Deep learning-based prediction of maxillary canine impaction on panoramic radiographs using YOLO-OBB derived DenSE-Net
- 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus
Abstract
Background 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. Methods 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. Results 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. Conclusions 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.
Related
- Evaluation of the Position of Impacted Maxillary Canines on Panoramic Radiographs Using Deep Learning with CBCT as the Reference Standard
- Deep Learning for Root Canal Filling Quality on Panoramic Radiographs with Detection and Grading Measured Separately
- AI-Based Automated Segmentation of the Mandibular Cortex and Mental Foramen for Radiomorphometric Measurement on Panoramic Radiographs: A Feasibility Study
- SBRF-Det: Scale-Boundary Representative Fusion for Multi-Finding Detection in Panoramic Dental Radiographs.
- RCTE: A multi-class object detection framework for dental panoramic radiographs
BibTeX
@article{annamalai2026deep,
title = {{Deep learning-based prediction of maxillary canine impaction on panoramic radiographs using YOLO-OBB derived DenSE-Net}},
author = {J. K. Adithya Annamalai and L. Rama Parvathy},
journal = {BMC Oral Health},
year = {2026},
month = jul,
doi = {10.1186/s12903-026-09302-6},
url = {https://doi.org/10.1186/s12903-026-09302-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deep learning-based prediction of maxillary canine impaction on panoramic radiographs using YOLO-OBB derived DenSE-Net
AU - J. K. Adithya Annamalai
AU - L. Rama Parvathy
PY - 2026
DA - 2026/07/01
JO - BMC Oral Health
DO - 10.1186/s12903-026-09302-6
UR - https://doi.org/10.1186/s12903-026-09302-6
AB - Background 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. Methods 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. Results 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. Conclusions 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.
SN - 1472-6831
ER -