Pais Ana Isabel Lopes, Lino Alves Jorge, Belinha Jorge
Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal.
Biomimetics (Basel). 2025 Apr 13;10(4):238. doi: 10.3390/biomimetics10040238.
The design of orthopedic implants is a complex challenge, requiring the careful balance of mechanical performance and biological integration to ensure long-term success. This study focuses on the development of a porous femoral stem implant aimed at reducing stiffness and mitigating stress shielding effects. To accelerate the design process, neural networks were trained to predict the optimal density distribution of the implant, enabling rapid optimization. Two initial design spaces were evaluated, revealing the necessity of incorporating the femur's anatomical features into the design process. The trained models achieved a median error near 0 for both conventional and extended design spaces, producing optimized designs in a fraction of the computational time typically required. Finite element analysis (FEA) was employed to assess the mechanical performance of the neural network-generated implants. The results demonstrated that the neural network predictions effectively reduced stress shielding compared to a solid model in 50% of the test cases. While the graded porosity implant design did not show significant differences in stress shielding prevention compared to a uniform porosity design, it was found to be significantly stronger, highlighting its potential for enhanced durability. This work underscores the efficacy of neural network-accelerated design in improving implant development efficiency and performance.
骨科植入物的设计是一项复杂的挑战，需要在机械性能和生物整合之间仔细权衡，以确保长期成功。本研究专注于开发一种多孔股骨柄植入物，旨在降低刚度并减轻应力遮挡效应。为了加速设计过程，训练神经网络来预测植入物的最佳密度分布，从而实现快速优化。评估了两个初始设计空间，揭示了在设计过程中纳入股骨解剖特征的必要性。对于传统和扩展设计空间，训练后的模型中位数误差接近0，在通常所需计算时间的一小部分内生成了优化设计。采用有限元分析（FEA）来评估神经网络生成的植入物的机械性能。结果表明，在50%的测试案例中，与实体模型相比，神经网络预测有效地降低了应力遮挡。虽然梯度孔隙率植入物设计在防止应力遮挡方面与均匀孔隙率设计相比没有显著差异，但发现它明显更强，突出了其增强耐久性的潜力。这项工作强调了神经网络加速设计在提高植入物开发效率和性能方面的有效性。
