Physics-Informed Neural Networks (PINNs) for solving the forward and inverse problems of prostate biomechanics.

Abstract

This work introduces a novel integration of Physics-Informed Neural Networks (PINNs) with hyperelastic material modeling, employing the Neo-Hookean model to estimate the stiffness of soft tissue organs based on realistic anatomical geometries. Specifically, we propose the modeling of the prostate biomechanics as an initial application of this framework. By combining machine learning with principles of continuum mechanics, the methodology leverages finite element method (FEM) simulations and magnetic resonance imaging (MRI)-derived prostate models to address forward and inverse biomechanical problems. The PINN framework demonstrates the ability to provide accurate material property estimations, requiring limited data while overcoming challenges in data scarcity. This approach marks a significant advancement in patient-specific precision medicine, enabling improved diagnostics, personalized treatment planning, and broader applications in the biomechanical characterization of other soft tissues and organ systems.