Gait laboratory measurement of whole-body kinematics and ground reaction forces during a wide range of activities is frequently performed in joint replacement patient diagnosis, monitoring, and rehabilitation programs. These data are commonly processed in musculoskeletal modeling platforms such as OpenSim and Anybody to estimate muscle and joint reaction forces during activity. However, the processing required to obtain musculoskeletal estimates can be time consuming, requires significant expertise, and thus seriously limits the patient populations studied. Accordingly, the purpose of this study was to evaluate the potential of deep learning methods for estimating muscle and joint reaction forces over time given kinematic data, height, weight, and ground reaction forces for total knee replacement (TKR) patients performing activities of daily living (ADLs). 70 TKR patients were fitted with 32 reflective markers used to define anatomical landmarks for 3D motion capture. Patients were instructed to perform a range of tasks including gait, step-down and sit-to-stand. Gait was performed at a self-selected pace, step down from an 8” step height, and sit-to-stand using a chair height of 17”. Tasks were performed over a force platform while force data was collected at 2000 Hz and a 14 camera motion capture system collected at 100 Hz. The resulting data was processed in OpenSim to estimate joint reaction and muscle forces in the hip and knee using static optimization. The full set of data consisted of 135 instances from 70 patients with 63 sit-to-stands, 15 right-sided step downs, 14 left-sided step downs, and 43 gait sequences. Two classes of neural networks (NNs), a recurrent neural network (RNN) and temporal convolutional neural network (TCN), were trained to predict activity classification from joint angle, ground reaction force, and anthropometrics. The NNs were trained to predict muscle and joint reaction forces over time from the same input metrics. The 135 instances were split into 100 instances for training, 15 for validation, and 20 for testing.Introduction
Methods
The magnitude of principal strain is indicative of the risks of femoral fracture,1,2 while changes in femoral strain energy density (SED) after total hip arthroplasty (THA) have been associated with bone remodeling stimulus.3 Although previous modeling studies have evaluated femoral strains in the intact and implanted femur under walking loads through successfully predicting physiological hip contact force and femoral muscle forces,1,2,3 strains during ‘high load’ activities of daily living have not typically been evaluated. Hence, the objective of this study was to compare femoral strain between the intact and the THA implanted femur under peak loads during simulated walking, stair descent, and stumbling. CTs of three cadaveric specimens were used to develop finite element (FE) models of intact and implanted femurs. Implanted models included a commercially-available femoral stem (DePuy Synthes, Warsaw, IN, USA). Young's moduli of the composite bony materials were interpolated from Hounsfield units using a CT phantom and established relationships.4 Peak hip contact force and femoral muscle forces during walking and stair descent were calculated using a lower extremity musculoskeletal model5 and applied to the femur FE models (Fig. 1). While maintaining the peak hip contact forces, muscle forces were further adjusted using an iterative optimization approach in FE models to reduce the femur deflection to the reported physiological range (< 5 mm).2 Femoral muscle forces during stumbling were estimated utilizing the same optimization approach with literature-reported hip contact forces as input.6 Maximum and minimum principal strains were calculated for each loading scenario. Changes in SED between intact and THA models were calculated in bony elements around the stem.INTRODUCTION
METHODS
While computational models have been used for many years to contribute to pre-clinical, design phase iterations of total knee replacement implants, the analysis time required has limited the real-time use as required for other applications, such as in patient-specific surgical alignment in the operating room. In this environment, the impact of variation in ligament balance and implant alignment on estimated joint mechanics must be available instantaneously. As neural networks (NN) have shown the ability to appropriately represent dynamic systems, the objective of this preliminary study was to evaluate deep learning to represent the joint level kinetic and kinematic results from a validated finite element lower limb model with varied surgical alignment. External hip and ankle boundary conditions were created for a previously-developed finite element lower limb model [1] for step down (SD), deep knee bend (DKB) and gait to best reproduce in-vivo loading conditions as measured on patients with the Innex knee ( A NN was created utilizing the NN toolbox in MATLAB. Sequence data inputs were produced from the alignment and the external boundary conditions for each activity cycle. Sequence outputs for the model were the 6 degree of freedom kinetics and kinematics, totaling 12 outputs. All data was normalized across the entire data set. Ten percent of the simulation runs were removed at random from the training set to be used for validation, leaving 2220 simulations for training and 244 for validation. A nine-layer bi-long short-term memory (LSTM) NN was created to take advantage of bi-LSTM layers ability to learn from past and future data. Training on the network was undertaken using an RMSprop solver until the root mean square error (RMSE) stopped reducing. Evaluation of NN quality was determined by the RMSE of the validation set.INTRODUCTION
