• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• The korean journal of orthodontics
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• v.39 no.1
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• pp.28-35
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• 2009

Objective: The purpose of this study was to investigate the relationship between menarche and cervical vertebral maturation. Methods: Lateral cephalograms of 67 young korean girls within the range of 1 year before or after their menarche were gathered. The concavity of the cervical vertebrae base and the ratio of the base length to the 3rd and 4th cervical vertebrae anterior height were measured and analyzed. Results: The mean measured values were as follows, concavity of the 3rd cervical vertebrae base: 1.27(${\pm}0.18$) mm, concavity of the 4th cervical vertebrae base: 1.06(${\pm}0.15$) mm, ratio of the base length to the 3rd cervical vertebrae anterior height: 0.73(${\pm}0.06$) and ratio of the base length to the 4th cervical vertebrae anterior height: 0.70(${\pm}0.05$). There was a significant increase in the ratio of the base length to the 3rd vertebrae anterior height and the base concavity of the 3rd and 4th cervical vertebrae during the period of 1 year before to 1 year after their menarche. Conclusions: These characteristics of the 3rd and 4th cervical vertebrae on the lateral cephalogram can provide useful clues on evaluating the growth stage.