PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery

2026-05-31

AI Summary

This study addresses the trade-off between accuracy and efficiency, as well as biomechanical inconsistency, in soft tissue deformation prediction for orthognathic surgery. The authors propose a physics-informed neural network framework featuring an innovative interface–volume decoupled architecture that explicitly separates the discontinuous motion at the bone–soft tissue interface from the continuous hyperelastic deformation within the volumetric domain. By integrating a physics-guided simulation-to-real adaptation strategy, the method ensures internal biomechanical consistency without requiring ground-truth volumetric data. Experiments on 40 clinical cases demonstrate that the proposed approach outperforms existing methods in both surface accuracy and physical plausibility, while achieving significantly faster inference than finite element methods—effectively balancing precision, computational efficiency, and biomechanical fidelity.

Abstract

Predicting patient-specific facial soft-tissue deformation is critical for iterative orthognathic surgery planning. However, current computational methods face a strict accuracy-efficiency trade-off: high-fidelity Finite Element Methods (FEM) are computationally prohibitive, whereas pure deep learning models often produce biomechanically inconsistent results. While Physics-Informed Neural Networks (PINNs) offer a promising avenue, learning the complex heterogeneous mechanics of bone--soft-tissue interactions with only partial clinical supervision (i.e., outer facial surfaces) remains highly unstable. To overcome these challenges, we present PINNOCHIO, a novel physics-informed framework for facial soft-tissue simulation. PINNOCHIO introduces a hybrid sequential decomposition that explicitly decouples discontinuous bone--soft-tissue interface movements from continuous volumetric hyperelastic deformation. This structural separation enables stable training and facilitates a physics-enabled sim-to-real adaptation strategy, ensuring internal biomechanical consistency without requiring volumetric ground truth. Evaluated on a 40-patient clinical cohort, PINNOCHIO outperforms existing baselines in both surface accuracy and physical validity. Furthermore, it achieves a substantial speedup over FEM, successfully resolving the accuracy-efficiency trade-off to provide a highly reliable and practical tool for interactive surgical planning.

Problem

• orthognathic surgery
• soft-tissue deformation
• physics-informed neural networks
• bone-soft-tissue interaction
• accuracy-efficiency trade-off

Innovation

• Physics-Informed Neural Networks
• Hyperelastic Deformation
• Interface-Volume Decoupling
• Sim-to-Real Adaptation
• Orthognathic Surgery Simulation