PhysSFI-Net: Physics-informed Geometric Learning of Skeletal and Facial Interactions for Orthognathic Surgical Outcome Prediction

2026-01-05

arXiv.org

AI Summary

This study addresses the challenge of efficiently and accurately predicting postoperative facial soft tissue deformation following orthognathic surgery, a task for which existing methods are either computationally expensive or lack clinical interpretability. To this end, the authors propose PhysSFI-Net, a novel end-to-end framework that uniquely integrates physical priors with geometric deep learning to achieve anatomy-aware prediction. The method leverages a hierarchical graph neural network, attention mechanisms, LSTM-based sequence modeling, and a biomechanics-inspired high-resolution surface reconstruction module. Evaluated on 135 clinical cases, PhysSFI-Net achieves state-of-the-art performance with a point cloud error of 1.070 ± 0.088 mm, surface deviation of 1.296 ± 0.349 mm, and landmark error of 2.445 ± 1.326 mm—significantly outperforming the current best method, ACMT-Net—while maintaining high accuracy and clinical interpretability.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• orthognathic surgery
• soft tissue deformation
• facial morphology prediction
• surgical outcome simulation
• craniofacial modeling

Innovation

• Physics-informed deep learning
• Geometric deep learning
• Orthognathic surgery simulation
• Soft tissue deformation prediction
• Hierarchical graph attention