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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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    Effect of heat polymerization conditions and microwave on the flexural strength of polymethyl methacrylate
    (Medknow Publications, 2018) Özkır, Serhat Emre; Yılmaz, Burak; Ünal, Server Mutluay; Çulhaoğlu, Ahmet; Kurkcuoğlu Işın
    Objective: The objective of this study is the effect of different heat polymerization conditions on the strength of polymethyl methacrylate (PMMA) resin base is unknown. Distinguishing one method that provides improved mechanical properties may be beneficial to the clinical success of complete and partial dentures and overdentures. The purpose of this study was to evaluate the effect of different polymerization methods on the flexural strength of a dental PMMA resin. Materials and Methods: Forty PMMA specimens (64 mm × 10 mm × 4 mm) were prepared with 4 different polymerization methods (n = 10); heat polymerization at 74°C for 9 h, at 100°C for 40 min, and with 620 kPa pressure at 100°C for 20 min. The remaining group of specimens was microwave polymerized at 180 W for 6 min. All specimens were thermocycled at 5°C and 55°C for 5000 times. Three-point flexure test was used to measure the flexural strength of specimens. One-way ANOVA and Tukey Honestly Significant Difference were applied to analyze the differences in flexural strengths (? = 0.05). Results: The flexural strength of heat-polymerized groups was similar. The flexural strength of microwave polymerized group was significantly different and lower than the other groups (P < 0.05). Conclusion: Polymerizing conventional heat-polymerizing PMMA resin with microwave energy resulted in a significant decrease in flexural strength. The results of this study suggest that clinicians may benefit from using heat polymerization when processing PMMA denture bases instead of microvawe polymerization when tested brand is used. © 2018 European Journal of Dentistry.
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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.