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

作者全名：Ye, Huayu; Qin, Hongrui; Tang, Ying; Ungvijanpunya, Nicha; Gou, Yongchao

作者地址：[Ye, Huayu; Qin, Hongrui; Tang, Ying; Gou, Yongchao] Chongqing Med Univ, Dept Orthodont, Stomatol Hosp, 426 Songshi North Rd, Chongqing 401147, Peoples R China; [Ye, Huayu; Gou, Yongchao] Chongqing Key Lab Oral Dis & Biomed Sci, 426 Songshi North Rd, Chongqing 401147, Peoples R China; [Ye, Huayu; Gou, Yongchao] Chongqing Municipal Key Lab Oral Biomed Engn Highe, 426 Songshi North Rd, Chongqing 401147, Peoples R China; [Ungvijanpunya, Nicha] Chulalongkorn Univ, Fac Dent, 34 Henri Dunant Rd, Bangkok 10330, Thailand

通信作者：Gou, YC (通讯作者)，Chongqing Med Univ, Dept Orthodont, Stomatol Hosp, 426 Songshi North Rd, Chongqing 401147, Peoples R China.; Gou, YC (通讯作者)，Chongqing Key Lab Oral Dis & Biomed Sci, 426 Songshi North Rd, Chongqing 401147, Peoples R China.; Gou, YC (通讯作者)，Chongqing Municipal Key Lab Oral Biomed Engn Highe, 426 Songshi North Rd, Chongqing 401147, Peoples R China.

来源：PROGRESS IN ORTHODONTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001228659200001

JCR分区：Q1

影响因子：3.5

年份：2024

卷号：25

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：CVM method; Skeletal maturation; Cervical vertebrae; Lateral cephalogram

摘要：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.

基金机构：National Natural Science Foundation of China

基金资助正文：Not applicable.