Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models.

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.

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

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.

Numerical analysis of WaveOne Gold and 2Shape endodontic files during root canal treatment.

This study aims to develop and analyse a finite element model of the endodontic nickel-titanium (NiTi) instrument during the root canal treatment (RCT). The 3D model of the tooth and the endodontic instrument has been created using computer-aided design software. The nonlinear explicit dynamic analysis in the CAE package (ANSYS) has been used to analyse the mechanical behaviour of endodontic instruments such as total deformation, equivalent elastic strain, and equivalent stress during canal preparation. The mechanical behaviour of three commercially available endodontic NiTi alloy instruments such as WaveOne Gold (WOG), 2Shape 1 (TS1) and 2Shape 2 (TS2) endodontic files was evaluated using FEA. Consequently, the effect of deformation, equivalent stress and equivalent elastic strain on endodontic files during cleaning and shaping are investigated and compared. The results show that the total deformation and equivalent elastic strain are maximum in the TS1 endodontic file in comparison to TS2 and WOG files.

Force and vibration analysis in biomechanical preparation of root canals using reciprocating endodontic file system: In vitro study.

The shaping and cleaning of the root canal are very important in root canal treatment. The excessive force and vibration during biomechanical preparation of the root canal may result in failure of the endodontic file. In this study, force and vibration analysis was carried out during root canal preparation. The samples of human extracted (premolar) teeth were provided by the College of Dental Science and Hospital. Endodontic instruments for reciprocating motion, such as the WaveOne Gold file system, had been used for root canal preparation. Force and vibration signals were recorded by dynamometer and accelerometer, respectively. The acquired signals were denoised using the db4 (SWT denoising 1-D) wavelet. Four levels of decomposition were carried out for each signal. The signal denoising technique was used to remove unwanted noise from the acquired signal. FESEM analysis was used to visualize the levels of severity of endodontic files during the cleaning and shaping of the root canal. In most of the cases, the failure occurred due to the improper use of the root canal instrumentation. The optimum amount of force was used to avoid the file failure and provided the proper instrumentation. The curve fitting regression model was used to find the interdependency between force and vibration.