An artificial intelligence assisted framework for automated quantified root canal curvature measurement on periapical radiographs.
9 affiliations
- 1Department of Family Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan.
- 2Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
- 3Research Center for Semiconductor Materials and Advanced Optics, Chung Yuan Christian University, Taoyuan, Taiwan.
- 4Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan.
- 5Department of Electrical Engineering, Ming Chi University of Technology, New Taipei, Taiwan.
- 6Department of Electronic Engineering, Feng Chia University, Taichung, Taiwan.
- 7Department of Information Management, Chung Yuan Christian University, Taoyuan, Taiwan.
- 8Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.
- 9Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon, Philippines.
Abstract
Background/purpose
Root canal curvature is a complex anatomical variation that complicates endodontic treatments. Traditionally, dentists rely on two-dimensional periapical radiographs (PA) and manual methods to estimate curvature angles. However, this conventional approach is highly time-consuming and subjective, often leading to inconsistent measurements.Materials and methods
This study aims to develop a fully automated, objective framework for precise angular measurement and severity grading of root canal curvature. The YOLO26n-seg model was employed to segment roots and classify single- or multi-rooted teeth from PA. Furthermore, we proposed an image processing algorithm that standardized orientation, extracted centerlines, and applied three-point circle fitting to establish tangential lines for curvature measurement. The framework's performance was validated against a ground truth determined by five experienced dentists.Results
YOLO26n-seg achieved outstanding segmentation precision (98.8%), sensitivity (97.3%), and intersection over union (96.2%). For angular measurement, the framework demonstrated high accuracy with errors of merely 0.7-1.6 degrees compared to manual assessments, yielding a root mean square error of 1.2° and R² up to 98.9%. Moreover, the automated system reduced average processing time per PA from 39.9 to 70.8 s to just 0.6-1.7 s, an approximately 44-fold increase in efficiency.Conclusion
The proposed automated framework for root canal curvature measurement provides accurate and consistent geometric data, offering robust support for clinical diagnostic needs. By enhancing evaluation efficiency and reducing the diagnostic burden on clinicians, this system serves as a valuable auxiliary tool that further advances the standardization of dental imaging analysis.
Related
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- ROI-based binary caries segmentation in intraoral periapical radiographs using a ResNet-conditioned U-Net with FiLM-modulated decoder
BibTeX
@article{chen2026artificial,
title = {{An artificial intelligence assisted framework for automated quantified root canal curvature measurement on periapical radiographs.}},
author = {Ming-Yi Chen and Shih-Lun Chen and Yu-Jen Chang and Yuan-Jin Lin and Chiung-An Chen and Tsung-Yi Chen and Kuo-Chen Li and Wei-Chen Tu and Patricia Angela R Abu},
journal = {Journal of dental sciences},
year = {2026},
month = oct,
doi = {10.1016/j.jds.2025.07.3692},
url = {https://europepmc.org/article/MED/42923617},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An artificial intelligence assisted framework for automated quantified root canal curvature measurement on periapical radiographs.
AU - Ming-Yi Chen
AU - Shih-Lun Chen
AU - Yu-Jen Chang
AU - Yuan-Jin Lin
AU - Chiung-An Chen
AU - Tsung-Yi Chen
AU - Kuo-Chen Li
AU - Wei-Chen Tu
AU - Patricia Angela R Abu
PY - 2026
DA - 2026/10/01
JO - Journal of dental sciences
DO - 10.1016/j.jds.2025.07.3692
UR - https://europepmc.org/article/MED/42923617
AB - Background/purpose Root canal curvature is a complex anatomical variation that complicates endodontic treatments. Traditionally, dentists rely on two-dimensional periapical radiographs (PA) and manual methods to estimate curvature angles. However, this conventional approach is highly time-consuming and subjective, often leading to inconsistent measurements.Materials and methods This study aims to develop a fully automated, objective framework for precise angular measurement and severity grading of root canal curvature. The YOLO26n-seg model was employed to segment roots and classify single- or multi-rooted teeth from PA. Furthermore, we proposed an image processing algorithm that standardized orientation, extracted centerlines, and applied three-point circle fitting to establish tangential lines for curvature measurement. The framework's performance was validated against a ground truth determined by five experienced dentists.Results YOLO26n-seg achieved outstanding segmentation precision (98.8%), sensitivity (97.3%), and intersection over union (96.2%). For angular measurement, the framework demonstrated high accuracy with errors of merely 0.7-1.6 degrees compared to manual assessments, yielding a root mean square error of 1.2° and R² up to 98.9%. Moreover, the automated system reduced average processing time per PA from 39.9 to 70.8 s to just 0.6-1.7 s, an approximately 44-fold increase in efficiency.Conclusion The proposed automated framework for root canal curvature measurement provides accurate and consistent geometric data, offering robust support for clinical diagnostic needs. By enhancing evaluation efficiency and reducing the diagnostic burden on clinicians, this system serves as a valuable auxiliary tool that further advances the standardization of dental imaging analysis.
SN - 1991-7902
SN - 2213-8862
ER -