Design and analysis of robust total joint replacements: finite element model experiments with environmental variables

Physicomechanical, morphological and tribo-deformation characteristics of lightweight WC/AZ31B Mg-matrix biocomposites for hip joint applications

Exploring high strength materials with a higher concentration of reinforcements in the alloy proves to be a challenging task. This research has explored magnesium-based composites (AZ31B alloy) with tungsten carbide reinforcements, enhancing strength for medical joint replacements via league championship optimisation. The primary objective is to enhance medical joint replacement biomaterials employing magnesium-based composites, emphasising the AZ31B alloy with tungsten carbide reinforcements. The stir casting method is utilised in the manufacture of magnesium matrix composites (MMCs), including varied percentages of tungsten carbide (WC). The mechanical characteristics, such as micro-hardness, tensile strength, and yield strength, have been assessed and compared with computational simulations. The wear studies have been carried out to analyse the tribological behaviour of the composites. Additionally, this study investigates the prediction of stress and the distribution of forces inside bone and joint structures, therefore offering significant contributions to the field of biomedical research. This research contemplates the use of magnesium-based MMCs for the discovery of biomaterials suitable for medical joint replacement. The study focuses on the magnesium alloy AZ31B, with particles ranging in size from 40 to 60 microns used as the matrix material. Moreover, the outcomes have revealed that when combined with MMCs based on AZ31B-magnesium matrix, the WC particle emerges as highly effective reinforcements for the fabrication of lightweight, high-strength biomedical composites. This study uses the league championship optimisation (LCO) approach to identify critical variables impacting the synthesis of Mg MMCs from an AZ31B-based magnesium alloy. The scanning electron microscopy (SEM) images are meticulously analysed to depict the dispersion of WC particulates and the interface among the magnesium (Mg) matrix and WC reinforcement. The SEM analysis has explored the mechanisms underlying particle pull-out, the characteristics of inter-particle zones, and the influence of the AZ31B matrix on the enhancement of the mechanical characteristics of the composites. The application of finite element analysis (FEA) is being used in order to make predictions regarding the distribution of stress and the interactions of forces within the model of the hip joint. This study has compared the physico-mechanical and tribological characteristics of WC to distinct combinations of 0%, 5%, 10% and 15%, and its impact on the performance improvements. SEM analysis has confirmed the findings' improved strength and hardness, particularly when 10%-15% of WC was incorporated. Following the incorporation of 10% of WC particles within Mg-alloy matrix, the outcomes of the study has exhibited enhanced strength and hardness, which furthermore has been evident by utilising SEM analysis. Using ANSYS, structural deformation and stress levels are predicted, along with strength characteristics such as additional hardness of 71 HRC, tensile strength of 140-150 MPa, and yield strength closer to 100-110 MPa. The simulations yield significant insights into the behaviour of the joint under various loading conditions, thus enhancing the study's significance in biomedical environments.

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant

Today, different implant designs exist in the market; however, there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. Therefore, the aim of this project was to investigate if the geometry of a commercial short stem hip prosthesis can be further optimized to reduce stress shielding effects and achieve better short-stemmed implant performance. To reach this aim, the potential of machine learning techniques combined with parametric Finite Element analysis was used. The selected implant geometrical parameters were: total stem length (L), thickness in the lateral (R1) and medial (R2) and the distance between the implant neck and the central stem surface (D). The results show that the total stem length was not the only parameter playing a role in stress shielding. An optimized implant should aim for a decreased stem length and a reduced length of the surface in contact with the bone. The two radiuses that characterize the stem width at the distal cross-section in contact with the bone were less influential in the reduction of stress shielding compared with the other two parameters; but they also play a role where thinner stems present better results.

Minimizing Stress Shielding and Cement Damage in Cemented Femoral Component of a Hip Prosthesis through Computational Design Optimization

The average life expectancy of many people undergoing total hip replacement (THR) exceeds twenty-five years and the demand for implants that increase the load-bearing capability of the bone without affecting the short- or long-term stability of the prosthesis is high. Mechanical failure owing to cement damage and stress shielding of the bone are the main factors affecting the long-term survival of cemented hip prostheses and implant design must realistically adjust to balance between these two conflicting effects. In the following analysis we introduce a novel methodology to achieve this objective, the numerical technique combines automatic and realistic modeling of the implant and embedding medium, and finite element analysis to assess the levels of stress shielding and cement damage and, finally, global optimization, using orthogonal arrays and probabilistic restarts, were used. Applications to implants, fabricated using a homogeneous material and a functionally graded material, were presented.

