Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy

Efficient development of subject-specific finite element knee models: Automated identification of soft-tissue attachments

Musculoskeletal disorders impact quality of life and incur substantial socio-economic costs. While in vivo and in vitro studies provide valuable insights, they are often limited by invasiveness and logistical constraints. Finite element (FE) analysis offers a non-invasive, cost-effective alternative for studying joint mechanics. This study introduces a fully automated algorithm for identifying soft-tissue attachment sites to streamline the creation of subject-specific FE knee models from magnetic resonance images. Twelve knees were selected from the Osteoarthritis Initiative database and segmented to create 3D meshes of bone and cartilage. Attachment sites were identified in three conditions: manually by two evaluators and via our automated Python-based algorithm. All knees underwent FE simulations of a 90° flexion-extension cycle, and 68 kinematic, force, contact, stress and strain outputs were extracted. The automated process was compared against manual identification to assess intra-operator variability. The attachment site locations were consistent across all three conditions, with average distances of 3.0 ± 0.5 to 3.1 ± 0.6 mm and no significant differences between conditions (p = 0.90). FE outputs were analyzed using Pearson correlation coefficients, randomized mean square error, and pairwise dynamic time warping in conjunction with ANOVA and Kruskal-Wallis. There were no statistical differences in pairwise comparisons of 67 of 68 FE output variables, demonstrating the automated method's consistency with manual identification. Our automated approach significantly reduces processing time from hours to seconds, facilitating large-scale studies and enhancing reproducibility in biomechanical research. This advancement holds promise for broader clinical and research applications, supporting the efficient development of personalized musculoskeletal models.

Impact of knee geometry on joint contact mechanics after meniscectomy

Finite element modeling has served as a cornerstone in understanding knee joint mechanics post-meniscectomy, yet the influence of varying knee geometries remains unknown. The present study aimed to fill that gap by employing statistical shape modeling to generate knee models from MRI data of 31 human knees, capturing the population's knee size and shape variations. Finite element simulations were conducted to replicate intact, partial, and total medial meniscectomy conditions during standing. The results revealed a substantial shift in load distribution from the medial to lateral compartment following medial meniscectomy with its magnitude depending on knee geometry. Cartilages experienced variable degrees of pressure changes at different sites, which could also be different for fluid and contact pressures. While changes in joint size led to somewhat predictable alterations in contact pressure, variations in joint shape resulted in unexpected changes in contact and fluid pressures, emphasizing the need for computational simulations. The average knee geometry exhibited the lowest contact and fluid pressures under the given loading and boundary conditions, in contrast to knees with shapes deviating from the average. This study highlights the significance of individual knee shape in the biomechanical outcome of meniscectomy, potentially explaining the variability in clinical outcomes observed post-surgery.

Methodology for Robotic In Vitro Testing of the Knee

The knee joint plays a pivotal role in mobility and stability during ambulatory and standing activities of daily living (ADL). Increased incidence of knee joint pathologies and resulting surgeries has led to a growing need to understand the kinematics and kinetics of the knee. In vivo, in silico, and in vitro testing domains provide researchers different avenues to explore the effects of surgical interactions on the knee. Recent hardware and software advancements have increased the flexibility of in vitro testing, opening further opportunities to answer clinical questions. This paper describes best practices for conducting in vitro knee biomechanical testing by providing guidelines for future research. Prior to beginning an in vitro knee study, the clinical question must be identified by the research and clinical teams to determine if in vitro testing is necessary to answer the question and serve as the gold standard for problem resolution. After determining the clinical question, a series of questions (What surgical or experimental conditions should be varied to answer the clinical question, what measurements are needed for each surgical or experimental condition, what loading conditions will generate the desired measurements, and do the loading conditions require muscle actuation?) must be discussed to help dictate the type of hardware and software necessary to adequately answer the clinical question. Hardware (type of robot, load cell, actuators, fixtures, motion capture, ancillary sensors) and software (type of coordinate systems used for kinematics and kinetics, type of control) can then be acquired to create a testing system tailored to the desired testing conditions. Study design and verification steps should be decided upon prior to testing to maintain the accuracy of the collected data. Collected data should be reported with any supplementary metrics (RMS error, dynamic statistics) that help illuminate the reported results. An example study comparing two different anterior cruciate ligament reconstruction techniques is provided to demonstrate the application of these guidelines. Adoption of these guidelines may allow for better interlaboratory result comparison to improve clinical outcomes.

Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks

Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors; https://simtk.org/projects/femors-rbf; https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing.

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).

Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives

Stakeholders in the modeling and simulation (M&S) community organized a workshop at the 2019 Annual Meeting of the Orthopaedic Research Society (ORS) entitled "Reproducibility in Modeling and Simulation of the Knee: Academic, Industry, and Regulatory Perspectives." The goal was to discuss efforts among these stakeholders to address irreproducibility in M&S focusing on the knee joint. An academic representative from a leading orthopedic hospital in the United States described a multi-institutional, open effort funded by the National Institutes of Health to assess model reproducibility in computational knee biomechanics. A regulatory representative from the United States Food and Drug Administration indicated the necessity of standards for reproducibility to increase utility of M&S in the regulatory setting. An industry representative from a major orthopedic implant company emphasized improving reproducibility by addressing indeterminacy in personalized modeling through sensitivity analyses, thereby enhancing preclinical evaluation of joint replacement technology. Thought leaders in the M&S community stressed the importance of data sharing to minimize duplication of efforts. A survey comprised 103 attendees revealed strong support for the workshop and for increasing emphasis on computational modeling at future ORS meetings. Nearly all survey respondents (97%) considered reproducibility to be an important issue. Almost half of respondents (45%) tried and failed to reproduce the work of others. Two-thirds of respondents (67%) declared that individual laboratories are most responsible for ensuring reproducible research whereas 44% thought that journals are most responsible. Thought leaders and survey respondents emphasized that computational models must be reproducible and credible to advance knee M&S.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Assessing Model Calibration Workflows and Outcomes

Model reproducibility is a point of emphasis for the National Institutes of Health (NIH) and in science, broadly. As the use of computational modeling in biomechanics and orthopedics grows, so does the need to assess the reproducibility of modeling workflows and simulation predictions. The long-term goal of the KneeHub project is to understand the influence of potentially subjective decisions, thus the modeler's "art", on the reproducibility and predictive uncertainty of computational knee joint models. In this paper, we report on the model calibration phase of this project, during which five teams calibrated computational knee joint models of the same specimens from the same specimen-specific joint mechanics dataset. We investigated model calibration approaches and decisions, and compared calibration workflows and model outcomes among the teams. The selection of the calibration targets used in the calibration workflow differed greatly between the teams and was influenced by modeling decisions related to the representation of structures, and considerations for computational cost and implementation of optimization. While calibration improved model performance, differences in the postcalibration ligament properties and predicted kinematics were quantified and discussed in the context of modeling decisions. Even for teams with demonstrated expertise, model calibration is difficult to foresee and plan in detail, and the results of this study underscore the importance of identification and standardization of best practices for data sharing and calibration.

Validation and evaluation of subject-specific finite element models of the pediatric knee

Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.

The relative influence of model parameters on finite element analysis simulations of intervertebral body fusion device static compression performance

Non-clinical mechanical performance testing is a critical aspect of intervertebral body fusion device (IBFD) development and regulatory evaluation. Recently, stakeholders have begun leveraging computational modeling and simulations such as finite element analysis (FEA) in addition to traditional bench testing. FEA offers advantages such as reduced experiment time, lower costs associated with elimination of bench testing (e.g. specimen manufacture and test execution), and elucidating quantities of interest that traditional testing cannot provide (e.g. stress and strain distributions). However, best practices for FEA of IBFDs are not well defined, and modeler decision making can significantly influence simulation setup and results. Therefore, the goal of this study was to determine the relative influence of modeling parameters when using FEA to assess non-clinical mechanical performance of IBFDs. FEA was used to conduct a series of IBFD static uniaxial compression simulations. Several parameters relating to implant geometry, loading/boundary conditions, and material properties were carefully controlled to assess their relative influence on two output variables (IBFD stiffness and yield load). Results were most influenced by device geometry, while the effects of boundary conditions and material properties were more significant within IBFDs of identical or similar geometries. These results will aid stakeholders in the development of standardized best practices for using FEA to assess non-clinical mechanical performance of IBFDs.

