Relationship between Cervical Vertebral Maturation and Spheno-occipital Synchondrosis Closure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sigid Fu
Keyword(s):  
Cervical Vertebral Maturation

Multiple Regression Modeling for Age Estimation by Assessment and Comparison of Spheno-Occipital Synchondrosis Fusion and Cervical Vertebral Maturation Stages

2021 ◽  
Author(s):  
Ceren Aktuna Belgin ◽  
Seval Bayrak ◽  
Kaan Orhan ◽  
Handan Ankarali
Keyword(s):  
Regression Analysis ◽  
Multiple Regression ◽  
Age Estimation ◽  
Rank Correlation ◽  
Cone Beam ◽  
Spearman Rank Correlation Analysis ◽  
Maturation Stages ◽  
Multiple Regression Modeling ◽  
Cervical Vertebral Maturation ◽  
The Relationship

Abstract Objectives The aim of this study was (1) to evaluate the relationship between spheno-occipital synchondrosis (SOS) fusion stages, cervical vertebral maturation (CVM) stages, and clivus sizes with chronological age on cone-beam computed tomography (CBCT) images, and (2) to compare these methods for accurate age estimation using regression analysis. Materials and Methods The CBCT images of 200 individuals (102 females and 98 males) were included in the study. The SOS fusion stages and CVM stages were evaluated. The width and length of the clivus were measured. The effects of SOS fusion stages, CVM stages, clivus width, and clivus length on age estimation were evaluated by univariate tests and the effects of coexistence with ANCOVA and regression model. Spearman rank correlation analysis was also used to investigate the relationship between the SOS fusion stage, CVM stage, clivus width, and clivus length. Results The SOS stages, CVM stages, and clivus width were not shown statistically significant differences between the sexes (p-values = 0.205, 0.162, and 0.277, respectively), whereas clivus length was significantly longer in males (42.9 ± 4.26 mm) than in females (41.04 ± 3.74 mm). Multiple regression analysis showed 80% success when all parameters (SOS fusion stages, CVM stages, clivus width, and clivus length) were taken into consideration. Conclusion In conclusion, with the use of CVM stages and SOS fusion stages together, an accurate and reliable age estimation can be obtained in forensic medicine.

Constancy of cervical vertebral maturation indicator in adults: A cross-sectional study

2018 ◽  
Vol 16 (3) ◽  
pp. 486-498 ◽  
Author(s):  
Bhadrinath Srinivasan ◽  
Sridevi Padmanabhan ◽  
Arun B. Chitharanjan
Keyword(s):  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Cervical Vertebral Maturation

scholarly journals Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

2020 ◽  
Vol 49 (5) ◽  
pp. 20190441 ◽  
Author(s):  
Hakan Amasya ◽  
Derya Yildirim ◽  
Turgay Aydogan ◽  
Nazan Kemaloglu ◽  
Kaan Orhan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cervical Vertebrae ◽  
Clinical Decision ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Observer ◽  
Ann Model ◽  
Visual Evaluation ◽  
Cervical Vertebral Maturation

Objectives: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results. Methods: A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results. Results: Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes. Conclusions: This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

scholarly journals Validity and reliability of a method for assessment of cervical vertebral maturation

2012 ◽  
Vol 82 (2) ◽  
pp. 229-234 ◽  
Author(s):  
Xiao-Guang Zhao ◽  
Jiuxiang Lin ◽  
Jiu-Hui Jiang ◽  
Qingzhu Wang ◽  
Sut Hong NG
Keyword(s):  
Validity And Reliability ◽  
Cervical Vertebral Maturation

scholarly journals Cervical Vertebral Maturation Method: Reproducibility and Efficiency of Chronological Age Estimation

2021 ◽  
Vol 11 (7) ◽  
pp. 3160
Author(s):  
Lydia Schoretsaniti ◽  
Anastasia Mitsea ◽  
Kety Karayianni ◽  
Iosif Sifakakis
Keyword(s):  
Age Estimation ◽  
High Reliability ◽  
Intraclass Correlation ◽  
Growth Spurt ◽  
Chronological Age ◽  
Maturation Stage ◽  
Rater Reliability ◽  
Absolute Agreement ◽  
Cervical Vertebral Maturation ◽  
Cervical Maturation

The aim of this study was to investigate the reproducibility of the Cervical Vertebral Maturation (CVM) method and the potential for chronological age estimation using this method. The sample consisted of 474 lateral cephalometric radiographs, from orthodontic patients aged 6.4–22.4 years. Six raters were trained to the CVM method (Baccetti). All images were assessed twice. Intra- and inter-rater agreements were assessed by Cohen’s weighted kappa and intraclass correlation coefficient, respectively. Analysis of variance was performed to investigate the correlation between cervical maturation stages and chronological age. The age prediction potential of the method was tested by general linear model regression analysis. Intra-rater reliability ranged from 0.857 to 0.931. Intra-rater absolute agreement ranged from 77% to 87% however inter-rater absolute agreement was lower than 50%. Inter-rater reliability was higher than 0.9. The 3rd Cervical Maturation Stage (CS3) showed the lowest reproducibility. The mean age differences among the 6 CS stages were statistically significant and increased as the CS increased. CS and gender could roughly explain the 60% (adjusted R2 = 0.61) of the age variance of the sample. This CVM method proved able to show high reliability; however, it cannot predict accurately the pubertal growth spurt. A direct correlation was found between cervical stages and chronological age. This method provides a broad estimation of chronological age.

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