Racial and gender differences of cervical vertebral maturation staging between African Americans versus Caucasian patients of various age groups: A retrospective study

Gudhimella, Sudha; Gandhi, Vaibhav; Glatt, Brian; Wong, Ariel; Wood, Clara; Janakiraman, Nandakumar; Spiller, Robert E.; Chandiramani, Sunita

doi:10.1016/j.ortho.2021.100606

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Santiago et al highlighted that the methodological deficiencies in the analysis of skeletal maturation did not involve any form of randomization, blinding, or sample size calculation [ 14 ]. The previously proposed formulae are demonstrated to vary with polymorphism and sexual dimorphism and display several algorithmic errors, which limit their clinical predictive use of the CVM staging method [ 15 , 16 ]. Recent advances have seen intelligent machine learning algorithms emerge to assist in medical diagnosis, thus selecting the right technique is crucial, considering the anticipated outcome or prediction [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping an intelligent algorithm for predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy

Ye,

Qin,

Tang

et al. 2024

Prog Orthod.

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Backgrounds and objectives 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.

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Section: Introductionmentioning

confidence: 99%

Mapping an intelligent algorithm for predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy

Ye,

Qin,

Tang

et al. 2024

Prog Orthod.

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Analysis of the Relationship Between Body Mass Index (BMI) and Dento-Skeletal Maturation: A Cross-Sectional Case-Control Study

Verdecchia,

Torre,

Diaz

et al. 2024

Dentistry Journal

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Background/Objectives: The aim of this cross-sectional study was to establish if there is a relationship between body mass index (BMI) and skeletodental development in young obese patients in comparison with normal-weight patients. Methods: The sample consisted of 178 individuals (115 normal weight, 37 overweight and 26 obese), aged 6 to 16 years, with a mean biological age of 11.96 ± 2.44 years. Dental maturation (dental age) was determined by using Demerjian’s method; craniofacial growth pattern, skeletal and dental class were determined by using cephalometric measurements; skeletal maturation was determined by using Baccetti’s method. Data were statistically analyzed. Results: According to Demirjian’s method, the mean dental age of the overweight and obese subjects was significantly higher than that of the normal-weight subjects (p = 0.001 and p = 0.02, respectively). A higher rate of dental class III was detected in the overweight group (p = 0.002). Concerning cephalometric records, statistically significant differences (p = 0.018) were observed in the distribution of SNA and SNB values, higher values being found in obese subjects. No difference was detected in the distribution of the ANB angle between the groups. As regards craniofacial growth pattern, no significant differences were found between the groups. Children with obesity presented more advanced skeletal maturation, reaching statistically significant differences (p = 0.02), in comparison with the normal-weight subjects. Conclusions: In conclusion, obese children showed increased tooth and skeletal development in comparison to the normal-weight subjects. These findings may be of interest for orthodontists, who should consider weight status when performing orthodontic treatment in children and adolescents.

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Racial and gender differences of cervical vertebral maturation staging between African Americans versus Caucasian patients of various age groups: A retrospective study

Cited by 2 publications

References 21 publications

Mapping an intelligent algorithm for predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy

Mapping an intelligent algorithm for predicting female adolescents’ cervical vertebrae maturation stage with high recall and accuracy

Analysis of the Relationship Between Body Mass Index (BMI) and Dento-Skeletal Maturation: A Cross-Sectional Case-Control Study

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