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

Thakur, Vinod Singh; Kankar, Pavan Kumar; Parey, Anand; Jain, Arpit; Jain, Prashant K.

doi:10.1177/09544119211068950

Cited by 4 publications

(9 citation statements)

References 39 publications

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“…2,62,63 Fractures may also occur more frequently in instruments with a higher taper or greater stiffness than those with a lower taper or greater flexibility. 4,62,64 Instruments of a lower quality are worn out or damaged and more prone to fractures. An excessive amount of force can damage the root canal and increase the chances of perforation, so the optimum amount of force is required for the RCT.…”

Section: Discussionmentioning

confidence: 99%

“…The three variables in the WaveOne Gold file tapered at 7%, 6%, and 3% across the file's length, accordingly. 4 Dentsply Maillerfer, Switzerland, manufactures these endodontic files, which are used in accordance with the manufacturer's guidelines. The RCT experimental rig consists of two sub-systems; the first is for the biomechanical preparation, where the extracted teeth are cleaned, and samples are prepared, as shown in Figure 1.…”

Section: Biomechanical Preparation and Data Acquisitionmentioning

confidence: 99%

“…2,3 Endodontic files or instruments are necessary to remove debris and dentin tissues from the root canal. 4 These instruments are made of Nitinol (NiTi) alloys, commonly known as shape memory alloys, which are superelastic and flexible. [5][6][7] The endodontic instrument tip may fracture during the RCT, often without visible signs of deterioration.…”

Section: Introductionmentioning

confidence: 99%

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

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Biomechanical Preparation and Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

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“…1,6 The RCT is a complex operation that is normally undertaken by dentists or dental professionals. 2 The micro-crack can be initiated during the root canal preparation, and there may be chances of endodontic instrument failure. 7 In that case, the practitioner faces problems associated with the deterioration or degradation of endodontic instruments.…”

Section: Introductionmentioning

confidence: 99%

“…The endodontic file is an essential surgical instrument used for canal preparation during RCT. 1 The endodontic instrument is made up of a superalloy known as nickel-titanium (NiTi), 2 also known as nitinol alloy. 3 The NiTi instrument is more adaptable than typical stainless steel instruments.…”

Section: Introductionmentioning

confidence: 99%

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

Thakur,

Kankar,

Parey

et al. 2023

Proc Inst Mech Eng H

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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.

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A new method to measure the mechanical properties of rotary endodontic Ni-Ti files: Measurement of 6-axis force/torque and automatic root canal preparation

LEE,

LEE

2024

Dent. Mater. J.

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

Cited by 4 publications

References 39 publications

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

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

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

A new method to measure the mechanical properties of rotary endodontic Ni-Ti files: Measurement of 6-axis force/torque and automatic root canal preparation

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