Towards a Transferable Modeling Method of the Knee to Distinguish Between Future Healthy Joints from Osteoarthritic Joints: Data from the Osteoarthritis Initiative

Computational models can be used to predict the onset and progression of knee osteoarthritis. Ensuring the transferability of these approaches among computational frameworks is urgent for their reliability. In this work, we assessed the transferability of a template-based modeling strategy, based on the finite element (FE) method, by implementing it on two different FE softwares and comparing their results and conclusions. For that, we simulated the knee joint cartilage biomechanics of 154 knees using healthy baseline conditions and predicted the degeneration that occurred after 8 years of follow-up. For comparisons, we grouped the knees using their Kellgren-Lawrence grade at the 8-year follow-up time and the simulated volume of cartilage tissue that exceeded age-dependent thresholds of maximum principal stress. We considered the medial compartment of the knee in the FE models and used ABAQUS and FEBio FE softwares for simulations. The two FE softwares detected different volumes of overstressed tissue in corresponding knee samples (p < 0.01). However, both programs correctly distinguished between the joints that remained healthy and those that developed severe osteoarthritis after the follow-up (AUC = 0.73). These results indicate that different software implementations of a template-based modeling method similarly classify future knee osteoarthritis grades, motivating further evaluations using simpler cartilage constitutive models and additional studies on the reproducibility of these modeling strategies.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

A novel knee joint model in FEBio with inhomogeneous fibril-reinforced biphasic cartilage simulating tissue mechanical responses during gait: data from the osteoarthritis initiative

We developed a novel knee joint model in FEBio to simulate walking. Knee cartilage was modeled using a fibril-reinforced biphasic (FRB) formulation with depth-wise collagen architecture and split-lines to account for cartilage structure. Under axial compression, the knee model with FRB cartilage yielded contact pressures, similar to reported experimental data. Furthermore, gait analysis with FRB cartilage simulated spatial and temporal trends in cartilage fluid pressures, stresses, and strains, comparable to those of a fibril-reinforced poroviscoelastic (FRPVE) material in Abaqus. This knee joint model in FEBio could be used for further studies of knee disorders using physiologically relevant loading.

Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept

Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.

Assessment of reporting practices and reproducibility potential of a cohort of published studies in computational knee biomechanics

Reproducible research serves as a pillar of the scientific method and is a foundation for scientific advancement. However, estimates for irreproducibility of preclinical science range from 75% to 90%. The importance of reproducible science has not been assessed in the context of mechanics-based modeling of human joints such as the knee, despite this being an area that has seen dramatic growth. Framed in the context of five experienced teams currently documenting knee modeling procedures, the aim of this study was to evaluate reporting and the perceived potential for reproducibility across studies the teams viewed as important contributions to the literature. A cohort of studies was selected by polling, which resulted in an assessment of nine studies as opposed to a broader analysis across the literature. Using a published checklist for reporting of modeling features, the cohort was evaluated for both "reporting" and their potential to be "reproduced," which was delineated into six major modeling categories and three subcategories. Logistic regression analysis revealed that for individual modeling categories, the proportion of "reported" occurrences ranged from 0.31, 95% confidence interval (CI) [0.23, 0.41] to 0.77, 95% CI: [0.68, 0.86]. The proportion of whether a category was perceived as "reproducible" ranged from 0.22, 95% CI: [0.15, 0.31] to 0.44, 95% CI: [0.35, 0.55]. The relatively low ratios highlight an opportunity to improve reporting and reproducibility of knee modeling studies. Ongoing efforts, including our findings, contribute to a dialogue that facilitates adoption of practices that provide both credibility and translation possibilities.