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Integration of multi-objective structural optimization into cementless hip prosthesis design: Improved Austin-Moore model

A new strategy of multi-objective structural optimization is integrated into Austin-Moore prosthesis in order to improve its performance. The new resulting model is so-called Improved Austin-Moore. The topology optimization is considered as a conceptual design stage to sketch several kinds of hollow stems according to the daily loading cases. The shape optimization presents the detailed design stage considering several objectives. Here, A new multiplicative formulation is proposed as a performance scale in order to define the best compromise between several requirements. Numerical applications on 2D and 3D problems are carried out to show the advantages of the proposed model.

Influence of PEEK Coating on Hip Implant Stress Shielding: A Finite Element Analysis

Stress shielding is a well-known failure factor in hip implants. This work proposes a design concept for hip implants, using a combination of metallic stem with a polymer coating (polyether ether ketone (PEEK)). The proposed design concept is simulated using titanium alloy stems and PEEK coatings with thicknesses varying from 100 to 400 μm. The Finite Element analysis of the cancellous bone surrounding the implant shows promising results. The effective von Mises stress increases between 81 and 92% for the complete volume of cancellous bone. When focusing on the proximal zone of the implant, the increased stress transmission to the cancellous bone reaches between 47 and 60%. This increment in load transferred to the bone can influence mineral bone loss due to stress shielding, minimizing such effect, and thus prolonging implant lifespan.

Uncertainty quantification for personalized analyses of human proximal femurs

Computational models for the personalized analysis of human femurs contain uncertainties in bone material properties and loads, which affect the simulation results. To quantify the influence we developed a probabilistic framework based on polynomial chaos (PC) that propagates stochastic input variables through any computational model. We considered a stochastic E-ρ relationship and a stochastic hip contact force, representing realistic variability of experimental data. Their influence on the prediction of principal strains (ϵ1 and ϵ3) was quantified for one human proximal femur, including sensitivity and reliability analysis. Large variabilities in the principal strain predictions were found in the cortical shell of the femoral neck, with coefficients of variation of ≈40%. Between 60 and 80% of the variance in ϵ1 and ϵ3 are attributable to the uncertainty in the E-ρ relationship, while ≈10% are caused by the load magnitude and 5-30% by the load direction. Principal strain directions were unaffected by material and loading uncertainties. The antero-superior and medial inferior sides of the neck exhibited the largest probabilities for tensile and compression failure, however all were very small (pf<0.001). In summary, uncertainty quantification with PC has been demonstrated to efficiently and accurately describe the influence of very different stochastic inputs, which increases the credibility and explanatory power of personalized analyses of human proximal femurs.

A Genetic Algorithm Based Multi-Objective Shape Optimization Scheme for Cementless Femoral Implant

The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

Four decades of finite element analysis of orthopaedic devices: where are we now and what are the opportunities?

Finite element has been used for more than four decades to study and evaluate the mechanical behaviour total joint replacements. In Huiskes seminal paper "Failed innovation in total hip replacement: diagnosis and proposals for a cure", finite element modelling was one of the potential cures to avoid poorly performing designs reaching the market place. The size and sophistication of models has increased significantly since that paper and a range of techniques are available from predicting the initial mechanical environment through to advanced adaptive simulations including bone adaptation, tissue differentiation, damage accumulation and wear. However, are we any closer to FE becoming an effective screening tool for new devices? This review contains a critical analysis of currently available finite element modelling techniques including (i) development of the basic model, the application of appropriate material properties, loading and boundary conditions, (ii) describing the initial mechanical environment of the bone-implant system, (iii) capturing the time dependent behaviour in adaptive simulations, (iv) the design and implementation of computer based experiments and (v) determining suitable performance metrics. The development of the underlying tools and techniques appears to have plateaued and further advances appear to be limited either by a lack of data to populate the models or the need to better understand the fundamentals of the mechanical and biological processes. There has been progress in the design of computer based experiments. Historically, FE has been used in a similar way to in vitro tests, by running only a limited set of analyses, typically of a single bone segment or joint under idealised conditions. The power of finite element is the ability to run multiple simulations and explore the performance of a device under a variety of conditions. There has been increasing usage of design of experiments, probabilistic techniques and more recently population based modelling to account for patient and surgical variability. In order to have effective screening methods, we need to continue to develop these approaches to examine the behaviour and performance of total joint replacements and benchmark them for devices with known clinical performance. Finite element will increasingly be used in the design, development and pre-clinical testing of total joint replacements. However, simulations must include holistic, closely corroborated, multi-domain analyses which account for real world variability.

Correlation between the bone mineral density and stress on femur around a Duetto SI stem

In cementless stem fixation, BMD reduction around the stem is of concern because it may cause loosening. This BMD reduction is assumed to be caused by stem implantation-induced alteration of the physiological feedback system, which may cause stress shielding and result in loosening, but the causal relationship has not been clarified. In this study, using a Duetto SI stem, we investigated the correlation between the postoperative BMD around the stem and stress. In patients who underwent their first THA at the orthopedic department of our university, the BMD was measured using DEXA, and FEA was performed with an equivalent time course. Time-course changes in the BMD, von Mises stress, and triaxial stress in Gruen zones 1 through 7 were calculated from those measured at 2 weeks and 5 months after surgery. The BMD and von Mises stress showed a bidirectional correlation when Gruen's classification was plotted on the horizontal axis. An increase in stress loaded on bone was assumed to be a factor increasing the BMD. The Duetto SI stem was fixed on the distal side, suggesting its stable fixation. BMD measurement and FEA were useful for quantification of the bone dynamics around the stem from an early phase.

