AI-Powered Cephalometric Landmark Detection for Orthodontic Diagnosis: Framework, Dataset, and Detection Challenge

Quantitative cephalometric analysis is a standard clinical and research tool in modern orthodontics which plays an integral role in orthodontic diagnosis, maxillofacial surgery, and treatment planning. The accurate identification and reproducible localization of cephalometric landmarks allows the quantification and classification of anatomical abnormalities. The traditional manual way of marking cephalometric landmarks on lateral cephalograms is a very time-consuming job and is miles hard to achieve stable detection accuracy because of uneven professionalism of orthodontists. Endeavors to develop automated landmark detection systems have persistently been made but they are inadequate for clinical orthodontic applications because of low reliability of specific landmarks. 

This research proposed a novel multi-stage regression framework for automatic cephalometric landmark detection, which demonstrated significant improvement over traditional methods. The proposed framework is based on a two-stage approach that uses a multihead CNN architecture to achieve superior performance. The shared backbone of the network enables inter-module communication, which helps the modules learn from each other’s predictions and align themselves accordingly. This approach is especially valuable in clinical settings where accurate and efficient cephalometric landmark detection is crucial for diagnosis and treatment of various craniofacial abnormalities.