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.

Mastication is the motor task with the highest muscle activations of the jaw region, potentially leading to high temporomandibular joint (TMJ) loading. Since increased loading of the TMJ is associated with temporomandibular disorders (TMD), TMJ mechanics during chewing has potential clinical relevance in TMD treatment. TMD self-management guidelines suggest eating soft and small pieces of food to reduce TMJ pain. Since TMJ loading cannot be measured in vivo, due to patient safety restrictions, computer modeling is an important tool for investigations of the potential connection between TMJ loading and TMD. The objective of this study is to investigate the effect of food bolus variables on mechanical TMJ loading to help inform better self-management guidelines for TMD. A combined rigid-body-finite-element model of the jaw region was used to investigate the effect of bolus size, stiffness, and position. Mandibular motion and TMJ disc von Mises stress were reported. Computed mandibular motion generally agrees well with previous literature. Disc stress was higher during the closing phase of the chewing cycle and for the non-working side disc. Smaller and softer food boluses overall lead to less TMJ loading. The results reinforce current guidelines regarding bolus modifications and provide new potential guidelines for bolus positioning that could be verified through a future clinical trial. The paper presents a first in silico investigation of dynamic chewing with detailed TMJ stress for different bolus properties. The results help to strengthen the confidence in TMD self-management recommendations, potentially reducing pain levels of patients.

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease data sets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and mechanobiology. Our 3D convolutional neural network recognized TMJ disorder patients through participant-specific morphological features in condylar, ramus, and chin. Driven by deep learning model outputs, biomechanical modeling revealed that small mandibular size and flat condylar shape were associated with increased TMJ disorder risk through increased joint force, decreased tissue nutrient availability and cell ATP production, and increased TMJ disc strain energy density. Combining explainable deep learning and multiscale biomechanical modeling addresses the “mechanism unknown” limitation undermining translational confidence in clinical applications of deep learning and increases methodological accessibility for smaller clinical data sets by providing the crucial biomechanical context.

Functional impairment of the masticatory region can have significant consequences that range from a loss of quality of life to severe health issues. Increased temporomandibular joint loading is often connected with temporomandibular disorders, but the effect of morphological factors on joint loading is a heavily discussed topic. Due to the small size and complex structure of the masticatory region in vivo investigations of these connections are difficult to perform. We propose a novel in silico approach for the investigation of the effect of wear facet inclination and position on TMJ stress. We use a forward-dynamics tracking approach to simulate lateral bruxing on the canine and first molar using 6 different inclinations, resulting in a total of 12 simulated cases. By using a computational model, we control a single variable without interfering with the system. Muscle activation pattern, maximum bruxing force as well as TMJ disc stress are reported for all simulations. Muscle activation patterns and bruxing forces agree well with previously reported EMG findings and in vivo force measurements. The simulation results show that an increase in inclination leads to a decrease in TMJ loading. Wear facet position seems to play a smaller role with regard to bruxing force but might be more relevant for TMJ loading. Together these results suggest a possible effect of tooth morphology on TMJ loading during bruxism.

Increased mechanical loading of the temporomandibular joint (TMJ) is often connected with the onset and progression of temporomandibular joint disorders (TMD). The potential role of occlusal factors and sleep bruxism in the onset of TMD are a highly debated topic in literature, but ethical considerations limit in vivo examinations of this problem. The study aims to use an innovative in silico modeling approach to thoroughly investigate the connection between morphological parameters, bruxing direction and TMJ stress. A forward-dynamics tracking approach was used to simulate laterotrusive and mediotrusive tooth grinding for 3 tooth positions, 5 lateral inclination angles, 5 sagittal tilt angles and 3 force levels, giving a total of 450 simulations. Muscle activation patterns, TMJ disc von Mises stress as well as correlations between mean muscle activations and TMJ disc stress are reported. Computed muscle activation patterns agree well with previous literature. The results suggest that tooth inclination and grinding position, to a smaller degree, have an effect on TMJ loading. Mediotrusive bruxing computed higher loads compared to laterotrusive simulations. The strongest correlation was found for TMJ stress and mean activation of the superficial masseter. Overall, our results provide in silico evidence that TMJ disc stress is related to tooth morphology.