Abstract：
This study proposes an intelligent health prediction and fault prognosis of the endodontic file during the root canal treatment. Root canal treatment is the procedure of disinfecting the infected pulp through the canal with the help of an endodontic instrument. Force signals are acquired with the help of a dynamometer during the canal preparation, and statistical features are extracted. The extracted features are selected through the window-wise feature extraction process. Characteristic features for endodontic file prognostics include time-domain features of the signals are evaluated. The extracted feature has inappropriate information, that is, noise between the signals; hence the smoothing of the feature is required at this stage to observe a trend in the signals. Based on the smoothing feature and post-processing of the feature, defined the health index to calculate the health condition of the endodontic instruments. A machine learning algorithm and exponential degradation model are used to predict the health of the endodontic instrument during the root canal treatment. This model is used to forecast the degradation of the endodontic file so that actions can be taken before actual failures happen. The proposed methodology can analyze the failures and micro-crack initiation of the endodontic instruments. Endodontics practitioners can use the machine learning models as well as an exponential model for estimating the health condition of the endodontic instrument. This study may help the clinician to progress the efficiency of the root canal treatment and the competence of the endodontic instruments.
摘要：
本研究提出了根管治疗过程中牙髓档案的智能健康预测和故障预后。根管治疗是在牙髓器械的帮助下通过根管对感染的牙髓进行消毒的过程。在运河准备过程中，借助测力计获取力信号，并提取统计特征。通过窗口式特征提取过程来选择所提取的特征。牙髓文件预测的特征包括评估信号的时域特征。提取的特征有不适当的信息，也就是说,信号之间的噪声；因此，在此阶段需要对特征进行平滑处理，以观察信号的趋势。基于平滑特征和特征的后处理，定义了健康指数来计算牙髓器械的健康状况。使用机器学习算法和指数退化模型来预测根管治疗过程中牙髓仪的健康状况。该模型用于预测牙髓文件的退化，以便在实际故障发生之前采取行动。所提出的方法可以分析牙髓仪器的故障和微裂纹萌生。牙髓医生可以使用机器学习模型以及指数模型来估计牙髓仪器的健康状况。这项研究可能有助于临床医生提高根管治疗的效率和牙髓器械的能力。
