AIMC Topic: Mandibular Condyle

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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...

Fully automated evaluation of condylar remodeling after orthognathic surgery in skeletal class II patients using deep learning and landmarks.

Journal of dentistry
OBJECTIVE: Condylar remodeling is a key prognostic indicator in maxillofacial surgery for skeletal class II patients. This study aimed to develop and validate a fully automated method leveraging landmark-guided segmentation and registration for effic...

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.

Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.

BMC oral health
BACKGROUND: Analyzing the morphological growth changes upon mandibular advancement between Twin Block (TB) and Functional Regulator II (FR2) in Class II patients involves measuring the condylar and mandibular changes in terms of linear and volumetric...

Deep Learning-Based Three-Dimensional Analysis Reveals Distinct Patterns of Condylar Remodelling After Orthognathic Surgery in Skeletal Class III Patients.

Orthodontics & craniofacial research
OBJECTIVE: This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analysed with ...

Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.

PloS one
Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machi...

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

Oral radiology
OBJECTIVES: To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.

Automated condylar seating assessment using a deep learning-based three-step approach.

Clinical oral investigations
OBJECTIVES: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated c...

Multi-class segmentation of temporomandibular joint using ensemble deep learning.

Scientific reports
Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the...