Research progress on the application of deep learning in cephalometric analysis

Abstract

In orthodontic and orthognathic practice, cephalometric analysis is an integral tool throughout the clinical process. However, as landmark identification is still unautomated, both the conventional and semiautomated approaches are open to considerable subjectivity and could be time-consuming for inexperienced clinicians. Deep learning (DL), a state-of-the-art artificial intelligence (AI) technique, is highly effective in image recognition. In recent years, many studies have focused on the application of DL in cephalometric analysis, including automated landmark detection, automated diagnosis, cervical vertebral maturation stage determination, adenoid hypertrophy analysis and upper airway identification. Studies show that DL can effectively improve the efficiency of cephalometric analysis. In most studies, the accuracy of DL can reach more than 80%, and its difference from the gold standard is clinically acceptable, demonstrating good potential for future applications. However, most studies are limited to landmark detection, and the broadness and richness of the training dataset are limited. Future studies should broaden the research scope, improve the algorithm, elevate the richness of the datasets, and combine DL with other AI algorithms to improve its accuracy, stability and generalizability.

Keywords

• cephalometric analysis
• deep learning
• artificial intelligence
• lateral cephalogram
• frontal cephalogram
• automated landmark detection
• automated diagnosis
• adenoid hypertrophy
• upper airway identification
• cervical vertebral maturation analysis