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To establish an intelligent cervical vertebra maturity assessment system, and to evaluate the reliability and clinical value of the system. Sixty children aged were recruited in the study. Lateral cephalometric radiograph and cone beam CT (CBCT) were taken at the same period. Based on the CBCT data, the system automatically extracted the patient's facial area through Otsu's method, intercepted the sagittal plane by three-dimensional least squares method, captured the second to fourth cervical vertebrae by superpixel segmentation. And then selected points were marked automatically through morphological algorithm and manual method. Consistency test was performed on the two sets of data to compare the reliability of automated cervical morphology capture. According to the parameters of morphological identification, positioning and staging algorithms were designed to form the intelligent cervical vertebra maturity assessment system. The cervical vertebra maturity was also judged manually on the lateral cephalometric radiograph. The weighted Kappa test and the Gamma correlation coefficient were subsequently applied to evaluate the consistency and correlation. The results showed that the cervical vertebra features automatically captured based on CBCT data had a high accuracy on the overall morphological recognition. In the prediction of 8 inflection points out of 13 points, there was no significant difference between automatic and manual method on both X and Y axes (all &gt;0.05). The assessment results of the cervical vertebra maturity of the intelligent system had strong consistency and correlation with the manual recognition results (weighted Kappa value=0.877, Gamma value=0.991, both &lt;0.05). The intelligent cervical vertebrae maturity assessment system based on CBCT data established in this study presents reliable outcome and high degree of automation, indicating that the system may be used clinically.

Establishment of an intelligent cervical vertebrae maturity assessment system based on cone beam CT data