Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning

Abstract

Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third molar (M3) extraction have been proposed, none fully predict both extraction difficulty and post-extraction complications. This study proposes an automatic diagnosis method based on state-of-the-art semantic segmentation and classification models to predict the extraction difficulty of maxillary and mandibular M3s and possible complications (sinus perforation and inferior alveolar nerve (IAN) injury). A dataset of 4,903 orthopantomographys (OPGs), annotated by experts, was used. The proposed diagnosis method segments M3s (#18, #28, #38, #48), second molars (#17, #27, #37, #47), maxillary sinuses, and inferior alveolar canal (IAC) in OPGs using a segmentation model and extracts the region of interest (RoI). Using the RoI as input, the classification model predicts extraction difficulty and complication possibilities. The model achieved 87.97% and 88.85% accuracy in predicting maxillary and mandibular M3 extraction difficulty, with area under the receiver operating characteristic curve (AUROC) of 96.25% and 97.3%, respectively. It also predicted the possibility of sinus perforation and IAN injury with 91.45% and 88.47% accuracy, and AUROC of 91.78% and 94.13%, respectively. Our results show that the proposed method effectively predicts the extraction difficulty and complications of maxillary and mandibular M3s using OPG, and could serve as a decision support system for clinicians before surgery.

Keywords: Deep learning; Extraction difficulty; Orthopantomographys; Post-extraction complications; Third molar.

Fig. 1

Overall framework for the diagnosis of extraction difficulty and complications. (Segmentation) DeepLabv3 + segments the third molars, second molars, maxillary sinuses, and IACs in the panoramic image. The red box is the RoI of 700 × 700 size with maxillary third molar (#18). (Classification) The RoI of the panoramic image and the semantic mask are concatenated and used as inputs to the R50 + Vision Transformer to classify extraction difficulty and complications. (Diagnosis Result) Diagnosis result of the extraction difficulty and possibility of complications of third molars (#18, #28, #38, #48).

Fig. 2

Illustration of impaction types of maxillary and mandibular third molar based on Pell & Gregory and Winter’s classifications.

Fig. 3

(Confusion Matrix on Test Set) The confusion matrix shows correctly and wrongly classified test samples. The confusion matrix displays correctly classified samples along the diagonals and incorrectly classified samples along the off-diagonal elements. (Examples of Segmentation and Classification) Examples of segmentation and classification results on the test set. Left: original OPGs. Right: OPGs reflecting the results of segmentation and classification. The segmentation results of the third molars (#18, #28, #38, #48), second molars (#17, #27, #37, #47), maxillary sinus (left and right), and IAC (left and right) are expressed in different colors for each instance. The classification results are expressed in white in the order of tooth number, the extraction difficulty, and the possibility of complication.