Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone

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• 作者：S. Majid Nazemi^a ; ^{majid.nazemi@gmail.com} ; Morteza Amini^b ; Saija A. Kontulainen^c ; Jaques S. Milner^d ; David W. Holdsworth^d ; Bassam A. Masri^e ; David R. Wilson^e ; James D. Johnston^a ; ^{jd.johnston@usask.ca}
• 关键词：Finite element modeling ; Density-modulus relationships for bone ; Subchondral bone ; Proximal tibia ; Neural network
• 刊名：Clinical Biomechanics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：41
• 期：Complete
• 页码：1-8
• 全文大小：1015 K
• 卷排序：41

文摘

Modeled proximal tibial structural stiffness using finite element modeling Validated stiffness predictions using in situ macro-indentation testing Applied a neural network to derive optimized modeling parameters Finite element modeling explained 81% variance in structural stiffness Require de-blurring methods, anisotropy inclusion and a smaller voxel to improve predictions