Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.

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.

Transfer accuracy of 3D printed versus CAD/CAM milled surgical guides for temporary orthodontic implants: A preclinical micro CT study.

OBJECTIVES: Temporary anchorage devices (TADs) have become an integral part of comprehensive orthodontic treatments. This study evaluated the transfer accuracy of three-dimensional (3D) printed and computer-aided design/computer-aided manufacturing (CAD/CAM) milled surgical guides for orthodontic TADs using micro-computed tomography (CT) imaging in a preclinical trial. METHODS: Overall, 30 surgical guides were used to place TADs into typodonts; 3D printing and CAD/CAM milling were used to produce the guides. The virtual target positions of the TADs were compared to the real positions in terms of spatial and angular deviations using digital superimposition. Micro-CT imaging was used to detect the positions. To evaluate reliability, two investigators collected the measurements twice. Intra-rater and inter-rater correlations were tested. RESULTS: In total, 60 palatal TADs were evaluated. The mean coronal deviations in the print group ranged from 0.15 ± 0.20 mm to 0.71 ± 0.22 mm, whereas in the mill group, they ranged from 0.09 ± 0.15 mm to 0.83 ± 0.23 mm. At the apical tip, the overall deviations in the print group ranged from 0.14 ± 0.56 mm to 1.27 ± 0.66 mm, whereas in the mill group, they ranged from 0.15 ± 0.57 mm to 1.09 ± 0.44 mm. The mean intra-class and inter-class correlation coefficients ranged from 0.904 to 0.987. No statistically significant differences were found between the groups. CONCLUSIONS: CAD/CAM milled guides yielded spatial and angular accuracies comparable to those of 3D printed guides with notable deviations in the vertical positioning of TADs. CLINICAL SIGNIFICANCE: Digital planning of orthodontic temporary implants combines clinical predictability and the safety of surrounding tissue. Therefore, the transfer accuracy of the guides is crucial. This preclinical study was the first to evaluate CAD/CAM milling for orthodontic guides and found its accuracy comparable to that of the current "gold standard".

To what extent can mastication functionality be restored following mandibular reconstruction surgery? A computer modeling approach.

STATEMENT OF PROBLEM: Advanced cases of head and neck cancer involving the mandible often require surgical removal of diseased sections and subsequent replacement with donor bone. During the procedure, the surgeon must make decisions regarding which bones or tissues to resect. This requires balancing tradeoffs related to issues such as surgical access and post-operative function; however, the latter is often difficult to predict, especially given that long-term functionality also depends on the impact of post-operative rehabilitation programs. PURPOSE: To assist in surgical decision-making, we present an approach for estimating the effects of reconstruction on key aspects of post-operative mandible function. MATERIAL AND METHODS: We develop dynamic biomechanical models of the reconstructed mandible considering different defect types and validate them using literature data. We use these models to estimate the degree of functionality that might be achieved following post-operative rehabilitation. RESULTS: We find significant potential for restoring mandibular functionality, even in cases involving large defects. This entails an average trajectory error below 2 mm, bite force comparable to a healthy individual, improved condyle mobility, and a muscle activation change capped at a maximum of 20%. CONCLUSION: These results suggest significant potential for adaptability in the masticatory system and improved post-operative rehabilitation, leading to greater restoration of jaw function.

The effect of bolus properties on muscle activation patterns and TMJ loading during unilateral chewing.

Mastication is a vital human function and uses an intricate coordination of muscle activation to break down food. Collection of detailed muscle activation patterns is complex and commonly only masseter and anterior temporalis muscle activation are recorded. Chewing is the orofacial task with the highest muscle forces, potentially leading to high temporomandibular joint (TMJ) loading. Increased TMJ loading is often associated with the onset and progression of temporomandibular disorders (TMD). Hence, studying TMJ mechanical stress during mastication is a central task. Current TMD self-management guidelines suggest eating small and soft pieces of food, but patient safety concerns inhibit in vivo investigations of TMJ biomechanics and currently no in silico model of muscle recruitment and TMJ biomechanics during chewing exists. For this purpose, we have developed a state-of-the-art in silico model, combining rigid body bones, finite element TMJ discs and line actuator muscles. To solve the problems regarding muscle activation measurement, we used a forward dynamics tracking approach, optimizing muscle activations driven by mandibular motion. We include a total of 256 different combinations of food bolus size, stiffness and position in our study and report kinematics, muscle activation patterns and TMJ disc von Mises stress. Computed mandibular kinematics agree well with previous measurements. The computed muscle activation pattern stayed stable over all simulations, with changes to the magnitude relative to stiffness and size of the bolus. Our biomedical simulation results agree with the clinical guidelines regarding bolus modifications as smaller and softer food boluses lead to less TMJ loading. The computed mechanical stress results help to strengthen the confidence in TMD self-management recommendations of eating soft and small pieces of food to reduce TMJ pain.

