Specimen-specific finite element representations of implanted hip capsules

The hip capsule is a ligamentous structure that contributes to hip stability. This article developed specimen-specific finite element models that replicated internal-external (I-E) laxity for ten implanted hip capsules. Capsule properties were calibrated to minimize root mean square error (RMSE) between model and experimental torques. RMSE across specimens was 1.02 ± 0.21 Nm for I-E laxity and 0.78 ± 0.33 Nm and 1.10 ± 0.48 Nm during anterior and posterior dislocation, respectively. RMSE for the same models with average capsule properties was 2.39 ± 0.68 Nm. Specimen-specific models demonstrated the importance of capsule tensioning in hip stability and have relevance for surgical planning and evaluation of implant designs.

Simultaneous Evaluation of Tibiofemoral and Patellofemoral Mechanics in Total Knee Arthroplasty: A Combined Experimental and Computational Approach

Contemporary total knee arthroplasty (TKA) has not fully restored natural patellofemoral (P-F) mechanics across the patient population. Previous experimental simulations have been limited in their ability to create dynamic, unconstrained, muscle-driven P-F articulation while simultaneously controlling tibiofemoral (T-F) contact mechanics. The purpose of this study was to develop a novel experimental simulation and validate a corresponding finite element model to evaluate T-F and P-F mechanics. A commercially available wear simulator was retrofitted with custom fixturing to evaluate whole-knee TKA mechanics with varying patella heights during a simulated deep knee bend. A corresponding dynamic finite element model was developed to validate kinematic and kinetic predictions against experimental measurements. Patella alta reduced P-F reaction forces in early and midflexion, corresponding with an increase in T-F forces that indicated an increase in extensor mechanism efficiency. Due to reduced wrapping of the extensor mechanism in deeper flexion for the alta condition, peak P-F forces in flexion increased from 101% to 135% of the applied quadriceps load for the baja and alta conditions, respectively. Strong agreement was observed between the experiment and model predictions with root-mean-square errors (RMSE) for P-F kinematics ranging from 0.8 deg to 3.3 deg and 0.7 mm to 1.4 mm. RMSE for P-F forces ranged from 7.4 N to 53.6 N. By simultaneously controlling dynamic, physiological loading of the T-F and P-F joint, this novel experimental simulation and validated model will be a valuable tool for investigation of future TKA designs and surgical techniques.

Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software.

BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors

Through wearable sensors and deep learning techniques, biomechanical analysis can reach beyond the lab for clinical and sporting applications. Transformers, a class of recent deep learning models, have become widely used in state-of-the-art artificial intelligence research due to their superior performance in various natural language processing and computer vision tasks. The performance of transformer models has not yet been investigated in biomechanics applications. In this study, we introduce a Biomechanical Multi-activity Transformer-based model, BioMAT, for the estimation of joint kinematics from streaming signals of multiple inertia measurement units (IMUs) using a publicly available dataset. This dataset includes IMU signals and the corresponding sagittal plane kinematics of the hip, knee, and ankle joints during multiple activities of daily living. We evaluated the model's performance and generalizability and compared it against a convolutional neural network long short-term model, a bidirectional long short-term model, and multi-linear regression across different ambulation tasks including level ground walking (LW), ramp ascent (RA), ramp descent (RD), stair ascent (SA), and stair descent (SD). To investigate the effect of different activity datasets on prediction accuracy, we compared the performance of a universal model trained on all activities against task-specific models trained on individual tasks. When the models were tested on three unseen subjects' data, BioMAT outperformed the benchmark models with an average root mean square error (RMSE) of 5.5 ± 0.5°, and normalized RMSE of 6.8 ± 0.3° across all three joints and all activities. A unified BioMAT model demonstrated superior performance compared to individual task-specific models across four of five activities. The RMSE values from the universal model for LW, RA, RD, SA, and SD activities were 5.0 ± 1.5°, 6.2 ± 1.1°, 5.8 ± 1.1°, 5.3 ± 1.6°, and 5.2 ± 0.7° while these values for task-specific models were, 5.3 ± 2.1°, 6.7 ± 2.0°, 6.9 ± 2.2°, 4.9 ± 1.4°, and 5.6 ± 1.3°, respectively. Overall, BioMAT accurately estimated joint kinematics relative to previous machine learning algorithms across different activities directly from the sequence of IMUs signals instead of time-normalized gait cycle data.

