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Item Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones(2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.Item Evaluation of Mandibular Third Molar Region in a Turkish Population Using Cone-Beam Computed Tomography(2020) Orhan, Buyuk Kaan; Yilmaz, Dilek; Ozemre, Mehmet Ozgur; Kamburoglu, Kivanc; Gulen, Orhan; Gullsahi, Ayse; 0000-0002-4134-5756; 33135608; AAG-1491-2021Objectives: To evaluate impacted mandibular third molar tooth region and obtain linear measurements using CBCT images and to assess the relationship between the impacted third molar and the mandibular canal. Methods: CBCT scans of 351 patients (208 females, 143 males) were assessed. Age, gender, and impaction site were recorded for each patient. The relationship of third molars with the vertical axis of second molars, 2nd molar resorption and the relationship between third molar apices and the mandibular canal were assessed. In addition, the distance between ramus and second molar, mesiodistal width of the third molar, the angle between third molar and second molar, and width of the third molar capsule were measured. Binary Logistic Regression, Chi-Square Test, and General Linear Model were used for statistical analysis. Results: The highest percentage of impaction was found for mesioangular followed by transversal and vertical. The transversal impacted third molars revealed a significant association with adjacent second molar root resorption (p<0.001). There was a statistical significance between the second molar resorption and distance between ramus and second molar (p<0.001). The mesioangular impacted third molars revealed significant relation with the mandibular canal (p<0.05). The most frequent variation found was the dental canal followed by the retromolar canal. In general, higher measurement values were obtained for men when compared to women (p<0.05). Conclusion: CBCT assessment of the third molar region provided useful information regarding impacted mandibular third molar surgery operations.