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Öğe Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform(Elsevier Ireland Ltd, 2017) Akkoc, Betul; Arslan, Ahmet; Kok, HaticeBackground and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.Öğe Determination of growth and development periods in orthodontics with artificial neural network(Wiley, 2021) Kok, Hatice; Izgi, Mehmet Said; Acilar, Ayse MerveBackground We aimed to determine the growth-development periods and gender from the cervical vertebrae using the artificial neural network (ANN). Setting and Sample Population The cephalometric and hand-wrist radiographs obtained from 419 patients aged between 8 and 17 years were included in our study. Materials and Methods Our retrospective study consisted of 419 patients' cephalometric and hand-wrist radiographs. The cephalometric radiographs were divided into six cervical vertebrae stages (CVS). Correlations were evaluated between hand-wrist maturation level, CVS, and ages. Twenty-seven vertebral reference points are marked on the cephalometric radiograph, and 32 linear measurements were taken. With the combination of these measurements, 24 different data sets were formed to train ANN. Thus, 24 different ANN models for the determination of the growth-development periods were obtained. According to the results, seven ANN models that have a high success level and clinically applicable were selected. Also, an ANN model was done by all measurements and age for the determination of gender from cervical vertebrae. Results Significantly positive correlations between hand-wrist maturation level, CVS and ages were detected. The ANN-7 model (32 linear measurements and age) accuracy value was found 0.9427. The highest model accuracy, 0.8687, with the least linear measurements, was obtained by drawing 13 linear measurements, using vertical measurements and indents. Gender was determined using ANN (0.8950) on cervical vertebrae data. Conclusion The growth-development periods and gender were determined from the cervical vertebrae by using ANN. The success of the ANN algorithm has been satisfactory. Further studies are needed for a fully automatic decision support system.Öğe Do hemostatic agents affect shear bond strength and clinical bond failure rate of orthodontic brackets?(Taylor & Francis Ltd, 2018) Karabekiroglu, Said; Kok, HaticeTo evaluate the effects of different hemostatic agents on the shear bond strength (SBS) in vitro and clinical bond failure rate of orthodontic metal brackets in vivo. A total of 100 human premolar teeth were randomly divided into five groups: control, blood, Viscostat, hydrogen peroxide (H2O2), and epinephrine. Teeth were bonded with same light-cured adhesive and composite. After storage in distilled water for 24h, thermal cycling was used as an aging procedure on all samples. The brackets were subjected to an SBS test at a speed of 0.5mm/min until bracket debonding. SBS values and the adhesive remnant index were evaluated. Ninety-nine patients (52 female, 47 males) undergoing routine orthodontic treatment were recruited for this controlled clinical study at bonding stages. All patients with bleeding on the buccal surface of any premolar tooth or teeth at bonding were included in this study. Over 6months, the bond failure rate was calculated. Data were analyzed using one-way analysis of variance (ANOVA) and Tukey's post-hoc test (p<.05). The McNemar test was used to compare bracket-bond failure. ANOVA showed a significant difference (p<.001) between the groups. No significant differences were found between the hemostatic agent groups (p>0.05) in the in vitro part. The lowest failure rate was obtained in the control group rather than the hemostatic agent groups during clinical follow-up (p<0.05). Each of the hemostatic agents (Viscostat, H2O2, and epinephrine) can be used for bleeding management during the orthodontic bonding process. Epinephrine application showed a high bond-failure rate at clinical follow-up.Öğe Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination(Aves, 2021) Kok, Hatice; Izgi, Mehmet Said; Acilar, Ayse MerveObjective: This study aimed to evaluate the success rates of the artificial neural network models (NNMs) and naive Bayes models (NBMs) trained with various cervical vertebra ratios in cephalometric radiographs for determining growth and development. Methods: Our retrospective study was performed on 360 individuals between the ages of 8 and 17 years, whose cephalometric radiographs were taken. According to the evaluation of cephalometric radiographs, growth and development periods were divided into 6 vertebral stages. Each stage was considered as a group, each group had 30 girls and 30 boys. Twenty-eight cervical vertebral ratios were obtained by using 10 horizontal and 13 vertical measurements. These 28 vertebral ratios were combined in 4 different combinations, leading to 4 different datasets. Each dataset was split into 2 parts as training and testing. To prevent the overfitting, a 5-cross fold validation technique was also used in the training phase. The experiments were conducted on 2 different train/test ratios as 80%-20% and 70%-30% for both NNMs and NBMs. Results: The highest determination success rate was obtained in NNM 3 (0.95) and the lowest in NBM 4 (0.50). The determination success of NBM 1 and NBM 3 was almost similar (0.60). The success of NNM 2 did not differ much from that of NNM 1 (0.94). The determination success of stage 5 was relatively lower than the others in NNM 1 and NNM 2 (0.83). Conclusion: The NNMs were more successful than the NBMs in our developed models. It is important to determine the effective ratio and/or measurements that will be useful for differentiation.