Regional influence on the aesthetic preference of different Mongolian profiles: A comparative study of assessors from Northeast and Mainland India

Nongthombam, Haripriya; Kumar, Mukesh; Goyal, Manish; Abrar, Md; Shaha, Kalpit; Kumar, Sandeep

doi:10.1016/j.ortho.2023.100730

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, the facial profile esthetics is highly influenced by race. Nongthombam et al have reported the difference of facial profile preference in different ethnicity [ 39 ]. Hence, our results were limited to Mongolian race.…”

Section: Discussionmentioning

confidence: 99%

Facial profile evaluation and prediction of skeletal class II patients during camouflage extraction treatment: a pilot study

Guo,

Tian,

et al. 2023

Head Face Med

View full text Add to dashboard Cite

Background The evaluation of the facial profile of skeletal Class II patients with camouflage treatment is of great importance for patients and orthodontists. The aim of this study is to explore the key factors in evaluating the facial profile esthetics and to predict the posttreatment facial profile esthetics of skeletal Class II extraction patients. Methods 124 skeletal Class II extraction patients were included. The pretreatment and posttreatment cephalograms were analyzed by a trained expert orthodontist. The facial profile esthetics of pretreatment and posttreatment lateral photographs were evaluated by 10 expert orthodontists using the visual analog scale (VAS). The correlation between subjective facial profile esthetics and objective cephalometric measurements was assessed. Three machine-learning methods were used to predict posttreatment facial profile esthetics. Results The distances from lower and upper lip to the E plane and U1-APo showed the stronger correlation with profile esthetics. The changes in lower lip to the E plane and U1-APo during extraction exhibited the stronger correlation with changes in VAS score (r = − 0.551 and r = − 0.469). The random forest prediction model had the lowest mean absolute error and root mean square error, demonstrating a better prediction accuracy and fitting effect. In this model, pretreatment upper lip to E plane, pretreatment Pog-NB and the change of U1-GAll were the most important variables in predicting the posttreatment score of facial profile esthetics. Conclusions The maxillary incisor protrusion and lower lip protrusion are key objective indicators for evaluating and predicting facial profile esthetics of skeletal Class II extraction patients. An artificial intelligence prediction model could be a new method for predicting the posttreatment esthetics of facial profiles.

show abstract

Section: Discussionmentioning

confidence: 99%