Work

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

Year: 2024

Type: article

Abstract: Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making. • Finite element-based machine learning approach to predict childbirth injuries. • Mesh-morphing algorithm to obtain patient-specific models of the pelvic floor. • Random forest model yields lowest mean absolute error for stretch prediction. • Real-time biomechanical analysis achievable and suitable for clinical applications. • Use of artificial intelligence tools contributes to reducing maternal morbidity.

Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials

DOI: 10.1016/j.jmbbm.2024.106736