Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment

IF 2.4 3区医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE

Orthodontics & Craniofacial Research Pub Date : 2024-05-20 DOI:10.1111/ocr.12811

Begüm Köktürk, Hande Pamukçu, Ömer Gözüaçık

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引用次数: 0

Abstract

Introduction

The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision.

Methods

This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models.

Results

The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance.

Conclusion

The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.

Abstract Image

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评估用于正畸治疗中拔牙决策的不同机器学习算法。

简介拔牙决定对治疗过程和结果有重大影响。因此，采用更客观、更标准化的方法做出这一决定至关重要。本研究的目的是：（1）从七个机器学习（ML）模型中找出表现最佳的模型，从而规范拔牙决策，为缺乏经验的临床医生提供指导；（2）确定拔牙决策的重要变量：这项研究包括 1000 名接受拔牙或不拔牙正畸治疗的患者（500 名拔牙，500 名不拔牙）。研究的成功标准是四位经验丰富的正畸医生做出的决定。使用 36 个变量训练了 7 个 ML 模型，包括人口统计学信息、头颅测量和模型测量。首先进行拔牙决策，然后确定拔牙类型。准确率和接收者操作特征曲线（ROC）的曲线下面积（AUC）用于衡量 ML 模型的成功率：由梯度提升树、支持向量机和随机森林模型组成的堆叠分类器模型在提取决策方面表现最佳，AUC 为 91.2%。决定拔牙决定的最重要特征是上下颌牙弓长度差异、Wits评估和ANS-Me长度。同样，堆叠分类器在决定拔牙类型方面表现最佳，准确率为 76.3%。对拔牙类型决定最重要的变量是下颌牙弓长度差异、I类臼齿关系、头廓过度咬合、Wits Appraisal和L1-NB距离：堆积分类器模型在拔牙决策中表现最佳。虽然 ML 模型在拔牙决策中表现出较高的性能，但在拔牙类型决策中却无法达到相同的性能水平。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Orthodontics & Craniofacial Research 医学-牙科与口腔外科

CiteScore

5.30

自引率

3.20%

发文量

审稿时长

>12 weeks

期刊介绍： Orthodontics & Craniofacial Research - Genes, Growth and Development is published to serve its readers as an international forum for the presentation and critical discussion of issues pertinent to the advancement of the specialty of orthodontics and the evidence-based knowledge of craniofacial growth and development. This forum is based on scientifically supported information, but also includes minority and conflicting opinions. The objective of the journal is to facilitate effective communication between the research community and practicing clinicians. Original papers of high scientific quality that report the findings of clinical trials, clinical epidemiology, and novel therapeutic or diagnostic approaches are appropriate submissions. Similarly, we welcome papers in genetics, developmental biology, syndromology, surgery, speech and hearing, and other biomedical disciplines related to clinical orthodontics and normal and abnormal craniofacial growth and development. In addition to original and basic research, the journal publishes concise reviews, case reports of substantial value, invited essays, letters, and announcements. The journal is published quarterly. The review of submitted papers will be coordinated by the editor and members of the editorial board. It is policy to review manuscripts within 3 to 4 weeks of receipt and to publish within 3 to 6 months of acceptance.