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    Biomimetic dental implant production using selective laser powder bed fusion melting: In-vitro results
    (Elsevier, 2024) Önder, M. Ercüment; Çulhaoğlu, Ahmet; Özgül, Özkan; Tekin, Umut; Atil, Fethi; Taze, Cem; Yasa, Evren
    Instead of a textured surface with irregular pore size and distribution as in conventional dental implants, the use of lattice structures with regular geometric structure and controlled pore size produced by selective laser powder bed fusion melting (LPDF) technique will provide more predictable and successful results regarding osseointe- gration and mechanics. In this study, biomimetic dental implants with 2 different pore designs were fabricated by LPDF technique and compared with conventional dental implants in terms of surface characterization and resistance to biomechanical forces. Finite element analysis, scanning electron microscopy, computed micro to- mography scanning, ISO 14801 tests and detork tests were used for the comparison. The tested biomimetic implants were found to be as durable as conventional implants in terms of mechanical strength and detork values. They were also found to be 40-60% more advantageous than conventional dental implants with respect to surface area and volume. As a result, it was concluded that biomimetic dental implants with sufficient me- chanical strength and complex surface geometries can be made as designed without changing the reliable base material and can be produced using a different manufacturing method.
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    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, Mehmet
    Segmentation 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.