Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis.

This study's objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.

3D superimposition of craniofacial imaging-The utility of multicentre collaborations.

Clinical applications of 3D image registration and superimposition have contributed to better understanding growth changes and clinical outcomes. The use of 3D dental and craniofacial imaging in dentistry requires validate image analysis methods for improved diagnosis, treatment planning, navigation and assessment of treatment response. Volumetric 3D images, such as cone-beam computed tomography, can now be superimposed by voxels, surfaces or landmarks.

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA).

Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA.