or syndromes affecting the temporomandibular joint.
Dent Mater J
December 2024
Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University.
The purpose of this study was to construct an artificial intelligence object detection model to detect the articular disk from temporomandibular joint (TMJ) magnetic resonance (MR) images using YOLO series. The study included two experiments using datasets from different MR imaging machines. A total of 536 MR images were retrospectively examined.
View Article and Find Full Text PDFJ Stomatol Oral Maxillofac Surg
January 2025
Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University, Nanjing, China. Electronic address:
Purpose: To analyze dynamic and static changes in the disc-condyle relationship in patients with skeletal Class III malocclusion after orthognathic surgery.
Methods: The surgical group comprised 30 patients with skeletal Class III malocclusion, and the magnetic resonance imaging and mandibular movement data were obtained at T0 (preoperatively), T1 (3 months postoperatively), and T2 (at the end of orthodontic treatment). The control group included 20 patients with normal occlusion, and the mandibular movement data were recorded.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
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