Thermodynamics-informed graph neural networks for real-time simulation of digital human twins

TL;DR

This paper introduces a thermodynamics-informed graph neural network model for real-time, accurate simulation of hepatic tissue in digital human twins, enhancing precision medicine and haptic feedback capabilities.

Contribution

It presents a novel hybrid GNN model incorporating thermodynamic constraints for fast, generalizable soft tissue simulation in medical applications.

Findings

Predicts liver responses in under 8 milliseconds

Achieves less than 0.15% position error

Maintains stress tensor errors below 7%

Abstract

The growing importance of real-time simulation in the medical field has exposed the limitations and bottlenecks inherent in the digital representation of complex biological systems. This paper presents a novel methodology aimed at advancing current lines of research in soft tissue simulation. The proposed approach introduces a hybrid model that integrates the geometric bias of graph neural networks with the physical bias derived from the imposition of a metriplectic structure as soft and hard constrains in the architecture, being able to simulate hepatic tissue with dissipative properties. This approach provides an efficient solution capable of generating predictions at high feedback rate while maintaining a remarkable generalization ability for previously unseen anatomies. This makes these features particularly relevant in the context of precision medicine and haptic rendering. Based on the adopted methodologies, we propose a model that predicts human liver responses to traction and compression loads in as little as 7.3 milliseconds for optimized configurations and as fast as 1.65 milliseconds in the most efficient cases, all in the forward pass. The model achieves relative position errors below 0.15\%, with stress tensor and velocity estimations maintaining relative errors under 7\%. This demonstrates the robustness of the approach developed, which is capable of handling diverse load states and anatomies effectively. This work highlights the feasibility of integrating real-time simulation with patient-specific geometries through deep learning, paving the way for more robust digital human twins in medical applications.