An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs

Objectives Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. Methods The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Eskisehir Osmangazi University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. Results The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. Conclusions A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.

Automated summary

The study proposed an automatic detection system for numbering teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method, achieving high accuracy and sensitivity in the process.