Failure analysis of the cement mantle in total hip arthroplasty with an efficient probabilistic method

Accurate prediction of long-term behaviour of cemented hip implants is very important not only for patient comfort but also for elimination of any revision operation due to failure of implants. Therefore, a more realistic computer model was generated and then used for both deterministic and probabilistic analyses of the hip implant in this study. The deterministic failure analysis was carried out for the most common failure states of the cement mantle. On the other hand, most of the design parameters of the cemented hip are inherently uncertain quantities. Therefore, the probabilistic failure analysis was also carried out considering the fatigue failure of the cement mantle since it is the most critical failure state. However, the probabilistic analysis generally requires large amount of time; thus, a response surface method proposed in this study was used to reduce the computation time for the analysis of the cemented hip implant. The results demonstrate that using an efficient probabilistic approach can significantly reduce the computation time for the failure probability of the cement from several hours to minutes. The results also show that even the deterministic failure analyses do not indicate any failure of the cement mantle with high safety factors, the probabilistic analysis predicts the failure probability of the cement mantle as 8%, which must be considered during the evaluation of the success of the cemented hip implants.

Fluid-structure interactions in micro-interlocked regions of the cement-bone interface

Experimental tests and computational modelling were used to explore the fluid dynamics at the trabeculae-cement interlock regions found in the tibial component of total knee replacements. A cement-bone construct of the proximal tibia was created to simulate the immediate post-operative condition. Gap distributions along nine trabeculae-cement regions ranged from 0 to 50.4 μm (mean = 12 μm). Micro-motions ranged from 0.56 to 4.7 μm with a 1 MPa compressive load to the cement. Fluid-structure analysis between the trabeculae and the cement used idealised models with parametric evaluation of loading direction, gap closing fraction (GCF), gap thickness, loading frequency and fluid viscosity. The highest fluid shear stresses (926 Pa) along the trabecular surface were found for conditions with very thin and large GCFs, much larger than reported physiological levels (~1-5 Pa). A second fluid-structure model was created with a provision for bone resorption using a constitutive model with resorption velocity proportional to fluid shear rate. A lower cut-off was used, below which bone resorption would not occur (50 s(-1)). Results showed that there was initially high shear rates (>1000 s(-1)) that diminished after initial trabecular resorption. Resorption continued in high shear rate regions, resulting in a final shape with bone left deep in the cement layer, and is consistent with morphology found in post-mortem retrievals. Small gaps between the trabecular surface and the cement in the immediate post-operative state produce fluid flow conditions that appear to be supra-physiologic; these may cause fluid-induced lysis of trabeculae in the micro-interlock regions.

On the optimal shape of hip implants

The success of a total hip arthroplasty is strongly related to the initial stability of the femoral component and to the stress shielding effect. In fact, for cementless stems, initial stability is essential to promote bone ingrowth into the stem coating. An inefficient primary stability is also a cause of thigh pain. In addition, the bone adaptation after the surgery can lead to an excessive bone loss and, consequently, can compromise the success of the implant. These factors depend on prosthesis design, namely on material, interface conditions and shape. Although, surgeons use stems with very different geometries, new computational tools using structural optimization methods have been used to achieve a better design in order to improve initial stability and therefore, the implant durability. In this work, a multi-criteria shape optimization process is developed to study the relationship between implants performance and geometry. The multi-criteria objective function takes into account the initial stability of the femoral stem and the effect of stress shielding on bone adaptation after the surgery. Then, the optimized stems are tested using a concurrent model for bone remodeling and osseointegration to evaluate long-term performance. Additionally, the sensitivity to misalignments is analyzed, since femoral stems are often placed in varus or valgus position. Results show that the different criteria are contradictory resulting in different characteristics for the hip stem. However, the multi-criteria formulation leads to compromise solutions, with a combination of the geometric characteristics obtained for each criterion separately.

A review of probabilistic analysis in orthopaedic biomechanics

Probabilistic analysis methods are being increasingly applied in the orthopaedics and biomechanics literature to account for uncertainty and variability in subject geometries, properties of various structures, kinematics and joint loading, as well as uncertainty in implant alignment. As a complement to experiments, finite element modelling, and statistical analysis, probabilistic analysis provides a method of characterizing the potential impact of variability in parameters on performance. This paper presents an overview of probabilistic analysis and a review of biomechanics literature utilizing probabilistic methods in structural reliability, kinematics, joint mechanics, musculoskeletal modelling, and patient-specific representations. The aim of this review paper is to demonstrate the wide range of applications of probabilistic methods and to aid researchers and clinicians in better understanding probabilistic analyses.

