The aim of this study was to determine whether there are any differences in morphology between temporomandibular joint ankylosis (TMJA) of traumatic and infective origin. Cone beam computed tomography (CBCT) scans of 25 patients (28 joints) with TMJA of traumatic origin (trauma group) and 15 patients (15 joints) with TMJA of infectious origin (infection group) were included. The following morphological parameters were evaluated on multiple sections of the CBCT scans: lateral juxta-articular bone growth, residual condyle, residual glenoid fossa, ramus thickening, ankylotic mass fusion line, sclerosis of the ankylosed condyle and spongiosa of the glenoid fossa, and mastoid and glenoid fossa air cell obliteration. Lateral juxta-articular bone growth, juxta-articular extension of fusion, and the presence of normal medial residual condyle and residual glenoid fossa were exclusively found in post-traumatic TMJA. There were differences in ramus thickening (82.1% in trauma vs 53.3% in infection), sclerosis of the ankylosed condyle (100% in trauma vs 60% in infection), and sclerosis of the spongiosa of the glenoid fossa (100% in trauma vs 46.7% in infection) between the trauma and infection groups. Mastoid and glenoid fossa air cell obliteration was found more frequently in the infection group (mastoid obliteration: 23.1% in infection vs 4% in trauma; glenoid obliteration: 66.7% in infection vs 55.6% in trauma ). CBCT imaging can be helpful in differentiating between TMJA of traumatic and infectious origin.
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http://dx.doi.org/10.1016/j.ijom.2023.01.009 | DOI Listing |
Cureus
December 2024
Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, IND.
Introduction The role of the condylar position in the correct functioning of the stomatognathic system has been the center of the study. Using cone-beam computed tomography (CBCT), this study looked at the three-dimensional (3D) position of the condylar bone in patients from Class I, Class II, Division 1, and Division 2. Materials and methods This cross-sectional, retrospective study was conducted using 102 CBCT records, with 34 records allocated to each category of malocclusion classification, such as dentoskeletal Class I, skeletal Class II, and dental Class II, Division 1 and 2.
View Article and Find Full Text PDFOral Surg Oral Med Oral Pathol Oral Radiol
December 2024
Department of Surgery, Division of Oral and Maxillofacial Surgery, University of Cincinnati School of Medicine, Cincinnati, OH. Electronic address:
Am J Sports Med
January 2025
Department of Orthopaedics, China-Japan Union Hospital of Jilin University, Changchun, China.
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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