BackgroundGrowth 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.MethodsCephalometric 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.ResultsAccording 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.ConclusionIn 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.

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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