AIMC Topic: Temporomandibular Joint Disorders
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Evaluation of Effect of Glucosamine-Chondroitin Sulfate, Tramadol, and Sodium Hyaluronic Acid on Expression of Cytokine Levels in Internal Derangement of Temporomandibular Joint.
The journal of contemporary dental practice
Dec 1, 2018
AIM: Evaluation of the effect of glucosamine-chondroitin combination, tramadol, and sodium hyaluronic acid in temporomandibular joint (TMJ) disorders and its impact on the expression of various cytokines such as IL-6, IL-1β, TNF-α, and PGE2.
A web-based system for neural network based classification in temporomandibular joint osteoarthritis.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 1, 2018
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 classifie...
Perspectives on next steps in classification of oro-facial pain - part 1: role of ontology.
Journal of oral rehabilitation
Jul 25, 2015
The purpose of this study was to review existing principles of oro-facial pain classifications and to specify design recommendations for a new system that would reflect recent insights in biomedical classification systems, terminologies and ontologie...
Can sonographic features be efficacy predictors of robotic massage treatment for masseter and temporal muscle in patients with temporomandibular disorder with myofascial pain?
Cranio : the journal of craniomandibular practice
Nov 17, 2014
OBJECTIVE: The aim of this study was to detect sonographic predictors for the efficacy of massage treatment of masseter and temporal muscle in temporomandibular disorders (TMDs) patients with myofascial pain.
Magnetic resonance image generation using enhanced TransUNet in temporomandibular disorder patients.
Dento maxillo facial radiology
Jul 1, 2025
OBJECTIVES: Temporomandibular disorder (TMD) patients experience a variety of clinical symptoms, and MRI is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep l...
Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.
Dento maxillo facial radiology
May 1, 2025
OBJECTIVES: This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandib...
Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.
Clinical and experimental dental research
Feb 1, 2025
OBJECTIVES: Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Conseque...
Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis.
Dento maxillo facial radiology
Jan 1, 2025
OBJECTIVES: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.
Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.
Dento maxillo facial radiology
Jan 1, 2025
OBJECTIVES: To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.
Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density-weighted images with diagnostic support from the latest deep learning classification models?
Dento maxillo facial radiology
Jan 1, 2025
OBJECTIVES: This study aimed to clarify the performance of MRI-based deep learning classification models in diagnosing temporomandibular joint osteoarthritis (TMJ-OA) and to compare the developed diagnostic assistance with human observers.
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