Abstract
Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics. Accurate simulation of postoperative facial morphology is essential for preoperative planning. However, traditional biomechanical models are computationally expensive, while geometric deep learning approaches often lack interpretability. In this study, we develop and validate a physics-informed geometric deep learning framework named PhysSFI-Net for precise prediction of soft tissue deformation following orthognathic surgery. PhysSFI-Net consists of three components: a hierarchical graph module with craniofacial and surgical plan encoders combined with attention mechanisms to extract skeletal-facial interaction features; a Long Short-Term Memory (LSTM)-based sequential predictor for incremental soft tissue deformation; and a biomechanics-inspired module for high-resolution facial surface reconstruction. Model performance was assessed using point cloud shape error (Hausdorff distance), surface deviation error, and landmark localization error (Euclidean distances of craniomaxillofacial landmarks) between predicted facial shapes and corresponding ground truths. A total of 135 patients who underwent combined orthodontic and orthognathic treatment were included for model training and validation. Quantitative analysis demonstrated that PhysSFI-Net achieved a point cloud shape error of 1.070 +/- 0.088 mm, a surface deviation error of 1.296 +/- 0.349 mm, and a landmark localization error of 2.445 +/- 1.326 mm. Comparative experiments indicated that PhysSFI-Net outperformed the state-of-the-art method ACMT-Net in prediction accuracy. In conclusion, PhysSFI-Net enables interpretable, high-resolution prediction of postoperative facial morphology with superior accuracy, showing strong potential for clinical application in orthognathic surgical planning and simulation.
Abstract (translated)
正颌手术通过重新定位下颌骨来恢复咬合并增强面部美学。在术前规划中，准确模拟术后面部形态至关重要。然而，传统的生物力学模型计算成本高昂，而几何深度学习方法往往缺乏可解释性。在这项研究中，我们开发和验证了一个名为PhysSFI-Net的物理信息几何深度学习框架，用于精确预测正颌手术后的软组织变形。 PhysSFI-Net包括三个组成部分：结合注意力机制的分层图模块与颅面编码器和外科计划编码器相结合以提取骨骼面部交互特征；基于长短期记忆（LSTM）的序列预测器用于增量软组织变形；以及一种受生物力学启发的模块，用于高分辨率面部表面重建。通过预测面部形状与对应真实数据之间的点云形状误差（豪斯多夫距离）、表面偏差误差及颅面标志定位误差（欧几里得距离），评估模型性能。 共有135名接受综合正畸和正颌治疗的患者被纳入模型训练和验证。定量分析表明，PhysSFI-Net实现了1.070 +/- 0.088毫米的点云形状误差、1.296 +/- 0.349毫米的表面偏差误差以及2.445 +/- 1.326毫米的标志定位误差。比较实验表明，PhysSFI-Net在预测准确性方面优于最先进的方法ACMT-Net。 结论是，PhysSFI-Net能够实现可解释、高分辨率的术后面部形态预测，并具有更高的精度，在正颌手术规划和模拟的临床应用中展现出强大的潜力。
URL
https://arxiv.org/abs/2601.02088
