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. 2021 Sep 1;50(6):20200172.
doi: 10.1259/dmfr.20200172. Epub 2021 Mar 4.

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

Münevver Coruh Kılıc  1 , Ibrahim Sevki Bayrakdar  2 , Özer Çelik  3 , Elif Bilgir  2 , Kaan Orhan  4 , Ozan Barıs Aydın  1 , Fatma Akkoca Kaplan  2 , Hande Sağlam  2 , Alper Odabaş  3 , Ahmet Faruk Aslan  3 , Ahmet Berhan Yılmaz  5
Affiliations

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

Münevver Coruh Kılıc et al. Dentomaxillofac Radiol. .

Abstract

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.

Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix.

Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively.

Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

Keywords: Artificial intelligence; children; deciduous tooth; deep learning; panoramic radiography; pediatric dentistry; tooth detecting.

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Conflict of interest statement

Conflict of Interest: The authors declare that they have no conflict of interest.

Figures

Figure 1.
Figure 1.
System architecture of the Faster R-CNN algorithm R-CNN, region-based convolutional neural network
Figure 2.
Figure 2.
Diagram of the artificial intelligence model (CranioCatch): development steps
Figure 3.
Figure 3.
Tooth detection and numbering on pediatric panoramic radiographs using the artificial intelligence model (CranioCatch)
See this image and copyright information in PMC

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