PDF(Pubmed)
Abstract：
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.
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.
摘要：
目的：本研究旨在定义一种新颖的算法，能够以高召回率和准确性预测女性青少年的颈椎成熟阶段。
方法：共收集560例女性头颅图，切除椎体形状不清、鳞屑畸形的头颅。480部来自女性青少年的电影（平均年龄：11.5岁；年龄范围：6-19岁）用于模型开发阶段，80名受试者被随机分层分配到验证队列中,以进一步评估模型的性能.从第二至第四颈椎（C2-C4）的15个解剖点和25个定量参数中得出有意义的预测参数，以建立普通的Logistic回归模型。评估指标，包括精度，召回，和F1评分用于评估模型在每个鉴定的颈椎成熟期(iCS)中的功效。在混乱和错误预测的情况下，对模型进行了修改，以提高一致性。
结果：四个重要参数，包括实际年龄，D3与AH3的比率（D3：AH3），C4的前上角度（@4），将C3lp和C4up之间的距离（C3lp-C4up）放入普通回归模型中。建立了实现新算法的主要预测模型，并对所有阶段的性能进行了93.96%的准确性评估，精度为93.98%，93.98%用于召回，F1评分为93.95%。尽管基于混合逻辑的模型实现了高精度，在主要队列(89.17%)和验证队列(85.00%)中,iCS3的分期估计表现不佳.通过双变量logistic回归分析，在iCS3中进一步选择C4的后高度（PH4）以建立校正模型，因此，评估指标分别提升到95.83%和90.00%，分别。
结论：对颈椎成熟度（CVM）方法的无偏见和客观评估可以作为决策支持工具，协助评估成长中成年人的最佳治疗时机。我们提出的新逻辑模型为每个特定的CVM阶段提供了单独的公式，并获得了出色的性能，表明作为中国女性青少年临床颅面骨科成熟度评估基准的能力。
方法：共收集560例女性头颅图，切除椎体形状不清、鳞屑畸形的头颅。480部来自女性青少年的电影（平均年龄：11.5岁；年龄范围：6-19岁）用于模型开发阶段，80名受试者被随机分层分配到验证队列中,以进一步评估模型的性能.从第二至第四颈椎（C2-C4）的15个解剖点和25个定量参数中得出有意义的预测参数，以建立普通的Logistic回归模型。评估指标，包括精度，召回，和F1评分用于评估模型在每个鉴定的颈椎成熟期(iCS)中的功效。在混乱和错误预测的情况下，对模型进行了修改，以提高一致性。
结果：四个重要参数，包括实际年龄，D3与AH3的比率（D3：AH3），C4的前上角度（@4），将C3lp和C4up之间的距离（C3lp-C4up）放入普通回归模型中。建立了实现新算法的主要预测模型，并对所有阶段的性能进行了93.96%的准确性评估，精度为93.98%，93.98%用于召回，F1评分为93.95%。尽管基于混合逻辑的模型实现了高精度，在主要队列(89.17%)和验证队列(85.00%)中,iCS3的分期估计表现不佳.通过双变量logistic回归分析，在iCS3中进一步选择C4的后高度（PH4）以建立校正模型，因此，评估指标分别提升到95.83%和90.00%，分别。
结论：对颈椎成熟度（CVM）方法的无偏见和客观评估可以作为决策支持工具，协助评估成长中成年人的最佳治疗时机。我们提出的新逻辑模型为每个特定的CVM阶段提供了单独的公式，并获得了出色的性能，表明作为中国女性青少年临床颅面骨科成熟度评估基准的能力。
