Angle Quest: Deep Learning Approach for Tooth Segmentation and Axial Inclination Measurement in Orthopantomogram Radiographs

The axial inclination of the teeth plays a significant role in orthodontics, oral surgery, and prosthodontics, ensuring the equilibrium of occlusal forces in the stomatognathic system and dental parallelism. This paper proposes a novel, automated deep learning and computer vision approach to segment and determine the mesiodistal axial inclination of individual teeth on orthopantomogram radiographs. Although the dentistry domain has explored the deep learning domain widely to solve its numerous problems, automated axial inclination and tooth morphology remain unexplored. This paper presents a customized axial inclination methodology based on deep learning and computer vision techniques that reduces time and the errors associated with manual measurements. The paper presents instance-level OPG tooth segmentation on the manually annotated dataset collected from A. B. Shetty Memorial Institute of Dental Sciences. The paper evaluates instance segmentation models using precision, recall, Dice coefficient, and IoU. The generalization of the model is justified using 5-fold cross-validation based on both object-level and pixel-level metrics. The model shows 0.83 precision, 0.92 recall, 0.87 dice coefficient, and 0.77 IoU, projecting reliable instance segmentation. Additionally, compared to existing approaches, the proposed method is reliable and faster, reducing computational overhead. The designed approach yields considerable axial inclination values, which are statistically analyzed alongside manually computed values from dentists using an unpaired t-test (p < 0.05). The study presents a clinically relevant, reliable, scalable, and structured preserving approach that provides a strong foundation for AI-based orthodontic treatment and diagnosis.