Healthcare — Probabilistic Techniques for Bone as a Natural Composite

Abstract

The number of older adults in the USA is projected to continue growing, thus driving the demand for orthopedic surgery due to diseases like Osteoporosis and related bone fractures. To manage the healthcare costs, there has been increased interest in introducing predictive analytics for the assessment of bone quality and properties. Bone has been described as a nanocomposite with unique mechanical properties, governed by its structural organization and chemical composition. To develop predictive analytics in relation to bone health, there is a need to understand why cortical bone is so resistant to crack propagation and how it ultimately succumbs to fracture. Moreover, bone has a hierarchical structure through multiple length scales in which the variability of the properties at each length scale affects the overall mechanical properties. Since there is no comprehensive model to capture the mechanics of fracture, there is a need to develop a stochastic model incorporating uncertainty in the multiscale of the bone hierarchy. For this study the research question was: Can multiscale probabilistic techniques used in composites be applied to bones? To answer this question, the following specific aims were constructed: survey probabilistic analysis for bones, survey probabilistic analysis for composites, and present probabilistic multiscale techniques for composites that can be transferred to bones. As a methodology, a critical review was conducted on the probabilistic modeling of bone and composite materials. The uncertainties at the different scales were reviewed for bone and composites. An assessment was conducted whether multiscale probabilistic techniques used in composites can be applied to bones. It was shown that there are several studies of deterministically modeling of bone at different scales. It was shown that several probabilistic multiscale models of composites exist. An argument was made that the probabilistic multiscale models of composites may be extended and modified for application in bone. It was argued and shown that multiscale probabilistic techniques used in composites may be extended and modified to apply to bone. The contribution of this work is proposing a predictive analytic method for the assessment of bone quality and properties. The predictive analytics is anchored in probabilistic models for bone adopted and extended from models for composites.

Exploring Macroporosity of Additively Manufactured Titanium Metamaterials for Bone Regeneration with Quality by Design: A Systematic Literature Review

“…Bone's sub-microstructure, from 1 to 10 µm, is composed of lamellae which in cortical bone compromise the concentric layers of osteons and in cancellous bone lamellae forms the trabeculae volume [150,151]. The sub-nanostructure of bone, below a few hundred nanometres, is composed molecular constituent elements, such as collagen, non-collagenous organic proteins, and mineral.…”

“…The elastic modulus of cancellous bone is estimated to be 0.02-6 GPA and it has a high porosity of 50-90% [85,86]. The mechanical properties of cortical and cancellous bone are difficult to measure, and tend to vary depending on bone orientation, test methods, mathematical formulas, and assumptions [146,151,159]. According to Chen and Thouas [57], the elastic modulus of cortical bone is approximately 11-21 GPa in the longitudinal direction and 5-13 GPa in the transverse direction.…”

Risk-Based Analysis of Femoral Stem Considering Uncertainty in its Design Parameters

“…The aging of the population and the obesity epidemic in the USA have been pointed out among the main causes of the high incidence of arthritic hip joint diseases, driving the demand for orthopedic surgery [1,2]. Total hip arthroplasty (THA) is the most performed surgical procedure in such cases and, despite the satisfactory short-term and long-term outcomes, several technical issues related to the implant design may lead to early revision surgeries.…”