A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves

The biomechanics of wounds at physiologically relevant levels: Understanding skin's stress-shielding effect for the quantitative assessment of healing

Wounds are responsible for the decrease in quality of life of billions of people around the world. Their assessment relies on subjective parameters which often delays optimal treatments and results in increased healthcare costs. In this work, we sought to understand and quantify how wounds at different healing stages (days 1, 3, 7 and 14 post wounding) change the mechanical properties of the tissues that contain them, and how these could be measured at clinically relevant strain levels, as a step towards quantitative wound tracking technologies. To achieve this, we used digital image correlation and mechanical testing on a mouse model of wound healing to map the global and local tissue strains. We found no significant differences in the elastic and viscoelastic properties of wounded vs unwounded skin when samples were measured in bulk, presumably as these were masked by the protective mechanisms of skin, which redistributes the applied loads to mitigate high stresses and reduce tissue damage. By measuring local strain values and observing the distinct patterns they formed, it was possible to establish a connection between the healing phase of the tissue (determined by the time post-injury and the observed histological features) and the overall mechanical behaviour. Importantly, these parameters were measured from the surface of the tissue, using physiologically relevant strains without increasing the tissue's damage. Adaptations of these approaches for clinical use have the potential to aid in the identification of skin healing problems, such as excessive inflammation or lack of mechanical progression over time. An increase, decrease, or lack of change in the elasticity and viscoelasticity parameters, can be indicative of wound state, thus ultimately leading to improved diagnostic outcomes.

Bayesian Optimization-Based Inverse Finite Element Analysis for Atrioventricular Heart Valves

Inverse finite element analysis (iFEA) of the atrioventricular heart valves (AHVs) can provide insights into the in-vivo valvular function, such as in-vivo tissue strains; however, there are several limitations in the current state-of-the-art that iFEA has not been widely employed to predict the in-vivo, patient-specific AHV leaflet mechanical responses. In this exploratory study, we propose the use of Bayesian optimization (BO) to study the AHV functional behaviors in-vivo. We analyzed the efficacy of Bayesian optimization to estimate the isotropic Lee-Sacks material coefficients in three benchmark problems: (i) an inflation test, (ii) a simplified leaflet contact model, and (iii) an idealized AHV model. Then, we applied the developed BO-iFEA framework to predict the leaflet properties for a patient-specific tricuspid valve under a congenital heart defect condition. We found that the BO could accurately construct the objective function surface compared to the one from a [Formula: see text] grid search analysis. Additionally, in all cases the proposed BO-iFEA framework yielded material parameter predictions with average element errors less than 0.02 mm/mm (normalized by the simulation-specific characteristic length). Nonetheless, the solutions were not unique due to the presence of a long-valley minima region in the objective function surfaces. Parameter sets along this valley can yield functionally equivalent outcomes (i.e., closing behavior) and are typically observed in the inverse analysis or parameter estimation for the nonlinear mechanical responses of the AHV. In this study, our key contributions include: (i) a first-of-its-kind demonstration of the BO method used for the AHV iFEA; and (ii) the evaluation of a candidate AHV in-silico modeling approach wherein the chordae could be substituted with equivalent displacement boundary conditions, rendering the better iFEA convergence and a smoother objective surface.