Open Knee(s): A Free and Open Source Library of Specimen-Specific Models and Related Digital Assets for Finite Element Analysis of the Knee Joint

There is a growing interest in the use of virtual representations of the knee for musculoskeletal research and clinical decision making, and to generate digital evidence for design and regulation of implants. Accessibility to previously developed models and related digital assets can dramatically reduce barriers to entry to conduct simulation-based studies of the knee joint and therefore help accelerate scientific discovery and clinical innovations. Development of models for finite element analysis is a demanding process that is both time consuming and resource intensive. It necessitates expertise to transform raw data to reliable virtual representations. Modeling and simulation workflow has many processes such as image segmentation, surface geometry generation, mesh generation and finally, creation of a finite element representation with relevant loading and boundary conditions. The outcome of the workflow is not only the end-point knee model but also many other digital by-products. When all of these data, derivate assets, and tools are freely and openly accessible, researchers can bypass some or all the steps required to build models and focus on using them to address their research goals. With provenance to specimen-specific anatomical and mechanical data and traceability of digital assets throughout the whole lifecycle of the model, reproducibility and credibility of the modeling practice can be established. The objective of this study is to disseminate Open Knee(s), a cohort of eight knee models (and relevant digital assets) for finite element analysis, that are based on comprehensive specimen-specific imaging data. In addition, the models and by-products of modeling workflows are described along with model development strategies and tools. Passive flexion served as a test simulation case, demonstrating an end-user application. Potential roadmaps for reuse of Open Knee(s) are also discussed.

A Parameter Sensitivity Analysis on Multiple Finite Element Knee Joint Models

The reproducibility of computational knee joint modeling is questionable, with models varying depending on the modeling team. The influence of model variations on simulation outcomes should be investigated, since knowing the sensitivity of the model outcomes to model parameters could help determine which parameters to calibrate and which parameters could potentially be standardized, improving model reproducibility. Previous sensitivity analyses on finite element knee joint models have typically used one model, with a few parameters and ligaments represented as line segments. In this study, a parameter sensitivity analysis was performed using multiple finite element knee joint models with continuum ligament representations. Four previously developed and calibrated models of the tibiofemoral joint were used. Parameters of the ligament and meniscus material models, the cartilage contact formulation, the simulation control and the rigid cylindrical joints were studied. Varus-valgus simulations were performed, changing one parameter at a time. The sensitivity on model convergence, valgus kinematics, articulating cartilage contact pressure and contact pressure location were investigated. A scoring system was defined to categorize the parameters as having a “large,” “medium” or “small” influence on model output. Model outcomes were sensitive to the ligament prestretch factor, Young’s modulus and attachment condition parameters. Changes in the meniscus horn stiffness had a “small” influence. Of the cartilage contact parameters, the penalty factor and Augmented Lagrangian setting had a “large” influence on the cartilage contact pressure. In the rigid cylindrical joint, the largest influence on the outcome parameters was found by the moment penalty parameter, which caused convergence issues. The force penalty and gap tolerance had a “small” influence at most. For the majority of parameters, the sensitivity was model-dependent. For example, only two models showed convergence issues when changing the Quasi-Newton update method. Due to the sensitivity of the model parameters being model-specific, the sensitivity of the parameters found in one model cannot be assumed to be the same in other models. The sensitivity of the model outcomes to ligament material properties confirms that calibration of these parameters is critical and using literature values may not be appropriate.

Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography

Computational modeling is of growing importance in orthopedics and biomechanics as a tool to understand differences in pathology and predict outcomes from surgical interventions. However, the computational models of the knee have historically relied on in vitro data to create and calibrate model material properties due to the unavailability of accurate in vivo data. This work demonstrates the design and use of a custom device to quantify anterior-posterior (AP) and internal-external (IE) in vivo knee laxity, with an accuracy similar to existing in vitro methods. The device uses high-speed stereo radiography (HSSR) tracking techniques to accurately measure the resulting displacements of the femur, tibia, and patella bones during knee laxity assessment at multiple loads and knee flexion angles. The accuracy of the knee laxity apparatus was determined by comparing laxity data from two cadaveric specimens between the knee laxity apparatus and an existing in vitro robotic knee joint simulator. The accuracy of the knee laxity apparatus was within 1 mm (0.04 in.) for AP and 2.5 deg for IE. Additionally, two living subjects completed knee laxity testing to confirm the laboratory use of the novel apparatus. This work demonstrates the ability to use custom devices in HSSR to collect accurate data, in vivo, for calibration of computational models.

