Deep learning-based prediction of maxillary canine impaction on panoramic radiographs using YOLO-OBB derived DenSE-Net

Maxillary canine impaction is a condition where the upper canine tooth doesn’t erupt properly. The diagnosis can be confirmed using a panoramic radiograph or Cone Beam Computed Tomography (CBCT). Maxillary canines are important for facial aesthetics and function. Sector analysis and the use of reference lines on panoramic radiographs are used to assess maxillary canine impaction. This study aimed to develop and evaluate an AI-based framework for automated detection and assessment of impacted maxillary canines from panoramic radiographs. The Ericson and Kurol method and the Angulation and Distance method involve drawing multiple lines and calculating angles. Such evaluation of canine impaction using panoramic radiographs typically requires 8–12 minutes per patient, depending on anatomical complexity and clinician experience. This time burden becomes significant in high-volume clinical settings. This paper provides a solution that automates the line-marking and impaction favorability calculation process. The objective is to predict maxillary canine impaction through panoramic x-ray imagery, with angle and distance calculations. An existing tooth segmentation dataset was adapted for compatibility with the YOLO-OBB based novel Dental Squeeze-and-Excitation Network (DenSE-Net), which in turn helps detect the orientation of the tooth, making it possible to draw lines. The final optimized DenSE-Net model achieved strong detection performance, with [email protected] = 0.969, [email protected] = 0.851, and [email protected]:0.95 = 0.671, indicating accurate oriented tooth localization across the evaluated classes. The findings of this research emphasize the importance of early and accurate assessment of maxillary canine impaction using radiographic techniques, enabling effective treatment planning and improved aesthetic outcomes.