Intelligent quantitative assessment of skeletal maturation based on multi-stage model: a retrospective cone-beam CT study of cervical vertebrae

2021 ◽  
Author(s):  
Lizhe Xie ◽  
Wen Tang ◽  
Iman Izadikhah ◽  
Xiaoyu Chen ◽  
Zhenqi Zhao ◽  
...  
2011 ◽  
Vol 13 (11) ◽  
pp. 819-825 ◽  
Author(s):  
Xavier Jordi Juan-Senabre ◽  
Juan López-Tarjuelo ◽  
Antonio Conde-Moreno ◽  
Agustín Santos-Serra ◽  
Ángel L. Sánchez-Iglesias ◽  
...  

2017 ◽  
Vol 46 (7) ◽  
pp. 20170030 ◽  
Author(s):  
Cosimo Nardi ◽  
Cinzia Talamonti ◽  
Stefania Pallotta ◽  
Paola Saletti ◽  
Linda Calistri ◽  
...  

2016 ◽  
Vol 45 (1) ◽  
pp. 20150162 ◽  
Author(s):  
Marco A E Bonfim ◽  
André L F Costa ◽  
Acácio Fuziy ◽  
Michel E L Ximenez ◽  
Flávio A Cotrim-Ferreira ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Youn-Kyung Choi ◽  
Jinmi Kim ◽  
Tetsutaro Yamaguchi ◽  
Koutaro Maki ◽  
Ching-Chang Ko ◽  
...  

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age). We performed Pearson’s correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P<0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.


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