Knee pivot location in asymptomatic older adults

Representative data of asymptomatic, native-knee kinematics is important when studying changes in knee function across the lifespan. High-speed stereo radiography (HSSR) provides a reliable measure of knee kinematics to <1 mm of translation and 1° of rotation, but studies often have limited statistical power to make comparisons between groups or measure the contribution of individual variability. The purpose of this study is to examine in vivo condylar kinematics to quantify the transverse center-of-rotation, or pivot, location across the flexion range and challenge the medial-pivot paradigm in asymptomatic knee kinematics. We quantified the pivot location during supine leg press, knee extension, standing lunge, and gait for 53 middle-aged and older adults (27 men; 26 women: 50.8 ± 7.0 yrs, 1.75 ± 0.1 m, 79.1 ± 15.4 kg). A central- to medial-pivot location was identified for all activities with increased knee flexion associated with posterior translation of the center-of-rotation. The association between knee angle and anterior-posterior center-of-rotation location was not as strong as the relation between medial-lateral and anterior-posterior location, excluding gait. The Pearson's correlation for gait was stronger between knee angle and anterior-posterior center-of-rotation location (P < 0.001) than medial-lateral and anterior-posterior location (P = 0.0122). Individual variability accounted for a measurable proportion in variance explained of center-of-rotation location. Unique to gait, the lateral translation of center-of-rotation location resulted in the anterior translation of center-of-rotation at <10° knee flexion. Furthermore, no association between vertical ground-reaction force and center-of-rotation was identified.

Impact of patient, surgical, and implant design factors on predicted tray-bone interface micromotions in cementless total knee arthroplasty

Micromotion magnitudes exceeding 150 µm may prevent bone formation and limit fixation after cementless total knee arthroplasty (TKA). Many factors influence the tray-bone interface micromotion but the critical parameters and sensitivities are less clear. In this study, we assessed the impacts of surgical (tray alignment, tibial coverage, and resection surface preparation), patient (bone properties and tibiofemoral kinematics), and implant design (tray feature and surface friction) factors on tray-bone interface micromotions during a series of activities of daily living. Micromotion was estimated via three previously validated implant-bone finite element models and tested under gait, deep knee bending, and stair descent loads. Overall, the average micromotion across the tray-bone cementless contact interface ranged from 9.3 to 111.4 µm, and peak micromotion was consistently found along the anterior tray edge. Maximizing tibial coverage above a properly sized tibial tray (an average of 12.3% additional area) had minimal impact on micromotion. A 1 mm anterior tray alignment change reduced the average micromotion by an average of 16.1%. Two-degree tibial angular resection errors reduced the area for bone ingrowth up to 48.1%. Differences on average micromotion from ±25% changes in bone moduli were up to 75.5%. A more posterior tibiofemoral contact due to additional 100 N posterior force resulted in an average of 79.3% increase on average micromotion. Overall, careful surgical technique, patient selection, and controlling kinematics through articular design all contribute meaningfully to minimizing micromotion in cementless TKA, with centralizing the load transfer to minimize the resulting moment at the anterior tray perimeter a consistent theme.

Impact of bone health on the mechanics of plate fixation for Vancouver B1 periprosthetic femoral fractures

BACKGROUND

Condyle-spanning plate-screw constructs have shown potential to lower the risks of femoral refractures after the healing of a primary Vancouver type B1 periprosthetic femoral fracture. Limited information exists to show how osteoporosis (a risk factor for periprosthetic femoral fractures) may affect the plate fixation during activities of daily living.

METHODS

Using total hip arthroplasty and plate-implanted finite element models of three osteoporotic femurs, this study simulated physiological loads of three activities of daily living, as well as osteoporosis associated muscle weakening, and compared the calculated stress/strain, load transfer and local stiffness with experimentally validated models of three healthy femurs. Two plating systems and two construct lengths (a diaphyseal construct and a condyle-spanning construct) were modeled.

