The choice of the femoral center of rotation affects material loss in total knee replacement wear testing - A parametric finite element study of ISO 14243-3

Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint

The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.

Ankle and subtalar joint axes of rotation and center of rotation during walking and running in healthy individuals measured using dynamic biplane radiography

The goal of this study was to determine how foot type and activity level affect ankle and hindfoot motion. Dynamic biplane radiography and a validated volumetric registration process was used to measure ankle and hindfoot motion of 20 healthy adults during walking and running. The helical axes of motion (HAM) during stance were calculated at the tibiotalar and subtalar joints. The intersection of each HAM and the rotation plane of interest defined the tibiotalar and subtalar centers of rotation (COR). Correlations between foot type and hindfoot kinematics were calculated using Pearson's correlations. The effect of activity, phase of gait, and dominant vs. non-dominant limb on HAM and COR were evaluated using linear mixed effects models. Activity and phase of gait influenced the superior location of the tibiotalar (p < 0.041) and subtalar (p < 0.044) CORs. Activity and gait phase affected tibiotalar (p < 0.049) and subtalar (p < 0.044) HAM direction during gait. Both HAM orientation and COR location changed with activity and phase of gait. These ankle and hindfoot kinematics have implications for total ankle replacement design and musculoskeletal models that estimate force and moment generating capabilities of muscles.

Computational wear of knee implant polyethylene insert surface under continuous dynamic loading and posterior tibial slope variation based on cadaver experiments with comparative verification

Background

The effect of posterior tibial slope on the maximum contact pressure and wear volume of polyethylene (PE) insert were not given special attention. The effects of flexion angle, Anterior-Posterior (AP) Translation, and Tibial slope on the max contact pressure and wear of PE insert of TKR were investigated under loadings which were obtained in cadaver experiments by using Archard’s wear law. This study uses not only loads obtained from cadaver experiments but also dynamic flexion starting from 0 to 90 degrees.

Method

Wear on knee implant PE insert was investigated using a 2.5 size 3 dimensional (3D) cruciate sacrificing total knee replacement model and Finite Element Method (FEM) under loadings and AP Translation data ranging from 0 to 90 flexion angles validated by cadaver experiments. Two types of analyses were done to measure the wear effect on knee implant PE insert. The first set of analyses included the flexion angles dynamically changing with the knee rotating from 0 to 90 angles according to the femur axis and the transient analyses for loadings changing with a certain angle and duration.

Results

It is seen that the contact pressure on the PE insert decreases as the cycle increases for both Flexion and Flexion+AP Translation. It is clear that as the cycle increases, the wear obtained for both cases increases. The loadings acting on the PE insert cannot create sufficient pressure due to the AP Translation effect at low speeds and have an effect to reduce the wear, while the effect increases with the wear as the cycle increases, and the AP Translation now contributes to the wear at high speeds. It is seen that as the posterior tibial slope angle increases, the maximum contact pressure values slightly decrease for the same cycle.

Conclusions

This study indicated that AP Translation, which changes direction during flexion, had a significant effect on both contact pressure and wear. Unlike previous similar studies, it was seen that the amount of wear continues to increase as the cycle increases. This situation strengthens the argument that loading and AP Translation values that change with flexion shape the wear effects on PE Insert.

Optimal surgical component alignment minimizes TKR wear - An in silico study with nine alignment parameters

Currently, preclinical mechanical wear testing of total knee replacements (TKRs) is done using ideally aligned components using standardized TKR level walking under either force or displacement-control regimes. To understand the influence of implant alignment and testing control regime, we studied the effect of nine component alignment parameters on TKR volumetric wear in silico. We used a computational framework combining Latin Hypercube sampling design of experiments, finite element analysis, and a numerical model of polyethylene wear, to create a predictive model of how component alignment affects wear rate for each control regime. Nine component alignment parameters were investigated, five femoral variables and four tibial variables. To investigate perturbations of the nine implant alignment variables, two separate 300-point designs were executed, one for each control regime. The results were then used to generate surrogate statistical models using stepwise multiple linear regression. Wear at the neutral position was 4.5mm3/million cycle and 8.6mm3/million cycle for displacement and force-control, respectively. Stepwise multiple linear regression surrogate models were highly significant for each control regime, but force-control generated a stronger predictive model, with a higher R2, more included terms, and a lower RMSE. Both models predicted transverse plane rotational mismatch can lead to large changes in predicted wear; a transverse plane alignment mismatch of 15° can elicit a change in wear of up to 5mm3/million cycle, almost double that of neutral alignment. Therefore, transverse plane alignment is particularly important when considering failure of the implant due to wear.

