Prediction of the articular eminence shape in a patient with unilateral hypoplasia of the right mandibular ramus before and after distraction osteogenesis—A simulation study

Zee, Mark de; Cattaneo, Paolo M.; Svensson, Peter; Pedersen, Thomas Klit; Melsen, Birte; Rasmussen, John; Dalstra, Michel

doi:10.1016/j.jbiomech.2009.02.027

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…Rigid body models have been used previously to investigate a variety of tasks, including open-close movements (Tuijt et al, 2010) and mastication (Hannam et al, 2008). Moreover, they have been used to investigate changes of function due to surgery (de Zee et al, 2009; Stavness et al, 2010) or the effect of morphological changes on the involved structures (Marková and Gallo, 2016). Their main drawback is that they use quite simple representations of the TMJ itself, consisting of a combination of planes or spline functions and completely lacking a TMJ disk (Curtis, 2011; Hannam, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint

et al. 2019

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The masticatory region is an important human motion system that is essential for basic human tasks like mastication, speech or swallowing. An association between temporomandibular disorders (TMDs) and high temporomandibular joint (TMJ) stress has been suggested, but in vivo joint force measurements are not feasible to directly test this assumption. Consequently, biomechanical computer simulation remains as one of a few means to investigate this complex system. To thoroughly examine orofacial biomechanics, we developed a novel, dynamic computer model of the masticatory system. The model combines a muscle driven rigid body model of the jaw region with a detailed finite element model (FEM) disk and elastic foundation (EF) articular cartilage. The model is validated using high-resolution MRI data for protrusion and opening that were collected from the same volunteer. Joint stresses for a clenching task as well as protrusive and opening movements are computed. Simulations resulted in mandibular positions as well as disk positions and shapes that agree well with the MRI data. The model computes reasonable disk stress patterns for dynamic tasks. Moreover, to the best of our knowledge this model presents the first ever contact model using a combination of EF layers and a FEM body, which results in a clear decrease in computation time. In conclusion, the presented model is a valuable tool for the investigation of the human TMJ and can potentially help in the future to increase the understanding of the masticatory system and the relationship between TMD and joint stress and to highlight potential therapeutic approaches for the restoration of orofacial function.

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Section: Introductionmentioning

confidence: 99%

A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint

et al. 2019

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“…The eminence achieves 50% of its mature size by 3 y of age (Katsavrias 2002) following primary molar eruption and joint loading with the mandible in a retruded position during chewing. Thus, the form of the TMJ eminence reflects the changing mechanical environment and has been shown to develop in a manner consistent with a neuromuscular objective of minimization of joint loads (de Zee et al 2009). Stem cells within the temporal bone periosteum are the likely source of progenitor cells that produce the chondroid bone of the eminence in response to signals from mechanosensitive gene cascades.…”

Section: Introductionmentioning

confidence: 99%

Mechanobehavior and Ontogenesis of the Temporomandibular Joint

et al. 2018

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Craniofacial secondary cartilages of the mandibular condyle and temporomandibular joint (TMJ) eminence grow in response to the local mechanical environment. The intervening TMJ disc distributes normal loads over the cartilage surfaces and provides lubrication. A better understanding of the mechanical environment and its effects on growth, development, and degeneration of the TMJ may improve treatments aimed at modifying jaw growth and preventing or reversing degenerative joint disease (DJD). This review highlights data recorded in human subjects and from computer modeling that elucidate the role of mechanics in TMJ ontogeny. Presented data provide an approximation of the age-related changes in jaw-loading behaviors and TMJ contact mechanics. The cells of the mandibular condyle, eminence, and disc respond to the mechanical environment associated with behaviors and ultimately determine the TMJ components' mature morphologies and susceptibility to precocious development of DJD compared to postcranial joints. The TMJ disc may be especially prone to degenerative change due to its avascularity and steep oxygen and glucose gradients consequent to high cell density and rate of nutrient consumption, as well as low solute diffusivities. The combined effects of strain-related hypoxia and limited glucose concentrations dramatically affect synthesis of the extracellular matrix (ECM), which limit repair capabilities. Magnitude and frequency of jaw loading influence this localized in situ environment, including stem and fibrocartilage cell chemistry, as well as the rate of ECM mechanical fatigue. Key in vivo measurements to characterize the mechanical environment include the concentration of work input to articulating tissues, known as energy density, and the percentage of time that muscles are used to load the jaws out of a total recording time, known as duty factor. Combining these measurements into a mechanobehavioral score and linking these to results of computer models of strain-regulated biochemical events may elucidate the mechanisms responsible for growth, maintenance, and deterioration of TMJ tissues.

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“…Kinetic rigid‐body system models have already been employed to simulate the results of distraction osteogenesis and hemimandibulectomy. In the former, a subject‐specific model was developed to predict the shape of the articular eminence in a patient with unilateral hypoplasia of the right mandibular ramus before and after distraction osteogenesis (40). This study, carried out under isometric conditions, tested the hypothesis that minimisation of joint loading was related to the slope of the articular eminence, the latter being measured prior to and 6·5 years after surgery.…”

Section: Computational Methods In Craniomandibular Modelsmentioning

confidence: 99%

“…Numerous recent models simulate clinically relevant conditions, including maxillary and palatal expansion (79, 80), force transmission through the teeth (81), orthodontic loads and tooth movement (82–84), activators and headgear (85), distraction osteogenesis (24, 40, 86–89), the effects of tooth removal and shortened dental arches (90–92), temporomandibular joint dysfunction (93–96), the biomechanics of mandibular fractures and bone plates (97, 98), osseointegrated implants (99) and dental restorations (100).…”

Section: Craniomandibular Modelsmentioning

confidence: 99%

“…Using a dynamic model of distraction osteogenesis to simulate static tooth‐clenching before and after treatment, de Zee et al. (40) predicted and verified increases in the steepness of the articular eminence as muscle forces equalised bilaterally. This study was based on a hypothesis of joint load minimisation and eminence morphology suggested by previous numerical modelling and experimental work by Iwasaki et al.…”

Section: Craniomandibular Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Current computational modelling trends in craniomandibular biomechanics and their clinical implications

Hannam

2010

Journal of Oral Rehabilitation

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Computational models of interactions in the craniomandibular apparatus are used with increasing frequency to study biomechanics in normal and abnormal masticatory systems. Methods and assumptions in these models can be difficult to assess by those unfamiliar with current practices in this field; health professionals are often faced with evaluating the appropriateness, validity and significance of models which are perhaps more familiar to the engineering community. This selective review offers a foundation for assessing the strength and implications of a craniomandibular modelling study. It explores different models used in general science and engineering and focuses on current best practices in biomechanics. The problem of validation is considered at some length, because this is not always fully realisable in living subjects. Rigid-body, finite element and combined approaches are discussed, with examples of their application to basic and clinically relevant problems. Some advanced software platforms currently available for modelling craniomandibular systems are mentioned. Recent studies of the face, masticatory muscles, tongue, craniomandibular skeleton, temporomandibular joint, dentition and dental implants are reviewed, and the significance of non-linear and non-isotropic material properties is emphasised. The unique challenges in clinical application are discussed, and the review concludes by posing some questions which one might reasonably expect to find answered in plausible modelling studies of the masticatory apparatus.

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Prediction of the articular eminence shape in a patient with unilateral hypoplasia of the right mandibular ramus before and after distraction osteogenesis—A simulation study

Cited by 17 publications

References 18 publications

A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint

A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint

Mechanobehavior and Ontogenesis of the Temporomandibular Joint

Current computational modelling trends in craniomandibular biomechanics and their clinical implications

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