A Multi‐Task Learning V‐Net Model for Working Length Prediction in Volumetric Dental Cone Beam Computed Tomography Images

ABSTRACT The prevalence of pulpal and periapical diseases exceeds 50% in the general population. Root canal treatment is currently recognized as the gold standard treatment, in which precise measurement of working length (WL) is critical for treatment success. In this study, a total of 100 eligible extracted teeth were collected and scanned using cone‐beam computed tomography (CBCT) to obtain high‐resolution three‐dimensional images. For WL calculation, we employed a V‐net‐based segmentation network for simulated paths of the root canal file, incorporating an encoder–decoder structure and a multi‐task learning strategy, and achieved a sensitivity of 94.7%. Ablation studies revealed that integrating the decoder's mask branch, key point branch, and boundary branch significantly improved the segmentation accuracy. The WL calculation comprised three stages: skeleton extraction and noise suppression, branch extraction and generation of the simulated paths of the root canal file, and length calculation based on three‐dimensional spline curves. The model achieved an average prediction error of 0.28 mm and an accuracy of 86.67% in WL prediction. These findings indicate that this V‐net‐based multi‐branch framework for precise WL estimation from CBCT holds substantial clinical application potential. Future work will focus on enhancing generalization and addressing challenges posed by calcified or anatomically complex root canals.