An artificial intelligence assisted framework for automated quantified root canal curvature measurement on periapical radiographs

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.