Automatic machine learning-based classification of mandibular third molar impaction status

Abstract

ObjectiveThe goal of this study was to evaluate the performance of automatic machine learning (ML)-based classification of the impacted status of the mandibular third molar. MethodsThe dataset consisted of 1864 mandibular third molar images, and the impaction pattern for each mandibular third molar was annotated based on the Pell and Gregory classification and the Winter classification. To improve performance, data augmentation techniques were applied, including rotation, flip, and pseudo-images that mimic prostheses, and ML was performed using the VGG16 convolutional neural network. ResultsIn the Pell and Gregory Class classification, the performance of the model trained on the augmented dataset exhibited good classification performance in three metrics, obtaining accuracy of 0.8609, macro-average F1-score of 0.7624, and an area under the receiver operating characteristic curve (AUC) of macro-average receiver operating characteristic (ROC) of 0.9334. For Pell and Gregory Position classification, the model obtained an accuracy value of 0.8432, a macro-average F1-score of 0.8156, and an AUC of macro-average ROC value of 0.9395. For Winter classification, the model obtained an accuracy value of 0.7959, a macro-average F1-score of 0.6423, and an AUC of macro-average ROC value of 0.9549. ConclusionsThe constructed ML classification model for mandibular third molar impaction status demonstrated good performance when data augmentation was applied.