Differential gene expression and protein-protein interaction networks of human periodontal ligament stromal cells under mechanical tension.

Orthodontic treatment is based on complex strategies and takes up to years until a desired therapeutic outcome is accomplished, implying long periods of high costs and discomfort for the patient. Choosing the optimal settings for force intensities in the initial phase of orthodontic tooth movement is the key to successful orthodontic treatment. It is known that orthodontic tooth movement is mainly mediated by tensile and compressive forces that are communicated to the alveolar bone via the periodontal ligament. While the revelation of the complex molecular network was already approached by transcriptomic analysis of compressed periodontal ligament cells, the entity of molecular key players activated by tensile forces remains elusive. Therefore, the aim of this study was to assess the effect of mechanical tensile forces on the gene expression profile of human primary periodontal ligament stromal cells, mimicking the initial phase of orthodontic tooth movement. A transcriptomic analysis of tension-treated and untreated periodontal ligament stromal cells yielded 543 upregulated and 793 downregulated differentially expressed genes. Finally, six highly significant genes were found in the transcriptome that are related to biological processes with relevance to orthodontic tooth movement, including apelin, fibroblast growth factor receptor 2, noggin, sulfatase 1, secreted frizzled-related protein 4 and stanniocalcin 1. Additionally, differences of gene expression profiles between individual cell donors showed a high effect size. Closer understanding of the roles of the identified candidates in the initial phase of orthodontic tooth movement could help to clarify the underlying mechanisms, which will be essential for the development of personalized treatment strategies in orthodontics.

Wear Management of Colored Foils for the Assessment of Sleep Bruxism Patterns-A Prospective, Randomized Crossover Study.

The assessment of bruxism relies on clinical examinations, questionnaires, and polysomnography. The additional use of colored foils (BruxChecker®) could enable a more precise evaluation of bruxing patterns. To assess differences between use of the foils during stress periods or just on consecutive nights and to determine a reasonable duration of using the foils, 28 patients were classified according to the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) and were randomly assigned to wearing the 12 foils for six consecutive nights (alternately in the upper and lower jaw; "consecutive") or six nights within one month following days of high stress ("stress") in a crossover design. The sizes of the attrition areas were measured with ImageJ. Stress was evaluated using the Perceived Stress Scale. The Stress Coping Questionnaire (SVF-120) was used for assessing habitual stress coping strategies. Areas of attrition increased significantly from day 1/2 to 5/6, both for the upper and lower jaw. Molars in the mandible had significantly larger attrition areas than in the maxilla. No significant differences were detected between "consecutive" and "stress" phases. The foils were suitable for differentiating teeth more or less affected by bruxism and were able to show that areas of attrition increased with days, indicating that some adaptation takes place and several days of wearing the foils are required to show the full picture. However, no differences between low/moderate- and high-stress phases were detected.

The effect of tooth cusp morphology and grinding direction 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.

Effect of facet inclination and location on TMJ loading during bruxism: An in-silico study.

Introduction: 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. Objectives: We propose a novel in silico approach for the investigation of the effect of wear facet inclination and position on TMJ stress. Methods: 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. Results: 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. Conclusion: Together these results suggest a possible effect of tooth morphology on TMJ loading during bruxism.

Experimental validation of a micro-CT finite element model of a human cadaveric mandible rehabilitated with short-implant-supported partial dentures.

