Abstract：
OBJECTIVE: This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability.
METHODS: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.
RESULTS: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.
CONCLUSIONS: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.
METHODS: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.
RESULTS: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.
CONCLUSIONS: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.
摘要：
目的：本研究旨在为下颌骨缺损患者的术后面部轮廓引入一种新颖的预测模型，解决当前方法中无法保留几何特征和缺乏可解释性的局限性。
方法：利用表面网格理论和深度学习，我们的模型与传统的点云方法不同，采用曲面三角形网格。我们使用网格卷积受限玻尔兹曼机（MCRBM）模型提取潜在变量，以生成三维变形场，旨在增强几何信息的保存和可解释性。
结果：对我们模型的实验评估表明，预测精度为91.2％，这代表了对传统的基于机器学习的方法的显著改进。
结论：所提出的模型为口腔颌面外科术前计划提供了一种有前途的新工具。它显著提高了术后面部轮廓预测下颌骨缺损重建的准确性，与以前的方法相比，提供了实质性的进步。
方法：利用表面网格理论和深度学习，我们的模型与传统的点云方法不同，采用曲面三角形网格。我们使用网格卷积受限玻尔兹曼机（MCRBM）模型提取潜在变量，以生成三维变形场，旨在增强几何信息的保存和可解释性。
结果：对我们模型的实验评估表明，预测精度为91.2％，这代表了对传统的基于机器学习的方法的显著改进。
结论：所提出的模型为口腔颌面外科术前计划提供了一种有前途的新工具。它显著提高了术后面部轮廓预测下颌骨缺损重建的准确性，与以前的方法相比，提供了实质性的进步。
