OBJECTIVE: The present study was designed to define a novel algorithm capable of predicting female adolescents\' cervical vertebrae maturation stage with high recall and accuracy.
METHODS: A total of 560 female cephalograms were collected, and cephalograms with unclear vertebral shapes and deformed scales were removed. 480 films from female adolescents (mean age: 11.5 years; age range: 6-19 years) were used for the model development phase, and 80 subjects were randomly and stratified allocated to the validation cohort to further assess the model\'s performance. Derived significant predictive parameters from 15 anatomic points and 25 quantitative parameters of the second to fourth cervical vertebrae (C2-C4) to establish the ordinary logistic regression model. Evaluation metrics including precision, recall, and F1 score are employed to assess the efficacy of the models in each identified cervical vertebrae maturation stage (iCS). In cases of confusion and mispredictions, the model underwent modification to improve consistency.
RESULTS: Four significant parameters, including chronological age, the ratio of D3 to AH3 (D3:AH3), anterosuperior angle of C4 (@4), and distance between C3lp and C4up (C3lp-C4up) were administered into the ordinary regression model. The primary predicting model that implements the novel algorithm was built and the performance evaluation with all stages of 93.96% for accuracy, 93.98% for precision, 93.98% for recall, and 93.95% for F1-score were obtained. Despite the hybrid logistic-based model achieving high accuracy, the unsatisfactory performance of stage estimation was noticed for iCS3 in the primary cohort (89.17%) and validation cohort (85.00%). Through bivariate logistic regression analysis, the posterior height of C4 (PH4) was further selected in the iCS3 to establish a corrected model, thus the evaluation metrics were upgraded to 95.83% and 90.00%, respectively.
CONCLUSIONS: An unbiased and objective assessment of the cervical vertebrae maturation (CVM) method can function as a decision-support tool, assisting in the evaluation of the optimal timing for treatment in growing adults. Our novel proposed logistic model yielded individual formulas for each specific CVM stage and attained exceptional performance, indicating the capability to function as a benchmark for maturity evaluation in clinical craniofacial orthopedics for Chinese female adolescents.

To develop a prediction model that combined information derived from chronological age, sex, and the cervical vertebral maturation (CVM) method to predict the pubertal spurt in mandibular growth.
A total of 50 subjects (29 females, 21 males) were selected from the American Association of Orthodontists Foundation Craniofacial Growth Legacy Collection, the University of Michigan Growth Study, and the Denver Child Growth study. A total of 456 lateral cephalograms were analyzed, and a multilevel logistic model was applied. The outcome variable was the presence or absence of the mandibular pubertal growth peak. The predictive variables were chronological age up to the third order, sex, presence or absence of CS 3 interactions between age and sex, age and CS 3, sex and CS 3.
The mean age ± standard deviation (SD) at the first cephalogram was 8.2 ± 0.5 years, whereas the mean age at the last cephalogram was 16.5 ± 1.1 years. The mean interval ± SD between two consecutive cephalograms was 1.0 ± 0.1 years. The mean age ± SD at the lateral cephalogram obtained immediately before the mandibular pubertal growth peak was 12.1 ± 1.1 years for females and 13.2 ± 0.8 years for males. The greatest increase in mandibular length occurred after CS 3 in 78% of the subjects. The presence of CS 3, age, second-order age, sex, and the interaction between age and sex were all statistically significant predictors of the mandibular pubertal growth spurt.
CS 3, chronological age, and sex can be used jointly to predict the pubertal peak in mandibular growth.

The cervical vertebral maturation (CVM) method is used to determine the craniofacial skeletal maturational stage of an individual at a specific time point during the growth process. This diagnostic approach uses data derived from the second (C2), third (C3), and fourth (C4) cervical vertebrae, as visualized in a two-dimensional lateral cephalogram. Six maturational stages of those three cervical vertebrae can be determined, based on the morphology of their bodies. The first step is to evaluate the inferior border of these vertebral bodies, determining whether they are flat or concave (ie, presence of a visible notch). The second step in the analysis is to evaluate the shape of C3 and C4. These vertebral bodies change in shape in a typical sequence, progressing from trapezoidal to rectangular horizontal, to square, and to rectangular vertical. Typically, cervical stages (CSs) 1 and CS 2 are considered prepubertal, CS 3 and CS 4 circumpubertal, and CS 5 and CS 6 postpubertal. Criticism has been rendered as to the reproducibility of the CVM method. Diminished reliability may be observed at least in part due to the lack of a definitive description of the staging procedure in the literature. Based on the now nearly 20 years of experience in staging cervical vertebrae, this article was prepared as a \"user\'s guide\" that describes the CVM stages in detail in attempt to help the reader use this approach in everyday clinical practice.

OBJECTIVE: The aim of this study was to assess the feasibility of skeletal maturation analysis using the Cervical Vertebrae Maturation (CVM) method by means of dedicated software, developed in collaboration with Outside Format (Paullo-Milan), as compared with manual analysis.
METHODS: From a sample of patients aged 7-21 years, we gathered 100 lateral cephalograms, 20 for each of the five CVM stages. For each cephalogram, we traced cervical vertebrae C2, C3 and C4 by hand using a lead pencil and an acetate sheet and dedicated software. All the tracings were made by an experienced operator (a dentofacial orthopedics resident) and by an inexperienced operator (a student in dental surgery). Each operator recorded the time needed to make each tracing in order to demonstrate differences in the times taken.
RESULTS: Concordance between the manual analysis and the analysis performed using the dedicated software was 94% for the resident and 93% for the student. Interobserver concordance was 99%. The hand-tracing was quicker than that performed by means of the software (28 seconds more on average).
CONCLUSIONS: The cervical vertebrae analysis software offers excellent clinical performance, even if the method takes longer than the manual technique.