PURPOSE: This study aimed to address the predictive value of a micro-computed tomography (µCT)-based finite element (µFE) model of a human cadaveric edentulous posterior mandible, rehabilitated by short dental implants. Hereby, three different prosthetic/implant configurations of fixed partial dentures ("Sp"-3 splinted crowns on 3 implants, "Br" - Bridge: 3 splinted crowns on 2 implants, and "Si"- 3 single crowns) were analysed by comparing the computational predictions of the global stiffness with experimental data. METHODS: Experimental displacement of the bone/implant/prosthesis system was measured under axial and oblique loads of 100 N using an optical deformation system (GOM Aramis) and the overall movement of the testing machine (Zwick Z030). Together with the measured machine force, an "Aramis" (optical markers) and "Zwick" (test machine) stiffness were calculated. FE models were created based on µCT-scans of the cadaveric mandible sample (n = 1) before and after implantation and using stl-files of the crowns. The same load tests and boundary conditions were simulated on the models and the µFE-results were compared to experimental data using linear regression analysis. RESULTS: The regression line through a plot of pooled stiffness values (N/mm) for the optical displacement recording (true local displacement) and the test machine (machine compliance included) had a slope of 0.57 and a correlation coefficient R2 of 0.82. The average pooled correlation of global stiffness between the experiment and FE-analysis (FEA) showed a R2 of 0.80, but the FEA-stiffness was 7.2 times higher. The factor was highly dependent on the test configuration. Sp-configuration showed the largest stiffness followed by Br-configuration (17% difference in experiment and 21% in FEA). CONCLUSIONS: The current study showed good qualitative agreement between the experimental and predicted global stiffness of different short implant configurations. It could be deduced that 1:1 splinting of the short implants by the crowns is most favorable for the stiffness of the implant/prosthesis system. However, in the clinical context, the absolute in silico readings must be interpreted cautiously, as the FEA showed a considerable overestimation of the values.

An in silico investigation of the effect of bolus properties on TMJ loading during mastication.

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.

A Novel Quantitative Method for Tooth Grinding Surface Assessment Using 3D Scanning.

Sleep bruxism is an oral parafunction that involves involuntary tooth grinding and clenching. Splints with a colored layer that gets removed during tooth grinding are a common tool for the initial diagnosis of sleep bruxism. Currently, such splints are either assessed qualitatively or using 2D photographs, leading to a non-neglectable error due to the 3D nature of the dentition. In this study we propose a new and fast method for the quantitative assessment of tooth grinding surfaces using 3D scanning and mesh processing. We assessed our diagnostic method by producing 18 standardized splints with 8 grinding surfaces each, giving us a total of 144 surfaces. Moreover, each splint was scanned and analyzed five times. The accuracy and repeatability of our method was assessed by computing the intraclass correlation coefficient (ICC) as well reporting means and standard deviations of surface measurements for intra- and intersplint measurements. An ICC of 0.998 was computed as well as a maximum standard deviation of 0.63 mm2 for repeated measures, suggesting an appropriate accuracy of our proposed method. Overall, this study proposes an innovative, fast and cost effective method to support the initial diagnosis of sleep bruxism.

Bone healing around titanium implants in a preclinical model of bile duct ligation-induced liver injury.

OBJECTIVES: Chronic liver disease increases the risk for periodontal disease and osteoporotic fractures, but its impacts on bone regeneration remain unknown. Herein, we studied the impact of liver cirrhosis on peri-implant bone formation. MATERIAL AND METHODS: A total of 20 male Wistar rats were randomly divided into two groups: one with the common bile duct ligated (BDL) and the respective sham-treated control group (SHAM). After four weeks of disease induction, titanium mini-screws were inserted into the tibia. Successful induction of liver cirrhosis was confirmed by the presence of clinical symptoms. Another four weeks later, peri-implant bone volume per tissue volume (BV/TV) and bone-to-implant contact (BIC) were determined by histomorphometric analysis. RESULTS: Peri-implant bone formation was not significantly different between the SHAM and BDL groups. In the cortical compartment, the median percentage of peri-implant new bone was 10.1% (95% CI of mean 4.0-35.7) and 22.5% (13.8-30.6) in the SHAM and BDL groups, respectively (p = .26). Consistently, the new bone in direct contact with the implant was 18.1% (0.4-37.8) and 23.3% (9.2-32.8) in SHAM and BDL groups, respectively (p = .38). When measuring the medullary compartment, the new bone area was 7.1% (4.8-10.4) and 10.4% (7.2-13.5) in the SHAM and BDL groups, respectively (p = .17). Medullary new bone in direct contact with the implant was 10.0% (1.2-50.4) and 20.6% (16.8-35.3) in SHAM and BDL groups, respectively, and thus comparable between the two groups (p = .46). CONCLUSIONS: Bile duct ligation has no significant impact on the early stages of peri-implant bone formation.

Characterizing Motor Control of Mastication With Soft Actor-Critic.

