Andreassen Thor E, Hume Donald R, Hamilton Landon D, Hegg Stormy L, Higinbotham Sean E, Shelburne Kevin B
Center for Orthopaedic Biomechanics, Department of Mechanical and Materials Engineering, University of Denver, Denver, CO, United States.
Assistive and Restorative Technology Laboratory, Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, United States.
Front Bioeng Biotechnol. 2025 Aug 14;13:1554836. doi: 10.3389/fbioe.2025.1554836. eCollection 2025.
Despite the documented consequences of modeling decisions on the performance of computational models in orthopaedics and biomechanics, the influence of the input data has largely been ignored. Modeling the living knee is limited by methods to measure the quantities needed for ligament calibration; yet, this may be possible with new devices focused on non-invasive measurement of knee laxity. These devices offer measurements similar to those commonly obtained from cadaveric specimens but are limited by what can be practically and safely obtained from a living subject. Validation of models calibrated with data is crucial and increasingly important as personalized modeling becomes the basis for proposed digital twins, and clinical trial workflows. To support our overall goal of building subject-specific models of the living knee, we aimed to show that subject-specific computational models calibrated using measurements would have accuracy comparable to models calibrated using measurements. Two cadaveric knee specimens were imaged using a combination of computed tomography (CT) and surface scans. Knee laxity measurements were made with a custom apparatus used for the living knee and from a robotic knee simulator. Models of the knees were built following previous methods and then calibrated with either laxity data from the robotic knee simulator (RKS) or from the knee laxity apparatus (KLA). Model performance was compared by simulation of various activities and found to be similar between models calibrated with laxity targets from the RKS and the KLA. Model predictions during simulated anterior-posterior laxity tests differed by less than 2.5 mm and within 2.6° and 2.8 mm during a simulated pivot shift. Still, differences in the predicted ligament loads and calibrated material properties emerged, highlighting a need for methods to include ligament load as part of the calibration process. Overall, the results showed that currently available methods of measuring knee laxity are sufficient to calibrate models comparable with existing techniques, and the workflows described here may provide a basis for modeling the living knee. The experimental data, models, results, and tools are publicly available.
尽管在骨科和生物力学领域，已有文献记载建模决策对计算模型性能的影响，但输入数据的影响在很大程度上被忽视了。对活体膝关节进行建模受到韧带校准所需测量量的测量方法的限制；然而，专注于膝关节松弛度非侵入性测量的新设备可能使这成为可能。这些设备提供的测量结果与通常从尸体标本中获得的测量结果相似，但受到从活体受试者身上实际且安全获取的数据的限制。随着个性化建模成为拟议的数字孪生和临床试验工作流程的基础，用这些数据校准模型的验证至关重要且日益重要。为了支持我们构建活体膝关节特定受试者模型的总体目标，我们旨在表明，使用这些测量值校准的特定受试者计算模型的准确性将与使用那些测量值校准的模型相当。使用计算机断层扫描（CT）和表面扫描相结合的方法对两个尸体膝关节标本进行成像。使用用于活体膝关节的定制设备和机器人膝关节模拟器进行膝关节松弛度测量。按照先前的方法构建膝关节模型，然后用来自机器人膝关节模拟器（RKS）或膝关节松弛度设备（KLA）的松弛度数据进行校准。通过模拟各种活动比较模型性能，发现用RKS和KLA的松弛度目标校准的模型之间相似。在模拟前后松弛度测试期间，模型预测的差异小于2.5毫米，在模拟轴移期间差异在2.6°和2.8毫米以内。尽管如此，预测的韧带负荷和校准的材料特性仍存在差异，这突出表明需要将韧带负荷纳入校准过程的方法。总体而言，结果表明，目前可用的测量膝关节松弛度的方法足以校准与现有技术相当的模型，此处描述的工作流程可能为活体膝关节建模提供基础。实验数据、模型、结果和工具均可公开获取。
