Dental implant brand and angle identification using deep neural networks.

Autor: Tiryaki B; Research Assistant, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey., Ozdogan A; Associate Professor, Department of Prosthodontics, Faculty of Dentistry, Atatürk University, Erzurum, Turkey. Electronic address: alprozdgn@gmail.com., Guller MT; Lecturer, Department of Dentistry Services, Vocational School of Health Services, Erzincan Binali Yıldırım University, Erzincan, Turkey., Miloglu O; Professor, Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Atatürk University, Erzurum, Turkey., Oral EA; Associate Professor, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey., Ozbek IY; Professor, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey.
Jazyk: angličtina
Zdroj: The Journal of prosthetic dentistry [J Prosthet Dent] 2023 Sep 14. Date of Electronic Publication: 2023 Sep 14.
DOI: 10.1016/j.prosdent.2023.07.022
Abstrakt: Statement of Problem: Determining the brand and angle of an implant clinically or radiographically can be challenging. Whether artificial intelligence can assist is unclear.
Purpose: The purpose of the present study was to determine the brand and angle of implants from panoramic radiographs with artificial intelligence.
Material and Methods: Panoramic radiographs were used to classify the accuracy of different dental implant brands through deep convolutional neural networks (CNNs) with transfer-learning strategies. The implant classification performance of 5 deep CNN models was evaluated using a total of 11 904 images of 5 different implant types extracted from 2634 radiographs. In addition, the angle of implant images was estimated by calculating the angle of 2634 implant images by applying a regression model based on deep CNN.
Results: Among the 5 deep CNN models, the highest performance was obtained in the Visual Geometry Group (VGG)-19 model with a 98.3% accuracy rate. By applying a fusion approach based on majority voting, the accuracy rate was slightly improved to 98.9%. In addition, the root mean square error value of 2.91 degrees was obtained as a result of the regression model used in the implant angle estimation problem.
Conclusions: Implant images from panoramic radiographs could be classified with a high accuracy, and their angles estimated with a low mean error.
(Copyright © 2023 Editorial Council for The Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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