Today, hip prostheses are validated with Standards for fatigue testing: The Standard ISO 7206-4 requires to test 6 components at 230daN during 5 × 10⁶ cycles without crack. For the neck region of stemmed femoral components, the Standard ISO 7206-6 requires 6 tests at 534daN during 10 × 10⁶ cycles without crack. But these tests don't provide provide any indication on reliability level for an implantation in population.

At the same time, the number of hip prosthesis implantation is growing, patients are implanted younger and younger and they want to be able to maintain a “normal” life. This way the average “loading spectrum” is getting tougher and tougher, due to this modification of the use of prosthesis in comparison with some years ago. On the other hand, there is new materials, new processes (additive manufacturing), new methods (customized stems…) with no feedback on reliability. A method is then necessary to manage the reliability in fatigue for actual and new products.

The objective of this study is to establish a statistical distribution of loading of hip prosthesis in order to define at best a minimum value of strength required for a good fatigue design.

To define this strength, the Stress-Strength (well known in automotive sector) approach is applied (fig 1). This approach will allow better assess the reliability in a population, depending on the mean strength and the scattering in fatigue.

The first step is to establish the distribution of the loads for a hip prosthesis. Then, for a given risk level, the required strength can be defined, knowing the scattering of this strength.

The strategy to have the distribution is based on:

In vivo load recordings on hip prosthesis (find on Orthoload.com),

All these data are collected in the literature, and combined, then processed with the software DEFFI^®. The goal is first to assess the reliability reached by a “nominal” stem and compare it to the reliability described in implant registers. Another goal is to analyse the stress distribution and compare it to standard requests (ISO 7206-6), in order to assess the reliability of an implant that succeeded this standard.

A last, this method is a way to define the minimum strength for implants dedicated to particular populations: young and active patients, patients with high Body Weight, etc…

For any figures or tables, please contact authors directly (see Info & Metrics tab above).

There is a large variability associated with hip stem designs, patient anatomy, bone mechanical property, surgical procedure, loading, etc. Designers and orthopaedists aim at improving the performance of hip stems and reducing their sensitivity to this variability. This study focuses on the primary stability of a cementless short stem across the spectrum of patient morphology using a total of 109 femoral reconstructions, based on segmentation of patient CT scan data. A statistical approach is proposed for assessing the variability in bone shape and density [Blanc, 2012]. For each gender, a thousand new femur geometries were generated using a subset of principal components required to capture 95% of the variance in both female and male training datasets [Bah, 2013]. A computational tool (Figure 1) is then developed that automatically selects and positions the most suitable implant (distal diameter 6–17 mm, low and high offset, 126° and 133° CCD angle) to best match each CT-based 3D femur model (75 males and 34 females), following detailed measurements of key anatomical parameters. Finite Element contact models of reconstructed hips, subjected to physiologically-based boundary constraints and peak loads of walking mode [Speirs, 2007] were simulated using a coefficient of fricition of 0.4 and an interference-fit of 50μm [Abdul-Kadir, 2008]. Results showed that the maximum and average implant micromotions across the subpopulation were 100±7μm and 7±5μm with ranges [15μm, 350μm] and [1μm, 25μm], respectively. The computed percentage of implant area with micromotions greater than reported critical values of 50μm, 100μm and 150μm never exceeded 14%, 8% and 7%, respectively. To explore the possible correlations between anatomy and implant performance, response surface models for micromotion metrics were constructed using the so-called Kriging regression methodology, based on Gaussian processes. A clear nonlinear decreasing trend was revealed between implant average micromotion and the metaphyseal canal flare indexes (MCFI) measured in the medial-lateral (ML), anterio-posterior (AP) and femoral neck-oriented directions but also the average bone density in each Gruen zone. In contrast, no clear influence of the remaining clinically important parameters (neck length and offsets, femoral anteversion and CCD angle, standard canal flares, patient BMI and weight or stem size) to implant average micromotion was found. In conclusion, the present study demonstrates that the primary stability and tolerance of the short stem to variability in patient anatomy were high, suggesting no need for patient stratification. The developed methodology, based on detailed morphological analysis, accurate implant selection and positioning, prediction of implant micromotion and primary stability, is a novel and valuable tool to support implant design and planning of femoral reconstructive surgery.

