Biomechanics of the Temporomandibular Joint

Andrés, Crespo Reinoso Pablo; Emilio, Ruiz Delgado; James, Jerez Robalino

doi:10.5772/intechopen.103836

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Focusing on baropodometric analysis, our results seem to highlight that the presence of TMD can lead to a different distribution of plantar pressure. As a general comment, we can speculate that the physiology impacting the mechanical properties of the temporomandibular joint could also manifest in the feet since they are constantly subjected to cyclic loading [ 39 ]. In addition, as also reported in other studies [ 40 , 41 ], a malocclusion may lead to early changes in plantar pressure to be intended as a compensatory mechanism.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Taborri,

Molinaro,

Russo

et al. 2024

Sensors

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Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.

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Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Taborri,

Molinaro,

Russo

et al. 2024

Sensors

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show abstract

“…The temporal, masseter, external or lateral pterygoid muscles are responsible for closing the jaw. The internal or medial pterygoid, geniohyoideus, digastric and mylohyoideus muscles are responsible for opening the jaw (9).…”

Section: The Pterygomandibular Ligament (Ptml)mentioning

confidence: 99%

Exploring the Complexities of Temporomandibular Joint Function and Dysfunction: A Contemporary Review

Vinayak,

Ram,

Chandran

2024

Odovtos - Int J Dent Sc

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This contemporary review delves into the intricate realm of temporomandibular joint (TMJ) function and dysfunction, shedding light on its multifaceted nature and offering insights into diagnostic and therapeutic strategies. The TMJ, a vital component of the craniofacial complex, plays a pivotal role in everyday activities such as chewing, speaking, and yawning. Understanding its anatomy and physiology is paramount in the treatment and management of TMJ dysfunction. The biomechanics of TMJ function, from the articular disc to the surrounding ligaments and muscles, have been explored comprehensively. A critical aspect of this review revolves around the diverse etiological factors contributing to TMJ dysfunction. Genetic predispositions, parafunctions, occlusal irregularities, and psychological stress are dissected to illuminate the roles of these factors in the development of TMJ disorders. These complexities prompted the need for a robust classification system elucidating the clinical manifestations and diagnostic criteria for various TMJ disorders. Modern diagnostic modalities, including clinical assessment, imaging techniques, and joint aspiration, are scrutinized for their indispensable roles in accurate diagnosis. The comprehensive overview included management strategies, from conservative measures to surgical interventions. In recognizing the pivotal influence of psychosocial factors, this review underscores the importance of patient-centered care and cognitive-behavioral interventions. Furthermore, regenerative therapies, minimally invasive techniques, and personalized medicine are emerging trends in TMJ research. This review provides a contemporary roadmap for comprehending, diagnosing, and managing TMJ-related conditions.

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“…The articular disc divides the TMJ into upper and lower joint spaces, which do not communicate in physiological conditions. The TMJ allows rotation in the lower joint space and translation in the upper joint space [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

High-Field Magnetic Resonance Imaging of the Temporomandibular Joint Low Agreement with Clinical Diagnosis in Asymptomatic Females

et al. 2023

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(1) Background: The aim of this study was to investigate the agreement between a clinical diagnosis based on research diagnostic criteria/temporomandibular disorders (RDC/TMD) and high-field magnetic resonance imaging (MRI) findings of temporomandibular joints (TMJs) in asymptomatic females. (2) Methods: A prospective study on 100 females (200 TMJs) was performed, using clinical examinations (RDC/TMD) and same-day MRIs of TMJs on a 3T MR unit. The inclusion criteria were as follows: females, age > 18, the presence of upper and lower incisors, and an understanding of the Serbian language. Descriptive statistics (means and standard deviations) and ANOVA with a post hoc Tukey test for differences among the patient subgroups was performed. The agreement between the clinical and MRI findings was determined using Cohen’s kappa coefficient (k < 0.21 slight, 0.21–0.4 fair, 0.41–0.6 moderate, 0.61–0.8 substantial, and 0.81–1 almost perfect). The statistical significance was set at p ≤ 0.05. (3) Results: Normal findings were observed in 86.7%, disc dislocation (DD) was observed in 9.2%, and arthralgia/osteoarthritis/osteoarthrosis was observed in 2.6% of TMJs using RDC/TMD. On the MRI, normal findings were observed in 50.5%, disc dislocation was observed in 16.3%, and arthralgia/osteoarthritis/osteoarthrosis was observed in 23.5% of TMJs. The anterior DD with reduction showed fair agreement of the clinical and MRI findings (k = 0.240, p < 0.001) compared with the DD without reduction (k = 0.355, p < 0.001). Both showed high specificity (94.9% and 99.4%) but low sensitivity (24.2% and 25.0%). The sensitivity in osteoarthritic changes was low (4.8%), but the specificity remained high (96.2%). (4) Conclusions: The sensitivity of the clinical examination remains low compared with 3T MRI, especially in osteoarthritic changes and anterior DD with reduction. However, the number of false positive diagnoses using RDC/TMD is low in asymptomatic patients. RDC/TMD remains a sensible method for establishing a clinical diagnosis and avoiding the overtreatment of asymptomatic patients.

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Biomechanics of the Temporomandibular Joint

Cited by 4 publications

References 15 publications

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Exploring the Complexities of Temporomandibular Joint Function and Dysfunction: A Contemporary Review

High-Field Magnetic Resonance Imaging of the Temporomandibular Joint Low Agreement with Clinical Diagnosis in Asymptomatic Females

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