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.

The number of young people getting total hip arthroplasty surgery is on the rise and studies have shown that the average number of perfect health years after such surgery is being reduced to about 9 years; this is because of complications which can lead to the failure of such implants. Consequently, such failures cause the implant not to last as long as required. The uncertainty in design parameters, loading, and even the manufacturing process of femoral stems, makes it important to consider uncertainty quantification and probabilistic modeling approaches instead of the traditional deterministic approach when designing femoral stems. This paper proposes a probabilistic analysis method which considers uncertainties in the design parameters of femoral implants to determine its effect on the implant stiffness. Accordingly, this method can be used to improve the design reliability of femoral stems. A simplified finite element model of a femoral stem was considered and analyzed both deterministically and probabilistically using Monte Carlo simulation. The results showed that uncertainties in design parameters can significantly affect the resulting stiffness of the stem. This paper proposes an approach that can be considered a potential solution for improving, in general, the reliability of hip implants and the predicted stiffness values for the femoral stems so as to better mitigate the stress shielding phenomenon.

The advent of state-of-the-art additive manufacturing (AM) processes has facilitated the manufacturing of complex orthopedic metallic implants such as femoral stems with porous portions based on lattice structures. These struts often have rough and not smooth textured surfaces, for which the irregularities may influence mechanical properties. To make robust predictions about the behavior of this kind of system, the variability of the mechanical properties and its impact on the stem stiffness must be considered in the processes of modeling and design of porous femoral stems. Also, to improve the credibility of computational models used for hip implant analysis, which involves numerous uncertainties, there is a need for rigorous uncertainty quantification (UQ) framework for proper model assessment following a credible-modeling standard. In this sense, this work proposes a UQ framework for analyzing a femoral stem implant model, to clarify how uncertainties impact the key properties of a porous femoral stem. In this study, uncertainties in the strut thickness, pore size, Young modulus, and external forcing are considered. A robust UQ framework is proposed and validated using experimental results available from literature, following the guidelines set in an AMSE standard. This study has a contribution in assessing the effect of input parameter uncertainties on femoral stem stiffness and the surface-to-volume ratio of porous stems.