通过人类学测量预测上颌中切牙宽度的机器学习模型。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-07-08 Epub Date: 2023-10-18 DOI:10.2186/jpr.JPR_D_23_00114
Remya Ampadi Ramachandran, Merve Koseoglu, Hatice Özdemir, Funda Bayindir, Cortino Sukotjo
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引用次数: 0

摘要

目的:为了提高微笑美学,临床医生应该全面分析面部,并确保上颌前牙的尺寸与现有的人类学测量结果相一致。连合间(ICW)、极间(IAW)、中间角(MCW)、外角间(LCW)和瞳孔间(IPW)宽度用于确定上颌中切牙(CW)的宽度。本研究的目的是开发一种使用机器学习(ML)算法的自动化方法,通过人类学测量来预测土耳其年轻人群的中切牙宽度。这种自动化有助于数字牙科和临床决策。方法:在这项横断面研究的初始阶段,验证了几个ML回归模型,包括多元线性回归(MLR)、多层感知器(MLP)、决策树(DT)和随机森林(RF)模型,以确认中心宽度预测的准确性。ML模型的实现考虑了仅包含男性和女性测量以及组合的数据集,并针对无偏的总体数据集评估了每个模型的性能。结果:与其他算法相比,RF算法在所有情况下都表现出了改进的性能,准确率为96%,这代表了正确预测的百分比。该图揭示了RF模型在根据人类学测量预测CW方面的适用性,而不考虑候选人的性别。结论:这些结果证明了基于ML模型的人体测量来预测中切牙宽度的可能性。这些试验中准确的中切牙宽度预测也表明了所提出的模型用于增强临床决策的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning model to predict the width of maxillary central incisor from anthropological measurements.

Purpose: To improve smile esthetics, clinicians should comprehensively analyze the face and ensure that the sizes selected for the maxillary anterior teeth are compatible with the available anthropological measurements. The inter commissural (ICW), interalar (IAW), intermedial-canthus (MCW), interlateral-canthus (LCW), and interpupillary (IPW) widths are used to determine the width of maxillary central incisors (CW). The aim of this study was to develop an automated approach using machine learning (ML) algorithms to predict central incisor width in a young Turkish population using anthropological measurements. This automation can contribute to digital dentistry and clinical decision-making.

Methods: In the initial phase of this cross-sectional study, several ML regression models-including multiple linear regression (MLR), multi-layer-perceptron (MLP), decision-tree (DT), and random forest (RF) models-were validated to confirm the central width prediction accuracy. Datasets containing only male and female measurements, as well as combined were considered for ML model implementation, and the performance of each model was evaluated for an unbiased population dataset.

Results: Compared with the other algorithms, the RF algorithm showed improved performance for all cases, with an accuracy of 96%, which represents the percentage of correct predictions. The plot reveals the applicability of the RF model in predicting the CW from anthropological measurements irrespective of the candidate's sex.

Conclusions: These results demonstrated the possibility of predicting central incisor widths based on anthropometric measurements using ML models. The accurate central incisor width prediction from these trials also indicates the applicability of the proposed model to be deployed for enhanced clinical decision-making.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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