[Identification model of tooth number abnormalities on pediatric panoramic radiographs based on deep learning]

Objective: To identify tooth number abnormalities on pediatric panoramic radiographs based on deep learning. Methods: Eight hundred panoramic radiographs of children aged 4 to 11 years meeting the inclusion and exclusion criteria were selected and randomly assigned by writing programs in Python (version 3.9) to the training set (480 images), verification set (160 images) and internal test set (160 images), taken in Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology between November 2012 to August 2020. And all panoramic radiographs of children aged 4 to 11 years taken in the First Outpatient Department of Peking University School and Hospital of Stomatology from June 2022 to December 2022 were collected as the external test set (907 images). All of the 1 707 images were obtained by operators to determine the outline and to label the tooth position of each deciduous tooth, permanent tooth, permanent tooth germ and additional tooth. The deep learning model with ResNet-50 as the backbone network was trained on the training set, validated on the verification set, tested on the internal test set and external test set. The images of test sets were divided into two categories according to whether there was abnormality of tooth number, to calculate sensitivity, specificity, positive predictive value and negative predictive value, and then divided into four types of extra teeth and missing permanent teeth both existed, extra teeth existed only, missing permanent teeth existed only, and normal teeth number, to calculate Kappa values. Results: The sensitivity, specificity, positive predictive value and negative predictive value were 98.0%, 98.3%, 99.0% and 96.7% in the internal test set, and 97.1%, 98.4%, 91.9% and 99.5% in the external test set respectively, according to whether there was abnormality of tooth number. While images were divided into four types, the Kappa value obtained in the internal test set was 0.886, and that in the external test set was 0.912. Conclusions: In this study, a deep learning-based model for identifying abnormal tooth number of children was developed, which could identify the position of additional teeth and output the position of missing permanent teeth on the basis of identifying normal deciduous and permanent teeth and permanent tooth germs on panoramic radiographs, so as to assist in diagnosing tooth number abnormalities.