{Reference Type}: Journal Article
{Title}: Postoperative facial prediction for mandibular defect based on surface mesh deformation.
{Author}: Du W;Wang H;Zhao C;Cui Z;Li J;Zhang W;Yu Y;Peng X;
{Journal}: J Stomatol Oral Maxillofac Surg
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jul 30
{Factor}: 2.48
{DOI}: 10.1016/j.jormas.2024.101973
{Abstract}: <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.