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Öğe Evaluation of the Effect of Different Abutment Materials on the Final Color of the Restoration After Aging: An In Vitro Study(Quintessence Publishing Co Inc, 2022) Türksayar, Almira Ada Diken; Bulut, Ali Can; Atsu, Saadet SağlamTo compare the effect of thermomechanical aging on implant abutment color change when using different abutment backgrounds. Materials and Methods: In this study, three separate experimental groups (n = 10) with different implant abutment materials were used: zirconia, modified polyether ether ketone (MPEEK), and polyether ketone ketone (PEKK). Equal-sized glass-ceramic incisor crowns were cemented to the abutments using transparent dual-curing resin cement. The specimens were then subjected to the thermomechanical aging process for the clinical equivalent of 5 years of use. The color values of each specimen in the middle third and the incisal third were recorded by a digital spectrophotometer in the CIE L*a*b* color coordinates both before and after the aging process. Color differences between groups were compared using one-way analysis of variance (ANOVA), while Tukey test was used to compare differences within the groups (P = .05). Results: In terms of color change (Delta E00) values, the zirconia group was found to show statistically more color changes only in the middle third (P < .000), but there was no significant difference between the the MPEEK and PEKK groups. In all groups, the Delta E00 value was clinically acceptable (Delta E00 < 1.8). Conclusion: After the aging process, high-performance polymer abutments caused less color change than zirconia. Therefore, esthetically satisfying results can be obtained in the anterior region, especially when highly translucent crown materials are used.Öğe Segmentation of Teeth Region via Machine Learning in Panoramic X-Ray Dental Images(IEEE, 2020) Güven, Ali; Yetik, İmam Şamil; Çulhaoğlu, Ahmet; Orhan, Kaan; Kılıçarslan, MehmetSegmentation of teeth region from the dental panoramic X-Ray images is an important task in determining various diseases. The main goal of this article is to be able to automatically segment the region of teeth in panoramic x-ray images. First, the center point of the teeth area in the images was determined automatically. Then, a feature set was developed including intensity values of pixels, x-coordinate relative to this center point, y-coordinate relative to this point, and the pixel values obtained by subtraction of maximum and minimum values in 3x3 window. CatBoost algorithm was used for machine learning. When creating the machine learning model, k-fold cross validation of training data set and grid search optimization of hyper parameters, were applied to avoid over fitting of data set. The results were analyzed using the learning curve, F1, accuracy, recall, and precision scores.Öğe Semi-Supervised Method for Determining the Maxillary and Mandibular Boundaries on Panoramic Radiographs(Ieee, 2018) Ulku, Berkay Kagan; Yetik, Imam Samil; Culhaoglu, Ahmet Kursad; Orhan, Kaan; Kilicarslan, Mehmet AliX-ray imaging plays an important role in the detection and diagnosis of dental disorders that can not be detected with the eye. Examination, evaluation and accurate diagnosis of panoramic radiography images used in anomaly detection is a process requiring serious experience and expertise. In order to simplify this process and to increase accuracy, some studies have been carried out on both the imaging techniques and the development of the computer aided diagnosis systems by transferring these images to the digital medium. One of these studies is segmentation of various structures in dental X-ray images. In this study, instead of using random masks as initial inputs, we developed a special mask identification method for each image and gave the mask which is the output of this method as initial input to the segmentation algorithm to obtain more suitable elliptical forms closer to the actual(ground truth) results and to the maxillary and mandibular structures.
