Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus

TL;DR

This paper explores model selection and sensitivity analysis for the biomechanics of soft tissues, specifically the human knee meniscus, emphasizing the importance of probabilistic methods like Bayesian inference to handle data variability and uncertainty.

Contribution

It introduces a Bayesian framework for model selection and sensitivity analysis in soft tissue biomechanics, addressing data scarcity and variability issues.

Findings

Bayesian inference effectively quantifies uncertainties in material parameters.

Model selection impacts the interpretation of tissue mechanical behaviour.

Sensitivity analysis identifies key parameters influencing tissue response.

Abstract

Soft tissues - such as ligaments and tendons - primarily consist of solid (collagen, predominantly) and liquid phases. Understanding the interaction between such components and how they change under physiological loading sets the basis for elucidating the essential link between their internal structure and mechanical behaviour. In fact, the internal heterogeneous structure of this kind of tissues leads to a wide range of mechanical behaviours, which then determine their own function(s). Characterising these behaviours implies an important experimental effort in terms of tissue harvesting, samples preparation and implementation of testing protocols - which, often, are not standardised. These issues lead to several difficulties in both collecting and providing comparable and reliable information. In order to model the behaviours of heterogeneous tissues and identify material parameters, a large volume of reproducible experimental data is required; unfortunately, such an amount of information is often not available. In reality, most of the studies that are focused on the identification of material parameters, are largely based on small sets of experimental data, which present a large variability. Such a large variability opens on to uncertainties in the estimation of material parameters, as reported in the literature. Hence, the use of a rigorous probabilistic framework, that is able to address uncertainties due to paucity of data, is of paramount importance in the field of biomechanics; in this perspective, Bayesian inference represents a promising approach. This study was focused on the analysis of the knee meniscus as a paradigmatic example of human soft tissue.