The purposes of this study were to evaluate the effect of the combined use of object detection for the classification of the C-shaped canal anatomy of the mandibular second molar in panoramic radiographs and to perform an external validation on a multicenter dataset.
The panoramic radiographs of 805 patients were collected from 4 institutes across two countries. The CBCT data of the same patients were used as “Ground-truth”. Five datasets were generated: one for training and validation, and 4 as external validation datasets. Workflow 1 used manual cropping to prepare the image patches of mandibular second molars, and then classification was performed using EfficientNet. Workflow 2 used two combined methods with a preceding object detection (YOLOv7) performed for automated image patch formation, followed by classification using EfficientNet. Workflow 3 directly classified the root canal anatomy from the panoramic radiographs using the YOLOv7 prediction outcomes. The classification performance of the 3 workflows was evaluated and compared across 4 external validation datasets.
For Workflows 1, 2, and 3, the area under the receiver operating characteristic curve (AUC) values were 0.863, 0.861, and 0.876, respectively, for the AGU dataset; 0.935, 0.945, and 0.863, respectively, for the ASU dataset; 0.854, 0.857, and 0.849, respectively, for the ODU dataset; and 0.821, 0.797, and 0.831, respectively, for the ODU low-resolution dataset. No significant differences existed between the AUC values of Workflows 1, 2, and 3 across the 4 datasets.
The deep learning systems of the 3 workflows achieved significant accuracy in predicting the C-shaped canal in mandibular second molars across all test datasets.