FINDINGS

Osteoporotic femurs showed higher bone strain (21.9%) and higher peak plate stress (144.3%) as compared with healthy femurs. Compared with shorter diaphyseal constructs, condyle-spanning constructs of two plating systems reduced bone strains in both healthy and osteoporotic femurs (both applying 'the normal' and 'the weakened muscle forces') around the most distal diaphyseal screw and in the distal metaphysis, both locations where secondary fractures are typically reported. The lowered resultant compressive force and the increased local compressive stiffness in the distal diaphysis and metaphysis may be associated with strain reductions via condyle-spanning constructs.

INTERPRETATION

Strain reductions in condyle-spanning constructs agreed with the clinically reported lowered risks of femoral refractures in the distal diaphysis and metaphysis. Multiple condylar screws may mitigate the concentrated strains in the lateral condyle, especially in osteoporotic femurs.

Image-Free Robotic-Assisted Total Knee Arthroplasty Improves Implant Alignment Accuracy: A Cadaveric Study

BACKGROUND

Improving resection accuracy and eliminating outliers in total knee arthroplasty (TKA) is important to improving patient outcomes regardless of alignment philosophy. Robotic-assisted surgical systems improve resection accuracy and reproducibility compared to conventional instrumentation. Some systems require preoperative imaging while others rely on intraoperative anatomic landmarks. We hypothesized that the alignment accuracy of a novel image-free robotic-assisted surgical system would be equivalent or better than conventional instrumentation with fewer outliers.

METHODS

Forty cadaveric specimens were used in this study. Five orthopedic surgeons performed 8 bilateral TKAs each, using the VELYS Robotic-Assisted System (DePuy Synthes) and conventional instrumentation on contralateral knees. Pre-resection and postresection computed tomography scans, along with optical scans of the implant positions were performed to quantify resection accuracies relative to the alignment targets recorded intraoperatively.

RESULTS

The robotic-assisted cohort demonstrated smaller resection errors compared to conventional instrumentation in femoral coronal alignment (0.63° ± 0.50° vs 1.39° ± 0.95°, P < .001), femoral sagittal alignment (1.21° ± 0.90° vs 3.27° ± 2.51°, P < .001), and tibial coronal alignment (0.93° ± 0.72° vs 1.65° ± 1.29°, P = .001). All other resection angle accuracies were equivalent. Similar improvements were found in the femoral implant coronal alignment (0.89° ± 0.82° vs 1.42° ± 1.15°, P = .011), femoral implant sagittal alignment (1.51° ± 1.08° vs 2.49° ± 2.10°, P = .006), and tibial implant coronal alignment (1.31° ± 0.84° vs 2.03° ± 1.44°, P = .004). The robotic-assisted cohort had fewer outliers (errors >3°) for all angular resection alignments.

CONCLUSION

This in vitro study demonstrated that image-free robotic-assisted TKA can improve alignment accuracy compared to conventional instrumentation and reduce the incidence of outliers.

Simplified Mechanical Tests Can Simulate Physiological Mechanics of a Fixation Construct for Periprosthetic Femoral Fractures

Plate fractures after fixation of a Vancouver Type B1 periprosthetic femoral fracture (PFF) are difficult to treat and could lead to severe disability. However, due to the lack of direct measurement of in vivo performance of the PFF fixation construct, it is unknown whether current standard mechanical tests or previous experimental and computational studies have appropriately reproduced the in vivo mechanics of the plate. To provide a basis for the evaluation and development of appropriate mechanical tests for assessment of plate fracture risk, this study applied loads of common activities of daily living (ADLs) to implanted femur finite element (FE) models with PFF fixation constructs with an existing or a healed PFF. Based on FE simulated plate mechanics, the standard four-point-bend test adequately matched the stress state and the resultant bending moment in the plate as compared with femur models with an existing PFF. In addition, the newly developed constrained three-point-bend tests were able to reproduce plate stresses in models with a healed PFF. Furthermore, a combined bending and compression cadaveric test was appropriate for risk assessment including both plate fracture and screw loosening after the complete healing of PFF. The result of this study provides the means for combined experimental and computational preclinical evaluation of PFF fixation constructs.

