PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method called PINNOCHIO to better predict how a patient's facial soft tissues change during jaw surgery. Their approach combines physics rules with advanced neural networks, separating bone movement from soft tissue deformation to improve accuracy and training stability. Tested on 40 patients, this method was more accurate and physically realistic than other techniques and ran much faster than traditional simulations. This makes it a useful tool for planning surgeries in a practical and reliable way.
orthognathic surgeryfacial soft-tissue deformationFinite Element Methodsdeep learningPhysics-Informed Neural Networksbone-soft-tissue interfacehyperelastic deformationsim-to-real adaptationbiomechanical consistencysurgical planning
Authors
Jungwook Lee, Daeseung Kim, Kevin Gu, Zhangfeng Hu, Tianshu Kuang, Finn Hopeman, Michael A. K. Liebschner, Jaime Gateno, Pingkun Yan
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.