Naralan, Muhammed Enes; Ozdogan, Alper; Guller, Mustafa Taha; Miloglu, Ozkan

Statement of problem: Prosthodontic materials may cause unexpected artifacts in cone beam computed tomography (CBCT) images, but studies quantifying these artifacts are sparse. Purpose: The purpose of this in vitro study was to compare the artifact expression of fixed prosthodontic materials with different CBCT devices. Material and methods: Ten prosthodontic materials (Co-Cr-Mo alloy, interim acrylic resin, polyetheretherketone, feldspathic ceramic, lithium disilicate glass-ceramic, zirconia-reinforced lithium silicate ceramic, zircon core, and 3 monolithic zirconias) were scanned with 2 CBCT devices. The materials were placed in polymethyl methacrylate resin to simulate clinical conditions. To assess the impact of the devices on artifacts, the gray values of 8 areas in each material image were analyzed. The data were analyzed with the Kruskal-Wallis and Wilcoxon Signed-Rank tests (α=.05). Results: Statistically significant differences were found in the artifact expression of the materials (P<.001) and between CBCT devices (P<.001). Conclusions: The artifact expression of polymeric and ceramic materials in CBCT images was less than that of other materials. The milliampere-second (mAs) value of CBCT devices had a significant impact on the artifact level.

Tiryaki, Burcu; Ozdogan, Alper; Guller, Mustafa Taha; Miloglu, Ozkan; Oral, Emin Argun; Ozbek, Ibrahim Yucel

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.