Bischoff Jeffrey E, Dai Yifei, Goodlett Casey, Davis Brad, Bandi Marc
J Biomech Eng. 2014 Feb;136(2):021004. doi: 10.1115/1.4026258.
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.
在骨科分析中有效解决人群层面的变异性问题，需要涵盖目标人群的强大数据集，而将此类数据纳入功能生物力学模型的统计方法可极大地推动这一过程。目前不断有数据集发布，这些数据集不仅包含解剖学信息，还包括关键的力学数据，如组织或关节刚度、步态模式以及与一系列解剖结构和生理状况下关节功能分析相关的其他输入信息。统计建模可用于在这些数据所蕴含的各种结构和功能生物特征之间建立关联，并量化这些关联如何从健康状态转变为疾病状态，最终到关节重建或其他临床干预过程中的变化。主成分分析为有效且高效地将解剖结构、组织特性、关节动力学和运动学方面的变异性整合到关节功能的机理模型中提供了基础。借助此类模型，生物工程师不仅能够基于特定患者研究变异性对生物力学性能的影响，还能以一种可能预测更大患者群体情况的方式进行研究。本文的目的是展示统计建模在骨科领域的广泛应用，并讨论如何继续利用这些技术来增进对不同人群骨科系统生物力学的理解。
