Subject-specific musculoskeletal loading of the tibia: Computational load estimation

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• 作者：N. Garijo^a ; N. Verdonschot^b ; ^c ; K. Engelborghs^d ; J.M. Garcí ; a-Aznar^a ; M.A. Pé ; rez^a ; ^{angeles@unizar.es}
• 关键词：Artificial neural network ; Subject-specific ; Bone density ; Musculoskeletal model ; Bone remodelling problem/inverse bone remodelling model
• 刊名：Journal of the Mechanical Behavior of Biomedical Materials
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：65
• 期：Complete
• 页码：334-343
• 全文大小：2985 K
• 卷排序：65

文摘

The systematic development of subject-specific computer models for the analysis of personalized treatments is currently a reality. In fact, many advances have recently been developed for creating virtual finite element-based models. These models accurately recreate subject-specific geometries and material properties from recent techniques based on quantitative image analysis. However, to determine the subject-specific forces, we need a full gait analysis, typically in combination with an inverse dynamics simulation study. In this work, we aim to determine the subject-specific forces from the computer tomography images used to evaluate bone density. In fact, we propose a methodology that combines these images with bone remodelling simulations and artificial neural networks. To test the capability of this novel technique, we quantify the personalized forces for five subject-specific tibias using our technique and a gait analysis. We compare both results, finding that similar vertical loads are estimated by both methods and that the dominant part of the load can be reliably computed. Therefore, we can conclude that the numerical-based technique proposed in this work has great potential for estimating the main forces that define the mechanical behaviour of subject-specific bone.