1 Department of Endodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.
2 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
3 Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
4 Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada.
5 Department of Medicine, Section of Endocrinology, Nutrition, and Diabetes, Boston University Medical Center, Boston, MA, USA.
6 Department of Oral Health Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, Canada.
7 Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, School of Dentistry, Dental Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
8 Department of Oral and Maxillofacial Surgery, D

Background: Dentists begin the diagnosis by identifying and enumerating teeth. Panoramic radiographs are widely used for tooth identification due to their large field of view and low exposure dose. The automatic numbering of teeth in panoramic radiographs can assist clinicians in avoiding errors. Deep learning has emerged as a promising tool for automating tasks. Our goal is to evaluate the accuracy of a two-step deep learning method for tooth identification and enumeration in panoramic radiographs.

Materials and methods: In this retrospective observational study, 1007 panoramic radiographs were labeled by three experienced dentists. It involved drawing bounding boxes in two distinct ways: one for teeth and one for quadrants. All images were preprocessed using the contrast-limited adaptive histogram equalization method. First, panoramic images were allocated to a quadrant detection model, and the outputs of this model were provided to the tooth numbering models. A faster region-based convolutional neural network model was used in each step.

Results: Average precision (AP) was calculated in different intersection-over-union thresholds. The AP50 of quadrant detection and tooth enumeration was 100% and 95%, respectively.

Conclusion: We have obtained promising results with a high level of AP using our two-step deep learning framework for automatic tooth enumeration on panoramic radiographs. Further research should be conducted on diverse datasets and real-life situations.