A pilot study of a deep learning approach to submerged primary tooth classification and detection

Abstract

Aim: The aim of the study was to compare the success and reliability of an artificial intelligence (AI) application in the detection and classification of submerged teeth in panoramic radiographs.

Materials and methods: Convolutional neural network (CNN) algorithms were used to detect and classify submerged molars. The detection module, based on the stateof- the-art Faster R-CNN architecture, processed a radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it with that of experts in the field.

Result: The success rate of the classification and identification of the system was high when evaluated according to the reference standard. The system was extremely accurate in its performance in comparison with observers.

Conclusions: The performance of the proposed computeraided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an AI application to prevent errors. In addition, this will facilitate the diagnoses of pediatric dentists.

Keywords: artificial intelligence; deep learning; infraocclusion; panoramic images; submerged teeth.