Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, MO, USA.
J Dent Res. 2023 Aug;102(9):1022-1030. doi: 10.1177/00220345231175868. Epub 2023 Jul 18.
Dental adhesives provide retention to composite fillings in dental restorations. Microtensile bond strength (µTBS) test is the most used laboratory test to evaluate bonding performance of dental adhesives. The traditional approach for developing dental adhesives involves repetitive laboratory measurements, which consumes enormous time and resources. Machine learning (ML) is a promising tool for accelerating this process. This study aimed to develop ML models to predict the µTBS of dental adhesives using their chemical features and to identify important contributing factors for µTBS. Specifically, the chemical composition and µTBS information of 81 dental adhesives were collected from the manufacturers and the literature. The average µTBS value of each adhesive was labeled as either 0 (if <36 MPa) or 1 (if ≥36 MPa) to denote the low and high µTBS classes. The initial 9-feature data set comprised pH, HEMA, BisGMA, UDMA, MDP, PENTA, filler, fluoride, and organic solvent (OS) as input features. Nine ML algorithms, including logistic regression, k-nearest neighbor, support vector machine, decision trees and tree-based ensembles, and multilayer perceptron, were implemented for model development. Feature importance analysis identified MDP, pH, OS, and HEMA as the top 4 contributing features, which were used to construct a 4-feature data set. Grid search with stratified 10-fold cross-validation (CV) was employed for hyperparameter tunning and model performance evaluation using 2 metrics, the area under the receiver operating characteristic curve (AUC) and accuracy. The 4-feature data set generated slightly better performance than the 9-feature data set, with the highest AUC score of 0.90 and accuracy of 0.81 based on stratified CV. In conclusion, ML is an effective tool for predicting dental adhesives with low and high µTBS values and for identifying important chemical features contributing to the µTBS. The ML-based data-driven approach has great potential to accelerate the discovery of new dental adhesives and other dental materials.
牙科黏合剂为牙修复体中的复合填充物提供固位力。微拉伸黏结强度（µTBS）测试是评估牙科黏合剂黏结性能最常用的实验室测试。传统的开发牙科黏合剂的方法涉及反复的实验室测量，这耗费了大量的时间和资源。机器学习（ML）是加速这一过程的一种很有前途的工具。本研究旨在开发 ML 模型，通过其化学特性来预测牙科黏合剂的 µTBS，并确定对 µTBS 有重要贡献的因素。具体来说，从制造商和文献中收集了 81 种牙科黏合剂的化学组成和 µTBS 信息。将每种黏合剂的平均 µTBS 值标记为 0（如果<36 MPa）或 1（如果≥36 MPa），以表示低 µTBS 和高 µTBS 类别。初始的 9 特征数据集由 pH 值、HEMA、BisGMA、UDMA、MDP、PENTA、填料、氟化物和有机溶剂（OS）作为输入特征组成。为了开发模型，实现了包括逻辑回归、k-最近邻、支持向量机、决策树和基于树的集成以及多层感知器在内的 9 种 ML 算法。特征重要性分析确定 MDP、pH 值、OS 和 HEMA 为前 4 个重要特征，这些特征用于构建一个 4 特征数据集。采用网格搜索和分层 10 折交叉验证（CV）进行超参数调整，并使用两个指标（接收器操作特征曲线下的面积（AUC）和准确性）评估模型性能。4 特征数据集的性能略优于 9 特征数据集，基于分层 CV 的最高 AUC 得分为 0.90，准确性为 0.81。总之，ML 是一种预测具有低和高 µTBS 值的牙科黏合剂和识别对 µTBS 有重要贡献的化学特征的有效工具。基于 ML 的数据驱动方法具有加速发现新的牙科黏合剂和其他牙科材料的巨大潜力。
