Evaluation of High-Speed Handpiece Cutting Efficiency According to Bur Eccentricity: An In Vitro Study

Kim, Duk-Yeon; Lee, Kyu-Bok; Lee, Kyu-bok

doi:10.3390/app9163395

Cited by 2 publications

(3 citation statements)

References 18 publications

(20 reference statements)

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“…The learning effect (reduction in working time) according to repeated learning with the intraoral scanner has been confirmed in previous studies [ 26 , 27 , 28 ]. Similarly, in the present study, a significant decrease in the working time was observed with repetition of the intraoral scanning task ( p < 0.001).…”

Section: Discussionsupporting

confidence: 76%

“…In the present study, high muscle activation was observed in the shoulder muscle (T) during the intraoral scanning task and in the two arm muscles (EDC and FDS) and in the shoulder muscle (T) in the tooth preparation task ( Table 1 ). A previous study reported that a force of 0.9 N or more is applied to the teeth during tooth preparation for a desired shape [ 28 ]. Therefore, it can be inferred that the high activation of the arm muscles (EDC and FDS) during the tooth preparation task in the present study was because of gripping the dental ultra-fast handpiece and pressing it against the teeth ( Figure 3 ).…”

Section: Discussionmentioning

confidence: 99%

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How Does the Use of an Intraoral Scanner Affect Muscle Fatigue? A Preliminary In Vivo Study

Lee

Son

et al. 2022

Bioengineering

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The purpose of this study was to evaluate muscle activation and fatigue in the operator during tooth preparation and intraoral scanning by simulating these tasks in two types of dental unit chair systems (UCS). Six participants were recruited, and the above tasks were simulated. Electrodes were placed on the skin over five types of muscles (arm, neck, and shoulder muscles), and the maximal voluntary contraction (MVC) was measured. Electromyography (EMG) was assessed during the simulation, and EMG values were normalized using MVC. The root mean square (RMS) EMG (%MVC) and muscle fatigue (%) were calculated. Owing to a lack of normal distribution of the data, Mann–Whitney U test and Kruskal–Wallis H test were performed for statistical comparison, and Bonferroni adjustment was performed for multiple comparisons (α = 0.05). There was no significant difference in RMS EMG between the two types of dental UCS (intraoral scanning, p = 0.237; tooth preparation, p = 0.543). Moreover, the RMS EMG and muscle fatigue were not significantly different between the two tasks (p > 0.05). There was significant muscle fatigue after the intraoral scanner use was simulated thrice (p < 0.001). It is necessary to refrain from performing continuous intraoral scanning and tooth preparation and to take appropriate rest to reduce the incidence of musculoskeletal disorders in dentists in clinical settings.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

How Does the Use of an Intraoral Scanner Affect Muscle Fatigue? A Preliminary In Vivo Study

Lee

Son

et al. 2022

Bioengineering

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“…The mean clinical force applied to the DATH with a bur in the chuck was reported as 99.3 g, substantiating the range 50–150-g range [ 2 ]. A high-speed handpiece with cutting efficiency according to the bur eccentricity with a constant weight load of 0.9 N for 15 s was also studied in [ 3 ]. An experimental custom-made device was used to hold the high-speed handpiece and specimen in their cutting test.…”

Section: Introductionmentioning

confidence: 99%

Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network

Huang,

Chen

2024

Bioengineering

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The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time sequence, transmitting memory, learning, storing, and fast convergence for monitoring the health and diagnosing the rotor and collet failure of DATHs. A handpiece mimicking a dentist’s hand load of 100 g was employed to repeatedly mill a glass porcelain block back and forth for cutting. An accelerometer was employed to capture vibration signals during free-running of unrestrained operation of the handpiece, aiming to discern the characteristic features of these vibrations. These data were then utilized to create a diagnostic health classification (DHC) for further developing a TCN, a 1D convolutional neural network (CNN), and long short-term memory (LSTM) prediction models. The three frameworks were used and compared for machine learning to establish DHC prediction models for the DATH. The experimental results indicate that, in terms of DHC predicted for the experimental dataset, the square categorical cross-entropy loss function error of the TCN framework was generally lower than that of the 1D CNN, which did not have a memory framework or the drawback of the vanishing gradient problem. In addition, the TCN framework outperformed the LSTM model, which required a longer history to provide sufficient diagnostic ability. Still, high accuracies were achieved both in the direction of feed-drive milling and in the gravity of the handpiece through vibration signals. In general, the failure classification prediction model could accurately predict the health and failure mode of the dental handpiece before the use of the DATH when an embedded sensor was available. Therefore, this model could prove to be a beneficial tool for predicting the deterioration patterns of real dental handpieces in their remaining useful life.

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Evaluation of High-Speed Handpiece Cutting Efficiency According to Bur Eccentricity: An In Vitro Study

Cited by 2 publications

References 18 publications

How Does the Use of an Intraoral Scanner Affect Muscle Fatigue? A Preliminary In Vivo Study

How Does the Use of an Intraoral Scanner Affect Muscle Fatigue? A Preliminary In Vivo Study

Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network

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