A Method to Compare Heterogeneous Types of Bone and Cartilage Meshes

Accurately capturing the bone and cartilage morphology and generating a mesh remains a critical step in the workflow of computational knee joint modeling. Currently, there is no standardized method to compare meshes of different element types and nodal densities, making comparisons across research teams a significant challenge. The aim of this paper is to describe a method to quantify differences in knee joint bone and cartilages meshes, independent of bone and cartilage mesh topology. Bone mesh-to-mesh distances, subchondral bone boundaries, and cartilage thicknesses from meshes of any type of mesh are obtained using a series of steps involving registration, resampling, and radial basis function fitting after which the comparisons are performed. Subchondral bone boundaries and cartilage thicknesses are calculated and visualized in a common frame of reference for comparison. The established method is applied to models developed by five modeling teams. Our approach to obtain bone mesh-to-mesh distances decreased the divergence seen in selecting a reference mesh (i.e., comparing mesh A-to-B versus mesh B-to-A). In general, the bone morphology was similar across teams. The cartilage thicknesses for all models were calculated and the mean absolute cartilage thickness difference was presented, the articulating areas had the best agreement across teams. The teams showed disagreement on the subchondral bone boundaries. The method presented in this paper allows for objective comparisons of bone and cartilage geometry that is agnostic to mesh type and nodal density.

Creep behavior of human knee joint determined with high-speed biplanar video-radiography and finite element simulation

Creep and relaxation of knee cartilage and meniscus have been extensively studied at the tissue level with constitutive laws well established. At the joint level, however, both experimental and model studies have been focused on either elastic or kinematic responses of the knee, where the time-dependent response is typically neglected for simplicity. The objectives of this study were to quantify the in-vivo creep behavior of human knee joints produced by the cartilaginous tissues and to use the relevant data to validate a previously proposed poromechanical model. Two participants with no history of leg injury volunteered for 3T magnetic resonance imaging (MRI) of their unloaded right knees and for biplanar video-radiography (BVR) of the same knees during standing on an instrumented treadmill for 10 min. Approximately 550 temporal data points were obtained for the in-vivo displacement of the right femur relative to the tibia of the knee. Models of the bones and soft tissues were derived from the MRI. The bone models were used to reconstruct the 3D bone kinematics measured using BVR. Ground reaction forces were simultaneously recorded for the right leg, which were used as input for the subject-specific finite element knee models. Cartilaginous tissues were modeled as fluid-saturated fibril-reinforced materials. In-vivo creep of the knee was experimentally observed for both participants, i.e., the joint displacement increased with time while the reaction forces at the foot were approximately constant. The creep displacements obtained from the finite element models compared well with the experimental data when the tissue properties were calibrated (Pearson correlation coefficient = 0.99). The results showed the capacity of the poromechanical knee model to capture the creep response of the joint. The combined experimental and model study may be used to understand the fluid-pressure load support and contact mechanics of the joint using material properties calibrated from the displacement data, which enhance the fidelity of model results.

Total Knee Replacement: Subject-Specific Modeling, Finite Element Analysis, and Evaluation of Dynamic Activities

This study presents a semi-automatic framework to create subject-specific total knee replacement finite element models, which can be used to analyze locomotion patterns and evaluate knee dynamics. In recent years, much scientific attention was attracted to pre-clinical optimization of customized total knee replacement operations through computational modeling to minimize post-operational adverse effects. However, the time-consuming and laborious process of developing a subject-specific finite element model poses an obstacle to the latter. One of this work's main goals is to automate the finite element model development process, which speeds up the proposed framework and makes it viable for practical applications. This pipeline's reliability was ratified by developing and validating a subject-specific total knee replacement model based on the 6th SimTK Grand Challenge data set. The model was validated by analyzing contact pressures on the tibial insert in relation to the patient's gait and analysis of tibial contact forces, which were found to be in accordance with the ones provided by the Grand Challenge data set. Subsequently, a sensitivity analysis was carried out to assess the influence of modeling choices on tibial insert's contact pressures and determine possible uncertainties on the models produced by the framework. Parameters, such as the position of ligament origin points, ligament stiffness, reference strain, and implant-bone alignment were used for the sensitivity study. Notably, it was found that changes in the alignment of the femoral component in reference to the knee bones significantly affect the load distribution at the tibiofemoral joint, with an increase of 206.48% to be observed at contact pressures during 5° internal rotation. Overall, the models produced by this pipeline can be further used to optimize and personalize surgery by evaluating the best surgical parameters in a simulated manner before the actual surgery.

