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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Oriented tooth detection: a CBCT image processing method integrated with RoI transformer.

Ziyi Zhao1,2,3,4, Bo Wu1,2, Sha Su3

  • 1School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.

Dento Maxillo Facial Radiology
|July 11, 2025
PubMed
Summary

This study introduces a novel oriented object detection method for cone beam computed tomography (CBCT) dental images, significantly improving tooth detection and classification accuracy. The innovative approach enhances visualization and reduces errors, particularly in complex dental cases.

Keywords:
CBCT image processingRoI transformercone beam computed tomographyoriented object detectiontooth detection

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Area of Science:

  • * Dental Imaging and Radiology
  • * Artificial Intelligence in Medicine
  • * Computer Vision

Background:

  • * Cone beam computed tomography (CBCT) offers high-resolution 3D dental imaging.
  • * Accurate tooth detection and classification are crucial for dental diagnostics.
  • * Existing methods face challenges with natural tooth orientation and background interference.

Purpose of the Study:

  • * To introduce an innovative CBCT image processing method using oriented object detection and a Region of Interest (RoI) Transformer.
  • * To improve the accuracy of tooth detection and classification in panoramic radiographs (PAN) derived from CBCT.
  • * To enhance the visualization of tooth orientation and reduce false positives.

Main Methods:

  • * Application of an oriented object detection approach, novel to dental imaging, aligning with natural tooth growth patterns.
  • * Integration of a Region of Interest (RoI) Transformer for improved detection and classification of various tooth types (molars, premolars, canines, incisors).
  • * Utilization of post-processing techniques, including distance and grayscale value constraints, to refine results and handle cases with missing teeth.

Main Results:

  • * Achieved high performance metrics: 98.48% accuracy, 97.21% recall, 97.21% F1 score, and 98.12% mean Average Precision (mAP) in tooth detection.
  • * Demonstrated superior performance compared to conventional horizontal detection methods.
  • * Showcased enhanced visualization capabilities of tooth orientation.

Conclusions:

  • * The proposed oriented object detection method significantly enhances tooth detection accuracy in CBCT-derived PAN.
  • * Improved visualization of tooth orientation and reduced background interference contribute to more reliable dental image analysis.
  • * This innovative approach holds promise for advancing automated dental diagnostics.