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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.
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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.
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Artificial intelligence can be defined as "understanding human thinking and trying to develop computer processes that will produce a similar structure." Thus, it is an attempt by a programmed computer to think. According to a broader definition, artificial intelligence is a computer equipped with human intelligencespecific capacities such as acquiring information, perceiving, seeing, thinking, and making decisions. Quality demands in dental treatments have constantly been increasing in recent years. In parallel with this, using image-based methods and multimedia-supported explanation systems on the computer is becoming widespread to evaluate the available information. The use of artificial intelligence in dentistry will greatly contribute to the reduction of treatment times and the effort spent by the dentist, reduce the need for a specialist dentist, and give a new perspective to how dentistry is practiced. In this review, we aim to review the studies conducted with artificial intelligence in dentistry and to inform our dentists about the existence of this new technology.