Simulation of preoperative flexion contracture in a computational model of total knee arthroplasty: Development and evaluation

Preoperative flexion contracture is a risk factor for patient dissatisfaction following primary total knee arthroplasty (TKA). Previous studies utilizing surgical navigation technology and cadaveric models attempted to identify operative techniques to correct knees with flexion contracture and minimize undesirable outcomes such as knee instability. However, no consensus has emerged on a surgical strategy to treat this clinical condition. Therefore, the purpose of this study was to develop and evaluate a computational model of TKA with flexion contracture that can be used to devise surgical strategies that restore knee extension and to understand factors that cause negative outcomes. We developed six computational models of knees implanted with a posteriorly stabilized TKA using a measured resection technique. We incorporated tensions in the collateral ligaments representative of those achieved in TKA using reference data from a cadaveric experiment and determined tensions in the posterior capsule elements in knees with flexion contracture by simulating a passive extension exam. Subject-specific extension moments were calculated and used to evaluate the amount of knee extension that would be restored after incrementally resecting the distal femur. Model predictions of the extension angle after resecting the distal femur by 2 and 4 mm were within 1.2° (p ≥ 0.32) and 1.6° (p ≥ 0.25), respectively, of previous studies. Accordingly, the presented computational method could be a credible surrogate to study the mechanical impact of flexion contracture in TKA and to evaluate its surgical treatment.

An exploratory investigation of patellofemoral joint loadings during directional lunges in badminton

Anterior knee pain is a commonly documented musculoskeletal disorder among badminton players. However, current biomechanical studies of badminton lunges mainly report kinetic profiles in the lower extremity with few investigations of in-vivo loadings. The objective of this study was to evaluate tissue loadings in the patellofemoral joint via musculoskeletal modelling and Finite Element simulation. The collected marker trajectories, ground reaction force and muscle activation data were used for musculoskeletal modelling to compute knee joint angles and quadricep muscle forces. These parameters were then set as boundary conditions and loads for a quasistatic simulation using the Abaqus Explicit solver. Simulations revealed that the left-forward (LF) and backward lunges showed greater contact pressure (14.98-29.61%) and von Mises stress (14.17-32.02%) than the right-forward and backward lunges; while, loadings in the left-backward lunge were greater than the left-forward lunge by 13-14%. Specifically, the stress in the chondral layer was greater than the contact interface, particularly in the patellar cartilage. These findings suggest that right-side dominant badminton players load higher in the right patellofemoral joint during left-side (backhand) lunges. Knowledge of these tissue loadings may provide implications for the training of badminton footwork, such as musculature development, to reduce cartilage loading accumulation, and prevent anterior knee pain.

Assessing the use of finite element analysis for mechanical performance evaluation of intervertebral body fusion devices

BACKGROUND

Intervertebral body fusion devices (IBFDs) are a widely used type of spinal implant placed between two vertebral bodies to stabilize the spine for fusion in the treatment of spinal pathologies. Assessing mechanical performance of these devices is critical during the design, verification, and regulatory evaluation phases of development. While traditionally evaluated with physical bench testing, empirical assessments are at times supplemented with computational models and simulations such as finite element analysis (FEA). However, unlike many mechanical bench tests, FEA lacks standardized practices and consistency of implementation.

OBJECTIVES

The objectives of this study were twofold. First, to identify IBFD 510(k) submissions containing FEA and conduct a comprehensive review of the elements provided in the FEA reports. Second, to engage with spinal device manufacturers through an anonymous survey and assess their practices for implementing FEA.

METHODS

First, a retrospective analysis of 510(k) submissions for IBFDs cleared by the FDA between 2013 and 2017 was performed. The contents of FEA test reports were quantified according to FDA guidance. Second, a survey inquiring about the use of FEA was distributed to industry and academic stakeholders. The survey asked up to 20 questions relating to modeler experience and modeling practices.

RESULTS

Significant gaps were present in model test reports that deemed the data unreliable and, therefore, unusable for regulatory decision-making in a high percentage of submissions. Nonetheless, the industry survey revealed most stakeholders employ FEA during device evaluation and are interested in more prescriptive guidelines for executing IBFD models.