Increasing the height of the anterior lip on a tibial insert in a posterior stabilized knee prosthesis has little effect on the wear rate

A high anterior lip on a total knee prosthesis is an effective way of reducing anterior translation, but the effect on joint wear is unclear. Using finite element analysis (FEA), this study quantitatively compared wear rates and anterior contact stresses in three posterior stabilized knee prostheses with different heights for the anterior lip during six daily activities (walking, stair ascent, stair descent, sit-to-stand, pivot turn and crossover turn). The wear rate and location of maximum wear depth were similar for the three lip heights tested, but the knee with the highest anterior lip also showed slight anterior wear scaring due to articular contact stress during swing phase, which was highly dependent on the shape of the contact interface. This study illustrates that tibial inserts with a high anterior lip maintain a wear rate similar to moderate and low lip posterior stabilized designs.

Sensitivity of total knee replacement wear to variability in motion and load input: A parametric finite element analysis study

Polyethylene wear remains a contributor to long term failure in total knee replacements (TKRs). Advances in materials have improved polyethylene wear rates, therefore further wear reductions require a better understanding of patient-specific factors that lead to wear. Variability of gait within patients is considerable and could lead to significant variability in wear rates that cannot be predicted by standard testing methods. An in-silico study was performed to investigate the influence of gait variability on TKR polyethylene wear. Nine characteristic peaks within the load and motion profiles used for TKR wear testing were varied 75% to 125% from baseline (ISO-14243-3:2014) to generate 310 unique waveforms. Wear was calculated for 1-million cycles using a finite element TKR wear model. From the results, a surrogate model was developed using multiple linear regression, and used to predict how wear changes due to dispersion of motion and force peaks within a) ±5%, the maximum allowable input tolerance of ISO, and b) ±25%, more reflective of patient gait inter-variability. The range of wear within the ±5% tolerance was 0.65 mm³ /million cycles and was 3.24 mm³ /million cycles within the ±25% variability more in line with the dispersion observed within patients. Although no one kinematic or kinetic peak dominated variability in TKR volumetric wear, variability within flexion/extension peaks were the largest contributor to wear rate variability. Interaction between the peaks of different waveforms was also important. This study, and future studies incorporating patient-specific data, could help to explain the connection between patient-specific gait factors and wear rates.

An efficient and robust simulator for wear of total knee replacements

Wear on total knee replacements is an important criterion for their performance characteristics. Numerical simulations of such wear have seen increasing attention over the last years. They have the potential to be much faster and less expensive than the in vitro tests in use today. While it is unlikely that in silico tests will replace actual physical tests in the foreseeable future, a judicious combination of both approaches can help making both implant design and pre-clinical testing quicker and more cost-effective. The challenge today for the design of simulation methods is to obtain results that convey quantitative information and to do so quickly and reliably. This involves the choice of mathematical models as well as the numerical tools used to solve them. The correctness of the choice can only be validated by comparing with experimental results. In this article, we present finite element simulations of the wear in total knee replacements during the gait cycle standardized in the ISO 14243-1 document, used for compliance testing in several countries. As the ISO 14243-1 standard is precisely defined and publicly available, it can serve as an excellent benchmark for comparison of wear simulation methods. We use comparatively simple wear and material models, but we solve them using a new wear algorithm that combines extrapolation of the geometry changes with a contact algorithm based on nonsmooth multigrid ideas. The contact algorithm works without Lagrange multipliers and penalty parameters, achieving unparalleled stability and efficiency. We compare our simulation results with the experimental data from physical tests using two different actual total knee replacements. Even though the model is simple, we can predict the total mass loss due to wear after 5-million gait cycles, and we observe a good match between the wear patterns seen in experiments and our simulation results. When compared with a state-of-the-art penalty-based solver for the same model, we measure a roughly fivefold increase of execution speed.