What is the Effect of Posterior Osteophytes on Flexion and Extension Gaps in Total Knee Arthroplasty? A Cadaveric Study

Background

Posterior compartment knee osteophytes may pose a challenge in achieving soft-tissue balance during total knee arthroplasty (TKA). Obtaining symmetry of flexion and extension gaps involves balance of both bony and soft-tissue structures. We hypothesize that space-occupying posteromedial femoral osteophytes affect soft-tissue balance.

Methods

Five cadaveric limbs were acquired. Computed tomography scans were obtained to define the osseous contours. Three-dimensionally printed, specimen-specific synthetic posterior femoral osteophytes were fabricated in 10-mm and 15-mm sizes. TKAs were implanted. Medial and lateral compartment contact forces were measured during passive knee motion using pressure-sensing technology. For each specimen, trials were completed without osteophytes and with 10-mm and 15-mm osteophytes affixed to the posteromedial femoral condyle. Contact forces were obtained at full extension, 10°, 30°, 45°, 60°, and 90° of flexion. These were recorded across each specimen in each condition for three trials. Tukey post hoc tests were used with a repeated measures ANOVA for statistical data analysis.

Results

The presence of posteromedial osteophytes increased asymmetric loading from full extension to 45° of flexion, with statistically significant differences observed at full extension and 30°. A reduction in lateral compartment forces was noted. The 25%-75% bounds of variability in the contact force was less than 3.5 lbs.

Conclusions

Posteromedial femoral osteophytes caused an asymmetric increase in medial contact forces from full extension continuing into mid-flexion. The soft-tissue imbalance created from these osteophytes supports their removal before performing ligament releases to obtain desired soft-tissue balancing during TKA.

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance.

Development of axial compression and combined axial compression and torque loading configurations to reproduce strain in the implanted femur during activities of daily living

Femoral strain is indicative of the potential for bone remodeling (strain energy density, SED) and periprosthetic femoral fracture (magnitude of principal strains) after total hip arthroplasty (THA). Previous modeling studies have evaluated femoral strains in THA-implanted femurs under gait loads including both physiological hip contact force and femoral muscle forces. However, experimental replication of the complex muscle forces during activities of daily living (ADLs) is difficult for in vitro assessment of femoral implant or fixation hardware. Alternatively, cadaveric tests using simplified loading configurations have been developed to assess post-THA bone mechanics, although no current studies have demonstrated simplified loading configurations used in mechanical tests may simulate the physiological femoral strains under ADL loads. Using an optimization approach integrated with finite element analysis, this study developed axial compression and combined axial compression and torque testing configurations for three common ADLs (gait, stair-descent and sit-to-stand) via matching the SED profile of the femur in THA-implanted models of three specimens. The optimized simplified-loading models showed good agreement in predicting bone remodeling stimuli (post-THA change in SED per unit mass) and fatigue regions as compared with the ADL-loading models, as well as other modeling and clinical studies. The optimized simplified test configurations can provide a physiological-loading based pre-clinical platform for the evaluation of implant/fixation devices of the femur.

Impact of surgical alignment, tray material, PCL condition, and patient anatomy on tibial strains after TKA