METHODS
Real-time tracking of surgical tools has applications for assessment of surgical skill and OR workflow. Accordingly, efforts have been devoted to the development of low-cost systems that track the location of surgical tools in real-time without significant augmentation to the tools themselves. Deep learning methodologies have recently shown success in a multitude of computer vision tasks, including object detection, and thus show potential for the application of surgical tool tracking. The objective of the current study was to develop and evaluate a deep learning-based computer vision system using a single camera for the detection and pose estimation of multiple surgical tools routinely used in both knee and hip arthroplasty. A computer vision system was developed for the detection and 6-DoF pose estimation of two surgical tools (mallet and broach handle) using only RGB camera frames. The deep learning approach consisted of a single convolutional neural network (CNN) for object detection and semantic key point prediction, as well as an optimization step to place prior known geometries into the local camera coordinate system. Inference on a camera frame with size of 256-by-352 took 0.3 seconds. The object detection component of the system was evaluated on a manually-annotated stream of video frames. The accuracy of the system was evaluated by comparing pose (position and orientation) estimation of a tool with the ground truth pose as determined using three retroreflective markers placed on each tool and a 14 camera motion capture system (Vicon, Centennial CO). Markers placed on the tool were transformed into the local camera coordinate system and compared to estimated location.Introduction
Methods
Alignment of the acetabular cup and femoral components directly affects hip joint loading and potential for impingement and dislocation following total hip arthroplasty (THA) [1]. Changes to the lines of action and moment generating capabilities of the muscles as a result of component position may influence overall patient function. The objectives of this study were to assess the effect of component placement on hip joint contact forces (JCFs) and muscle forces during a high demand step down task and to identify important alignment parameters using a probabilistic approach. Three patients following THA (2 M: 28.3±2.8 BMI; 1 F: 25.7 BMI) performed lower extremity maximum isometric strength tests and a step down task as part of a larger IRB-approved study. Patient-specific musculoskeletal models were created by scaling a model with detailed hip musculature [2] to patient segment dimensions and mass. For each model, muscle maximum isometric strengths were optimized to minimize differences between model-predicted and measured preoperative maximum isometric joint torques at the hip and knee. Baseline simulations used patient-specific models with corresponding measured kinematics and ground reaction forces to predict hip JCFs and muscle forces using static optimization. To assess the combined effects of stem and cup position and orientation, a 1000 trial Monte Carlo simulation was performed with input variability in each degree of freedom based on the ±1 SD range in component placement relative to native geometry reported by Tsai et al. [3] (Figure 1). Maximum confidence bounds (1–99%) were predicted for the hip JCF magnitude and muscle forces for three prime muscles involved in the task (gluteus medius, gluteus minimus and psoas). HJC confidence bounds were compared to Orthoload measurements from telemetric implants from 6 patients performing the step down task. Sensitivity of hip JCF and muscle force outputs was quantified by Pearson Product-Moment correlation between the input parameter and the value of each output averaged across four points in the cycle.Introduction
Methods
Patellar resurfacing affects patellofemoral (PF) kinematics, contact mechanics, and loading on the patellar bone. Patients with total knee arthroplasty (TKA) often exhibit adaptations in movement patterns that may be linked to quadriceps deficiency and the mechanics of the reconstructed knee [1]. Previous comparisons of PF kinematics between dome and anatomic resurfacing have revealed differences in patellar sagittal plane flexion [2], but further investigation of PF joint mechanics is required to understand how these differences influence performance. The purpose of this study was to compare PF mechanics between medialized dome and medialized anatomic implants using subject-specific computational models. A high-speed stereo radiography (HSSR) system was used to capture 3D sub-mm measurement of bone and implant motion [3]. HSSR images were collected for 10 TKA patients with Attune® (DePuy Synthes, Warsaw, IN) posterior-stabilized, rotating-platform components, 5 with medialized dome and 5 with medialized anatomic patellar components (3M/7F, 62.5±6.6 years, 2.2±0.6 years post-surgery, BMI: 26.2±3.5 kg/m2), performing two activities of daily living: knee extension and lunge (Figure 1). Relative motions were tracked using Autoscoper (Brown University, Providence, RI) for implant geometries obtained from the manufacturer. A statistical shape model was used to predict the patella and track motions [4]. Subject-specific finite element models of the experiment were developed for all subjects and activities [5]. The model included implant components, patella, quadriceps, patellar tendon, and medial and lateral PF ligaments (Figure 2a). While tibiofemoral kinematics were prescribed based on experimental data, the PF joint was unconstrained. A constant 1000N quadriceps load was distributed among four muscle groups. Soft tissue attachments and pre-strain in PF ligaments were calibrated to match experimental kinematics [5]. Model outputs included PF kinematics, patellar and contact force ratios, patellar tendon angle, and moment arm.Introduction
