Deep Learning–Based Facial and Skeletal Transformations for Surgical Planning

作者：Jiahao Bao, X. Zhang, Shuguang Xiang, Hao Liu, Ming Cheng, Yang Yang, Xiaolin Huang, W. Xiang, Wenpeng Cui, Hong Lai, Shuo Huang, Yan Wang, Dianwei Qian, Hong Yu · 发表于：Journal of Dental Research · 年份：2024 · DOI：10.1177/00220345241253186 · 被引用次数：26 · 研究领域：Dental Radiography and Imaging、Anatomy and Medical Technology、3D Shape Modeling and Analysis

The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of cr...