Development of a YOLOv9 model with angle loss for automatic detection of landmarks and cephalometric analysis

Abstract Artificial intelligence has been widely applied to identify anatomical landmarks on lateral cephalometric radiographs, reducing localization errors and improving the efficiency of cephalometric analysis. This study aimed to develop and evaluate an automated cephalometric landmark detection system based on You Only Look Once version 9 (YOLOv9) with an angle-based loss function and automated Steiner cephalometric analysis using radiographs from an Indonesian population. The proposed angle-based loss was incorporated into YOLOv9 to enforce geometric consistency among anatomically related landmarks. Model performance was evaluated using mean radial error (MRE), successful detection rate (SDR), and mean average precision (mAP). Automated Steiner measurements derived from the predicted landmarks were compared with expert annotations using the mean absolute error (MAE). The proposed model achieved an overall MRE of 0.99 mm, an SDR of 86.8% at a 2-mm threshold, and a mAP of 0.754. Automated Steiner analysis yielded a mean absolute error of 1.38° compared with expert measurements. Compared with the baseline YOLOv9 model, the proposed angle-based loss modestly improved overall landmark localization performance while preserving anatomical relationships among predicted landmarks. These findings suggest that the proposed framework can support automated cephalometric analysis. However, further validation using independent datasets is required to confirm its generalizability and clinical applicability.