Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

•The finite element method is computationally intensive, rigid body models cannot predict stress.•Neural network models are created to predict stress from multibody reaction forces.•Neural networks learn from finite element and multibody model solutions.•End goal: musculoskeletal models that simultaneously predict muscle force and cartilage stress. Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.