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Bone-Implant Osseointegration Monitoring Using Electro-mechanical Impedance Technique and Convolutional Neural Network: A Numerical Study

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Abstract

Accurate quantification of the jawbone-implant interface plays a pivotal role in assessing the mechanical stability of dental implant structures. This study proposes a methodology integrating the electro-mechanical impedance (EMI)-based technique with a deep learning algorithm to autonomously monitor the bone-implant interface during the osseointegration process. We develop a 1D convolutional neural network (1D CNN) model, which automatically processes raw EMI data and extracts optimal features for predicting osseointegration ratios. To validate our approach, we conduct predictive 3D numerical modelling of the PZT-implant-bone system. This model simulates the implant’s EMI response under varying degrees of osseointegration. Next, we employ traditional statistical metrics to monitor osseointegration and discuss their limitations. Finally, we apply the proposed 1D CNN model to predict bone-implant osseointegration rate. We train and test the network using the simulated EMI data with added noise to account for real-world conditions. The results show that the trained model achieves a minimal testing error of just 2.4%. Even when 60% of testing cases are not trained, the model maintains a prediction accuracy exceeding 94%.

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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)

    PubMed  Google Scholar 

  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)

    CAS  PubMed  Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    Google Scholar 

  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)

    CAS  Google Scholar 

  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)

    Google Scholar 

  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)

    CAS  Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    CAS  PubMed  Google Scholar 

  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.2022.912081

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jiao, P., et al.: Piezoelectric sensing techniques in structural health monitoring: a state-of-the-art review. Sensors 20(13), 3730 (2020)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  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)

    CAS  Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  Google Scholar 

  18. Bahl, S., et al.: Smart materials types, properties and applications: a review. Mater. Today 28, 1302–1306 (2020)

    CAS  Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  Google Scholar 

  21. Park, G., Inman, D.J.: Structural health monitoring using piezoelectric impedance measurements. Philos Trans A 2007(365), 373–392 (1851)

    Google Scholar 

  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)

    ADS  Google Scholar 

  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)

    ADS  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    Google Scholar 

  33. Nguyen, T.-T., et al.: Deep learning-based autonomous damage-sensitive feature extraction for impedance-based prestress monitoring. Eng. Struct. 259, 114172 (2022)

    Google Scholar 

  34. Kiranyaz, S., et al.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)

    Google Scholar 

  35. Le, B.-T., et al.: Fault assessment in piezoelectric-based smart strand using 1D convolutional neural network. Buildings 12(11), 1916 (2022)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  PubMed  PubMed Central  Google Scholar 

  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)

    ADS  Google Scholar 

  40. Min, J., et al.: Development of a low-cost multifunctional wireless impedance sensor node. Smart Struct. Syst. 6, 689–709 (2010)

    Google Scholar 

  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)

    PubMed Central  Google Scholar 

  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)

    Google Scholar 

  43. Caliendo, C.: Acoustic wave conductometric sensors. In: Narayan, R. (ed.) Encyclopedia of Sensors and Biosensors, 1st edn., pp. 591–616. Elsevier, Oxford (2023)

    Google Scholar 

  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)

    Google Scholar 

  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.3061

    Article  Google Scholar 

  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)

    Google Scholar 

  47. Park, G., et al.: Overview of piezoelectric impedance-based health monitoring and path forward. Shock Vib. Digest 35(6), 451–464 (2003)

    Google Scholar 

  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)

    Google Scholar 

  49. Shao, J., et al.: Bolt looseness detection based on piezoelectric impedance frequency shift. Appl. Sci. 6(10), 298 (2016)

    Google Scholar 

  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)

    ADS  MathSciNet  PubMed  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  CAS  PubMed  Google Scholar 

  57. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Google Scholar 

  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)

    CAS  PubMed  Google Scholar 

  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)

    Google Scholar 

  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)

    ADS  PubMed  PubMed Central  Google Scholar 

  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).

  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).

  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)

    Google Scholar 

  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)

  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).

  66. Demkowicz, L.: A note on symmetry boundary conditions in finite element methods. Appl. Math. Lett. 4(5), 27–30 (1991)

    MathSciNet  Google Scholar 

  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)

    CAS  Google Scholar 

  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)

    PubMed  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

  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)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  73. Pham, H.C., et al.: Bolt-loosening monitoring framework using an image-based deep learning and graphical model. Sensors 20(12), 3382 (2020)

    ADS  PubMed  PubMed Central  Google Scholar 

  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)

  75. Melville, J., et al.: Structural damage detection using deep learning of ultrasonic guided waves, vol. 1949 (2017)

  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)

    Google Scholar 

  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)

    CAS  PubMed  PubMed Central  Google Scholar 

  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)

    PubMed  Google Scholar 

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Acknowledgements

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, 700000, Vietnam

    Tran-De-Nhat Truong, Manh-Hung Tran, Chi-Khai Nguyen & Duc-Duy Ho

  2. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc City, Ho Chi Minh City, 700000, Vietnam

    Tran-De-Nhat Truong, Thanh-Truong Nguyen, Manh-Hung Tran, Chi-Khai Nguyen & Duc-Duy Ho

  3. Water Resources Research and Development Centre, Ministry of Energy, Water Resources and Irrigation, Government of Nepal, Pulchowk, Lalitpur, Nepal

    Ananta Man Singh Pradhan

  4. Industrial Maintenance Training Center, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, 700000, Vietnam

    Thanh-Truong Nguyen

  5. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Thanh-Canh Huynh

  6. Faculty of Civil Engineering, Duy Tan University, Danang, 550000, Vietnam

    Thanh-Canh Huynh

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  1. Tran-De-Nhat Truong
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  2. Ananta Man Singh Pradhan
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  3. Thanh-Truong Nguyen
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  7. Thanh-Canh Huynh
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T-CH and D-DH proposed the idea and conducted the simulations. All the authors prepared and reviewed the manuscript.

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Correspondence to Duc-Duy Ho or Thanh-Canh Huynh.

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Truong, TDN., Pradhan, A.M.S., Nguyen, TT. et al. Bone-Implant Osseointegration Monitoring Using Electro-mechanical Impedance Technique and Convolutional Neural Network: A Numerical Study. J Nondestruct Eval 43, 10 (2024). https://doi.org/10.1007/s10921-023-01021-0

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