Elsevier

Journal of Biomechanics

Volume 48, Issue 5, 18 March 2015, Pages 750-760
Journal of Biomechanics

Review
Patient-specific bone modeling and analysis: The role of integration and automation in clinical adoption

https://doi.org/10.1016/j.jbiomech.2014.12.018Get rights and content

Abstract

Patient-specific analysis of bones is considered an important tool for diagnosis and treatment of skeletal diseases and for clinical research aimed at understanding the etiology of skeletal diseases and the effects of different types of treatment on their progress. In this article, we discuss how integration of several important components enables accurate and cost-effective patient-specific bone analysis, focusing primarily on patient-specific finite element (FE) modeling of bones. First, the different components are briefly reviewed. Then, two important aspects of patient-specific FE modeling, namely integration of modeling components and automation of modeling approaches, are discussed. We conclude with a section on validation of patient-specific modeling results, possible applications of patient-specific modeling procedures, current limitations of the modeling approaches, and possible areas for future research.

Introduction

There is currently a general trend in healthcare for personalized medicine that includes use of imaging techniques, genetic analyses, and extensive biomarker evaluation to determine dedicated diagnosis or treatment protocols that differ for each specific patient. Such protocols appreciate the differences with respect to e.g. shape, morphology, genetics, and overall physiology of each individual in a much more extensive manner. As biomechanical engineers, we are interested in mechanical functioning of bodies, e.g. the shape and morphology of bone on the one hand and the structural performance related to its mechanical function on the other hand. The relationship between form and function is of primary importance since such types of biomechanical analysis could help us understand the evolutionary biomechanics of species as well as to study biological tissues with the structural and mechanical aspects as their major function.

This relationship brings us to Wolff׳s law, a topic that Rik Huiskes was so engaged with and many of Rik׳s PhD students were steered into this area. The topic certainly can contribute to orthopedics implant design or patient-specific modeling that might guide new diagnosis and/or treatment methods, something that Rik Huiskes pursued in his early career. During a later phase, he became more interested in the scientific issues themselves. As he phrased it so vividly: ‘If bone is the answer, then what is the question’ (Huiskes, 2000). Whereas the engineering approach followed by Julius Wolff more or less describes bone as an optimized material with maximal strength and minimal weight, Rik argued that there are no mathematical optimization rules for bone architecture. He claimed that bone follows only a biological regulatory process, producing a structure adapted to mechanical demands by the nature of its characteristics, adequate for evolutionary endurance. However, in biomechanical bone analysis, the mathematically described regulatory process could help us better understand the etiology of skeletal diseases and develop better techniques for their diagnosis and treatment.

Development of better techniques for diagnosis and treatment of skeletal diseases requires application of bone analysis techniques to specific patients. To do patient-specific bone analysis, one needs additional information pertaining to individual patients so as to personalize the biomechanical models of bone behavior, improve the sensitivity and specificity of the obtained analytical results, and provide the patients with personalized healthcare. That is why patient-specific bone analysis often starts from a set of images that are meant to provide the additional information needed for personalization of biomechanical models.

Images alone are, however, not enough for providing all the information that is required for patient-specific analysis of bones. At least two other types of information are needed, namely functional data and material properties. Functional data including the kinematics of patient׳s movements and the external forces exerted to the patient body during those movements (kinetic data) is needed to determine the internal musculoskeletal loads experienced by bones including joint reaction forces and muscle forces.

As for the material properties of bones, empirical relationships between bone density values measured using imaging modalities and elastic properties of bones are often used for obtaining patient-specific material properties. However, the empirical relationships are not available for all bones and may be drastically different from one patient to another (Eberle et al., 2013).

Several analytical tools (Fig. 1) have been developed to utilize the above-mentioned sources of information and establish the patient-specific relationship between bone shape, morphology, loading, and mechanical performance. The analytical tools may (1) process the image information and provide information regarding bone shape and morphology such as image processing algorithms and statistical models of shape and appearance, (2) use image information for determining the spatial distribution of mechanical properties such as material mapping relationships and material models, or (3) use functional data from musculoskeletal models for determining the loading conditions experienced by bones.

The purpose of this paper is not to review the patient-specific modeling technology, as this has been recently performed (Poelert et al., 2013). Instead, we focus on emerging tools, trends, and approaches that could revolutionize patient-specific modeling of bones and greatly facilitate the clinical acceptance of the technology.

If patient-specific bone analysis is going to be used in clinical settings, it needs to satisfy two criteria. First, it should provide clear added value for clinical diagnosis and treatment above that of currently available clinical tools. Second, it should be time and cost-effective. The first criterion relates to the accuracy of the analyses whereas the second relates to their cost-effectiveness. In this paper, we argue that integration and automation of the above-mentioned analytical tools could enable accurate and cost-effective patient-specific analysis of bones. Patient-specific finite element (FE) models are often used to intimately integrate the above-mentioned sources of information in analytical tools. In the following section, we introduce the different analytical tools and sources of information important for patient-specific bone analysis (Fig. 1). The different variants of every analytical tool or source of information will be discussed in the light of accuracy and cost. In the third section, we discuss possible ways of integrating and automating patient-specific bone analysis mainly in the context of patient-specific FE modeling. The paper concludes with a section discussing the limitations of currently available approaches and the required developments for clinical acceptance.

Section snippets

Components of patient-specific procedures

The different components used in patient-specific bone analyses (Fig. 1) are reviewed in this section. The different variants of every component are listed in Fig. 1. From left to right, the accuracy as well as the associated costs of the variants of different components increases. The cost is defined in a broad sense here, and includes a combination of monetary cost, time cost, and side effects such as the dosage of ionizing radiation. The variants that are currently seriously underdeveloped

Integration and automation

As previously mentioned, we argue here that clinical adoption of patient-specific bone analysis in general and patient-specific FE modeling in particular is dependent on two parameters: accuracy and cost-effectiveness of the available technology. Integration and automation are two important concepts that could help in improving both accuracy and cost-effectiveness of patient-specific bone analysis. By the term integration we mean combining the inputs and outputs of the components reviewed in

Discussion

Patient-specific bone modeling and analysis were discussed in the previous sections. It is clear that an accurate and cost-effective patient-specific modeling platform could be used for many diagnosis and treatments of several musculoskeletal pathologies. Important examples include design of optimal patient-specific implants and bone substitutes based on the possibilities of additive manufacturing technology that are well suited for the efficient production of a single implant with a dedicated

Conflict of interest

We hereby state that none of the authors has any financial or personal relationships with other people or organizations that could inappropriately influence (bias) our work.

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