Methods
Patellar crepitus and clunk are tendofemoral-related complications predominantly associated with posterior-stabilizing (PS) total knee arthroplasty (TKA) designs [1]. Contact between the quadriceps tendon and the femoral component can cause irritation, pain, and catching of soft-tissue within the intercondylar notch (ICN). While the incidence of tendofemoral-related pathologies has been documented for some primary TKA designs, literature describing revision TKA is sparse. Revision components require a larger boss resection to accommodate a constrained post-cam and stem/sleeve attachments, which elevates the entrance to the ICN, potentially increasing the risk of crepitus. The objective of this study was to evaluate tendofemoral contact in primary and revision TKA designs, including designs susceptible to crepitus, and newer designs which aim to address design features associated with crepitus. Six PS TKA designs were evaluated during deep knee bend using a computational model of the Kansas knee simulator (Figure 1). Prior work has demonstrated that tendofemoral contact predictions from this model can differentiate between TKA patients with patellar crepitus and matched controls [2]. Incidence of crepitus of up to 14% has been reported in Insall-Burstein® II and PFC® Sigma® designs [3]. These designs, in addition to PFC® Sigma® TC3 (revision component), were included in the analyses. Primary and revision components of newer generation designs (NexGen®, Attune® and Attune® Revision) were also included. Designs were evaluated in a patient model with normal Insall-Salvati ratio and a modified model with patellar tendon length reduced by two standard deviations (13mm) to assess worst-case patient anatomy.Introduction
Methods
Within total hip replacement, articulation of the femoral head near the rim of the acetabular liner creates undesirable conditions leading to a propensity for dislocation[1], increased contact stresses[2], increased load and torque imparted on the acetabular component[3], and increased wear[4]. Propensity for rim loading is affected by prosthesis placement, as well as the kinematics and loading of the patient. The present study investigates these effects. CT scans from an average-sized patientwere segmented for the hemipelvis and femur of interest. DePuy Synthes implant models were aligned in a neutral position in Hypermesh. The acetabular liner was assigned deformable solid material properties, and the remainder of the model was assigned rigid properties. Joint reaction forces and kinematics of hip flexion were taken from the public Orthoload database to represent ADLs [5]: Active flexion lying on a table, gait, bending to lift and move a load, and sit-stand. The pelvis was fully constrained, while three-degree-of-freedom (3-DOF) forces were applied to the femur. Hip flexion was kinematically-prescribed while internal-external (I-E) and adduction-abduction (Ad-Ab) DOFs were constrained. Angles of acetabular implant positioning were based on published data by Rathod [6]. Femoral implant position was chosen based on cadaveric in vitro DePuy Synthes measurements of variation in femoral prosthesis position reported previously [7]. Acetabular and Femoral alignment angles were represented for nominal position, as well as positioning + 1σ and + 2σ from the mean in both anteversion and inclination for acetabular components, and both Varus/Valgus and Flexion (angle in sagittal plane) for the femoral component. The analyses were automated within Matlab to execute 68 finite element analyses in Abaqus Explicit and structured in a DOE style analysis with Cup inclination, Cup version, Stem Flexion, and Stem Varus/Valgus, and Activity as variables of interest (64 runs + 4 centerpoints = 68 analyses). From a previous study it was known that acetabular component inclination had the greatest effect on contact pressure location [7], so all data were analyzed relative to inclination, allowing other positioning variables to be represented as variation per inclination position. Results are presented as a percentage, with 0% being pole loading and 100% being rim loading, to normalize for head diameter.INTRODUCTION
METHODS
