Comparison between two techniques for modeling interface conditions in a porous coated hip endoprosthesis

Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R² = 0.905-0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.

Reduced stress shielding with limited micromotions using a carbon fibre composite biomimetic hip stem: a finite element model

Total hip arthroplasty (THA) enjoys excellent rates of success in older patients, but younger patients are still at risk of aseptic loosening and bone resorption from stress shielding. One solution to the stress shielding problem is to use a hip stem with mechanical properties matching those of cortical bone. The objective of the present study was to investigate numerically the biomechanical performance of such a biomimetic hip stem based on a hydroxyapatite (HA)-coated carbon fibre composite. A finite element model (FEM) of the biomimetic stem was constructed. Contact elements were studied to model the bone–implant interface in a non-osseointegrated and osseointegrated state in the best way. Three static load cases representing slow walking, stair climbing, and gait in a healthy individual were considered. Stress shielding and bone–implant interface micromotions were evaluated and compared with the results of a similar FEM based on titanium alloy (Ti–6Al–4V). The composite stems allowed for reduced stress shielding when compared with a traditional Ti–6Al–4V stem. Micromotions were slightly higher with the composite stem, but remained below 40 μm on most of the HA-coated surface. It is concluded that a biomimetic composite stem might offer a better compromise between stress shielding and micromotions than the Ti–6Al–4V stem with the same external geometry.

Predicting the subject-specific primary stability of cementless implants during pre-operative planning: Preliminary validation of subject-specific finite-element models

Pre-operative planning help the surgeon in taking the proper clinical decision. The ultimate goal of this work is to develop numerical models that allow the surgeon to estimate the primary stability during the pre-operative planning session. The present study was aimed to validate finite-element (FE) models accounting for patient and prosthetic size and position as planned by the surgeon. For this purpose, the FE model of a cadaveric femur was generated starting from the CT scan and the anatomical position of a cementless stem derived by a skilled surgeon using a pre-operative CT-based planning simulation software. In-vitro experimental measurements were used as benchmark problem to validate the bone-implant relative micromotions predicted by the patient-specific FE model. A maximum torque in internal rotation of 11.4 Nm was applied to the proximal part of the hip stem. The error on the maximum predicted micromotion was 12% of the peak micromotion measured experimentally. The average error over the entire range of applied torques was only 7% of peak measurement. Hence, the present study confirms that it is possible to accurately predict the level of primary stability achieved for cementless stems using numerical models that account for patient specificity and surgical variability.

The role of parameter identification in finite element contact analyses with reference to orthopaedic biomechanics applications

A finite element model accounting for large sliding frictional contact requires, depending on the type of contact algorithm in use, the definition of many numerical parameters such as contact stiffness, convergence norm and tolerance, compenetration monitoring, over-relaxing factors, etc. All these parameters do not have a physical meaning and thus they cannot be measured experimentally. This makes their identification quite complex. The aim of this study was to investigate the role of parameter identification on the accuracy of results produced by finite element models accounting for bone-implant frictional contact, when the Penalty method is used. The sensitivity analysis of several numerical parameters that may govern the state of results was carried out. Two parameters, contact stiffness and convergence tolerance, were found to play a crucial role in establishing the accuracy of the finite element results. Based on the achieved results it was stated that any numerical-only study involving contact non-linearity and omitting careful qualification of the model limits should be rejected from any peer-reviewed journal.

Large-sliding contact elements accurately predict levels of bone-implant micromotion relevant to osseointegration

Primary stability is recognised as an important determinant in the aseptic loosening failure process of cementless implants. An accurate evaluation of the bone-implant relative micromotion is becoming important both in pre-clinical and clinical studies. If the biological threshold for micro-movements is in the range 100-200 micrometer then, in order to be discriminative, any method used to evaluate the primary stability should have an accuracy of 10-20 micrometer or better. Additionally, such method should also be able to report the relative micromotion at each point of the interface. None of the available experimental methods satisfies both requirements. Aim of the present study is to verify if any of the current finite element modelling techniques is sufficiently accurate in predicting the primary stability of a cementless prosthesis to be used to decide whether the micromotion may or may not jeopardise the implant osseointegration. The primary stability of an anatomic cementless stem, as measured in vitro, was used as a benchmark problem to comparatively evaluate different contact modelling techniques. Frictionless contact, frictional contact and press-fitted frictional contact conditions were modelled using alternatively node-to-node, node-to-face and face-to-face contact elements. The model based on face-to-face contact elements accounting for frictional contact and initial press-fit was able to predict the micromotion measured experimentally with an average (RMS) error of 10 micrometer and a peak error of 14 micrometer. All the other models presented errors higher than 20 micrometer assumed in the present study as an accuracy threshold.