OPTIMISATION OF HIP STEM SHAPE AND MATERIAL PROPERTIES TO MINIMISE BONE RESORPTION AND HEAD DISPLACEMENT
Notice bibliographique
Résumé
Introduction Stress shielding is one of the major concerns of load bearing implants (e.g. hip prostheses). Stiff implants cause stress shielding, which is thought to contribute to bone resorption1. On the contrary, low-stiffness implants generate high interfacial stresses that have been related to pain and interfacial micro-movements². Different attempts have been made to reduce these problems by optimizing either the stem design3 or using functionally graded implants (FGI) where the stem9s mechanical properties are optimized4. In this way, new additive manufacturing technologies allow fabricating porous materials with well-controlled mesostructure, which allows tailoring their mechanical properties. In this work, Finite Element (FE) simulations are used to develop an optimization methodology for the shape and material properties of a FGI hip stem. The resorbed bone mass fraction and the stem head displacement are used as objective functions. Methodology The 2D-geometry of a femur model (Sawbones®) with an implanted Profemur-TL stem (Wright Medical Technology Inc.) was used for FE simulations. The stem geometry was parameterized using a set of 8 variables (Figure 1-a). To optimize the stem9s material properties, a grid was generated with equally spaced points for a total of 96 points (Figure 1-b). Purely elastic materials were used for the stem and the bone. Two bone qualities were considered: good (Ecortical=20 GPa, Etrabecular=1.5 GPa) and medium (Ecortical=15 GPa, Etrabecular=1 GPa). Poisson ratio was fixed to v=0.3. Loading corresponded to stair climbing. Hip contact force along with abductors, vastus lateralis and vastus medialis muscles were considered5 for a bodyweight of 847 N. The resorbed bone mass fraction was evaluated from the differences in strain energy densities between the intact bone and the implanted bone2. The displacement of the load point on the femoral head was computed. The optimization problem was formulated as the minimization of the resorbed bone mass fraction and the head displacement. It was solved using a genetic algorithm. Results For the Profemur-TL design, bone resorption was around 36% and 56% for good and medium bone qualities, respectively (Fig. 2). The corresponding head displacements were 11.75 mm and 21.19 mm. Optimized solutions showed bone resorption from 15% to 26% and from 44% to 65% for good and medium bone qualities, respectively. Corresponding head displacements ranged from 11.85 mm to 12.25 mm and from 16.9 mm to 22.6 mm. Conclusion The obtained set of solutions constitutes an improvement of the implant performance for this functionally graded implant (FGI) compared to the original implant for both bone qualities. From these simulations, the final solution for the FGI could be chosen based on manufacturing restrictions or another performance indicator.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».