Bone remodeling after total knee arthroplasty is regulated by the changes in strain energy density (SED), however, the critical parameters influencing post-operative SED distributions are not fully understood. This study aimed to investigate the impact of surgical alignment, tray material properties, posterior cruciate ligament (PCL) balance, tray posterior slope, and patient anatomy on SED distributions in the proximal tibia. Finite element models of two tibiae (different anatomy) with configurations of two implant materials, two surgical alignments, two posterior slopes, and two PCL conditions were developed. The models were tested under the peak loading conditions during gait, deep knee bending, and stair descent. For each configuration, the contact forces and locations and soft-tissue loads of interest were taken into consideration. SED in the proximal tibia was predicted and the changes in strain distributions were compared for each of the factors studied. Tibial anatomy had the most impact on the proximal bone SED distributions, followed by PCL balancing, surgical alignment, and posterior slope. In addition, the thickness of the remaining cortical wall after implantation was also a significant consideration when evaluating tibial anatomy. The resulting SED changes for alignment, posterior slope, and PCL factors were mainly due to the differences in joint and soft-tissue loading conditions. A lower modulus tray material did result in changes in the post-operative strain state, however, these were almost negligible compared to that seen for the other factors.

Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.

Computational evaluation of TKR stability using feedback-controlled compressive loading

Pre-clinical assessment of stability in total knee replacement is crucial for developing preferred implant performance. Current total knee replacement patients often experience joint instability that the human body addresses with compensatory strategies. Specifically, an increased quadriceps-hamstrings co-contraction serves to increase joint stability through an increased compressive force across the tibiofemoral joint. The aim of this study is to introduce a novel method to evaluate total knee replacement by determining the compressive loading required to achieve natural knee stability. Four current total knee replacement geometries in both their cruciate-retaining and posterior-stabilized forms are modeled in a finite-element framework. The finite-element model is initially validated experimentally using traditional knee laxity testing with a constant compressive load and anterior-posterior displacement or internal-external rotation. Model predictions of constraint are in reasonable agreement with experimental results (average root mean square errors: 0.46 Nm, 62.5 N). The finite-element model is subsequently interfaced with a feedback controller to vary the compressive force that the implant requires in order to match experimental natural knee internal-external and anterior-posterior stability at different flexion angles. Results show that the lower constraint total knee replacement designs require on average 66.7% more compressive load than the higher constraint designs to achieve natural knee constraint. As expected, total knee replacement stability and compressive load requirements to replicate natural kinematics vary with inclusion of tibiofemoral ligaments. The current study represents a novel approach to evaluate stability in existing total knee replacement geometries and to design implants that better restore natural knee mechanics. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1901-1909, 2018.

Statistical modeling to characterize relationships between knee anatomy and kinematics

The mechanics of the knee are complex and dependent on the shape of the articular surfaces and their relative alignment. Insight into how anatomy relates to kinematics can establish biomechanical norms, support the diagnosis and treatment of various pathologies (e.g., patellar maltracking) and inform implant design. Prior studies have used correlations to identify anatomical measures related to specific motions. The objective of this study was to describe relationships between knee anatomy and tibiofemoral (TF) and patellofemoral (PF) kinematics using a statistical shape and function modeling approach. A principal component (PC) analysis was performed on a 20-specimen dataset consisting of shape of the bone and cartilage for the femur, tibia and patella derived from imaging and six-degree-of-freedom TF and PF kinematics from cadaveric testing during a simulated squat. The PC modes characterized links between anatomy and kinematics; the first mode captured scaling and shape changes in the condylar radii and their influence on TF anterior-posterior translation, internal-external rotation, and the location of the femoral lowest point. Subsequent modes described relations in patella shape and alta/baja alignment impacting PF kinematics. The complex interactions described with the data-driven statistical approach provide insight into knee mechanics that is useful clinically and in implant design.

Mechanics of post-cam engagement during simulated dynamic activity

Posterior-stabilized (PS) total knee arthroplasty (TKA) components employ a tibial post and femoral cam mechanism to guide anteroposterior knee motion in lieu of the posterior cruciate ligament. Some PS TKA patients report a clicking sensation when the post and cam engage, while severe wear and fracture of the post; we hypothesize that these complications are associated with excessive impact velocity at engagement. We evaluated the effect of implant design on engagement dynamics of the post-cam mechanism and resulting polyethylene stresses during dynamic activity. In vitro simulation of a knee bend activity was performed for four cadaveric specimens implanted with PS TKA components. Post-cam engagement velocity and flexion angle at initial contact were determined. The experimental data were used to validate computational predictions of PS mechanics using the same loading conditions. A lower limb model was subsequently utilized to compare engagement mechanics of eight TKA designs, relating differences between implants to geometric design features. Flexion angle and post-cam velocity at engagement demonstrated considerable ranges among designs (23°-89°, and 0.05-0.22 mm/°, respectively). Post-cam velocity was correlated (r = 0.89) with tibiofemoral condylar design features. Condylar geometry, in addition to post-cam geometry, played a significant role in minimizing engagement velocity and forces and stresses in the post. This analysis guides selection and design of PS implants that facilitate smooth post-cam engagement and reduce edge loading of the post.

