Informatics-enhanced prediction of failure strength in skeletal muscle tissue

Skeletal muscles are susceptible to injury during daily activities and competitive sports. Reliable prediction of tissue failure strength is a major challenge due to the large uncertainty in the mechanical behavior of skeletal muscle tissue. The present study reveals a strong correlation between histological characteristics and skeletal muscle failure strength by means of mechanical and histological experiments. We propose a data-driven hybrid modeling approach that enables an effective integration of data science and informatics tools to capture tissue failure strength. Uncertainty in the tissue failure strength is propagated into the posterior information of reduced model parameters via the Bayesian inference framework and parameter space compression. A histological enhancement to the softening hyperelasticity model is made by linking a quantified tissue-scale histological characteristic and stiff model parameters using artificial neural networks. The model is applied to skeletal muscle tissue from different species and sites to assess its predictive capabilities for physiological differences. The results show that the approach can achieve reliable predictions of skeletal muscle tissue failure strength. The proposed approach can be extended to different scales to enrich the understanding of structure–property linkages for biomaterials.