Resonant frequency analysis of dental implants

Rittel, D.; Dorogoy, A.; Haïat, Guillaume; Shemtov‐Yona, Keren

doi:10.1016/j.medengphy.2019.02.008

Cited by 18 publications

(11 citation statements)

References 37 publications

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“…The better sensitivity of QUS compared to RFA to variations of the BIC can be explained physically. The ISQ is related to the resonance frequency of the bone-implant system, which depends on properties of the entire host bone that vibrates when excited mechanically (24). However, QUS measurements are only sensitive on bone tissue located at a distance lower than around 30 µm from the implant surface (28)(29)(30), which corresponds to the region of interest where osseointegration phenomena are known to occur (31,53).…”

Section: Comparison Between Rfa and Qus Techniquesmentioning

confidence: 99%

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Assessment of dental implant stability using resonance frequency analysis and quantitative ultrasound methods

et al. 2021

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Section: Comparison Between Rfa and Qus Techniquesmentioning

confidence: 99%

“…The correlation between the ISQ and bone implant contact (BIC) is relatively weak and remains a subject of debate (19)(20)(21)(22)(23). Moreover, sensitivity issues of ISQ to changes of periprosthetic bone tissue have been raised, due to the fact that only the first bending mode is considered (24).…”

Section: Introductionmentioning

confidence: 99%

Assessment of dental implant stability using resonance frequency analysis and quantitative ultrasound methods

et al. 2021

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“…In order to more intuitively reflect the fitting ability of the proposed model, a size conforming to the design standards is used for fitting the corresponding S 11 curve. According to the optimization results, a set of antenna parameters whose performance satisfies the design index is [45,18,14,14]. As shown in Fig.5, the X-axis is the frequency sweep range, which is from 1GHz to 5GHz.…”

Section: ) Return Loss Fittingmentioning

confidence: 99%

“…Resonant frequency is an important technical index in the optimal design of antennas. Obtaining resonant frequency quickly by known structural parameters of the antenna is often used in the research of modern antenna design [13]- [14]. Both GP model and SVM model are widely used in antenna resonant frequency modeling [15]- [16].…”

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confidence: 99%

Antenna Optimization Based on Co-Training Algorithm of Gaussian Process and Support Vector Machine

2020

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For the optimal design of electromagnetic components, surrogate model methods can usually be used, but obtaining labeled training samples from full-wave electromagnetic simulation software is most time-consuming. How to use relatively few labeled samples to obtain a relatively high-precision surrogate model is the current electromagnetic research hotspot. This paper proposes a semi-supervised co-training algorithm based on Gaussian process (GP) and support vector machine (SVM). By using a small number of initial training samples, the initial GP model and initial SVM model can be trained by some basic parameter settings. Moreover, the accuracy of these two models can be improved by using the differences between these two models and combining with unlabeled samples for jointly training. In the co-training process, to ensure the performance of the proposed algorithm, a stop criterion set in advance to control the number of unlabeled samples introduced. Therefore, the accuracy of the model can be prevented from being reduced by introducing too much unlabeled samples, which can find the best solution in the limited time. The proposed co-training algorithm is evaluated by benchmark functions, optimal design of Yagi microstrip antenna (MSA) and GPS Beidou dual-mode MSA. The results show that the proposed algorithm fits the benchmark functions well. For the problem of resonant frequency modeling of the above two different MSAs, under the condition of using the same labeled samples, the predictive ability of the proposed algorithm is improved compared with the traditional supervised learning method. Moreover, for the groups of antenna sizes that meet the design requirements, the fitting effects of their return loss curve (S 11) are well. The effectiveness of the proposed co-training algorithm has been well verified, which can be used to replace the time-consuming electromagnetic simulation software for prediction. INDEX TERMS Semi-supervised learning (SSL), Gaussian process (GP), support vector machine (SVM), co-training, antenna optimization.

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“…The direct with an easy implementable normal static load case is not possible because the ISQ value based on RFA (resonance frequency analysis) measurement and it is a dynamic load. Due to this, the ISQ is determinable just indirect way or direct with a modal analysis [15][16][17]. The ISQ value is an integer between 0 and 100, where four main range was determined to show the osseointegration stages.…”

Section: Introductionmentioning

confidence: 99%

Calculating ISQ Primary Stability of a Dental Implant through Micromotion

Pammer

2019

Period. Polytech. Mech. Eng.

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There are several types of primary and secondary stability measuring methods, but there are no calculating methods to determine direct primary stability. The aim of this work is to make a calculation method for primary stability. The out coming result of the calculation should be the same form and unit as available in the clinical and used RFA (Resonance Frequency Analysis) method, especially the ISQ (Implant Stability Quotient). Dental implant analog screws were inserted in bone modelling standard PUR (Polyurethane) solid foam blocks, and the insertion torque and the micromotion was monitored. The ISQ values of the inserted screws were measured also. On the basis of results, the characteristic equation was determined, which showed an excellent correlation (r = 0.96) between the micro mobility and ISQ. To simulate the micro mobility of an inserted screw with FEA (Finite Element Analysis) in any case of the change the bone material properties is not difficult instead of in vitro and in vivo examinations. Using the simulation results and the characteristic equation the clinically used ISQ value could be determinable. Thanks to this simple method, it is easy to monitor virtually the stability change in any lesion of bone structure. As a result of the conducted measurements and simulations, it can be concluded that the ISQ value, which represent the implant primary stability, can be calculated via FEA. With this simulation method, it is possible to predict and monitor pre-clinically the primary stability of dental implants with new geometries.

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Resonant frequency analysis of dental implants

Cited by 18 publications

References 37 publications

Assessment of dental implant stability using resonance frequency analysis and quantitative ultrasound methods

Assessment of dental implant stability using resonance frequency analysis and quantitative ultrasound methods

Antenna Optimization Based on Co-Training Algorithm of Gaussian Process and Support Vector Machine

Calculating ISQ Primary Stability of a Dental Implant through Micromotion

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