Bone-Implant Osseointegration Monitoring Using Electro-mechanical Impedance Technique and Convolutional Neural Network: A Numerical Study
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Dental Implant Techniques and Outcomes · Orthopedic Infections and Treatments · Orthopaedic implants and arthroplasty
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (78)
Show source-provided references
1. LaMalfa Ribolla, E., Rizzo, P.: Modeling the electromechanical impedance technique for the assessment of dental implant stability. J. Biomech. 48(10), 1713–1720 (2015)10.1016/j.jbiomech.2015.05.020 ↗
2. Adell, R., et al.: A 15-year study of osseointegrated implants in the treatment of the edentulous jaw. Int. J. Oral Surg. 10(6), 387–416 (1981)10.1016/S0300-9785(81)80077-4 ↗
3. Atsumi, M., Park, S.H., Wang, H.L.: Methods used to assess implant stability: current status. Int. J. Oral Maxillofac. Implants 22(5), 743–754 (2007)
4. Lam, A., Koudela, C.L.: Chapter 25—Dental basics. In: Weinzweig, J. (ed.) Plastic surgery secrets plus, 2nd edn., pp. 165–170. Mosby, Philadelphia (2010)10.1016/B978-0-323-03470-8.00025-9 ↗
5. Ribolla, E.L.M., Rizzo, P., Gulizzi, V.: On the use of the electromechanical impedance technique for the assessment of dental implant stability: modeling and experimentation. J. Intell. Mater. Syst. Struct. 26(16), 2266–2280 (2015)10.1177/1045389X14554129 ↗
6. Wang, Y., Zhang, Y., Miron, R.: Health, maintenance, and recovery of soft tissues around implants: soft tissues around implants. Clin. Implant Dent. Rel. Res. 18, 618–634 (2015)10.1111/cid.12343 ↗
7. Kittur, N., et al.: Dental implant stability and its measurements to improve osseointegration at the bone-implant interface: a review. Mater. Today 43, 1064–1070 (2021)
8. Blanes, R.J., et al.: A 10-year prospective study of ITI dental implants placed in the posterior region. I: Clinical and radiographic results. Clin. Oral Implants Res. 18(6), 699–706 (2007)10.1111/j.1600-0501.2006.01306.x ↗
9. Reuben, R.L.: 13—Acoustic emission and ultrasound for monitoring the bone-implant interface. In: Piattelli, A. (ed.) Bone Response to Dental Implant Materials, pp. 247–259. Woodhead Publishing, Duxford (2017)10.1016/B978-0-08-100287-2.00013-6 ↗
10. Ossi, Z., et al.: In vitro assessment of bone-implant interface using an acoustic emission transmission test. Proc. Inst. Mech. Eng. H 226(1), 63–69 (2012)10.1177/0954411911428696 ↗
11. Geckili, O., et al.: Comparative ex vivo evaluation of two electronic percussive testing devices measuring the stability of dental implants. J. Periodontol. 85(12), 1786–1791 (2014)10.1902/jop.2014.140206 ↗
12. Pattijn, V., et al.: The resonance frequencies and mode shapes of dental implants: rigid body behaviour versus bending behavior. A numerical approach. J. Biomech. 39(5), 939–947 (2006)10.1016/j.jbiomech.2005.01.035 ↗
13. Peres, I., Rolo, P., Soares dos Santos, M.P.: Multifunctional smart bone implants: fiction or future?—A new perspective. Front. Bioeng. Biotechnol. (2022). https://doi.org/10.3389/fbioe.2022.91208110.3389/fbioe.2022.912081 ↗
14. Jiao, P., et al.: Piezoelectric sensing techniques in structural health monitoring: a state-of-the-art review. Sensors 20(13), 3730 (2020)10.3390/s20133730 ↗
15. Le, T.-C., et al.: Piezoelectric impedance-based structural health monitoring of wind turbine structures: current status and future perspectives. Energies 15(15), 5459 (2022)10.3390/en15155459 ↗
