Introduction:

Computer-assisted surgery (CAS) aims to improve component positioning and mechanical alignment in Total Knee Arthroplasty (TKA). Robotic cutting-guides have been integrated into CAS systems with the intent to improve bone-cutting precision and reduce navigation time by precisely automating the placement of the cutting-guide. The objectives of this study were to compare the intra-operative efficiency and accuracy of a robotic-assisted TKA procedure to a conventional computer-assisted TKA procedure where fixed sequential cutting-blocks are navigated free-hand.

The alignment of prostheses components has a major impact on the longevity of total knee protheses as it significantly influences the biomechanics and thus also the load distribution in the knee joint.

Knee joint loads depend on three factors: (1) geometrical conditions such as bone geometry and implant position/orientation, (2) passive structures such as ligaments and tendons as well as passive mechanical properties of muscles, and (3) active structures that are muscles. The complex correlation between implant position and clinical outcome of TKA and later in vivo joint loading after TKA has been investigated since 1977. These investigations predominantly focused on component alignment relative to the mechanical leg axis (Mikulicz-line) and more recently on rotational alignment perpendicular to the mechanical axis. In general four different approaches can be used to study the relationship between implant position and knee joint loads: In anatomical studies (1), the influence of the geometrical conditions and passive structures can be analyzed under the constraint that the properties of vital tissue are only approximated. This could be overcome with an intraoperative load measurement approach (2). Though, this set up does not consider the influence of active structures. Although post-operative in vivo load measurements (3) provide information about the actual loading condition including the influence of active structures, this method is not applicable to investigate the influence of different implant positions. Using mathematical approaches (4) including finite element analysis and multi-body-modeling, prostheses positions can be varied freely. However, there exists no systematical analysis of the influence of prosthesis alignment on knee loading conditions not only in axial alignment along and rotational alignment perpendicular to the mechanical axis but in all six degrees of freedom (DOF) with a validated mathematical model. Our goal was therefore to investigate the correlation between implant position and joint load in all six DOF using an adaptable biomechanical multi-body model.

A model for the simulation of static single leg stance was implemented as an approximation of the phase with the highest load during walking cycle. This model is based on the AnyBody simulation software (AnyBody Technology A/S, Denmark). As an initial approach, with regard to the simulation of purely static loading the knee joint was implemented as hinge joint. The patella was realised as a deflection point, a so called “ViaNode,” for the quadriceps femoris muscle. All muscles were implemented based on Hill's muscle model. The knee model was indirectly validated by comparison of the simulation results for single and also double leg stance with in-vivo measurements from the Orthoload database (www.orthoload.de). For the investigation of the correlation between implant position and knee load, major boundary conditions were chosen as follows:•

Flexion angle was set to 20° corresponding to the position with the highest muscle activity during gait cycle.

Comparing the simulation results of single and double leg stance with the in-vivo measurements from the Orthoload database, changes in knee joint forces showed similar trends and the slope of changes in torques transmitted by the joint was equal. Against the background of unknown geometrical conditions in the Orthoload measurements and the simplification (hinge joint) of the initial multi-body-model compared to real knee joints, the developed model provides a reasonable basis for further investigations already – and will be refined in future works.

As influencing parameters are very complex, a non-ambiguous interpretation of force/torque changes in the knee joint as a function of changes in component positions was in many cases hardly possible. Changes in patella force on the other hand could be traced back to geometrical and force changes in the quadriceps femoris muscle. Positional changes mostly were in good agreement with our hypotheses based on literature data when knee load and patellar forces respectively were primarily influenced by active structures, e.g. with regard to the danger of patella luxation in case of increased internal rotation of the tibial component. Whereas simulations also showed results contradicting our expectations for positional changes mainly affecting passive structures, e.g. cranial/caudal translation of the femoral component. This shows the major drawback of the implemented model: Intra-articular passive structures such as cruciate and collateral ligaments were not represented. Additionally kinematic influences on knee and patella loading were not taken into account as the simulations were made under static conditions. Implementation of relative movements of femoral, tibial and patella components and simulation under dynamic conditions might overcome this limitation. Furthermore, the boundary condition of complete muscle adaptations might be critical, as joint loads might be significantly higher shortly after operation. This could lead to a much longer and possibly ineffective rehabilitation process.

