• Selin Yesiltepe, Ibrahim Sevki Bayrakdar, Kaan Orhan, Özer Çelik, Elif Bilgir, Ahmet Faruk Aslan, Alper Odabaş, Andre Luiz Ferreira Costa, and Rohan Jagtap.
• Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Aydın Adnan Menderes University, Aydın, Turkey.
• Med Princ Pract. 2022 Jan 1; 31 (6): 555561555-561.

ObjectiveThe 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 MethodsIn 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.ResultsFifty 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.ConclusionDeep 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.© 2022 The Author(s). Published by S. Karger AG, Basel.

16 keywords...

hide…