CONCLUSIONS

This study showed that while inconsistencies and gaps in FEA execution do exist within the spinal device community, the stakeholders are eager to work together in developing standardized approaches for executing computational models to support mechanical performance assessment of spinal devices in regulatory submissions.

Biomechanical modelling of the facet joints: a review of methods and validation processes in finite element analysis

There is an increased interest in studying the biomechanics of the facet joints. For in silico studies, it is therefore important to understand the level of reliability of models for outputs of interest related to the facet joints. In this work, a systematic review of finite element models of multi-level spinal section with facet joints output of interest was performed. The review focused on the methodology used to model the facet joints and its associated validation. From the 110 papers analysed, 18 presented some validation of the facet joints outputs. Validation was done by comparing outputs to literature data, either computational or experimental values; with the major drawback that, when comparing to computational values, the baseline data was rarely validated. Analysis of the modelling methodology showed that there seems to be a compromise made between accuracy of the geometry and nonlinearity of the cartilage behaviour in compression. Most models either used a soft contact representation of the cartilage layer at the joint or included a cartilage layer which was linear elastic. Most concerning, soft contact models usually did not contain much information on the pressure-overclosure law. This review shows that to increase the reliability of in silico model of the spine for facet joints outputs, more needs to be done regarding the description of the methods used to model the facet joints, and the validation for specific outputs of interest needs to be more thorough, with recommendation to systematically share input and output data of validation studies.

Changes in Knee Joint Mechanics After Medial Meniscectomy Determined With a Poromechanical Model

The menisci play a vital role in the mechanical function of knee joint. Unfortunately, meniscal tears often occur. Meniscectomy is a surgical treatment for meniscal tears; however, mechanical changes in the knee joint after meniscectomy is a risk factor to osteoarthritis (OA). The objective of this study was to investigate the altered cartilage mechanics of different medial meniscectomies using a poromechanical model of the knee joint. The cartilaginous tissues were modeled as nonlinear fibril-reinforced porous materials with full saturation. The ligaments were considered as anisotropic hyperelastic and reinforced by a fibrillar collagen network. A compressive creep load of ¾ body weight was applied in full extension of the right knee during 200 s standing. Four finite element models were developed to simulate different meniscectomies of the joint using the intact model as the reference for comparison. The modeling results showed a higher load support in the lateral than medial compartment in the intact joint, and the difference in the load share between the compartments was augmented with medial meniscectomy. Similarly, the contact and fluid pressures were higher in the lateral compartment. On the other hand, the medial meniscus in the normal joint experienced more loading than the lateral one. Furthermore, the contact pressure distribution changed with creep, resulting in a load transfer between cartilage and meniscus within each compartment while the total load born by the compartment remained unchanged. This study has quantified the altered contact mechanics on the type and size of meniscectomies, which may be used to understand meniscal tear or support surgical decisions.

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

Development of robust finite element models of porcine tibiofemoral joints loaded under varied flexion angles and tibial freedoms

The successful development of cartilage repair treatments for the knee requires understanding of the biomechanical environment within the joint. Computational finite element models play an important role in non-invasively understanding knee mechanics, but it is important to compare model findings to experimental data. The purpose of this study was to develop a methodology for generating subject-specific finite element models of porcine tibiofemoral joints that was robust and valid over multiple different constraint scenarios. Computational model predictions of two knees were compared to experimental studies on corresponding specimens loaded under several different constraint scenarios using a custom designed experimental rig, with variations made to the femoral flexion angle and level of tibial freedom. For both in vitro specimens, changing the femoral flexion angle had a marked effect on the contact distribution observed experimentally. With the tibia fixed, the majority of the contact region shifted to the medial plateau as flexion was increased. This did not occur when the tibia was free to displace and rotate in response to applied load. These trends in contact distribution across the medial and lateral plateaus were replicated in the computational models. In an additional model with the meniscus removed, contact pressures were elevated by a similar magnitude to the increase seen when the meniscus was removed experimentally. Overall, the models were able to capture specimen-specific trends in contact distribution under a variety of different loads, providing the potential to investigate subject-specific outcomes for knee interventions.

Use of Computational Modeling to Study Joint Degeneration: A Review

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient’s individualized risk assessment as screening tool for use in clinical practice.