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

IF 2.4 2区 医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE
Runzhi Guo, Yuan Tian, Xiaobei Li, Weiran Li, Danqing He, Yannan Sun
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Abstract

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.

伪装提取治疗期间骨骼II类患者面部轮廓评估和预测:一项初步研究。
背景:骨骼ⅱ类患者伪装治疗后面部轮廓的评估对患者和正畸医师具有重要意义。本研究旨在探讨评估骨骼II类拔牙患者面部轮廓美学的关键因素,并预测其术后面部轮廓美学。方法:纳入124例骨骼II类拔牙患者。治疗前和治疗后的脑电图由训练有素的专家正畸医生进行分析。采用视觉模拟量表(visual analogue scale, VAS)对10名正畸专家治疗前后侧位照片的面部轮廓美学进行评价。评估主观面部轮廓美学与客观头侧测量的相关性。使用三种机器学习方法来预测治疗后的面部轮廓美学。结果:下唇、上唇到E面及U1-APo的距离与外形美观有较强的相关性。拔牙过程中下唇向E面和U1-APo的变化与VAS评分的变化相关性较强(r = - 0.551和r = - 0.469)。随机森林预测模型的平均绝对误差和均方根误差最小,具有较好的预测精度和拟合效果。在该模型中,预处理上唇至E面、预处理Pog-NB和U1-GAll的变化是预测治疗后面部轮廓美学评分的最重要变量。结论:上颌切牙突出和下唇突出是评价和预测骨骼ⅱ类拔牙患者面部轮廓美学的关键客观指标。人工智能预测模型是一种预测面部轮廓后处理美学的新方法。
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来源期刊
Head & Face Medicine
Head & Face Medicine DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.70
自引率
3.30%
发文量
32
审稿时长
>12 weeks
期刊介绍: Head & Face Medicine is a multidisciplinary open access journal that publishes basic and clinical research concerning all aspects of cranial, facial and oral conditions. The journal covers all aspects of cranial, facial and oral diseases and their management. It has been designed as a multidisciplinary journal for clinicians and researchers involved in the diagnostic and therapeutic aspects of diseases which affect the human head and face. The journal is wide-ranging, covering the development, aetiology, epidemiology and therapy of head and face diseases to the basic science that underlies these diseases. Management of head and face diseases includes all aspects of surgical and non-surgical treatments including psychopharmacological therapies.
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