Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment

被引:5
作者
Wang, Xin [1 ]
Zhao, Xiaoke [2 ,3 ,4 ]
Song, Guangying [1 ,5 ]
Niu, Jianwei [2 ,3 ,4 ]
Xu, Tianmin [1 ,5 ]
机构
[1] Peking Univ Sch & Hosp Stomatol, Dept Orthodont, Beijing, Peoples R China
[2] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China
[3] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp BDBC, Beijing, Peoples R China
[4] Beihang Univ, Hangzhou Innovat Res Inst, Beijing, Peoples R China
[5] NHC Res Ctr Engn & Technol Computerized Dent, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
cephalometric analysis; facial harmony; machine learning; malocclusion; orthodontic treatment;
D O I
10.3389/fphys.2022.862847
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors.Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined.Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment.Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.
引用
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页数:13
相关论文
共 39 条
[1]  
Borzabadi-Farahani Ali, 2012, Prog Orthod, V13, P304, DOI 10.1016/j.pio.2012.03.003
[2]  
Burden DJ., 2007, SEMIN ORTHOD, V13, P76, DOI DOI 10.1053/J.SODO.2007.03.002
[3]   A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data [J].
Chang, Wenbing ;
Liu, Yinglai ;
Xiao, Yiyong ;
Yuan, Xinglong ;
Xu, Xingxing ;
Zhang, Siyue ;
Zhou, Shenghan .
DIAGNOSTICS, 2019, 9 (04)
[4]   A data-driven approach to predicting diabetes and cardiovascular disease with machine learning [J].
Dinh, An ;
Miertschin, Stacey ;
Young, Amber ;
Mohanty, Somya D. .
BMC MEDICAL INFORMATICS AND DECISION MAKING, 2019, 19 (01)
[5]  
Downs W.B., 1956, ANGLE ORTHOD, V26, P191
[6]  
Fortes Helena Nunes da Rocha, 2014, Dental Press J. Orthod., V19, P66, DOI 10.1590/2176-9451.19.2.066-075.oar
[7]  
Fu MK, 1965, J BEIJING MED SCH, V4, P251
[8]  
Hammond P, 2001, METHOD INFORM MED, V40, P365
[9]   Sagittal lip positions in different skeletal malocclusions: a cephalometric analysis [J].
Joshi, Merina ;
Wu, Li Peng ;
Maharjan, Surendra ;
Regmi, Mukunda Raj .
PROGRESS IN ORTHODONTICS, 2015, 16
[10]  
Kambara T., 2016, J OSAKA DENT U, V40, P153, DOI [10.18905/jodu.40.2_153, DOI 10.18905/JODU.40.2_153]