This work was motivated by the need to capture the spectrum of anatomical shape variability rather than relying on analyses of single bones. A novel tool was developed that combines image-based modelling with statistical shape analysis to automatically generate new femur geometries and measure anatomical parameters to capture the variability across the population. To demonstrate the feasibility of the approach, the study used data from 62 Caucasian subjects (31 female and 31 male) aged between 43 and 106 years, with CT voxel size ranging 0.488 × 0.488 × 1.5 mm to 0.7422 × 0.7422 × 0.97 mm.

The scans were divided into female and male subgroups and high-quality subject-specific tetrahedral finite element (FE) meshes resulting from segmented femurs formed the so-called training samples. A source mesh of a segmented femur (25580 nodes, 51156 triangles) from the Visible Human dataset [Spitzer, 1996] was used for elastic surface registration of each considered target male and female subjects, followed by applying a mesh morphing strategy.

To represent the variations in bone morphology across the population, gender-based Statistical Shape Models (SSM) were developed, using Principal Component Analysis. These were then sampled using the principal components required to capture 95% of the variance in each training dataset to generate 1000 new anatomical shapes [Bryan, 2010; Blanc, 2012] and to automatically measure key anatomical parameters known to critically influence the biomechanics after hip replacement (Figure 1).

Analysis of the female and male training datasets revealed the following data for the five considered anatomical parameters: anteversion angle (12.6 ± 6.4° vs. 6.2 ± 7.5°), CCD angle (124.8 ± 4.7° vs. 126.3 ± 4.6°), femoral neck length (48.7 ± 3.8 mm vs. 52 ± 5 mm), femoral head radius (21.5 ± 1.3 mm vs. 24.9 ± 1.5 mm) and femur length (431.0 ± 17.6 mm vs. 474.5 ± 26.3 mm). However, using the SSM generated pool of 1000 femurs, the following data were computed for females against males: anteversion angle (10.5 ± 14.3° vs. 7.6 ± 7.2°), CCD angle (123.9 ± 5.8° vs. 126.7 ± 4°), femoral neck length (46.7 ± 7.7 mm vs. 51.5 ± 4.4 mm), femoral head radius (21.4 ± 1.2 mm vs. 24.9 ± 1.4 mm) and femur length (430.2 ± 16.1 mm vs. 473.9 ± 25.9 mm).

The highest variability was found in the anteversion of the females where the standard deviation in the SSM-based sample was increased to 14.3° from 6.4° in the original training dataset (Figures 2 & 3). The mean values for both females (10.5°) and males (7.6 °) were found close to the values of 10° and 7° reported in [Mishra, 2009] in 31 females and 112 males with a [2°, 25°] and [2°, 35°] range, respectively.

Femoral neck length of the female (male) subjects was 47.3 ± 6.2 mm (51.8 ± 4.1 mm) compared to 48.7 ± 3.8 mm (52 ± 5 mm) in the training dataset and 63.65 ± 5.15 mm in [Blanc, 2012] with n = 142, 54% female, 46% male and a [50.32–75.50 mm] range. For the measured CCD angle in both female (123.9 ± 5.8°) and male (126.7 ± 4°) subjects, a good correlation was found with reported values of 128.4 ± 4.75° [Atilla, 2007], 124.7 ± 7.4° [Noble, 1988] and 129.82 + 5.37° [Blanc, 2012].

In conclusion, the present study demonstrates that the proposed methodology based on gender-specific statistical shape modelling can be a valuable tool for automatically generating a large specific population of femurs to support implant design and planning of femoral reconstructive surgery.