Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA.
Indiana University School of Dentistry, Indianapolis, Indiana, USA.
Orthod Craniofac Res. 2023 Nov;26(4):552-559. doi: 10.1111/ocr.12641. Epub 2023 Mar 9.
To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population.
The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns.
The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%.
All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.
探究机器学习（ML）在预测异质人群中正畸拔牙模式的准确性。
本回顾性研究的资料来源于 366 例接受正畸拔牙治疗的患者的记录。数据集按种族/民族和性别分层，随机分为训练集（70%）和测试集（30%）。使用 55 项头影测量和人口统计学输入数据来训练和测试多种 ML 算法。拔牙模式根据之前的治疗计划进行标记。随机森林（RF）、逻辑回归（LR）和支持向量机（SVM）算法用于预测患者的拔牙模式。
U/L4s（RF：81.63%，LR：63.27%，SVM：63.27%）和 U4s 单独拔牙模式（RF：61.11%，LR：72.22%，SVM：72.22%）的分类准确率最高。然而，所有方法对 U4/L5s、U5/L4s 和 U/L5s 拔牙模式的分类准确率均较低（<50%）。对于总体准确率，RF 最高，为 54.55%，其次是 SVM 为 52.73%，LR 为 49.09%。
所有测试的监督 ML 技术在预测 U/L4s 和 U4s 拔牙模式方面都具有良好的准确性。然而，它们对 U4/L5s、U5/L4s 和 U/L5s 拔牙模式的预测效果较差。磨牙关系、下颌拥挤度和覆颌是确定拔牙模式的最具预测性指标。
