Knee arthroscopy is typically approached from the anterior, posteromedial and posterolateral portals. Access to the posterior compartments through these portals can cause iatrogenic cartilage damage and create difficulties in viewing the structures of the posterior compartments. The purpose of this study was to assess the feasibility of needle arthroscopy using direct posterior portals as both working and visualising portals. For workability, the needle scope was inserted advanced from anterior between the cruciate ligament bundle and the lateral wall of the medial femoral condyle until the posterior compartments were visualised. For visualisation, direct postero-lateral and -medial portals were established. The technique was performed in 9 knees by two experienced researchers. Workability and instrumentation of the posteromedial compartment and meniscus was achieved in 56%. The posterior horns could not be visualised in four specimens as the straight lens could not provide a more medial field of view. Visualisation from the direct medial posterior portal allowed a clear view of the medial meniscus, femoral condyle and posterior cruciate ligament in all specimens. Workability and instrumentation of the posterolateral compartment was not possible with the needle scope. Direct posterior approaches for the posteromedial compartment access are challenging with the current needle scope options and could only be achieved in over 50%. The postero-lateral compartment was not accessible. An angled lens or a flexible Needle scope would be better suited for developing this technique further

Joint assessment through manual physical examination is a fundamental skill that must be acquired by orthopaedic surgeons. These joint assessments allow surgeons to identify soft tissue injuries (e.g. ligament tears) which are critical in identifying appropriate treatment options. The difficulty in communicating the feeling of different joint conditions and the limited opportunities for practice can make these skills challenging to learn, resulting in reduced treatment effectiveness and increased costs. This research seeks to improve the training of joint assessment with the creation of a haptic joint simulator that can train surgeons with increased effectiveness. A first of its kind haptic simulator is presented, which incorporates: a newly defined kinetic knee simulation, a haptic device for user interaction, and a haptic control algorithm. The knee model has been specifically created for this application and allows six degree-of-freedom manipulation of the tibia while considering the effects of ten knee ligament bundles. The model has been mathematically formulated to allow for the high update rates necessary for smooth and stable haptic simulation. Two quantitative assessments were made of the model to confirm its clinical validity. The first was against the widely used OpenSim biomechanical simulation software. Simulations of the model's performance for both anterior-posterior draw tests and varus-valgus rotation tests showed less than 0.7%RMSE for force and 5.5%RMSE for moments. Crucially, the proposed model could generate updated forces in less than 1ms, compared to 188ms for OpenSim. The second validation of the model was against a cadaveric knee that was tested using a validated robotic testing platform. This comparison showed that the model could generate similar force- motion pathways to the cadaveric knee after the model's parameters were scaled to match. Having demonstrated that it is possible to create a computational knee model that has good conformance to gold-standard knee simulations and cadaveric recordings, while updating at less than 1ms, this research has overcome a major hurdle. The next stage of this research will be to incorporate the knee model into a full haptic simulator and perform skill acquisition trials. Given the effectiveness of past haptic training systems in aiding clinical skills acquisition, this research offers a promising way to improve surgeon training, and therefore also patient diagnosis and treatment