Consideration of biomechanical aspects during computer assisted orthopaedic surgery (CAOS) is recommendable in order to obtain satisfactory long-term results in total hip arthroplasty (THA). In addition to the absolute value of the hip joint resultant force R the pre- and post-operative orientation of R is an important aspect in the context of the development of a planning module for computer-assisted THA and furthermore for planning of acetabular orientation in periacetabular osteotomy interventions. It is possible to estimate the orientation of hip joint resultant force R for individual patients based on geometrical and anthropometrical parameters. The aim of this study was to examine how far the choice of the mathematical model influences the computational results for the orientation of R in the frontal plane. A further aspect was the comparison of the results with in-vivo data published in the open access OrthoLoad database (www.orthoload.com).

Our comparative study included the 2D-models suggested by Pauwels, Blumentritt and Debrunner as well as the 3D-model suggested by Iglič and three patient datasets from the Orthoload database. As computation of R according to each model relies on standardized X-ray imaging, three anterior-posterior (a.p.) digitally reconstructed radiographs (DRRs) were generated from CT data (x21_x21, x8_x8, x12_x12). The orientation of R was expressed in terms of the angle δ for these three patient individual datasets. The angle δ is defined as the angle between the longitudinal axis and R. The computation results were also compared with in vivo telemetric measurement data from the OrthoLoad database. The following data were used to evaluate R in the frontal plane: the highest load peak of the single leg stance (static conditions) of three patients (EBL, HSR, KWR) respectively in the same manner for planar gait (dynamic conditions) of one patient (KWR). The mean value of the orientation of R under static conditions in single leg stance was calculated in order to get a reference value. For the orientation of R under dynamic conditions δ was calculated by using only the highest peak of three cycles (heel strike to toe off) determined in one single patient (among the three patients involved in the measurements under static conditions) of the database.

The following values of δ were obtained:

Pauwels: 18.26°/20.34°/17.31° (x21_x21/x8_x8/x12_x12) Debrunner: 12.37°/14.30°/12.59° Blumentritt: 5.18°/6.52°/6.14° Iglič: 9.24°/9.01°/9.20°

OrthoLoad database (in-vivo): 28.41°/17.08°/13.32°-static (EBL/HSR/KWR) 16.44°-dynamic (KWR)

The differences in the computational results appear to depend more on the hip model than on the variability of patient-specific geometrical and anthropometrical parameters. The results obtained with in-vivo measurement data are best approximated by using Pauwels' model. The mean values of Pauwels (18.64°), Debrunner (13.09°) and Iglič (9.15°) are a little bit more vertically orientated than the mean value of the static in-vivo results (19.60°). Only Pauwels' model result has a larger angle δ than the in-vivo dynamic result (KWR = 16.44°). By comparing the in-vivo values obtained under dynamic conditions, i.e. gait, (16.44°) with the static in-vivo values of the same patient (13.32°), it could be recognized that the static values are a little bit more vertically orientated than the dynamic result. But both are in the same range as the mathematical models.

The computational biomechanical hip models try to approximate the physiological conditions of the hip joint and the OrthoLoad database represents the physiological reconstructed (artificial) hip joint. Therefore, we think our validation approach is useful for a comparison of the biomechanical computation models.

In contrast, Blumentritt's model outcomes have the largest deviation from the other models as well as from the in-vivo data (static and dynamic conditions). Blumentritt used the weight bearing surface as a reference. He defined it being perpendicular to the longitudinal axis [3]. He postulated that a valid and optimal orientation of R is approximately perpendicular on the weight bearing surface respectively parallel to the longitudinal axis. This approach for validation is questionable because the results show that in the three included and analysed DDR's the orientation is in the mean value 5.95° to the longitudinal axis. It can be concluded that Blumentritt's model assumptions have to be carefully reviewed due to the deviations from in-vivo measurement data.

Among the limitations of our study is the fact that the OrthoLoad database offers only a small number of patient datasets. There is only one dataset for the direct comparison of static (single leg stance) and dynamic (free planar gait) in-vivo measurement data of the same patient included. Furthermore, the individual anatomic geometry data of the patients included in the database are not revealed. Additionally, a source of errors could be an inaccuracy during the data acquisition from the DRR.

Further research seems to be recommendable in the context of implementing a biomechanical hip model in a planning module for computer-assisted THA or periacetabular osteotomy interventions, respectively. Sensitivity analyses and parameter studies for different mathematical models using a multi-body-simulation system are objectives of our ongoing work.