16. Zhu, J., Wang, Y., Qing, X.: A real-time electromechanical impedance-based active monitoring for composite patch bonded repair structure. Compos. Struct. 212, 513–523 (2019)10.1016/j.compstruct.2019.01.035 ↗
17. Li, H., et al.: Integrated electromechanical impedance technique with convolutional neural network for concrete structural damage quantification under varied temperatures. Mech. Syst. Signal Process. 152, 107467 (2021)10.1016/j.ymssp.2020.107467 ↗
18. Bahl, S., et al.: Smart materials types, properties and applications: a review. Mater. Today 28, 1302–1306 (2020)
19. Sony, S., Laventure, S., Sadhu, A.: A literature review of next-generation smart sensing technology in structural health monitoring. Struct. Control. Health Monit. 26(3), e2321 (2019)10.1002/stc.2321 ↗
20. Boemio, G., Rizzo, P., Nardo, L.D.: Assessment of dental implant stability by means of the electromechanical impedance method. Smart Mater. Struct. 20(4), 045008 (2011)10.1088/0964-1726/20/4/045008 ↗
21. Park, G., Inman, D.J.: Structural health monitoring using piezoelectric impedance measurements. Philos Trans A 2007(365), 373–392 (1851)
22. Min, J., Park, S., Yun, C.-B.: Impedance-based structural health monitoring using neural networks for autonomous frequency range selection. Smart Mater. Struct. 19(12), 125011 (2010)10.1088/0964-1726/19/12/125011 ↗
23. Qiu, H., Li, F.: Bolt looseness monitoring based on damping measurement by using a quantitative electro-mechanical impedance method. Smart Mater. Struct. 31(9), 095022 (2022)10.1088/1361-665X/ac80e1 ↗
24. Huynh, T.-C., Kim, J.-T.: Compensation of temperature effect on impedance responses of PZT interface for prestress-loss monitoring in PSC girders. Smart Struct. Syst. 17(6), 881–901 (2016)10.12989/sss.2016.17.6.881 ↗
25. Abdeljaber, O., et al.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275, 1308–1317 (2018)10.1016/j.neucom.2017.09.069 ↗
26. Abdeljaber, O., et al.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)10.1016/j.jsv.2016.10.043 ↗
27. Cha, Y.-J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civil Infrastruct. Eng. 32(5), 361–378 (2017)10.1111/mice.12263 ↗
28. Azimi, M., Pekcan, G.: Structural health monitoring using extremely compressed data through deep learning. Comput.-Aided Civil and Infrastruct. Eng. 35(6), 597–614 (2020)10.1111/mice.12517 ↗
29. Huynh, T.-C.: Vision-based autonomous bolt-looseness detection method for splice connections: design, lab-scale evaluation, and field application. Autom. Constr. 124, 103591 (2021)10.1016/j.autcon.2021.103591 ↗
30. Ince, T., et al.: Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Industr. Electron. 63(11), 7067–7075 (2016)10.1109/TIE.2016.2582729 ↗
31. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2016)10.1109/TBME.2015.2468589 ↗
32. Acharya, U.R., et al.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415–416, 190–198 (2017)10.1016/j.ins.2017.06.027 ↗
33. Nguyen, T.-T., et al.: Deep learning-based autonomous damage-sensitive feature extraction for impedance-based prestress monitoring. Eng. Struct. 259, 114172 (2022)10.1016/j.engstruct.2022.114172 ↗
34. Kiranyaz, S., et al.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)10.1016/j.ymssp.2020.107398 ↗
35. Le, B.-T., et al.: Fault assessment in piezoelectric-based smart strand using 1D convolutional neural network. Buildings 12(11), 1916 (2022)10.3390/buildings12111916 ↗
36. Yan, Q., et al.: Intelligent monitoring and assessment on early-age hydration and setting of cement mortar through an EMI-integrated neural network. Measurement 203, 111984 (2022)10.1016/j.measurement.2022.111984 ↗
