组织工程与重建外科杂志2024，Vol.20Issue(6)：605-616,12.DOI:10.3969/j.issn.1673-0364.2024.06.002
基于人工智能技术的颅颌面畸形自动化头影测量研究
Automated cephalometric analysis of craniomaxillofacial deformities based on artificial intelligence technologies
摘要
Abstract
Objective To develop a new automatic landmark detection framework for the diagnosis and treatment of patients with severe craniomaxillofacial (CMF) deformities,addressing the issues of limited data quantity and large morphological differences. Methods This study proposed a method based on a three-dimensional (3D) point cloud deformation model and deep learning networks. First,normal human data was deformed to simulate severe CMF patient data for data augmentation. Then,a coarse-to-fine strategy was adopted,where initial coarse localization of landmarks was performed using a 3D point cloud convolutional neural network (CNN) semantic segmentation model,followed by fine localization using different models based on whether the landmarks are located in bone defect areas. Results The experiments demonstrated that the proposed method outperformed existing technologies in the detection of both normal and defective landmarks. The average errors for normal landmarks and defective landmarks detected under CT scanning were 1.19 mm and 1.13 mm,respectively,and under CBCT scanning were 0.91 mm and 0.94 mm,respectively. Conclusion The new method can effectively improve the accuracy of landmark detection for severe CMF deformities,which is significant for clinical surgical design and patient treatment.关键词
颅面畸形/标记点检测/深度学习/三维头影测量/点云Key words
Craniomaxillofacial deformities/Landmark detection/Deep learning/Three-dimensional cephalometric measurements/Point cloud分类
医药卫生引用本文复制引用
许梦,罗召阳,宋涛..基于人工智能技术的颅颌面畸形自动化头影测量研究[J].组织工程与重建外科杂志,2024,20(6):605-616,12.基金项目
中国医学科学院医学与健康科技创新工程(2021-I2M-1-052) （2021-I2M-1-052）
中国医学科学院整形外科医院科学基金(YS202007). （YS202007）
