A Gaussian process approach for rapid evaluation of skin tension.

Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessing in vivo skin tension or its natural pre-stretch. This study aims to utilise modern machine learning (ML) methods to develop a model that uses non-invasive measurements of surface wave speed to predict clinically useful skin properties such as stress and natural pre-stretch. A large dataset consisting of simulated wave propagation experiments was created using a simplified two-dimensional finite element (FE) model. Using this dataset, a sensitivity analysis was performed, highlighting the effect of the material parameters and material model on the Rayleigh and supersonic shear wave speeds. Then, a Gaussian process regression model was trained to solve the ill-posed inverse problem of predicting stress and pre-stretch of skin using measurements of surface wave speed. This model had good predictive performance (R2 = 0.9570) and it was possible to interpolate simplified parametric equations to calculate the stress and pre-stretch. To demonstrate that wave speed measurements could be obtained cheaply and easily, a simple experiment was devised to obtain wave speed measurements from synthetic skin at different values of pre-stretch. These experimental wave speeds agree well with the FE simulations, and a model trained solely on the FE data provided accurate predictions of synthetic skin stiffness. Both the simulated and experimental results provide further evidence that elastic wave measurements coupled with ML models are a viable non-invasive method to determine in vivo skin tension. STATEMENT OF SIGNIFICANCE: To prevent unfavourable patient outcomes from reconstructive surgery, it is necessary to determine relevant subject-specific skin properties. For example, during a skin graft, it is necessary to estimate the pre-stretch of the skin to account for shrinkage upon excision. Existing methods are invasive or rely on the experience of the clinician. Our work aims to present an innovative framework to non-invasively determine in vivo material properties using the speed of a surface wave travelling through the skin. Our findings have implications for the planning of surgical procedures and provides further motivation for the use of elastic wave measurements to determine in vivo material properties.

A machine learning approach to predict in vivo skin growth.

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict in vivo skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. R2 = 0.9353) as well as the subject-specific shear modulus (R2 = 0.9801), growth rate (R2 = 0.8649), and natural pre-stretch (R2 = 0.9783) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.

A machine learning approach to predict in vivo skin growth.

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict in vivo skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. R 2 = 0.9353 ) as well as the subject-specific shear modulus ( R 2 = 0.9801 ), growth rate ( R 2 = 0.8649 ), and natural pre-stretch ( R 2 = 0.9783 ) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.

Analysis of In Vivo Skin Anisotropy Using Elastic Wave Measurements and Bayesian Modelling.

In vivo skin exhibits viscoelastic, hyper-elastic and non-linear characteristics. It is under a constant state of non-equibiaxial tension in its natural configuration and is reinforced with oriented collagen fibers, which gives rise to anisotropic behaviour. Understanding the complex mechanical behaviour of skin has relevance across many sectors including pharmaceuticals, cosmetics and surgery. However, there is a dearth of quality data characterizing the anisotropy of human skin in vivo. The data available in the literature is usually confined to limited population groups and/or limited angular resolution. Here, we used the speed of elastic waves travelling through the skin to obtain measurements from 78 volunteers ranging in age from 3 to 93 years old. Using a Bayesian framework allowed us to analyse the effect that age, gender and level of skin tension have on the skin anisotropy and stiffness. First, we propose a new measurement of anisotropy based on the eccentricity of angular data and conclude that it is a more robust measurement when compared to the classic "anisotropic ratio". Our analysis then concluded that in vivo skin anisotropy increases logarithmically with age, while the skin stiffness increases linearly along the direction of Langer Lines. We also concluded that the gender does not significantly affect the level of skin anisotropy, but it does affect the overall stiffness, with males having stiffer skin on average. Finally, we found that the level of skin tension significantly affects both the anisotropy and stiffness measurements employed here. This indicates that elastic wave measurements may have promising applications in the determination of in vivo skin tension. In contrast to earlier studies, these results represent a comprehensive assessment of the variation of skin anisotropy with age and gender using a sizeable dataset and robust modern statistical analysis. This data has implications for the planning of surgical procedures and questions the adoption of universal cosmetic surgery practices for very young or elderly patients.

Ultrasound shear wave elasticity imaging quantifies coronary perfusion pressure effect on cardiac compliance.

Diastolic heart failure (DHF) is a major source of cardiac related morbidity and mortality in the world today. A major contributor to, or indicator of DHF is a change in cardiac compliance. Currently, there is no accepted clinical method to evaluate the compliance of cardiac tissue in diastolic dysfunction. Shear wave elasticity imaging (SWEI) is a novel ultrasound-based elastography technique that provides a measure of tissue stiffness. Coronary perfusion pressure affects cardiac stiffness during diastole; we sought to characterize the relationship between these two parameters using the SWEI technique. In this work, we demonstrate how changes in coronary perfusion pressure are reflected in a local SWEI measurement of stiffness during diastole. Eight Langendorff perfused isolated rabbit hearts were used in this study. Coronary perfusion pressure was changed in a randomized order (0-90 mmHg range) and SWEI measurements were recorded during diastole with each change. Coronary perfusion pressure and the SWEI measurement of stiffness had a positive linear correlation with the 95% confidence interval (CI) for the slope of 0.009-0.011 m/s/mmHg ( R(2) = 0.88 ). Furthermore, shear modulus was linearly correlated to the coronary perfusion pressure with the 95% CI of this slope of 0.035-0.042 kPa/mmHg ( R(2) = 0.83). In conclusion, diastolic SWEI measurements of stiffness can be used to characterize factors affecting cardiac compliance specifically the mechanical interaction (cross-talk) between perfusion pressure in the coronary vasculature and cardiac muscle. This relationship was found to be linear over the range of pressures tested.