The influence of total knee arthroplasty geometry on mid-flexion stability: an experimental and finite element study

Fluoroscopic evaluation of total knee arthroplasty (TKA) has reported sudden anterior translation of the femur relative to the tibia (paradoxical anterior motion) for some cruciate-retaining designs. This motion may be tied to abrupt changes in the femoral sagittal radius of curvature characteristic of traditional TKA designs, as the geometry transitions from a large load-bearing distal radius to a smaller posterior radius which can accommodate femoral rollback. It was hypothesized that a gradually reducing radius may attenuate sudden changes in anterior-posterior motion that occur in mid-flexion with traditional discrete-radius designs. A combined experimental and computational approach was employed to test this hypothesis. A previously developed finite element (FE) model of the Kansas knee simulator (KKS), virtually implanted with multiple implant designs, was used to predict the amount of paradoxical anterior femoral slide during a simulated deep knee bend. The model predicted kinematics demonstrated that incorporating a gradually reducing radius in mid-flexion reduced the magnitude of paradoxical anterior translation between 21% and 68%, depending on the conformity of the tibial insert. Subsequently, both a dual-radius design and a modified design incorporating gradually reducing radii were tested in vitro in the KKS for verification. The model-predicted and experimentally observed kinematics exhibited good agreement, while the average experimental kinematics demonstrated an 81% reduction in anterior translation with the modified design. The FE model demonstrated sufficient sensitivity to appropriately differentiate kinematic changes due to subtle changes in implant design, and served as a useful pre-clinical design-phase tool to improve implant kinematics.

Relative contributions of design, alignment, and loading variability in knee replacement mechanics

Substantial variation in total knee replacement (TKR) outcomes exists within the patient population. Some of this variability is due to differences in the design of the implanted components and variation in surgical alignment, while other variability is due to differences in the applied forces and torques due to anatomic and physiological differences within a patient population. We evaluated the relative contributions of implant design, surgical alignment, and patient-specific loading variability to overall tibiofemoral joint mechanics to provide insight into which measures can be influenced through design and surgical decisions, and which are inherently dependent on variation within the patient population and should be considered in the robustness of the implant design and surgical procedure. Design, surgical, and loading parameters were assessed using probabilistic finite element methods during simulated stance-phase gait and squat activities. Patient-specific loading was found to be the primary contributor to joint loading and kinematics during low flexion, particularly under conditions of high external loads (for instance, the gait cycle with high internal-external torque), while design and surgical factors, particularly femoral posterior radius and posterior slope of the tibial insert became increasingly important in TKR performance in deeper flexion.

Identifying alignment parameters affecting implanted patellofemoral mechanics

Complications of the patellofemoral (PF) joint remain a common cause for revision of total knee replacements. PF complications, such as patellar maltracking, subluxation, and implant failure, have been linked to femoral and patellar component alignment. In this study, a dynamic finite element model of an implanted PF joint was applied in conjunction with a probabilistic simulation to establish relationships between alignment parameters and PF kinematics, contact mechanics, and internal stresses. Both traditional sensitivity analysis and a coupled probabilistic and principal component analysis approach were applied to characterize relationships between implant alignment and resulting joint mechanics. Critical alignment parameters, and combinations of parameters, affecting PF mechanics were identified for three patellar designs (dome, modified dome, and anatomic). Femoral internal-external (I-E) alignment was identified as a critical alignment factor for all component designs, influencing medial-lateral contact force and anterior-posterior translation. The anatomic design was sensitive to patellar flexion-extension (F-E) alignment, while the dome, as expected, was less influenced by rotational alignment, and more by translational position. The modified dome was sensitive to a combination of superior-inferior, F-E, and I-E alignments. Understanding the relationships and design-specific dependencies between alignment parameters can aid preoperative planning, and help focus instrumentation design on those alignment parameters of primary concern.