37. Nguyen, T.-T., et al.: A method for automated bolt-loosening monitoring and assessment using impedance technique and deep learning. Dev. Built Environ. 14, 100122 (2023)10.1016/j.dibe.2023.100122 ↗
38. Pham, Q.-Q., et al.: Raspberry Pi platform wireless sensor node for low-frequency impedance responses of PZT interface. Sensors 22(24), 9592 (2022)10.3390/s22249592 ↗
39. Park, S., Shin, H.-H., Yun, C.-B.: Wireless impedance sensor nodes for functions of structural damage identification and sensor self-diagnosis. Smart Mater. Struct. 18(5), 055001 (2009)10.1088/0964-1726/18/5/055001 ↗
40. Min, J., et al.: Development of a low-cost multifunctional wireless impedance sensor node. Smart Struct. Syst. 6, 689–709 (2010)10.12989/sss.2010.6.5_6.689 ↗
41. Conceição, C., Completo, A., Soares dos Santos, M.P.: Ultrasensitive capacitive sensing system for smart medical devices with ability to monitor fracture healing stages. J. R. Soc. Interface. 20(199), 20220818 (2023)10.1098/rsif.2022.0818 ↗
42. Liang, C., Sun, F.P., Rogers, C.A.: Coupled electro-mechanical analysis of adaptive material systems—determination of the actuator power consumption and system energy transfer. J. Intell. Mater. Syst. Struct. 5(1), 12–20 (1994)10.1177/1045389X9400500102 ↗
43. Caliendo, C.: Acoustic wave conductometric sensors. In: Narayan, R. (ed.) Encyclopedia of Sensors and Biosensors, 1st edn., pp. 591–616. Elsevier, Oxford (2023)10.1016/B978-0-12-822548-6.00107-2 ↗
44. Xu, Y., Liu, G.: A modified electro-mechanical impedance model of piezoelectric actuator-sensors for debonding detection of composite patches. J. Intell. Mater. Syst. Struct. 13(6), 389–396 (2002)10.1177/104538902761696733 ↗
45. Nguyen, T.-T., et al.: Analytical impedance model for piezoelectric-based smart Strand and its feasibility for prestress force prediction. Struct. Control. Health Monit. (2022). https://doi.org/10.1002/stc.306110.1002/stc.3061 ↗
46. Nguyen, T.-T., Ho, D.-D., Huynh, T.-C.: Electromechanical impedance-based prestress force prediction method using resonant frequency shifts and finite element modelling. Dev. Built Environ. 12, 100089 (2022)10.1016/j.dibe.2022.100089 ↗
47. Park, G., et al.: Overview of piezoelectric impedance-based health monitoring and path forward. Shock Vib. Digest 35(6), 451–464 (2003)10.1177/05831024030356001 ↗
48. Kim, J.-T., et al.: Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches. Eng. Struct. 32(1), 115–128 (2010)10.1016/j.engstruct.2009.08.021 ↗
49. Shao, J., et al.: Bolt looseness detection based on piezoelectric impedance frequency shift. Appl. Sci. 6(10), 298 (2016)10.3390/app6100298 ↗
50. Wang, B., et al.: Impedance-based pre-stress monitoring of rock bolts using a piezoceramic-based smart washer—a feasibility study. Sensors (Basel, Switzerland) 17(2), 250 (2017)10.3390/s17020250 ↗
51. Wang, L., et al.: Synchronous detection of bolts looseness position and degree based on fusing electro-mechanical impedance. Mech. Syst. Signal Process. 174, 109068 (2022)10.1016/j.ymssp.2022.109068 ↗
52. Wang, F., et al.: A novel fractal contact-electromechanical impedance model for quantitative monitoring of bolted joint looseness. IEEE Access 6, 40212–40220 (2018)10.1109/ACCESS.2018.2855693 ↗
53. Nguyen, T.-T., et al.: Deep learning-based functional assessment of piezoelectric-based smart interface under various degradations. Smart Struct. Syst. 28(1), 69–87 (2021)
54. Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification (2015)
