Deep Learning for Root Canal Filling Quality on Panoramic Radiographs with Detection and Grading Measured Separately

Background/Objectives: The European Society of Endodontology names two filling requirements with a reproducible link to outcome: apical extent and material density. Panoramic radiography shows every treated tooth in one exposure, but systems built on it pool detection with grading into one average precision that hides which of the two has failed. This study graded both axes on panoramic radiographs with detection and discrimination measured separately.

Methods

In this retrospective study, two specialist endodontists read 830 panoramic radiographs independently, and a third adjudicated every disagreement. The 1668 root fillings of 806 radiographs were encoded into a binary and an axis-resolved label space, and 160 radiographs were locked away before training. The remaining 646 were evaluated under five-fold cross-validation, with four architectural interventions tested alongside input resolution. Localisation was measured by class-agnostic recall, grading by matched-box accuracy and per-axis F1, and label spaces were compared by paired bootstrap.