Journal of dental sciences
An artificial intelligence assisted framework for automated quantified root canal curvature measurement on periapical radiographs
By Chen MY, Chen SL, Chang YJ and 6 others.
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.