55. Gu, J., et al.: Recent advances in convolutional neural networks. arXiv preprint arXiv:1512.07108 (2015).
56. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)10.1038/nature14539 ↗
57. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)10.1162/neco.1989.1.4.541 ↗
58. Geng, J.-P., Tan, K.B.C., Liu, G.-R.: Application of finite element analysis in implant dentistry: a review of the literature. J. Prosthet. Dent. 85(6), 585–598 (2001)10.1067/mpr.2001.115251 ↗
59. Nguyen, T.-H., et al.: Numerical simulation of single-point mount PZT-interface for admittance-based anchor force monitoring. Buildings 11(11), 550 (2021)10.3390/buildings11110550 ↗
60. Huynh, T.-C., Dang, N.-L., Kim, J.-T.: Preload monitoring in bolted connection using piezoelectric-based smart interface. Sensors 18(9), 2766 (2018)10.3390/s18092766 ↗
61. Kitts, D.J., Zagrai, A.N.: Finite element modeling and effect of electrical/mechanical parameters on electromechanical impedance damage detection. In: ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems (2009).10.1115/SMASIS2009-1296 ↗
62. Tashakori, S., et al.: Evaluating the performance of the SuRE method for inspection of bonding using the COMSOL finite element analysis package. In: Health Monitoring of Structural and Biological Systems XIII. SPIE (2019).10.1117/12.2517425 ↗
63. Rugina, C., Enciu, D., Tudose, M.: Numerical and experimental study of circular disc electromechanical impedance spectroscopy signature changes due to structural damage and sensor degradation. Struct. Health Monit. 14(6), 663–681 (2015)10.1177/1475921715610927 ↗
64. Lobur, M., Vivchar, D., Jaworski, N.: MEMS pressure sensors design by the COMSOL system. In: 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) (2016)10.1109/TCSET.2016.7451997 ↗
65. Massimino, G., et al.: Air-coupled array of Pmuts at 100 kHz with PZT active layer: multiphysics model and experiments. In: 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) (2019).10.1109/EuroSimE.2019.8724514 ↗
66. Demkowicz, L.: A note on symmetry boundary conditions in finite element methods. Appl. Math. Lett. 4(5), 27–30 (1991)10.1016/0893-9659(91)90138-L ↗
67. Chen, L.-J., et al.: Finite element analysis of stress at implant–bone interface of dental implants with different structures. Trans. Nonferr. Met. Soc. China 21(7), 1602–1610 (2011)10.1016/S1003-6326(11)60903-5 ↗
68. Beaupré, G., Orr, T., Carter, D.: An approach for time-dependent bone modeling and remodeling—theoretical development. J. Orthop. Res. 8(5), 651–661 (1990)10.1002/jor.1100080506 ↗
69. Hambli, R.: Numerical procedure for multiscale bone adaptation prediction based on neural networks and finite element simulation. Finite Elem. Anal. Des. 47(7), 835–842 (2011)10.1016/j.finel.2011.02.014 ↗
70. Giurgiutiu, V., Zagrai, A., Jing Bao, J.: Piezoelectric wafer embedded active sensors for aging aircraft structural health monitoring. Struct. Health Monitor. 1(1), 41–61 (2002)10.1177/147592170200100104 ↗
71. Wandowski, T., et al.: Methods for assessment of composite aerospace structures. In: Araujo, A.L. Mota Soares, C.A. (eds) Smart Structures and Materials: Selected Papers from the 7th ECCOMAS Thematic Conference on Smart Structures and Materials, pp. 227–244. Springer, Cham (2017)10.1007/978-3-319-44507-6_12 ↗
72. Baptista, F.G., et al.: An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring. Sensors 14(1), 1208–1227 (2014)10.3390/s140101208 ↗
73. Pham, H.C., et al.: Bolt-loosening monitoring framework using an image-based deep learning and graphical model. Sensors 20(12), 3382 (2020)10.3390/s20123382 ↗
74. Yuan, F.-G., et al.: Machine learning for structural health monitoring: challenges and opportunities. In: SPIE Smart Structures + Nondestructive Evaluation, vol. 11379. SPIE (2020)10.1117/12.2561610 ↗
75. Melville, J., et al.: Structural damage detection using deep learning of ultrasonic guided waves, vol. 1949 (2017)10.1063/1.5031651 ↗
76. Spencer, B.F., Jr., Hoskere, V., Narazaki, Y.: Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering 5(2), 199–222 (2019)10.1016/j.eng.2018.11.030 ↗
77. Wang, R., Sui, J., Wang, X.: Natural piezoelectric biomaterials: a biocompatible and sustainable building block for biomedical devices. ACS Nano 16(11), 17708–17728 (2022)10.1021/acsnano.2c08164 ↗
78. Vidal, J.V., et al.: Dual vibration and magnetic energy harvesting with bidomain LiNbO3-based composite. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(6), 1219–1229 (2020)10.1109/TUFFC.2020.2967842 ↗