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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]