AIMC Topic: Temporomandibular Joint

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Distinguishing acute and chronic TMD in adolescent patients.

Scientific reports
This retrospective cross-sectional study aimed to elucidate the clinical and imaging characteristics of chronic temporomandibular disorder (TMD) compared to acute TMD in adolescents, and to identify factors associated with symptom chronicity. The stu...

Instantaneous center of rotation, the first step to build up the digital laboratory of complex motions.

PloS one
Calculating instantaneous centers of rotation to describe combined rotational and translational motions has a long history in many fields of applied science and basic rigid body kinematics. However, only some theoretical studies have explored the fun...

Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm.

BMC oral health
The temporomandibular joint (TMJ) constitutes a bilateral ginglymoarthrodial joint, wherein each condyle interacts with its corresponding glenoid fossa of the temporal bone. There is a critical need to understand better and accurately characterize th...

Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ.

Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning.

Journal of dentistry
OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging (MRI) using artificial intelligence (AI). Using deep learning techniques, the model first automatica...

Automated diagnosis and classification of temporomandibular joint degenerative disease via artificial intelligence using CBCT imaging.

Journal of dentistry
OBJECTIVES: In this study, artificial intelligence (AI) techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.

Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data.

Scientific reports
This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collecte...

Detecting the articular disk in magnetic resonance images of the temporomandibular joint using YOLO series.

Dental materials journal
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 da...

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study.

Journal of stomatology, oral and maxillofacial surgery
OBJECTIVE: The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologis...

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Medical & biological engineering & computing
Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medic...