Abstract:

Cephalometric analysis technology based on two-dimensional images has always been the‘golden standard’.Still，there are the problems of‘anatomical errors’caused by the distortion of two-dimensional images and overlapping of anatomical landmarks，and‘artificial errors’caused by manual punctuation.Three-dimensional（3D）cephalometric analysis，which has been widely used in clinical diagnosis，and playing a more and more important role for resolving the‘anatomical error’problem.Automatic cephalometric analysis，which uses image processing and deep learning for identifying and punctuating cephalometric landmarks automatically，could be used for resolving the‘manual error’problem.Convolutional neural network（CNN）based on deep learning is currently the most effective technology of image processing and target detection，which has shown its great potential for automatic target detection of 3D cephalometric landmarks.Based on literature review，we summarized the current status of 3D cephalometric analysis and the research progress of CNN for automatic target detection of 3D cephalometric landmarks.

Key words: Convolutional neural network, Dentofacial deformity, Cephalometric analysis, Three-dimensional anatomical landmark, Deep learning