A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs

Selin Yesiltepe; Ibrahim Sevki Bayrakdar; Kaan Orhan; Özer Çelik; Elif Bilgir; Ahmet Faruk Aslan; Alper Odabaş; Andre Luiz Ferreira Costa; Rohan Jagtap

doi:10.1159/000527145

A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs

Med Princ Pract. 2022;31(6):555-561. doi: 10.1159/000527145. Epub 2022 Sep 27.

Authors

Selin Yesiltepe¹, Ibrahim Sevki Bayrakdar^{2

3}, Kaan Orhan^{4

5}, Özer Çelik^{3

6}, Elif Bilgir², Ahmet Faruk Aslan⁶, Alper Odabaş⁶, Andre Luiz Ferreira Costa⁷, Rohan Jagtap⁸

Affiliations

¹ Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Aydın Adnan Menderes University, Aydın, Turkey.
² Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey.
³ Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
⁴ Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey.
⁵ Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey.
⁶ Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey.
⁷ Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, Brazil.
⁸ Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, Mississippi, USA.

Abstract

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations.

Subject and methods: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements.

Results: Fifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively.

Conclusion: Deep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts.

Keywords: Artificial intelligence; Deep learning; Dentistry; Idiopathic osteosclerosis; Panoramic radiography.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence
Deep Learning*
Humans
Osteosclerosis* / diagnostic imaging
Radiography, Panoramic

Grants and funding

This work was supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under grant number 202045E06.