Automated Cephalometric Points Marking System

Background/Objectives: Modern artificial intelligence methods are increasingly used in medical and dental image analysis to support diagnosis and treatment planning. In orthodontics, automatic detection of cephalometric landmarks from X-ray images remains a challenging and clinically relevant task. Methods: This study proposes a multi-model approach for cephalometric landmark detection based on the ALD algorithm and three derived models trained with extended image augmentation techniques. The applied augmentations, including contrast and negative transformations, improved the detection of specific anatomical landmarks. The final detection strategy integrates outputs from all four models, selecting the most accurate prediction for each landmark based on historical performance results. Results: The proposed method was evaluated on real datasets. It achieved a mean radial error (MRE) of 2.12 mm compared to 2.26 mm for the baseline model, and a successful detection rate (SDR) of 72.22% within a 2.5 mm threshold compared to 68.87% for the baseline model. Conclusions: The results demonstrate that the ensemble-based approach improves landmark detection accuracy and has the potential to support clinical orthodontic workflows.