Introduction. The intrinsic constraint of a total knee replacement (TKR) implant system is considered an important characteristic which plays a large role in determining stability following surgery. Established techniques for evaluating the constraint of TKR implants, as described in ASTM F 1223-14, do not necessarily map directly to physiologically relevant loading scenarios where instability can occur, and thus give an incomplete picture of the constraint characteristics of a candidate implant design. Sophisticated joint motion simulators now allow for more physiologically representative joint loading (eg. gait), including the contributions of virtual soft tissues. In this study, we employ a function-based constraint measurement technique for evaluating the kinematics of two TKR designs during gait. Furthermore, we employ simulated soft tissues in order to create three “virtual” knees on which the TKR are tested. Methods. The constraint characteristics of TKR implants were evaluated using a function-based measurement technique on a VIVO joint motion simulator (AMTI, Waltham, MA). The AVG75 standardized load and motion profiles for gait (Bergmann et al. 2014), were applied to an ultra-congruent cruciate-sacrificing TKR (Zimmer-Biomet, Warsaw, IN). Ligaments were simulated as point-to-point spring elements between the femur and tibia (3 bundles for MCL, 3 bundles for LCL). Ligament bundle origin, insertion, stiffness, and resting length properties were adapted from the publically available MB Knee project (. simtk.org/home/mb_knee. ) to create three knees. AP and IE kinematics were recorded during simulated gait after approximately 500 “learning” cycles at 0.75 Hz. Trials were then repeated with superimposed AP forces or IE torques. The amount of superimposed load varied with the amount of compressive load, such that the superimposed load was ±25 N AP force or ±1 Nm IE torque, per 1000 N of compressive force. AP and IE laxities were calculated based on changes in AP and IE motions, respectively (Fig 1). Experiments were repeated with a second TKR design; using the same femoral component but replacing the ultra-congruent UHMWPE bearing with a 3D printed ABS plastic bearing featuring a less congruent sagittal profile. In total, there were 2 implants × 3 virtual knees × 5 simulated loading profiles = 30 different simulated gait trials. Results. The baseline (normal gait) AP and IE motions for both TKR designs, averaged across three knees, are shown in Fig. 2. The average AP and IE laxities for each knee are shown in Table 1, with results averaged for each TKR design. Discussion. Differences in AP motion between the two TKR designs are large compared to the differences in IE motion. Predictably, the overall AP and IE motions and average laxities for the less congruent TKR are greater. While this trend was generally consistent across all knees, the actual differences in laxities between the two TKR designs varied between knees. This suggests that the importance of the intrinsic constraint of TKR varies on subject-to-subject basis, and thus variable soft tissue stabilization models should be considered during pre-clinical testing. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Introduction. Improper soft-tissue balancing can result in postoperative complications after total knee arthroplasty (TKA) and may lead to early revision. A single-use tibial insert trial with embedded sensor technology (VERASENSE from OrthoSensor Inc., Dania Beach, FL) was designed to provide feedback to the surgeon intraoperatively, with the goal to achieve a “well-balanced” knee throughout the range of motion (Roche et al. 2014). The purpose of this study was to quantify the effects of common soft-tissue releases as they related to sensor measured joint reactions and kinematics. Methods. Robotic testing was performed using four fresh-frozen cadaveric knee specimens implanted with appropriately sized instrumented trial implants (geometry based on a currently available TKA system). Sensor outputs included the locations and magnitudes of medial and lateral reaction forces. As a measure of tibiofemoral joint kinematics, medial and lateral reaction locations were resolved to femoral anterior-posterior displacement and internal-external tibial rotation (Fig 1.). Laxity style joint loading included discrete applications of ± 100 N A-P, ± 3 N/m I-E and ± 5 N/m varus-valgus (V-V) loads, each applied at 10, 45, and 90° of flexion. All tests included 20 N of compressive force. Laxity tests were performed before and after a specified series of soft-tissue releases, which included complete transection of the posterior cruciate ligament (PCL), superficial medial collateral ligament (sMCL), and the popliteus ligament (Table 1). Sensor outputs were recorded for each quasi-static test. Statistical results were quantified using regression formulas that related sensor outputs (reaction loads and kinematics) as a function of tissue release across all loading conditions. Significance was set for p-values ≤ 0.05. Results. Tissue releases, and in particular the sMCL and PCL, led to multiple findings, many of which were dependent on flexion (Table 2). For PCL resection, at 10° of flexion lateral and total joint loads decreased, whereas at 45 and 90° lateral load increased. In addition, there was a significant anterior shift of the femur that increased with flexion angle, while tibial rotation was only affected at 90°. sMCL release decreased the total load across all flexion angles, and impacted the medial load at 10° only. The only structure for which no significant relationship was discovered was the deep medial collateral ligament, as this variable was confounded on others. Discussion. One critical aspect of TKA is achieving appropriate soft-tissue balance to maximize postoperative performance. In this study, the sensor provided a direct measurement of joint loading and kinematics, which were related to surgically relevant soft-tissue releases. Results showed the sMCL to decrease joint loads and flexion dependent changes after PCL release, likely an indication of bundle specific response. Future work should be performed to examine the roles of individual ligament bundles, as well as graded effects of tissue releases. Overall, the results corroborate previous findings and provide a new and direct look at the role of ligaments in TKA. Significance. This study quantified relationships between surgically relevant tissue states and joint response in TKA. The data has the potential to be applied intraoperatively to guide soft-tissue releases. To view tables/figures, please contact authors directly

INTRODUCTION. Understanding the relationship between knee specific tissue behavior and joint contact mechanics remains an area of focus. Seminal work from 1990's established the possibility to optimize tissue properties for recreation of laxity driven kinematics (Mommersteeg et al., 1996). Yet, the uniqueness and validity of such predictions could be strengthened, especially as they relate to joint contact conditions. Understanding this interplay has implications for the long term performance of joint replacements. Development of instrumented knee implants, highlighted by a single use tibial insert trial with embedded sensor technology (VERASENSE, Orthosensor Inc.), may offer an avenue to establish the relationship between tissue state and joint mechanics. Utilization of related data also has the potential to confirm computational predictions, where both rigid body motions and associated reactions are explicitly accounted for. Hence, the goal of this work was to evaluate an approach for optimization of ligament properties using joint mechanics data from an instrumented implant during laxity style testing. Such a framework could be used to inform joint balancing techniques, improve long term implant performance, and alternatively, qualify factors that may lead to poor outcomes. METHODS. Experimentation was performed on a 52 year old male, left, cadaveric specimen. Joint arthroplasty was performed using standard practice by an experienced orthopedic surgeon. To mimic passive intraoperative loading, laxity loading at 10°, 45° and 90° flexion, which consisted of discrete application of anterior-posterior (± 100N), varus-valgus (± 5 Nm) and internal-external (± 3 Nm) loads at each angle, was performed using a simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH). Experimental results included relative tibiofemoral kinematics and sensor measured metrics (Fig 1). The finite element model was developed from specimen-specific MRIs and solved using Abaqus/Explicit. The model included the rigid bones, appropriately placed implants and relevant soft-tissue structures (Fig. 1). Ligament stiffness values were adopted from the literature and included a 6% strain toe region. Sets of nonlinear springs, defined using MR imaging, comprised each ligament/bundle. Optimization was performed, which minimized the root mean squared difference between VERASENSE measured tibiofemoral mechanics and the model predicted values. Ligament slack lengths were the control variables and the objective included each loading state and all contact metrics (θ, AFD, ML, and LL). RESULTS AND DISCUSSION. The model successfully recreated joint kinematics with average errors of 4° for rotations and 3 mm for translations, across all flexion angles (Fig 2). Though a systematic offset in θ was observed, model versus experiment contact locations were also in good agreement. Reaction forces were generally over-predicted by the model, but retained the overall trend (Fig 2). Sensitivity analysis also supported this finding. In light of the larger focus of this project, testing also included systematic removal of key tissues followed by repeat testing, as evaluated across numerous specimens. Overall, the presented framework represents a promising step towards establishing simulation based tools able to support exploratory studies as well as the clinical decision making process. Future work will evaluate efficacy across numerous specimens and assess sensitivity to key modeling and experimental parameters. To view tables/figures, please contact authors directly