The human masticatory system is a complex functional unit characterized by a multitude of skeletal components, muscles, soft tissues, and teeth. Muscle activation dynamics cannot be directly measured on live human subjects due to ethical, safety, and accessibility limitations. Therefore, estimation of muscle activations and their resultant forces is a longstanding and active area of research. Reinforcement learning (RL) is an adaptive learning strategy which is inspired by the behavioral psychology and enables an agent to learn the dynamics of an unknown system via policy-driven explorations. The RL framework is a well-formulated closed-loop system where high capacity neural networks are trained with the feedback mechanism of rewards to learn relatively complex actuation patterns. In this work, we are building on a deep RL algorithm, known as the Soft Actor-Critic, to learn the inverse dynamics of a simulated masticatory system, i.e., learn the activation patterns that drive the jaw to its desired location. The outcome of the proposed training procedure is a parametric neural model which acts as the brain of the biomechanical system. We demonstrate the model's ability to navigate the feasible three-dimensional (3D) envelope of motion with sub-millimeter accuracies. We also introduce a performance analysis platform consisting of a set of quantitative metrics to assess the functionalities of a given simulated masticatory system. This platform assesses the range of motion, metabolic efficiency, the agility of motion, the symmetry of activations, and the accuracy of reaching the desired target positions. We demonstrate how the model learns more metabolically efficient policies by integrating a force regularization term in the RL reward. We also demonstrate the inverse correlation between the metabolic efficiency of the models and their agility and range of motion. The presented masticatory model and the proposed RL training mechanism are valuable tools for the analysis of mastication and other biomechanical systems. We see this framework's potential in facilitating the functional analyses aspects of surgical treatment planning and predicting the rehabilitation performance in post-operative subjects.

A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint.

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.

In vivo prediction of temporomandibular joint disc thickness and position changes for different jaw positions.

Temporomandibular joint disorders (TMD) are common dysfunctions of the masticatory region and are often linked to dislocation or changes of the temporomandibular joint (TMJ) disc. Magnetic resonance imaging (MRI) is the gold standard for TMJ imaging but standard clinical sequences do not deliver a sufficient resolution and contrast for the creation of detailed meshes of the TMJ disc. Additionally, bony structures cannot be captured appropriately using standard MRI sequences due to their low signal intensity. The objective of this study was to enable researchers to create high resolution representations of all structures of the TMJ and consequently investigate morphological as well as positional changes of the masticatory system. To create meshes of the bony structures, a single computed tomography (CT) scan was acquired. In addition, a high-resolution MRI sequence was produced, which is used to collect the thickness and position change of the disc for various static postures using bite blocks. Changes in thickness of the TMJ disc as well as disc translation were measured. The newly developed workflow successfully allows researchers to create high resolution models of all structures of the TMJ for various static positions, enabling the investigation of TMJ disc translation and deformation. Discs were thinnest in the lateral part and moved mainly anteriorly and slightly medially. The procedure offers the most comprehensive picture of disc positioning and thickness changes reported to date. The presented data can be used for the development of a biomechanical computer model of TMJ anatomy and to investigate dynamic and static loads on the components of the system, which could be useful for the prediction of TMD onset.

Fast Forward-Dynamics Tracking Simulation: Application to Upper Limb and Shoulder Modeling.

OBJECTIVE: Musculoskeletal simulation can be used to estimate muscle forces in clinical movement studies. However, such simulations typically only target movement measurements and are not applicable to force exertion tasks which are commonly used in rehabilitation therapy. Simulations can also produce nonphysiological joint forces or be too slow for real-time clinical applications, such as rehabilitation with real-time feedback. The objective of this study is to propose and evaluate a new formulation of forward-dynamics assisted tracking simulation that incorporates measured reaction forces as targets or constraints without any additional computational cost. METHODS: We illustrate our method with idealized proof-of-concept models and evaluate it with two upper limb cases: Tracking of hand reaction forces during an isometric force-generation task and constraining glenohumeral joint reaction forces for stability during arm elevation. RESULTS: We show that the addition of reaction force optimization terms within our simulations generates plausible muscle force predictions for these tasks, which are strongly related to reaction forces in addition to movement. Execution times for all models tested were not different when run with or without the reaction force optimization term, ensuring that the simulations are fast enough for real-time clinical applications. CONCLUSION: Our novel reaction force optimization term leads to more realistic shoulder reaction forces, without any additional computational costs. SIGNIFICANCE: Our formulation is not only valuable for shoulder simulations, but could be used in various clinical situations (e.g., for different joints and rehabilitation therapy tasks) where the direction and/or magnitude of reaction forces are of interest.