Yazar "Izgi, Mehmet Said" seçeneğine göre listele

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    Determination of growth and development periods in orthodontics with artificial neural network
    (Wiley, 2021) Kok, Hatice; Izgi, Mehmet Said; Acilar, Ayse Merve
    Background 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.
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    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 Merve
    Objective: 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.
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    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 Said
    Background 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.
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    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 Said
    Background 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.