Date: June 2026.
Source: arXiv preprint 2606.01572.
Abstract: Predicting patient-specific facial soft-tissue deformation is critical for iterative orthognathic surgery planning. 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. PINNOCHIO introduces a hybrid sequential decomposition that explicitly decouples discontinuous bone-soft-tissue interface movements from continuous volumetric hyperelastic deformation, enabling stable learning under surface-only supervision (i.e., outer facial surfaces from 3dMD scans) and achieving competitive accuracy with a substantial speedup over FEM for real-time iterative surgical planning.

Article: PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery.
Authors: Jungwook Lee, Daeseung Kim, Kevin Gu, Zhangfeng Hu, Tianshu Kuang, Finn Hopeman, Michael A.K. Liebschner, Jaime Gateno, Pingkun Yan. Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA.

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