Analyzing Uncertainty of an Ankle Joint Model with Genetic Algorithm

Abstract

Recent studies in biomechanical modeling suggest a paradigm shift, in which the parameters of biomechanical models would no longer treated as fixed values but as random variables with, often unknown, distributions. In turn, novel and efficient numerical methods will be required to handle such complicated modeling problems. The main aim of this study was to introduce and verify genetic algorithm for analyzing uncertainty in biomechanical modeling. The idea of the method was to encode two adversarial models within one decision variable vector. These structures would then be concurrently optimized with the objective being the maximization of the difference between their outputs. The approach, albeit expensive numerically, offered a general formulation of the uncertainty analysis, which did not constrain the search space. The second aim of the study was to apply the proposed procedure to analyze the uncertainty of an ankle joint model with 43 parameters and flexible links. The bounds on geometrical and material parameters of the model were set to 0.50 mm and 5.00% respectively. The results obtained from the analysis were unexpected. The two obtained adversarial structures were almost visually indistinguishable and differed up to 38.52% in their angular displacements.

Achieving Precision Healthcare through Nanomedicine and Enhanced Model Systems

“…61,62 However, they rely on accurate input data and assumptions, which may introduce uncertainties and simplifications. 63,64 For example, while there are numerous studies addressing inorganic or carbon-based NC transport, polymeric NC models are more difficult to build due to more complex structural properties and more sensitive physicochemical parameters. 65 The complexity of the NC-cell interactions also poses an additional challenge where a vast majority of descriptors must be included to replicate the dynamic nature of cellular membranes and the glycocalyx.…”

“…Based on mathematical equations and computer-driven analysis, models such as finite element analysis (FEA) or multibody dynamic simulations can provide valuable insights into tissue mechanics, function, and NC toxicity. , They offer a cost-effective approach to study joint biomechanics and allow rapid exploration of different scenarios and parameter variations. Several studies have addressed joint behavior under various conditions including loading, lubrication, and mechanics of the cartilage and surrounding tissues. , However, they rely on accurate input data and assumptions, which may introduce uncertainties and simplifications. , For example, while there are numerous studies addressing inorganic or carbon-based NC transport, polymeric NC models are more difficult to build due to more complex structural properties and more sensitive physicochemical parameters . The complexity of the NC-cell interactions also poses an additional challenge where a vast majority of descriptors must be included to replicate the dynamic nature of cellular membranes and the glycocalyx. , Such drawbacks may make computational models generalized or biased, missing out on biologically relevant complexities of in vivo joint physiology.…”

Arbitrary Prestrain Values for Ligaments Cause Numerical Issues in a Multibody Model of an Ankle Joint

“…In order to simplify the problem of prestrain, the assumed model of the ankle was chosen to be highly symmetrical (based on [10,33]-see Figure 1). It contained:  six nonlinear cables substituting the ligaments,  two Hertzian contact pairs, based on spheres, representing a ball-and-socket joint, which modeled the cartilage,  two rigid segments representing the tibia/fibula (TFS) and the talus/calcaneus (TCS).…”