2026-10-19T01:09:13Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/105812026-10-18T21:13:19Zopenairedrivercom_ebyu_10000col_ebyu_10001

Dental implant brand and angle identification using deep neural networksTiryaki, BurcuOzdogan, AlperGuller, Mustafa TahaMiloglu, OzkanOral, Emin ArgunOzbek, Ibrahim YucelDeep LearningDental ImplantsHumansNeural Networks, ComputerRadiography, Panoramicartificial neural networkclassificationhumanpanoramic radiographytooth implantStatement 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. © 2025 Editorial Council for The Journal of Prosthetic DentistryElsevier Inc.2025Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10581https://doi.org/10.1016/j.prosdent.2023.07.0220022-3913enJournal of Prosthetic Dentistryinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10581