Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth "grand challenge competition to predict in vivo knee loads" dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R2) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > -0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > -0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

Soft-tissue balancing for total knee arthroplasty (TKA) remains subjective and highly dependent on surgical expertise. Pre-operative planning may support the clinician in taking decisions by integrating subject-specific computer models that predict functional outcome. However, validation of these models is essential before they can be applied in clinical practice. The aim of this study was to evaluate a knee modelling workflow by comparing experimental cadaveric measures to model-based kinematics and ligament length changes. Subject-specific models for three cadaveric knees were constructed from medical images. The implanted knees were mounted onto a mechanical rig to perform squatting, measuring kinematics and ligament length changes with optical markers and extensometers. Coronal malrotation was introduced using tibial inserts with a built-in slope. The model output agreed well with the experiment in all alignment conditions. Kinematic behaviour showed an average RMSE of less than 2.7mm and 2.3° for translations and rotations. The average RMSE was below 2.5% for all ligaments. These results show that the presented model can quantitatively predict subject-specific knee behaviour following TKA, allowing evaluation of implant alignment in terms of kinematics and ligament length changes. In future work, the model will be used to evaluate subject-specific implant position based on ligament behaviour.

Knee joint laxity or instability is a common problem that may have detrimental consequences for patients. Unfortunately, assessment of knee joint laxity is limited by current methodologies resulting in suboptimal diagnostics and treatment. This paper presents a novel method for accurately measuring non-invasive knee joint laxity in four degrees-of-freedom (DOF). An arthrometer, combining a parallel manipulator and a six-axis force/moment sensor, average mean difference for translations of 0.08 mm and an average limit of agreement between-1.64 mm and 1.80 mm. The average mean difference for rotations was 0.10°and the limit of agreement were between-0.85° and 1.05°. The presented method eliminates several limitations present in current methods and may prove a valuable tool for assessing knee joint laxity. Current stress radiography is also only assessing single plane laxity, but is not limited by low reliability (Schulz et al., 2005) or STA (Garavaglia et al., 2007). The radiation exposure from such measurements may, however, induce serious health risks for both the patient and operator making the method unsuitable for many applications (Balonov and Shrimpton, 2012). Joint instability must be assessed in multiple DOF in order to fully understand the complexity of the joint structures and the interplay between ligaments (Hirschmann and Müller, 2015). Neither clinical tests, arthrometry nor 1D stress radiography possesses the potential to obtain this information. Furthermore, these methods potentially over-constrain the joint during measurements, by restricting out-of-plane motion, making it appear more stable and potentially shift the load distribution unnaturally in the joint (Woo et al., 1999). Methods capable of measuring unconstrained knee joint laxity in multiple DOF not restricted by the above limitations are a prerequisite to progress the field of research in knee instability and advance current clinical assessment of joint instability. Novel methods utilizing robotics for assessing knee joint laxity has, therefore, recently emerged (Branch et al., 2015; Lorenz et al., 2015). These methods exercise high repeatability and accuracy in the application of forces, however, they are still limited by their inability to measure true bone motion due to STA. By combining robotic technology with new medical image modalities, the aforementioned limitation can be overcome which may make it possible to conceive a laxity measurement method with high accuracy, multiplanar assessment and unaffected by STA. Therefore, this paper proposes such a novel method for measuring knee joint laxity in multiple DOF, combining parallel manipulator technology and low dose biplanar x-ray acquisition. Methods Development An in vivo arthrometer was custom developed, combining a parallel manipulator (H-820, Physik Instrumente, Germany) and a six axis force/moment (F/M) sensor (Omega85 SI-1900-80, ATI Industrial Automation, USA) (see Fig. 1). The parallel manipulator has a manufacturer reported repeatability of ±1 µm and the F/M sensor has a re...