三维颅颌面软硬组织智能头影测量分析的研究进展OA

In recent years,deep learning has achieved remarkable advances in medical image analysis.Automated land-mark identification and cephalometric analysis of craniofacial hard and soft tissues are key components of three-dimen-sional(3D)craniofacial assessment.These techniques have shown great potential for improving measurement efficiency,reducing interobserver variability,and supporting personalized diagnosis and treatment planning.This article reviews the development of two-dimensional automated cephalometric analysis and summarizes recent progress in deep learning-based methods for 3D automated landmark identification and cephalometric analysis of craniofacial hard and soft tissues.Major challenges are discussed,including the lack of standardized measurement protocols,dependence on large annotated datasets,limited model generalizability,and insufficient clinical interpretability.Future perspectives are also presented,with a focus on the establishment of large-scale clinical databases,the development of standardized measure-ment systems,and the application of multimodal and multitask models for clinical decision support.This re-view aims to provide a reference for future research and clinical translation of 3D automated craniofacial land-mark identification and cephalometric analysis.