%0 Journal Article
%T Postoperative facial prediction for mandibular defect based on surface mesh deformation.
%A Du W
%A Wang H
%A Zhao C
%A Cui Z
%A Li J
%A Zhang W
%A Yu Y
%A Peng X
%J J Stomatol Oral Maxillofac Surg
%V 0
%N 0
%D 2024 Jul 30
%M 39089509
%F 2.48
%R 10.1016/j.jormas.2024.101973
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.<BR><B>CONCLUSIONS: </B>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.