Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography

Abstract

Background: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. Methods: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. Results: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43–94.26%, 52.63–60.77%, 72.01–75.84%, and 76.36–79.00%, respectively. Conclusion: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.