Bayesian Calibration of Computational Knee Models to Estimate Subject-Specific Ligament Properties, Tibiofemoral Kinematics, and Anterior Cruciate Ligament Force With Uncertainty Quantification.

High-grade knee laxity is associated with early anterior cruciate ligament (ACL) graft failure, poor function, and compromised clinical outcome. Yet, the specific ligaments and ligament properties driving knee laxity remain poorly understood. We described a Bayesian calibration methodology for predicting unknown ligament properties in a computational knee model.

Tekscan analysis programs (TAP) for quantifying dynamic contact mechanics.

This short communication provides details on customized Tekscan Analysis Programs (TAP) which extract comprehensive contact mechanics metrics from piezoelectric sensors in articulating joints across repeated loading cycles. The code provides functionality to identify regions of interest (ROI), compute contact mechanic metrics, and compare contact mechanics across multiple test conditions or knees. Further, the variability of identifying ROIs was quantified between seven different users and compared to an expert.

Reducing uncertainty when using knee-specific finite element models by assessing the effect of input parameters.

Little is known about knee-specific factors that influence contact mechanics. Finite Element (FE) models offer a powerful tool to study contact mechanics, but there often exists ambiguity in the exact values of the inputs (e.g.

A statistically-augmented computational platform for evaluating meniscal function.

Meniscal implants have been developed in an attempt to provide pain relief and prevent pathological degeneration of articular cartilage. However, as yet there has been no systematic and comprehensive analysis of the effects of the meniscal design variables on meniscal function across a wide patient population, and there are no clear design criteria to ensure the functional performance of candidate meniscal implants. Our aim was to develop a statistically-augmented, experimentally-validated, computational platform to assess the effect of meniscal properties and patient variables on knee joint contact mechanics during the activity of walking.

Using a statistically calibrated biphasic finite element model of the human knee joint to identify robust designs for a meniscal substitute.

This paper describes a methodology for selecting a set of biomechanical engineering design variables to optimize the performance of an engineered meniscal substitute when implanted in a population of subjects whose characteristics can be specified stochastically. For the meniscal design problem where engineering variables include aspects of meniscal geometry and meniscal material properties, this method shows that meniscal designs having simultaneously large radial modulus and large circumferential modulus provide both low mean peak contact stress and small variability in peak contact stress when used in the specified subject population. The method also shows that the mean peak contact stress is relatively insensitive to meniscal permeability, so the permeability used in the manufacture of a meniscal substitute can be selected on the basis of manufacturing ease or cost.

Changes in dynamic medial tibiofemoral contact mechanics and kinematics after injury of the anterior cruciate ligament: a cadaveric model.

The effects of tears of the anterior cruciate ligament on knee kinematics and contact mechanics during dynamic everyday activities, such as gait, remains unclear. The objective of this study was to characterize anterior cruciate ligament-deficient knee contact mechanics and kinematics during simulated gait. Nine human cadaveric knees were each augmented with a sensor capable of measuring dynamic normal contact stresses on the tibial plateau, mounted on a load-controlled simulator, and subjected to physiological, multidirectional, dynamic loads to mimic gait.

Simultaneous Determination of Tuning and Calibration Parameters for Computer Experiments.

Tuning and calibration are processes for improving the representativeness of a computer simulation code to a physical phenomenon. This article introduces a statistical methodology for simultaneously determining tuning and calibration parameters in settings where data are available from a computer code and the associated physical experiment. Tuning parameters are set by minimizing a discrepancy measure while the distribution of the calibration parameters are determined based on a hierarchical Bayesian model.

Hip resurfacing increases bone strains associated with short-term femoral neck fracture.

Short-term femoral neck fracture is a primary complication associated with contemporary hip resurfacing. Some fractures are associated with neck notching, while others occur in the absence of notching. These unexplained fractures may be due to large magnitude strains near the implant rim, which could cause bone damage accumulation and eventual neck fracture.

Demographic and behavioral modifiers of arsenic exposure pathways: a Bayesian hierarchical analysis of NHEXAS data.

We introduce a Bayesian hierarchical statistical model that describes subpopulation-specific pathways of exposure to arsenic. Our model is fitted to data collected as part of the National Human Exposure Assessment Survey (NHEXAS) and builds on the structural-equation-based analysis of the same data by Clayton et al. (Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12, 29-43).

Robust design for acetabular cup stability accounting for patient and surgical variability.

The stability of cementless acetabular cups depends on a close fit between the components and reamed acetabular cavities to promote bone ingrowth. Cup performance and stability are affected by both design and environmental (patient-dependent and surgical) factors. This study used a statistically based metamodel to determine the relative influences of design and environmental factors on acetabular cup stability by incorporating a comprehensive set of patient-dependent and surgical variables.

Acetabular cup geometry and bone-implant interference have more influence on initial periprosthetic joint space than joint loading and surgical cup insertion.

Environmental variations in patient-dependent and surgical factors were modeled using robust optimization with a finite element acetabular cup-pelvis model. A previously developed statistical optimization scheme was used to: (1) determine the cup geometry and the optimal cup-bone interference that maximized bone-implant contact areas and minimized changes in the gap volume between the implant and bone surface during gait loading and unloading; and (2) determine the relative contributions of design, patient-dependent, and surgical factors to variations in bone-implant contact areas and a change in gap volume. The statistical analyses indicated that the design variables, namely the equatorial diameter and eccentricity, explained most of the variations in the performance measures.