Öğe Gender Determination from Teeth Images via Hybrid Feature Extraction Method(Springer International Publishing Ag, 2020) Uzbas, Betul; Arslan, Ahmet; Kok, Hatice; Acilar, Ayse MerveTeeth are a significant resource for determining the features of an unknown person, and gender is one of the important pieces of demographic information. For this reason, gender analysis from teeth is a current topic of research. Previous literature on gender determination have generally used values obtained through manual measurements of the teeth, gingiva, and lip area. However, such methods require extra effort and time. Furthermore, since sexual dimorphism varies among populations, it is necessary to know the optimum values for each population. This study uses a hybrid feature extraction method and a Support Vector Machine (SVM) for gender determination from teeth images. The study group was composed of 60 Turkish individuals (30 female, 30 male) between the ages of 19 and 27. Features were automatically extracted from the intraoral images through a hybrid method that combines two-dimensional Discrete Wavelet Transformation (DWT) and Principle Component Analysis (PCA). Classification was performed from these features through SVM. The system can be easily used on any population and can perform fast and low-cost gender determination without requiring any extra effort.Öğe Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images(Pergamon-Elsevier Science Ltd, 2016) Akkoc, Betul; Arslan, Ahmet; Kok, HaticeGender is one of the intrinsic properties of identity, with performance enhancement reducing the cluster when a search is performed. Teeth have durable and resistant structure, and as such are important sources of identification in disasters (accident, fire, etc.). In this study, gender determination is accomplished by maxillary tooth plaster models of 40 people (20 males and 20 females). The images of tooth plaster models are taken with a lighting mechanism set-up. A gray level co-occurrence matrix of the image with segmentation is formed and classified via a Random Forest (RF) algorithm by extracting pertinent features of the matrix. Automatic gender determination has a 90% success rate, with an applicable system to determine gender from maxillary tooth plaster images. (C) 2016 Elsevier Ltd. All rights reserved.Öğe Investigation of the relationship between Sella Turcica Bridge and Ponticulus Posticus: A Lateral Cephalometric Study(Soc Chilena Anatomia, 2017) Tassoker, Melek; Kok, Hatice; Ozcan, SevgiThe ponticulus posticus (PP) is a bridge of bone sometimes found on the atlas vertebra surrounding the vertebral artery and the first cervical nerve root. Sella turcica bridging (STB) is the fusion of anterior and posterior clinoid processes. The objective of this study was to find out the association between STB and PP. For the study, 752 digital lateral cephalograms were retrieved from the archived records of Necmettin Erbakan University, Faculty of Dentistry, Konya, Turkey. There was a significant relationship between the presence of STB and PP (p=0.000, p<0.001). This study indicates that there is a significant correlation between the presence of STB and PP.Öğe Is There a Possible Association between Skeletal Face Types and Third Molar Impaction? A Retrospective Radiographic Study(Karger, 2019) Tassoker, Melek; Kok, Hatice; Sener, SevgiObjective: Third molar impaction is seen much more than impaction of any other tooth as they are the last teeth to erupt. Inadequate retromolar space and the direction of eruption may be contributing factors. The aim of this study was to investigate the relationship between third molar impaction and different skeletal face types. Subjects and Methods: Panoramic and lateral cephalometric radiographs of 158 orthodontic patients (aged 19-25 years) were retrieved from the archived records of the Necmettin Erbakan University Faculty of Dentistry, Konya, Turkey. Third molar impaction was classified on the basis of Winter's classification. The skeletal facial type was determined by a measure of the angle created by the lines Ba-Na and Pt-Gn. The mean was 90 +/- 2 and this value was regarded as mesofacial. An angle of >93 degrees was regarded as brachyfacial and an angle of < 87 degrees as dolichofacial. Results: The overall presence of mandibular and maxillary third molar impactions was 65.2 and 38.6%, respectively. Although there was a statistically significant difference between different skeletal facial types and mandibular third molar impaction (p < 0.05), no statistically significant differences were observed between different skeletal facial types and maxillary third molar impaction (p >0.05). Brachyfacials demonstrated a lower prevalence of third molar impaction than dolichofacials. Conclusions: Different skeletal face types were associated with mandibular third molar impaction. Brachyfacials, who have a greater horizontal facial growth pattern than dolichofacials, showed a lower prevalence of impacted mandibular third molars. (c) 2018 The Author(s) Published by S. Karger AG, BaselÖğe Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics(Springer, 2019) Kok, Hatice; Acilar, Ayse Merve; Izgi, Mehmet SaidBackground Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. Methods Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. Results According to confusion matrices decision tree, CSV1 (97.1%)-CSV2 (90.5%), SVM: CVS3 (73.2%)-CVS4 (58.5%), and kNN: CVS 5 (60.9%)-CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. Conclusion In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.Öğe Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics(Springer, 2019) Kok, Hatice; Acilar, Ayse Merve; Izgi, Mehmet SaidBackground Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. Methods Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. Results According to confusion matrices decision tree, CSV1 (97.1%)-CSV2 (90.5%), SVM: CVS3 (73.2%)-CVS4 (58.5%), and kNN: CVS 5 (60.9%)-CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. Conclusion In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.