Temporomandibular Disorders. Part 1: Anatomy, Aetiology, Diagnosis and Classification

Oluwajana, Funmi; Clarke, Pete; Foster-Thomas, Emma; James, Martin; Crawford, Charles

doi:10.12968/denu.2022.49.4.320

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Practically, TMJ-OA presents as restricted lower jaw movement due to pain, crepitus, and local paraspinal tenderness in the joint promotion. OA is diagnosed when a radiographic scan indicates structural bone change [4][5][6]. OA may also be utilized to measure the condylar and Ramal asymmetry of the mandible in those with Juvenile Idiopathic Arthritis using orthopantomography (JIA).…”

Section: Introductionmentioning

confidence: 99%

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

2022

IJIES

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Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative disorder affecting the TMJ and is distinguished by the gradual deterioration of the joint's interior surfaces. To identify and classify the TMJ-OA from panoramic dental X-ray images, many deep learning models were developed. Amongst, a faster region-based convolutional neural network (FRCNN) can find the condylar area and recognize its abnormalities by learning adequate features with a limited number of images. Nonetheless, the accuracy was not effective for larger databases. Hence in this article, an optimized generative adversarial network (OGAN) model is proposed to create larger panoramic dental X-ray images for TMJ-OA recognition. This GAN model utilizes the generator to produce synthetic panoramic dental images and trains the discriminator to decide whether the created images are real or counterfeit. Besides, an Elephant Herding Optimization (EHO) algorithm is adopted to select the most optimal hyperparameters of the GAN according to the clan and separating factors. Then, the created synthetic panoramic images are added to the actual database and partitioned into 2 distinct collections: training and test collections. The training collection is utilized to train the FRCNN, which extracts the condylar area from every image and identifies the abnormalities. Further, the trained model is tested using the test set to analyze the efficiency of TMJ-OA recognition. Finally, the experimental results exhibit that the OGAN-FRCNN model achieves an accuracy of 94.59% on the panoramic dental X-ray database, whereas the classical models such as VGG16,

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Section: Introductionmentioning

confidence: 99%

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

2022

IJIES

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Is bruxism associated with temporomandibular joint disorders? A systematic review and meta-analysis

et al. 2023

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Temporomandibular disorders. Part 2: non-surgical management

et al. 2022

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For optimal success, a multidisciplinary team approach that uses a combination of non-surgical treatment modalities is recommended in the care of TMD patients. In this article, the second in a series of six focusing on the diagnosis and management of temporomandibular disorders (TMD), the importance of self-management, psychological interventions and physical therapies is discussed. Intra-oral appliances will be covered in a dedicated article later in this series. CPD/Clinical Relevance: Clinicians should have both an understanding and appreciation for the non-surgical management options available to patients diagnosed with TMD.

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Temporomandibular Disorders. Part 1: Anatomy, Aetiology, Diagnosis and Classification

Cited by 4 publications

References 32 publications

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

Is bruxism associated with temporomandibular joint disorders? A systematic review and meta-analysis

Temporomandibular disorders. Part 2: non-surgical management

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