“How does the knee move?” is a question of fundamental importance for treatment of knee injuries and knee replacement design. Unfortunately, we lack unambiguous and comprehensive knee function data sets and/or consensus on how healthy knees move. One can just as easily find reports stating the natural knee has a center of axial rotation in the medial compartment of the knee as in the lateral. This is due to technical and practical issues: It is extremely difficult to accurately measure knee motions during ambulatory activities and, when that can be done, very few studies have examined a range of weightbearing activities in the same study cohort. The purpose of this study is to report knee kinematics in a cohort of healthy older subjects whose motions were examined during four different movements, three of them weightbearing ambulation, using a high-speed stereoradiographic system. Six healthy consenting subjects (age = 61 ± 5 years, body mass = 75 ± 8 kg, BMI = 27 ± 4) were observed using a high-speed stereoradiographic system while completing four tasks. Subjects were instructed to perform an unloaded, seated knee extension from high flexion to full extension; to walk at a self selected pace; to step down from a 7 inch platform; and to walk and perform a 90° direction change (pivoting). Stereoradiographic images (1080 × 1080 pixels) were acquired at 100 images/second using 40cm image intensifiers and pulsed x-ray exposures. The three-dimensional knee kinematics were measured using the XROMM software suite ( Average CoRs for all four activities were in the posterior-medial quadrant of the knee, with the CoR for open-chain knee extension being the most medial and gait the most lateral (Table 1, Figure 2). One-way ANOVA showed average CoRs are different (p « 0.001). There was considerable variation in individual CoRs, for example, with two knees showing lateral CoRs for gait and the remaining knees having medial CoRs. It should not be surprising that natural knee motions vary with dynamic activity, yet knee kinematics often are presented as being one stereotypic, monolithic pattern of motion. Our data show that the same healthy subjects performing different dynamic activities manifest different knee motions, with open-chain knee extension having the most medial CoR and gait the most lateral. This finding is consistent with previous reports comparing stair climbing and gait in knees with various implant designs. Additional experimental data and, ultimately, validated numerical simulations should facilitate an increasingly accurate process for designing improved treatments for diseased and damaged knees.
The current standard for alignment in total knee arthroplasty (TKA) is neutral mechanical axis within 3° of varus or valgus deviation [1]. This configuration has been shown to reduce wear and optimally distribute load on the polyethylene insert [2]. Two key factors (patient-specific hip-knee-ankle (HKA) angle and surgical component alignment) influence load distribution, kinematics and soft-tissue strains across the tibiofemoral (TF) joint. Improvements in wear characteristics of TKA materials have facilitated a trend for restoring the anatomic joint line [3]. While anatomic component alignment may aid in restoring more natural kinematics, the influence on joint loads and soft-tissue strains should be evaluated. The purpose of the current study was to determine the effect of varus component alignment in combination with a variety of HKA limb alignments on joint kinematics, loads and soft-tissue strain. A dynamic three-dimensional finite element model of the lower limb of a TKA patient was developed. Detailed description of the model has been previously published [4]. The model included femur, tibia and patella bones, TF ligaments, patellar tendon, quadriceps and hamstrings, and was virtually implanted with contemporary cruciate-retaining fixed-bearing TKA components. The model was initially aligned in ideal mechanical alignment with neutral HKA limb alignment. A design-of-experiments (DOE) study was performed whereby component placement was altered from neutral to 3° and 7° varus alignment, and HKA angle was altered from neutral to ±3° and ±7° (valgus and varus) (Figure 1).Introduction
Methods
A large number of total knee arthroplasty (TKA) patients, particularly in Japan, India and the Middle East, exhibit anatomy with substantial proximal tibial torsion. Alignment of the tibial components with the standard anterior-posterior (A-P) axis of the tibia can result in excessive external rotation of the tibial components with respect to femoral component alignment. This in turn influences patellofemoral (PF) mechanics and forces required by the extensor mechanism. The purpose of the current study was to determine if a rotating-platform (RP) TKA design with an anatomic patellar component reduced compromise to the patellar tendon, quadriceps muscles and PF mechanics when compared to a fixed-bearing (FB) design with a standard dome-shaped patellar component. A dynamic three-dimensional finite element model of the knee joint was developed and used to simulate a deep knee bend in a patient with excessive external tibial torsion (Figure 1). Detailed description of the model has been previously published [1]. The model included femur, tibia and patellar bones, TKA components, patellar ligament, quadriceps muscles, PF ligaments, and nine primary ligaments spanning the TF joint. The model was virtually implanted with two contemporary TKA designs; a FB design with domed patella, and a RP design with anatomic patella. The FB design was implanted in two different alignment conditions; alignment to the tibial A-P axis, and optimal alignment for bone coverage. Four different loading conditions (varying internal-external (I-E) torque and A-P force) were applied to the model to simulate physiological loads during a deep knee bend. Quadriceps muscle force, patellar tendon force, and PF and TF joint forces were compared between designs.Introduction
Methods
