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. 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. 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. 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.

中文翻译：

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人工智能算法在正畸学中通过颈椎椎骨生长发育确定的用途和比较。

生长和发育可以通过头颅X线片上定义的颈椎阶段来确定。人工智能能够通过使用不同的算法来执行多种活动，例如在生活的许多领域进行预测分类。在这项研究中，我们旨在确定经常发生的颈椎椎骨阶段（CVS）使用了七个人工智能分类器，并比较了这些算法的性能。我们的研究包括从300位年龄在8至17岁之间的个人获得的头颅X线照片。在第二，第三和第四颈椎上定义了19个参考点，并进行了20种不同的线性测量。选择并比较了分类领域中常用的七个人工智能算法。这些算法是k最近邻（k-NN），朴素贝叶斯（NB），决策树（Tree），人工神经网络（ANN），支持向量机（SVM），随机森林（RF）和逻辑回归（Log .Regr。）算法。根据混淆矩阵决策树，CSV1（97.1％）– CSV2（90.5％），SVM：CVS3（73.2％）– CVS4（58.5％）和kNN：CVS 5（60.9％）– CVS 6（78.7％）为确定颈椎阶段最精确的算法。在确定除CVS5以外的所有阶段中，ANN算法具有第二高的准确度值（分别为93％，89.7％，68.8％，55.6％和78％）（47.4％的第三高准确度值）。根据预测CSV类的算法的平均排名，ANN是最稳定的算法，其平均排名为2.17。在我们的实验研究中，kNN和Log.Regr。算法具有最低的精度值。SVM-RF-Tree和NB算法具有不同的精度值。人工神经网络可能是确定CVS的首选方法。