%0 Journal Article
%T Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.
%A Mun SB
%A Kim J
%A Kim YJ
%A Seo MS
%A Kim BC
%A Kim KG
%J BMC Oral Health
%V 24
%N 1
%D 2024 Aug 16
%M 39152384
%F 3.747
%R 10.1186/s12903-024-04721-9
%X <B>BACKGROUND: </B>We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.