A finite element-based machine learning framework to predict the mechanical behavior of the pelvic floor muscles during childbirth

Abstract

The medical community has been focusing on gaining a deeper understanding of birth trauma, which affects millions of women worldwide. Maternal lesions can be challenging to diagnose and expensive to examine. To better comprehend the mechanism of injuries occurring in the pelvic floor muscles (PFM), biomechanical simulations can be a valuable tool. However, utilizing the finite element method (FEM) to conduct simulations can be a time-consuming process. To overcome this issue, the present study aims to develop a machine learning (ML) framework to predict stresses on the PFM during childbirth by training ML algorithms on FEM simulation data. To generate the dataset for the ML algorithm’s training, childbirth simulations were performed using different material properties to characterize the PFM. Four ML algorithms were employed, namely Random Forest (RF), Extreme Gradient Boosting (XGBT), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), considering two scenarios: (1) stress prediction for the maximum stretch level of the muscle, and (2) for multiple levels of fetal descent. Results showed that the ANN performed best in the former, with a mean absolute error (MAE) of 0.191 MPa. In the latter, XGBT provided lower errors for 20 and 35 mm of fetal descent, with MAE values of 0.002 and 0.028 MPa, respectively. Nevertheless, the ANN yielded better predictions for 50 and 65 mm, with MAE values of 0.214 and 0.187 MPa, respectively. The present work represents the first attempt to use FEM-based ML algorithms with childbirth simulations to obtain near-real-time predictions in routine clinical procedures.