Clinical and Computational Evaluation of an Anatomic Patellar Component.

BACKGROUND: Anatomic patellar components for total knee arthroplasty (TKA) have demonstrated favorable in vivo kinematics. A novel failure mechanism in TKA patients with an anatomic patellar component was observed prompting an investigation to identify patient- and implant-related factors associated with suboptimal performance. METHODS: A retrospective evaluation was performed comparing 100 TKA patients with an anatomic patellar component to 100 gender-, age-, and body mass index-matched patients with a medialized dome component. All surgeries were performed with the same posterior-stabilized TKA system with minimum of 1-year follow-up. Several radiographic parameters were assessed. A separate computational evaluation was performed using finite-element analysis, comparing bone strain energy density through the patella bone remnant. RESULTS: Patients with an anatomic patellar component had significantly higher rates of anterior knee pain (18 versus 2%, P < .001), chronic effusions (18 versus 2%, P < .001), and superior patellar pole fragmentation (36 versus 13%, P < .001) compared to those with a dome component. Radiographically, the anatomic group demonstrated more lateral patellar subluxation (2.3 versus 1.1 mm, P < .001) and lateral tilt (5.4 versus 4.0 mm, P = .013). Furthermore, there were more revisions in the anatomic group (7 versus 3, P = .331). On computational evaluation, all simulations demonstrated increased bone strain energy density at the superior patellar pole with the anatomic patella. Resection thickness <13 mm resulted in over 2-fold higher strain energy density, and negative resection angle of 7° resulted in 6-fold higher superior pole strain energy. CONCLUSIONS: Patients with an anatomic patellar component showed higher rates of anterior knee pain, chronic effusion, and superior pole fragmentation compared to patients with a dome patella, with higher superior patellar pole strain energy confirmed on computational evaluation. Avoiding higher resection angles and excessive patellar resection may improve the performance and survivorship of the anatomic patella.

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.

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.

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.

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.

Impact of periprosthetic femoral fracture fixation plating constructs on local stiffness, load transfer, and bone strains.

Secondary femoral fractures after the successful plate-screw fixation of a primary Vancouver type B1 periprosthetic femoral fracture (PFF) have been associated with the altered state of stress/strain in the femur as the result of plating. The laterally implanted condyle-spanning plate-screw constructs have shown promises clinically in avoiding secondary bone and implant failures as compared with shorter diaphyseal plates. Though the condyle-spanning plating has been hypothesized to avoid stress concentration in the femoral diaphysis through increasing the working length of the plate, biomechanical evidence is lacking on how plate length may impact the stress/strain state of the implanted femur. Through developing and experimentally validating finite element (FE) models of 3 cadaveric femurs, this study investigated the impact of plating on bone strains, load transfer and local stiffness, which were compared between FE models of 2 different plating systems that each had a diaphyseal configuration and a condyle-spanning configuration. Under simulated gait-loading, the condyle-spanning constructs of both plating systems were shown to lower the bone strains around the distal fixation screws (up to 24.8% reduction in maximum principal strain and 26.6% reduction in minimum principal strain) and in the distal metaphyseal shaft of the femur (up to 15.9% and 25.7% reductions in maximum and minimum principal strains, respectively), where secondary bone fractures have been typically reported. In the distal diaphyseal and metaphyseal shaft of femur, FE models of the condyle-spanning constructs were shown to increase the local compressive stiffness (up to 152.9% increases under simulated gait-loading) and decrease the transfer of compressive load (37.1% decreases under simulated gait-loading), which may be indicative of the lowered risks of bone damage.

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.

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.

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 framework for population-based evaluation of TKR-implanted patellofemoral joint mechanics.

Differences in patient anatomy are known to influence joint mechanics. Accordingly, intersubject anatomical variation is an important consideration when assessing the design of joint replacement implants. The objective of this study was to develop a computational workflow to perform population-based evaluations of total knee replacement implant mechanics considering variation in patient anatomy and to assess the potential for an efficient sampling strategy to support design phase screening analyses. The approach generated virtual subject anatomies using a statistical shape model of the knee and performed virtual implantation to size and align the implants. A finite-element analysis simulated a deep knee bend activity and predicted patellofemoral (PF) mechanics. The study predicted bounds of performance for kinematics and contact mechanics and investigated relationships between patient factors and outputs. For example, the patella was less flexed throughout the deep knee bend activity for patients with an alta patellar alignment. The results also showed the PF range of motions in AP and ML were generally larger with increasing femoral component size. Comparison of the 10-90% bounds between sampling strategies agreed reasonably, suggesting that Latin Hypercube sampling can be used for initial screening evaluations and followed up by more intensive Monte Carlo simulation for refined designs. The platform demonstrated a functional workflow to consider variation in joint anatomy to support robust implant design.

