Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

被引:5
作者
Cai, Jingyi [1 ,2 ]
Min, Ziyang [1 ,2 ]
Deng, Yudi [1 ,2 ]
Jing, Dian [3 ,4 ]
Zhao, Zhihe [1 ,2 ]
机构
[1] Sichuan Univ, West China Hosp Stomatol, State Key Lab Oral Dis, 14 3rd Sect,South Renmin Rd, Chengdu 610041, Sichuan, Peoples R China
[2] Sichuan Univ, West China Hosp Stomatol, Natl Clin Res Ctr Oral Dis, 14 3rd Sect,South Renmin Rd, Chengdu 610041, Sichuan, Peoples R China
[3] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Sch Med, Dept Orthodont, 639 Zhizaoju Rd, Shanghai 200011, Peoples R China
[4] Shanghai Jiao Tong Univ, Coll Stomatol, 639 Zhizaoju Rd, Shanghai 200011, Peoples R China
基金
中国国家自然科学基金;
关键词
Orthodontics; Occlusal plane; Machine learning; Back-propagation artificial neural network; Aesthetic improvement; ARTIFICIAL NEURAL-NETWORK; INTELLIGENCE; GROWTH; INCLINATION;
D O I
10.1186/s12903-023-03817-y
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
BackgroundAdequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning.Materials and methodsCephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison.ResultsThe regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II.ConclusionsRotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.
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页数:14
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