Xu Meng, Liu Bingyang, Luo Zhaoyang, Ma Hengyuan, Sun Min, Wang Yongqian, Yin Ningbei, Tang Xiaojun, Song Tao
Cleft Lip and Palate Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College.
Maxillofacial Surgery Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing.
J Craniofac Surg. 2023;34(5):1485-1488. doi: 10.1097/SCS.0000000000009299. Epub 2023 Mar 22.
Deep learning algorithms based on automatic 3-dimensional (D) cephalometric marking points about people without craniomaxillofacial deformities has achieved good results. However, there has been no previous report about cleft lip and palate. 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 cleft lip and palate based on the relationships between points. The authors used the PointNet++ model to investigate the automatic 3D cephalometric marking points. And the mean distance error of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 150 patients were enrolled. The mean distance error for all 27 landmarks was 1.33 mm, and 9 landmarks (30%) showed SDRs at 2 mm over 90%, and 3 landmarks (35%) showed SDRs at 2 mm under 70%. The automatic 3D cephalometric marking points take 16 seconds per dataset. In summary, our training sets were derived from the cleft lip with/without palate computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional neural network algorithm may be suitable for 3D cephalometry system in cleft lip and palate cases. More accurate results may be obtained if the cleft lip and palate training set is expanded in the future.
基于自动三维（3D）头影测量标记点的深度学习算法在无颅颌面畸形人群中已取得良好效果。然而，此前尚无关于唇腭裂的相关报道。本研究的目的是应用一种基于三维点云图卷积神经网络的新型深度学习方法，根据点之间的关系预测和定位唇腭裂患者的标志点。作者使用PointNet++模型研究自动三维头影测量标记点。并使用中心坐标位置的平均距离误差和成功检测率（SDR）来评估系统标记的准确性。共纳入150例患者。所有27个标志点的平均距离误差为1.33毫米，9个标志点（30%）在2毫米处的SDR超过90%，3个标志点（35%）在2毫米处的SDR低于70%。每个数据集的自动三维头影测量标记点耗时16秒。总之，我们的训练集来自唇裂伴/不伴腭裂的计算机断层扫描，以获得准确结果。基于图卷积神经网络算法的三维头影测量系统可能适用于唇腭裂病例的三维头影测量系统。如果未来扩大唇腭裂训练集，可能会获得更准确的结果。
