Introduction

Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee.

In knee osteoarthritis (OA) patients, a focal cartilage defect is commonly found, especially in the medial compartment. In addition, cartilage softening is often observed at the defect rim. Both factors may alter the loading distribution and thereby the contact pressures, previously related to cartilage degeneration. To determine contact pressure in-vivo during motion, computational modelling can be used. The aim of this study was to analyse knee cartilage pressures during walking in healthy and damaged cartilage using a multi-scale modelling approach. Using 3D motion capture and musculoskeletal models, multi-body simulations of the stance phase of gait calculated knee kinematics and muscle, ligament and contact forces. These were subsequently imposed to a finite element (FE) model including tibial and femoral bones and cartilage. FE analyses were performed using intact cartilage as well as including a medial tibial cartilage defect, with and without softening of the defect rim. Specifically during loading response, a medial cartilage defect reduced the contact surface (−28%) and thereby increased the contact pressure (+33%) compared to intact cartilage, particularly on the medial compartment (+75% in contact pressure). Including softening of the cartilage rim increased the contact area (+22%) and decreased contact pressures (−9%) compared to the defect. This indicates that a focal defect increases the cartilage loading. This is partially compensated by softening of the cartilage rim. Therefore, the role of focal defects in altered cartilage loading and consequent OA development always needs to be discussed acknowledging the cartilage status at the defect rim.

Introduction

Modification in joint loading, and specifically shear stress, is found to be an important mechanical factor in the development of osteoarthritis (OA). Cartilage shear stresses can be investigated using finite element (FE) modelling, where typically in vivo joint loading as measured by an instrumented hip prosthesis is used as boundary condition. However, subject-specific gait characteristics substantially affect joint loading. The goal of this study is to investigate the effect of subject-specific joint loading as calculated using a subject-specific musculoskeletal model and integrated motion capture data on acetabular shear stress.

Several studies have shown that gait kinematics[1–3] and hip contact forces (HCFs)[4, 5] of patients following total hip arthroplasty (THA) do not return to normal, although improvements in kinematics are found compared to the pre-surgery. However, the evolution of HCFs after surgery has not been investigated. The goal of this study is to evaluate HCFs during gait in OA patients before and at 2 evaluation moments post-THA.

Fourteen unilateral hip OA patients before and 3- and 12-months post-THA surgery walked at self-selected speed, as well as 18 healthy control subjects. 3D marker trajectories were captured using Vicon (Oxford Metrics, UK) and force data was measured using two AMTI force platforms (Watertown, MA). A musculoskeletal model consisting of 14 segments, 19 degrees of freedom and 88 musculotendon actuators and including wrapping surfaces around the hip joint was used[6]. All analyses were performed in OpenSim 3.1[7]. The model was scaled to the dimensions of each subject using the marker positions of a static pose. A kalman smoother procedure was used to calculate joint angles[8]. Muscle forces were calculated using static optimization, minimizing the sum of squared muscle activations. HCFs were calculated and normalized to body weight (BW). First and second peak HCFs were determined and used for statistical analysis. To determine differences between HCFs of OA patients at the different evaluation moments, a Friedman test was used. In case of a significant difference, post-hoc rank-based multiple comparison tests with a Bonferonni adjustment was used. To compare controls and patients at each evaluation moment separate Man-Whitney U tests were used. Differences in HCFs between the affected and non-affected legs were expressed by a symmetry index (SI), i.e. the ratio between the HCFs of the affected leg over the non-affected leg, averaged over the stance phase of the gait cycle.

At the first and second HCF peaks, no significant differences were found between pre-, 3- and 12-months post-surgery (first peak average HCF: 2.68, 2.72 and 2.78BW respectively; second peak average HCF: 3.21, 3.83 and 3.77BW respectively). Compared to controls, significant differences are found for all evaluation moments at the first and second HCF peaks (average HCF controls: 3.43 and 5.15BW respectively). The SI was below 1 pre- and 3-months post-surgery (0.88 and 0.85 respectively), indicating decreased loading of the affected compared to the non-affected leg. At 12-months post-surgery SI was close to 1 (0.98).

As reported before[4, 5], first or second peak HCFs do not return to normal after THA. Although HCFs increase after THA compared to pre-surgery, significant differences with controls remain. Surprisingly, no significant differences are found between the different evaluation moments of the patients, indicating no clear improvements are found after THA. Further, average HCF peaks at 3- and 12-months post-surgery are similar, indicating no further improvements are found 3-months post-surgery. However, the SI was above or close to 1 at 12-months post-surgery, indicating hip loading evolved to a more symmetrical loading 12-months post-surgery.

Children with cerebral palsy (CP) often present femoral bone deformities not accounted for in generic musculoskeletal models [1,2]. MRI-based models can be used to include subject-specific muscle paths [3,4], although this is a time-demanding process. Recently, non-rigid deformation techniques have been used to transform generic bone geometry, including muscle points, onto personalized bones [5]. However, it is still unknown to what extent such an approximation of subject-specific detail affects calculated hip contact forces (HCFs) during gait in CP children.

Seven children diagnosed with diplegic CP walked independently at self-selected speed. 3D marker trajectories were captured using Vicon (Oxford Metrics, UK) and force data was measured using two AMTI force platforms (Watertown, MA). MR-images were acquired (Philips Ingenia 1.5T) of all subjects lying supine. Firstly, a generic model [6] was scaled using the marker positions of a static pose. Secondly, a MRI-model containing the subject-specific bone structures and muscle paths of all hip and upper leg muscles was created [3]. Thirdly, the generic femur and pelvis geometries and muscle points were transformed onto the image-based femur and pelvis using an advanced non-rigid deformation procedure (Materialise N.V.). For all models, further analyses were performed in OpenSim 3.1 [7]. A kalman smoother procedure was used to calculate joint angles [8]. Muscle forces were calculated using a static optimization minimizing the sum of squared muscle activities. Next, HCFs were calculated and normalized to body weight (BW). First and second peak HCFs were determined and used for a Kruskal-Wallis test to determine differences between models. In case of a significant difference, a post-hoc rank-based multiple comparison test with Bonferonni adjustment was used. Further, average absolute differences in muscle points between the models was calculated, as well as average differences in moment arm lengths (MALs), reflecting muscle function.

Where the scaled generic muscle points differed on average 2.49cm from the MRI points, the non-rigidly deformed points differed 1.54cm from the MRI muscle points. Specifically, the tensor fascia latae differed most between the deformed and MRI models (11.7cm). When considering MALs, the gluteii muscles present an altered function for the generic and deformed models compared to the MRI model for all degrees of freedom of the hip at the time of both HCF peaks. The differences between models resulted in a significantly increased second peak HCF for the MRI models compared to the generic models (first peak average HCF: 3.88BW, 3.95BW and 4.90BW; second peak average HCF: 3.03BW, 4.89BW and 5.32BW for the generic, MRI and non-rigidly deformed models respectively). Although not significantly different, the deformed models calculated slightly increased HCFs compare to the MRI models.

The generic models underestimated HCFs compared to the MRI models, while the non-rigidly deformed models slightly overestimated HCFs. However, differences between the deformed and MRI models in terms of muscle points and MALs remain, specifically for the gluteii muscles. Therefore, further user-guided modification of the model based on MR-images will be necessary.