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Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach.
Cai, Jingyi; Min, Ziyang; Deng, Yudi; Jing, Dian; Zhao, Zhihe.
Affiliation
  • Cai J; State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China.
  • Min Z; State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China.
  • Deng Y; State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China.
  • Jing D; Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University, No.639, Zhizaoju Road, Huangpu District, Shanghai, 200011, China. janetjingdian@hotmail.com.
  • Zhao Z; State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China. zhzhao@scu.edu.cn.
BMC Oral Health ; 24(1): 30, 2024 01 06.
Article de En | MEDLINE | ID: mdl-38184528
ABSTRACT

BACKGROUND:

Adequate 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

METHODS:

Cephalometric 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.

RESULTS:

The 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.

CONCLUSIONS:

Rotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.
Sujet(s)
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Occlusion dentaire Type d'étude: Prognostic_studies Limites: Humans Langue: En Journal: BMC Oral Health Sujet du journal: ODONTOLOGIA Année: 2024 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Occlusion dentaire Type d'étude: Prognostic_studies Limites: Humans Langue: En Journal: BMC Oral Health Sujet du journal: ODONTOLOGIA Année: 2024 Type de document: Article Pays d'affiliation: Chine
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