The role of patient, surgical, and implant design variation in total knee replacement performance

Clinical studies demonstrate substantial variation in kinematic and functional performance within the total knee replacement (TKR) patient population. Some of this variation is due to differences in implant design, surgical technique and component alignment, while some is due to subject-specific differences in joint loading and anatomy that are inherently present within the population. Combined finite element and probabilistic methods were employed to assess the relative contributions of implant design, surgical, and subject-specific factors to overall tibiofemoral (TF) and patellofemoral (PF) joint mechanics, including kinematics, contact mechanics, joint loads, and ligament and quadriceps force during simulated squat, stance-phase gait and stepdown activities. The most influential design, surgical and subject-specific factors were femoral condyle sagittal plane radii, tibial insert superior-inferior (joint line) position and coronal plane alignment, and vertical hip load, respectively. Design factors were the primary contributors to condylar contact mechanics and TF anterior-posterior kinematics; TF ligament forces were dependent on surgical factors; and joint loads and quadriceps force were dependent on subject-specific factors. Understanding which design and surgical factors are most influential to TKR mechanics during activities of daily living, and how robust implant designs and surgical techniques must be in order to adequately accommodate subject-specific variation, will aid in directing design and surgical decisions towards optimal TKR mechanics for the population as a whole.

Evaluating knee replacement mechanics during ADL with PID-controlled dynamic finite element analysis

Validated computational knee simulations are valuable tools for design phase development of knee replacement devices. Recently, a dynamic finite element (FE) model of the Kansas knee simulator was kinematically validated during gait and deep flexion cycles. In order to operate the computational simulator in the same manner as the experiment, a proportional-integral-derivative (PID) controller was interfaced with the FE model to control the quadriceps actuator excursion and produce a target flexion profile regardless of implant geometry or alignment conditions. The controller was also expanded to operate multiple actuators simultaneously in order to produce in vivo loading conditions at the joint during dynamic activities. Subsequently, the fidelity of the computational model was improved through additional muscle representation and inclusion of relative hip-ankle anterior-posterior (A-P) motion. The PID-controlled model was able to successfully recreate in vivo loading conditions (flexion angle, compressive joint load, medial-lateral load distribution or varus-valgus torque, internal-external torque, A-P force) for deep knee bend, chair rise, stance-phase gait and step-down activities.

Verification of predicted knee replacement kinematics during simulated gait in the Kansas knee simulator

Evaluating total knee replacement kinematics and contact pressure distributions is an important element of preclinical assessment of implant designs. Although physical testing is essential in the evaluation process, validated computational models can augment these experiments and efficiently evaluate perturbations of the design or surgical variables. The objective of the present study was to perform an initial kinematic verification of a dynamic finite element model of the Kansas knee simulator by comparing predicted tibio- and patellofemoral kinematics with experimental measurements during force-controlled gait simulation. A current semiconstrained, cruciate-retaining, fixed-bearing implant mounted in aluminum fixtures was utilized. An explicit finite element model of the simulator was developed from measured physical properties of the machine, and loading conditions were created from the measured experimental feedback data. The explicit finite element model allows both rigid body and fully deformable solutions to be chosen based on the application of interest. Six degrees-of-freedom kinematics were compared for both tibio- and patellofemoral joints during gait loading, with an average root mean square (rms) translational error of 1.1 mm and rotational rms error of 1.3 deg. Model sensitivity to interface friction and damping present in the experimental joints was also evaluated and served as a secondary goal of this paper. Modifying the metal-polyethylene coefficient of friction from 0.1 to 0.01 varied the patellar flexion-extension and tibiofemoral anterior-posterior predictions by 7 deg and 2 mm, respectively, while other kinematic outputs were largely insensitive.