Orthognathic surgery consultation is essential to help patients understand the changes to their facial appearance after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. To overcome these challenges, this study aims to develop a fully automated pipeline that generates accurate and efficient 3D previews of postsurgical facial appearances for patients with orthognathic treatment without requiring additional medical images. The study introduces novel aesthetic losses, such as mouth-convexity and asymmetry losses, to improve the accuracy of facial surgery prediction. Additionally, it proposes a specialized parametric model for 3D reconstruction of the patient, medical-related losses to guide latent code prediction network optimization, and a data augmentation scheme to address insufficient data. The study additionally employs FLAME, a parametric model, to enhance the quality of facial appearance previews by extracting facial latent codes and establishing dense correspondences between pre- and post-surgery geometries. Quantitative comparisons showed the algorithm's effectiveness, and qualitative results highlighted accurate facial contour and detail predictions. A user study confirmed that doctors and the public could not distinguish between machine learning predictions and actual postoperative results. This study aims to offer a practical, effective solution for orthognathic surgery consultations, benefiting doctors and patients.

翻译：正颌手术咨询对于帮助患者理解术后面部外观变化至关重要。然而，由于治疗前后数据有限及治疗过程的复杂性，当前的可视化方法往往效率低下且不够精确。为克服这些挑战，本研究旨在开发一套全自动流程，无需额外医学影像即可为正颌治疗患者生成准确高效的术后面部外观三维预览。研究引入了新颖的美学损失函数，如嘴部凸度损失与不对称性损失，以提高面部手术预测的精度。此外，本研究提出了用于患者三维重建的专用参数化模型、指导潜在编码预测网络优化的医学相关损失函数，以及应对数据不足的数据增强方案。研究还采用参数化模型FLAME，通过提取面部潜在编码并建立手术前后几何形态间的密集对应关系，以提升面部外观预览的质量。定量比较证明了算法的有效性，定性结果则凸显了其在面部轮廓与细节预测方面的准确性。用户研究证实，医生与公众无法区分机器学习预测结果与实际术后效果。本研究旨在为正颌手术咨询提供实用有效的解决方案，使医患双方共同受益。