On comprehensive analysis of root canal shaping ability of three endodontic files of different kinematics

Nayak, Ankit; Jain, Prashant K.; Kankar, Pavan Kumar; Jain, Niharika

doi:10.1177/09544119211014670

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In endodontics, AI and ML may predict periapical diseases, vertical root fracture, apical foramen, root morphologies, retreatment, and endodontic surgeries in the field of dentistry. [12][13][14][15][48][49][50][51][52] The current study includes the diagnosis of endodontic instrument failure. The typical endodontic RCT mechanics makes it laborious and prone to error in fault diagnosis estimation.…”

Section: Discussionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Few authors worked in RCT mechanics, endodontics, and even dentistry. 14,18,40–46 This study implements a novel approach that helps the practitioner to understand the health condition and fault prognosis of the endodontic instrument based on machine learning models. It can assist in preventing endodontic instrument failure while performing the RCT.…”

Section: Discussionmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It is critical for proper treatment of root canal as it affects all following procedures, including chemical disinfection and filling of the root canal. 3,4 Many Nickel-titanium (Ni-Ti) files are introduced in the market with variable shapes. Fanta AF blue S one file (Shanghai Fanta dental materials Co., Ltd. China) is an AF-H heat-treated Ni-TI file with S-shape crosssection from the middle of the file upward, and an almost oval cross-section at the tip.…”

Section: Introductionmentioning

confidence: 99%

Comparing shaping Ability of Fanta, OneCurve, and ProTaper Next Ni-Ti Rotary Files Systems.

Mansour¹,

Yehia²,

Obeid³

2022

Mansoura Journal of Dentistry

View full text Add to dashboard Cite

On comprehensive analysis of root canal shaping ability of three endodontic files of different kinematics

Cited by 6 publications

References 21 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

Comparing shaping Ability of Fanta, OneCurve, and ProTaper Next Ni-Ti Rotary Files Systems.

Contact Info

Product

Resources

About