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Engineering  2013 

Using Ultrasonic Spectrometry to Estimate the Stability of a Dental Implant Phantom

DOI: 10.4236/eng.2013.510B117, PP. 570-574

Keywords: Dental Implant Stability, Partial Least Squares, Ultrasonic Spectrometry, Spectral Analysis

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Abstract:

A challenging problem in dental implant surgery is to evaluate the stability of the implant. In this simulation study, an experimental phantom is used to represent a jawbone with a dental implant. It is made of a little pool filled with soft-tissue equivalent material and a disc of fresh Oakwood with a metal screw. Varying levels of contact between screw and wood are simulated by screwing in or out the screw. Initially, the screw is screwed in and fixed firmly in wood. Thereafter, the screw is screwed out, a half turn each time, to increase the gap gradually between wood and screw. Pulse-echo ultrasound is used and the power spectra of the received echo-signals are computed. These spectra are normalized then analyzed by using the partial least squares method to estimate the corresponding implant stiffness grade in terms of number of turns when beginning from the initial tight-screw state then screwing out the screw. A coefficient of determination R2 of 96.4% and a mean absolute error of ±0.23 turns are achieved when comparing real and estimated values of stiffness grades, indicating the efficiency of this approach.

References

[1]  P. I. Branemark, U. Breine, R. Adell, B. O. Hansson, J. Lindstrom and A. Ohlsson, “Intra-Osseous Anchorage of Dental Prostheses: I. Experimental Studies,” Journal of Plastic and Reconstructive Surgery and Hand Surgery, Informa Healthcare, Vol. 3, No. 2, 1969, pp. 81-100.
[2]  D. W. Fitting and L. Adler, “Ultrasonic Spectral Analysis for Nondestructive Evaluation,” Plenum Press, New York, 1981. http://dx.doi.org/10.1007/978-1-4613-3126-1
[3]  S. F. Lin, L. C. Pan, S. Y. Lee, Y. H. Peng and T. C. Hsiao, “Resonance Frequency Analysis for Osseointegration in Four Surgical Conditions of Dental Implants,” Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 3, 2001, pp. 2998?-3001.
[4]  R. L. C. Pan and S. H. Ying, “Mechanical Properties of Bone-Implant Interface: An in Vitro Model for the Comparison of Stability Parameters Affecting Various Stages during Osseointegration for Dental Implant,” IEMBS’04 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, 2005, pp. 5050-5052.
[5]  P. Valderrama, T. W. Oates, A. A. Jones, J. Simpson, J. D. Schoolfield and D. L. Cochran, “Evaluation of Two Different Resonance Frequency Devices to Detect Implant Stability: A Clinical Trial,” Journal of Periodontology, American Academy of Periodontology, Vol. 78, No. 2, 2007, pp. 262-272. http://dx.doi.org/10.1902/jop.2007.060143
[6]  V. Pattijn, S. V. N. Jaecques, E. De Smet, L. Muraru, C. Van Lierde, G. Van der Perre, I. Naert and J. V. Sloten, “Resonance Frequency Analysis of Implants in the Guinea Pig Model: Influence of Boundary Conditions and Orientation of the Transducer,” Medical Engineering & Physics, Vol. 29, No. 2, 2007, pp. 182-190. http://dx.doi.org/10.1016/j.medengphy.2006.02.010
[7]  M. S. De Almeida, C. D. Maciel and J. C. Pereira, “Proposal for an Ultrasonic Tool to Monitor the Osseointegration of Dental Implants,” Sensors, Molecular Diversity Preservation International, Vol. 7, No. 7, 2007, pp. 1224- 1237.
[8]  V. Mathieu, F. Anagnostou, E. Soffer and G. Haiat, “Ultrasonic Evaluation of Dental Implant Biomechanical Stability: An in Vitro Study,” Ultrasound in Medicine & Biology, Vol. 37, No. 2, 2011, pp. 262-270. http://dx.doi.org/10.1016/j.ultrasmedbio.2010.10.008
[9]  A. Walker, “The Encyclopedia of Wood,” Quatro Publishing, London 2005, p. 192.
[10]  A. Tampieri, S. Sprio, A. Rufini, I. G. Lesci and N. Roveri, “From Wood to Bone: Multi-Step Process to Convert Hierarchical Structures into Biomimetic Hydroxyapatite Scaffolds for Bone Tissue Engineering,” Journal of Materials Chemistry, Vol. 19, No. 28, 2009, pp. 4973- 4980. http://dx.doi.org/10.1039/b900333a
[11]  E. L. Madsen, J. A. Zagzebski, R. A. Banjavie and R. E. Jutila, “Tissue Mimicking Materials for Ultrasound Phantoms,” Medical Physics, Vol. 5, 1978, p. 391. http://dx.doi.org/10.1118/1.594483
[12]  R. Rosipal and N. Kramer, “Overview and Recent Advances in Partial Least Squares,” Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005; Revised Selected Papers, Springer-Verlag Inc., New York, 2006, pp. 34-51. http://dx.doi.org/10.1007/11752790_2
[13]  H. Wold, “Nonli-near Estimation by Iterative Least Squares Procedures,” In: F. N. David, Ed., Festschrift for J. Neyman, Wiley, New York, 1966, p. 411.
[14]  H. Wold, “Path Models with Latent Variables: The NIPALS Approach,” Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling, 1975, pp. 307-357.
[15]  Y. C. Eldar and A. V. Oppenheim, “MMSE Whitening and Subspace Whitening,” IEEE Transactions on Information Theory, Vol. 49, No. 7, 2003, pp. 1846-1851. http://dx.doi.org/10.1109/TIT.2003.813507
[16]  H. Hamid Muhammed, “Hyperspectral Crop Reflectance Data for Characterising and Estimating Fungal Disease Severity in Wheat,” Biosystems Engineering, Vol. 91, No. 1, 2005, pp. 9-20. http://dx.doi.org/10.1016/j.biosystemseng.2005.02.007
[17]  H. Abdi, “Partial Least Square Regression,” Encyclopedia for Research Methods for the Social Sciences, 2003.
[18]  M. Rhiel, M. B. Cohen, D. W. Murhammer and M. A. Arnold, “Nonde-structive Near-Infrared Spectroscopic Measurement of Multiple Analytes in Undiluted Samples of Serum-Based Cell Culture Media,” University Of Iowa, 2001.
[19]  R. K. Schenk and D. Buser, “Osseointegration: A Reality,” Periodontology, Vol. 17, No. 1, 1998, pp. 22-35.

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