Development of a statistical shape-function model of the implanted knee for real-time prediction of joint mechanics.

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implanted knee to instantaneously predict joint mechanics in an efficient manner. Finite element methods were combined with Latin hypercube sampling and regression analyses to produce modeling equations relating nine implant design and six surgical alignment parameters to tibiofemoral (TF) joint mechanics outcomes during a deep knee bend. A SFM was developed and TF contact mechanics, kinematics, and soft tissue loads were instantaneously predicted from the model. Average normalized root-mean-square error predictions were between 2.79% and 9.42%, depending on the number of parameters included in the model. The statistical shape-function model generated instantaneous joint mechanics predictions using a maximum of 130 training simulations, making it ideally suited for integration into a patient-specific design and alignment optimization pipeline. Such a tool may be used to optimize kinematic function to achieve more natural motion or minimize implant wear, and may aid the engineering and clinical communities in improving patient satisfaction and surgical outcomes.

Validation of model-predicted tibial tray-synthetic bone relative motion in cementless total knee replacement during activities of daily living.

As fixation of cementless total knee replacement components during the first 4-6 weeks after surgery is crucial to establish bony ingrowth into the porous surface, several studies have quantified implant-bone micromotion. Relative motion between the tray and bone can be measured in vitro, but the full micromotion contour map cannot typically be accessed experimentally. Finite element models have been employed to estimate the full micromotion map, but have not been directly validated over a range of loading conditions. The goal of this study was to develop and validate computational models for the prediction of tray-bone micromotion under simulated activities of daily living. Gait, stair descent and deep knee bend were experimentally evaluated on four samples of a cementless tibial tray implanted into proximal tibial Sawbones™ constructs. Measurements of the relative motion between the tray and the anterior cortical shell were collected with digital image correlation and used to validate a finite element model that replicated the experiment. Additionally, a probabilistic analysis was performed to account for experimental uncertainty and determine model sensitivity to alignment and frictional parameters. The finite element models were able to distinguish between activities and capture the experimental trends. Best-matching simulations from the probabilistic analysis matched measured displacement with an average root mean square (RMS) difference of 14.3 µm and Pearson-product correlation of 0.93, while the mean model presented an average RMS difference of 27.1 µm and a correlation of 0.8. Maximum deviations from average experimental measurements were 40.5 and 87.1 µm for the best-matching and average simulations, respectively. The computational pipeline developed in this study can facilitate and enhance pre-clinical assessment of novel implant components.

Loading and kinematic profiles for patellofemoral durability testing.

Patellar complications after total knee replacement (TKR), such as maltracking, fracture, wear, and loosening, can lead to implant failure and revision surgery. However, few in vitro patellofemoral durability tests for the implanted joint have been developed. Existing standards for patellofemoral loading profiles (ISO 14243-5, draft) are generic (not implant-specific) and do not include patient variability. The goal of this study was to derive implant-specific loading profiles to simulate a motor task that reaches high knee flexion and include patient variability. In vivo data, including motion capture and stereo-radiographic images at the knee, were collected for eleven rotating platform TKR patients performing a single-leg lunge activity. Quadriceps forces during the activity were estimated for each patient from marker data and ground forces with a musculoskeletal model. Patellofemoral contact forces were estimated with patient-specific finite element models of the implanted knees. Stereo-radiography patellofemoral kinematics and estimated contact loads were combined to derive seven loading profiles that span the observed inter-patient variability. The loading profiles were experimentally evaluated in a 6-degree-of-freedom testing machine and worst-case loading profiles were identified. The two profiles that generated the highest stresses in the patellar button (43% and 46% of the volume surpassed yield stress, respectively) included the largest internal (4.4°) and external (13.0°) patellar rotation, and greater medio/lateral contact forces (up to 915 N). The same profiles were also tested in a finite element model of the experimental simulator, which was able to adequately replicate location and magnitude of the peak deformations measured in the prostheses after the experiment. The kinematic and loading profiles developed in this study simulated a high-demand motor task and incorporated inter-patient variability, capturing worst-case patellofemoral configurations, and can be utilized for pre-clinical testing of new patellar designs.