Evaluation of In Vivo Knee Load Predictions using Instrumented Implants

描述(申请人提供)：这个项目的目标是严格评估肌肉骨骼模型在行走过程中可靠地预测膝关节肌肉和关节接触力的能力。了解这些内部负荷可以改善影响行走能力的神经肌肉骨骼疾病的诊断和治疗(例如，中风、脑瘫、骨关节炎)。由于临床上不能测量内部负荷，肌肉骨骼模型已成为进行评估的主要手段。然而，如果模型估计不准确，基于这些估计的临床评估或治疗可能无效，甚至有害。我们建议使用从植入测力膝关节置换物的患者体内获得的接触力来评估行走过程中膝关节肌肉和关节接触力的肌肉骨骼模型估计。这些独特的内部负荷测量将使我们能够直接评估接触力估计值，间接评估肌肉力估计值。对于接受测试的五名患者，我们将收集广泛的运动数据(胫骨接触力、运动捕捉、地面反作用力、肌电、透视、肌肉力量)。然后，我们将增强OpenSim开源肌肉骨骼建模软件的新功能(例如，“快速”接触模型建模方法，基于肌电测量预测肌肉力量的新优化方法)，以允许构建每个患者的高保真肌肉骨骼模型。将使用现有的和新的肌肉和接触力预测方法来评估每个患者特定模型再现患者胫骨接触力、肌电和其他运动数据的能力。我们还将在ASME夏季生物工程会议上举行年度比赛，研究人员将使用我们提供的数据和模型来预测体内的胫骨接触力，而不需要事先知道它们。这项肌肉骨骼模型验证工作将是有史以来进行的最广泛的工作，产生的数据、模型和想法将为未来几年的进一步评估研究提供基础。 公共卫生相关性：肌肉骨骼模型可以促进为中风、脑性瘫痪和骨关节炎等神经肌肉骨骼疾病设计有效的定制治疗方法。然而，在将它们用于这一目的之前，他们的预测需要得到验证。这项研究提出了独特的数据和方法来执行这种验证，重点是在步行过程中的膝盖。

DESCRIPTION (provided by applicant): The goal of this project is to critically evaluate the ability of musculoskeletal models to predict muscle and joint contact forces in the knee reliably during walking. Knowledge of these internal loads could improve the diagnosis and treatment of neuromusculoskeletal disorders that affect walking ability (e.g., stroke, cerebral palsy, osteoarthritis). Because internal loads cannot be measured clinically, musculoskeletal models have become the primary means for developing estimates. However, if model estimates are inaccurate, clinical assessments or treatments based on these estimates could be ineffective or even harmful. We propose to evaluate musculoskeletal model estimates of muscle and joint contact forces in the knee during walking using in vivo contact force measurements obtained from patients implanted with force-measuring knee replacements. These unique internal load measurements will allow us to evaluate contact force estimates directly and muscle force estimates indirectly. For each of the five patients tested, we will collect a broad range of movement data (tibial contact force, motion capture, ground reaction force, EMG, fluoroscopic, muscle strength). We will then enhance OpenSim open-source musculoskeletal modeling software with new capabilities (e.g., "fast" contact model modeling methods, new optimization methods for predicting muscle forces based on EMG measurements) to permit construction of a high-fidelity musculoskeletal model of each patient. The ability of each patient-specific model to reproduce the patient's tibial contact force, EMG, and other movement data will be evaluated using existing and new muscle and contact force prediction methods. We will also hold an annual competition at the ASME Summer Bioengineering Conference where researchers will use data and models we make available to predict the in vivo tibial contact forces without knowing them in advance. This musculoskeletal model validation effort will be the most extensive ever performed, and the data, models, and ideas generated will provide a foundation for further evaluation studies for years to come. PUBLIC HEALTH RELEVANCE: Musculoskeletal models could facilitate the design of effective, customized treatments for neuromusculoskeletal disorders such as stroke, cerebral palsy, and osteoarthritis. However, before they can be used for this purpose, their predictions need to be validated. This study proposes unique data and methods to perform such a validation with a focus on the knee during walking.