Using a New Deep Learning Method for 3D Cephalometry in Patients With Hemifacial Microsomia

Abstract

Abstract Deep learning algorithms based on automatic 3D cephalometric marking points about people without craniomaxillofacial deformities have achieved good results. However, there has been no previous report about hemifacial microsomia (HFM). The purpose of this study is to apply a new deep learning method based on a 3D point cloud graph convolutional neural network to predict and locate landmarks in patients with HFM based on the relationships between points. The authors used a PointNet++ model to investigate the automatic 3D cephalometry. And the mean distance error (MDE) of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 135 patients were enrolled. The MDE for all 32 landmarks was 1.46 ± 1.308 mm, and 10 landmarks showed SDRs at 2 mm over 90%, and only 4 landmarks showed SDRs at 2 mm under 60%. Compared with the manual reproducibility, the standard distance deviation and coefficient of variation values for the MDE of the artificial intelligence system was 0.67 and 0.43, respectively. In summary, our training sets were derived from HFM computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional network algorithm may be suitable for the 3D cephalometry system in HFM cases. More accurate results may be obtained if the HFM training set is expanded in the future.