Yasin Yasa1, Özer Çelik2, Ibrahim Sevki Bayrakdar3, Adem Pekince4, Kaan Orhan5,6, Serdar Akarsu7, Samet Atasoy7, Elif Bilgir3, Alper Odabaş2, Ahmet Faruk Aslan2. 1. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey. 2. Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey. 3. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey. 4. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey. 5. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey. 6. Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey. 7. Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey.
Abstract
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 Ordu 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.
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 Ordu 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.
Entities:
Keywords:
Artificial intelligence; bite-wing radiography; deep learning; tooth detection
Authors: Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga Journal: Healthcare (Basel) Date: 2022-07-08
Authors: María Prados-Privado; Javier García Villalón; Antonio Blázquez Torres; Carlos Hugo Martínez-Martínez; Carlos Ivorra Journal: Biomed Res Int Date: 2021-12-14 Impact factor: 3.411