Research Papers

Incorporating Population-Level Variability in Orthopedic Biomechanical Analysis: A Review

[+] Author and Article Information
Jeffrey E. Bischoff

Zimmer, Inc.,
P.O. Box 708,
Warsaw, IN 46581-0708
e-mail: Jeff.bischoff@zimmer.com

Yifei Dai

Zimmer, Inc.,
P.O. Box 708,
Warsaw, IN 46581-0708

Casey Goodlett, Brad Davis

Kitware, Inc.,
Carrboro, NC 27510

Marc Bandi

Zimmer, GmbH,
Winterthur, Switzerland

1Corresponding author.

Manuscript received October 4, 2013; final manuscript received December 9, 2013; accepted manuscript posted December 16, 2013; published online February 5, 2014. Editor: Beth Winkelstein.

J Biomech Eng 136(2), 021004 (Feb 05, 2014) (12 pages) Paper No: BIO-13-1465; doi: 10.1115/1.4026258 History: Received October 04, 2013; Revised December 09, 2013; Accepted December 16, 2013

Effectively addressing population-level variability within orthopedic analyses requires robust data sets that span the target population and can be greatly facilitated by statistical methods for incorporating such data into functional biomechanical models. Data sets continue to be disseminated that include not just anatomical information but also key mechanical data including tissue or joint stiffness, gait patterns, and other inputs relevant to analysis of joint function across a range of anatomies and physiologies. Statistical modeling can be used to establish correlations between a variety of structural and functional biometrics rooted in these data and to quantify how these correlations change from health to disease and, finally, to joint reconstruction or other clinical intervention. Principal component analysis provides a basis for effectively and efficiently integrating variability in anatomy, tissue properties, joint kinetics, and kinematics into mechanistic models of joint function. With such models, bioengineers are able to study the effects of variability on biomechanical performance, not just on a patient-specific basis but in a way that may be predictive of a larger patient population. The goal of this paper is to demonstrate the broad use of statistical modeling within orthopedics and to discuss ways to continue to leverage these techniques to improve biomechanical understanding of orthopedic systems across populations.

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Grahic Jump Location
Fig. 1

Typical framework for generating a statistical model from population data (left) and as applied specifically for analysis of tissue morphology (right)

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Fig. 2

Regions of variation on a resected tibia between Caucasian males and females for the first five modes of variation (PCs 1–5). Images in the top row are scaled consistently according to the maximum deviation across all modes (PC1); images in the bottom row are scaled based on the maximum deviation in the particular mode.

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Fig. 3

Primary modes of variation for the knee, shown at ± two standard deviations and encompassing variation in multiple anatomies as well as joint configuration. Figure reproduced from Ref. [60] with permission from the publisher.

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Fig. 4

Statistical analysis of nonlinear stiffness. (a) Variation in anterior-posterior constraint force as a function of AP displacement across specimens at full extension, illustrating typical nonlinear soft tissue behavior; (b) first mode of variation (mean ± two standard deviations, with mean response shown in red) for AP constraint force as function of AP displacement and flexion; (c) second mode of variation for AP constraint force.

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Fig. 6

A vision for more unified statistical modeling for biomechanical analyses. (a) Independent statistical models for different types of model inputs; (b), (c) statistical models from some merging of datasets; (d) single statistical model that spans all model inputs.

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Fig. 5

Basic roadmap for utilizing shape models to support structural analysis (figures reprinted with permission from publishers)



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