Effect of geometrical uncertainty on cemented hip implant structural integrity

A large number of parameters such as material properties, geometry, and structural strength are involved in the design and analysis of cemented hip implants. Uncertainties in these parameters have a potential to compromise the structural performance and lifetime of implants. Statistical analyses are well suited to investigating this type of problem as they can estimate the influence of these uncertainties on the incidence of failure. Recent investigations have focused on the effect of uncertainty in cement properties and loading condition on the integrity of the construct. The present study hypothesizes that geometrical uncertainties will play a role in cement mantle failure. Finite element input parameters were simulated as random variables and different modes of failure were investigated using a response surface method (RSM). The magnitude of random von Mises stresses varied up to 8 MPa, compared with a maximum nominal value of 2.38 MPa. Results obtained using RSM are shown to match well with a benchmark direct Monte Carlo simulation method. The resulting probability that the maximum cement stress will exceed the nominal stress is 62%. The load and the bone and prosthesis geometries were found to be the parameters most likely to influence the magnitude of the cement stresses and therefore to contribute most to the probability of failure.

What design factors influence wear behavior at the bearing surfaces in total joint replacements?

Bearing surface wear in total joint replacements arises from local stresses that exceed the mechanical strength of the articulating materials. Because both the tensile/compressive principal stresses and maximum shear stress near the bearing surface increase when contact stresses increase, minimizing contact stresses has been a central design goal, especially in total knees. Wear rates increase with factors such as increased sliding distance in metal-on-polyethylene bearings, or suboptimal fluid film lubrication in the case of hard-on-hard total hip implants. These factors in turn depend directly on implant design. Advanced preclinical assessment technologies such as laboratory physical simulators and finite element analyses have provided means by which the dependence of wear rate on mechanical design factors can be quantified. However, untoward complexities occurring in vivo, such as impingement or third-body challenge, can appreciably compromise wear performance even for implants that are well-designed in terms of bearing surface stress minimization.

Effects of knee simulator loading and alignment variability on predicted implant mechanics: a probabilistic study

Inherent variability in total knee arthroplasty loading and alignment, present in vivo and in simulator testing, may ultimately influence polyethylene tibial insert wear and long-term performance. The effect of this variability was quantified on implant kinematics and contact mechanics during simulated gait loading conditions using semi-constrained and unconstrained fixed bearing, cruciate retaining implants. A probabilistic finite element model of the Stanmore knee wear simulator was utilized to estimate the envelope of anterior-posterior (AP) and internal-external (IE) position and contact pressure and to evaluate the variability in corresponding ranges of motion (ROM). Variability levels were represented by standard deviations of up to 10% of the maximum value for load inputs and 0.25 mm and 0.5 degrees for component alignment inputs. Model predictions compared well with experimental simulator results for the semi-constrained implant, with predicted positional envelopes of up to 1.8 mm (AP) an 3.4 degrees (IE) for the semi-constrained and up to 2.6 mm (AP) and 3.7 degrees (IE) for the unconstrained implant at the variability levels evaluated. ROM varied by up to 22%, while peak contact pressure variations averaged less than 2 MPa for both designs. For each implant, loading variability was more influential during the swing phase of gait, while alignment variability affected kinematics more during stance. The relative rank of sensitivities showed differences between the two designs, providing insight into critical parameters affecting kinematics and contact characteristics.

Biomechanical analysis of the shear behaviour adjacent to an axially loaded implant

Good mechanical fixation of an implant to the surrounding bone is important for its longevity, and is influenced by both biological and mechanical factors. This study parametrically evaluates the mechanics of the interface with a computationally efficient analytic structural model of the shear stress field and global shear stiffness of an axially loaded implant. The utility of the analytic model was first established by validating its assumptions with a case-specific finite element model. We then used the analytic model for a sensitivity analysis of the relationship between the pattern of tissue growth and shear properties of the interface for our previously reported loaded in vivo experimental micromotion device. The bone located directly at the implant surface was found to be the most effective site for increasing interface stiffness. This suggests that the implant surface is the most desirable site for bone growth, yet is also the most mechanically challenging environment due to its maximal shear stresses. Thus, these findings support the further investigation of osteo-conductive coatings and other biological stimuli to overcome the challenging mechanics, and to promote bone growth directly at the implant surface. The model also demonstrated that the mechanical contribution to the global implant shear stiffness of a commonly observed isolated sclerotic bone rim is very limited. The results of this sensitivity analysis agree with experimental studies with the micromotion device, and with clinical studies reporting good results with osteo-conductive coatings.