Xiao Pengwei, Schilling Caroline, Wang Xiaodu
Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA.
Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA.
J Funct Biomater. 2024 Aug 22;15(8):239. doi: 10.3390/jfb15080239.
Understanding bone surface curvatures is crucial for the advancement of bone material design, as these curvatures play a significant role in the mechanical behavior and functionality of bone structures. Previous studies have demonstrated that bone surface curvature distributions could be used to characterize bone geometry and have been proposed as key parameters for biomimetic microstructure design and optimization. However, understanding of how bone surface curvature distributions correlate with bone microstructure and mechanical properties remains limited. This study hypothesized that bone surface curvature distributions could be used to predict the microstructure as well as mechanical properties of trabecular bone. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the histomorphometric parameters (e.g., BV/TV, BS, Tb.Th, DA, Conn.D, and SMI), geometric parameters (e.g., plate area PA, plate thickness PT, rod length RL, rod diameter RD, plate-to-plate nearest neighbor distance NND, rod-to-rod nearest neighbor distance NND, plate number PN, and rod number RN), as well as the apparent stiffness tensor of trabecular bone using various bone surface curvature distributions, including maximum principal curvature distribution, minimum principal curvature distribution, Gaussian curvature distribution, and mean curvature distribution. The results showed that the surface curvature distribution-based deep learning model achieved high fidelity in predicting the major histomorphometric parameters and geometric parameters as well as the stiffness tenor of trabecular bone, thus supporting the hypothesis of this study. The findings of this study underscore the importance of incorporating bone surface curvature analysis in the design of synthetic bone materials and implants.
了解骨表面曲率对于骨材料设计的进展至关重要，因为这些曲率在骨结构的力学行为和功能中起着重要作用。先前的研究表明，骨表面曲率分布可用于表征骨几何形状，并已被提议作为仿生微观结构设计和优化的关键参数。然而，对于骨表面曲率分布如何与骨微观结构和力学性能相关联的理解仍然有限。本研究假设骨表面曲率分布可用于预测松质骨的微观结构以及力学性能。为了验证这一假设，训练并验证了一个卷积神经网络（CNN）模型，以使用各种骨表面曲率分布来预测组织形态计量学参数（例如，骨体积分数、骨表面积、骨小梁厚度、骨小梁间距、骨小梁连接性密度和结构模型指数）、几何参数（例如，骨板面积、骨板厚度、骨小梁长度、骨小梁直径、骨板到骨板最近邻距离、骨小梁到骨小梁最近邻距离、骨板数量和骨小梁数量）以及松质骨的表观刚度张量，包括最大主曲率分布、最小主曲率分布、高斯曲率分布和平均曲率分布。结果表明，基于表面曲率分布的深度学习模型在预测主要组织形态计量学参数、几何参数以及松质骨的刚度张量方面具有高保真度，从而支持了本研究的假设。本研究结果强调了在合成骨材料和植入物设计中纳入骨表面曲率分析的重要性。
