Characterization of the Nonlinear Biaxial Mechanical Behavior ‎of Human Ureter using Constitutive Modeling and Artificial ‎Neural Networks

Characterization of the mechanical properties of soft biological tissues is a fundamental issue in a variety of medical applications. As such, constitutive modeling of biological tissues that serves to establish a relationship between the kinematic variables has been used to formulate the tissue’s mechanical response under various loading conditions. However, the validation of the developed analytical and numerical models is accompanied by a length of computation time. Hence, the need for new advantageous methods like artificial intelligence (AI), aiming at minimizing the computation time for real-time applications such as in robotic-assisted surgery, sounds crucial. In this study, at first, the mechanical nonlinear characteristics of human ureter were obtained from planar biaxial test data, in which the examined specimens were simultaneously loaded along their circumferential and longitudinal directions. To do so, the biaxial stress-strain data was used to fit the well-known Fung and Holzapfel-Delfino hyperelastic functions using the genetic optimization algorithm. Next, the potential of Artificial Neural Networks (ANN), as an alternative method for prediction of the mechanical response of the tissue was evaluated such that, multilayer perceptron feedforward neural network with different architectures was designed and implemented and then, trained with the same experimental data. The results showed both approaches were practically able to predict the ureter nonlinearity and in particular, the ANN model can follow up the tissue nonlinearity during the entire loading phase in both low and high strain amplitudes (RMSE 0.02). Such results confirmed that neural networks can be a reliable alternative for modeling the nonlinear mechanical behavior of soft biological tissues