Related Experiment Video
Updated: Jun 16, 2025
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
Published on: August 5, 2021
Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography
Sae Byeol Mun1, Jeseong Kim2, Young Jae Kim3
1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Republic of Korea.
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.
