Abstract：
This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist\'s control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments\' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.
摘要：
这项工作提供了一种创新的根管治疗（RCT）期间牙髓仪器故障检测方法。有时候,牙髓器械容易从尖端骨折，原因不确定牙医的控制。牙髓医生的全面评估和决策支持系统可以避免几次破损。这项研究提出了一种基于机器学习和人工智能的方法，可以帮助诊断仪器的健康状况。在RCT期间,使用测力计记录力信号。从获得的信号中，提取统计特征。因为少数阶级（即有缺陷/中等阶级）的实例较少，需要对数据集进行过采样，以避免偏差和过拟合。因此,采用合成少数群体过采样技术(SMOTE)来增加少数群体。Further,使用机器学习技术评估性能，即高斯朴素贝叶斯(GNB)，二次支持向量机(QSVM)，精细k最近邻(FKNN)，和合奏袋装树（EBT）。EBT模型相对于GNB提供了出色的性能，QSVM，还有FKNN.机器学习（ML）算法可以通过监测力信号来准确检测牙髓仪器的故障。EBT和FKNN分类器训练得非常好，曲线下面积值为1.0和0.99，预测精度为98.95和97.56%，分别。ML可以潜在地增强临床结果，促进学习，减少进程故障，提高治疗效果，并提高仪器性能，有助于优越的RCT过程。这项工作使用ML方法进行牙髓仪器的故障检测，为从业者提供足够的决策支持系统。
