Acoustic emission in orthopaedics: A state of the art review

doi:10.1016/j.jbiomech.2016.10.038

Journal of Biomechanics

Volume 49, Issue 16, 8 December 2016, Pages 4065-4072

https://doi.org/10.1016/j.jbiomech.2016.10.038 Get rights and content

Abstract

Acoustic emission (AE) is the phenomenon of sonic and ultrasonic wave generation by materials as they undergo deformation and fracture processes. AE monitoring is widely used throughout civil and mechanical engineering as a highly sensitive and non-destructive technique for structural health monitoring. Advances in computational power and digital data storage have generated much further interest in the possible applications of AE technology. Of particular interest has been its application within the field of Orthopaedic surgery.

This paper examines the current literature surrounding the use of AE technology within Orthopaedics and provides a comprehensive overview of its current applications within Orthopaedic surgery.

The use of AE technology in Orthopaedics is wide ranging and is discussed under the themes of: the study of the biomechanical properties of bone and fracture mechanics, research into failure mechanisms associated with cemented implants, prosthetic design, diagnostic value of AE and clinical application.

AE technology is of great benefit as an Orthopaedic research tool where AE counts can be used to provide a surrogate marker for damage accumulation and flaws can be monitored as they develop. More recently there has been increased interest in the possible clinical applications of AE technology and an appreciation of the potential benefits for the diagnosis and treatment of Orthopaedic pathology.

Despite the challenges involved when adopting AE techniques in vivo the potential of AE technology within Orthopaedics is significant. Already widely used in the research setting, clinical application has shown enormous potential and is a rapidly expanding area of contemporary research.

Aim

This analysis will review and summarise the current literature relating to the use of AE technology within Orthopaedic surgery

Introduction

The phenomenon of sonic and ultrasonic wave generation by materials undergoing deformation and fracture processes is termed Acoustic Emission (AE). AE results from the rapid release of energy during crack formation and propagation within a material and can be detected using specialist sensors. AE technology has been widely applied within civil and mechanical engineering to provide a highly sensitive and non-destructive method to monitor the health of structures. The technique enables both the detection and the location of structural flaws as they develop and, as such, the technology has proven invaluable in investigating the dynamic behaviour of materials (Ono, 2011).

A typical AE system consists of sensors, preamplifiers and a data acquisition system. AE sensors used in biomechanical engineering are typically piezoelectric, employing ceramic elements to generate electric signals in response to mechanical strain. Sensors are typically attached to the surface of the material under investigation using a thermo-plastic adhesive, with multiple sensors required to allow AE source triangulation. The analog signals detected by the sensors are amplified using preamplifiers and fed into a data acquisition system. The signals are converted into digital data, filtered and useful signals (“hits”) registered according to pre-defined parameters. The most common method for AE signal detection is based on threshold discrimination, where an amplitude threshold is defined and any signal exceeding this amplitude triggers a “hit” measurement (Muravin, 2009).

Fig. 1 demonstrates a typical AE signal triggering a “hit”. Annotations outline the standardised terms relating to the relevant parameters of AE signals and hits (MISTRAS, 1992).

Due to the large amounts of data generated through AE measurement research in this field has historically been limited and has principally centred on uses in engineering. Recent advances in computational power and digital data storage have, however, led to a marked expansion in AE research and to the development of increasingly widespread applications of the technology (Rashid and Pullin, 2014).

An area of recent particular interest is the application of AE technology to Orthopaedic surgery, where the non-destructive and highly sensitive nature of AE analysis make it ideally suited.

Section snippets

AE in Orthopaedics

Early applications of AE within Orthopaedics involved its use in vitro to characterise the structural and biomechanical properties of bone (Katz, 1979). It was established that the technique is particularly well suited to the dynamic analysis of bone deformation and fracture processes to which it was, and still is, frequently applied.

Subsequent work has seen AE technology applied within the field of arthroplasty research, with researchers taking advantage of its capability to monitor structural

Discussion

Acoustic Emission technology has been widely used in Orthopaedic research as a tool to assess micro-damage accumulation. More recently, however, there has been increased interest in the possible clinical application of the technology and an appreciation of the potential benefits of applying AE analysis during both the diagnosis and treatment of Orthopaedic pathology. This progression has led to an increased appreciation of, and interest in, the breadth of data available during AE monitoring.

Conclusion

Acoustic Emission technology has yet to find widespread clinical application within the field of Orthopaedic surgery. The technology has, however, been widely used in research where it has mostly been adopted as a research tool using AE signals as a surrogate marker for damage accumulation.

The technology has proven invaluable in the evaluation of the biomechanical properties of bone where it has been widely applied during bending and torsional testing (Aggelis et al., 2015) as well as

Perspectives - The future of AE in orthopaedics

Despite the promise shown in vitro, translation of AE techniques to in vivo applications faces many challenges. In addition to the complex structure of bone interfering with AE transmission, AE technology suitable for clinical application needs to be developed. When considering remote data collection, for example, AE sensors that are wireless and lightweight would be required. Identifying the relevant AE signals amongst extraneous noise is also a substantial challenge (Agcaoglu and Akkus, 2013).

Conflict of Interest

The author declares no conflict of interest associated with this article.

No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

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High-frequency acoustic waves produced by the sudden release of energy within a material caused by mechanical loading, deformation, corrosion, or other forms of damage are detected by Acoustic Emission (AE). It is a non-destructive testing technique utilized to monitor the structural health of materials and components and to detect and locate defects or damage that could lead to failure. Researchers in the biomedical field have lately focused on AE due to its potential for early detection, tracking, and managing various health issues, such as identifying and evaluating joint disorders, tissue damage, etc. However, due to the variance in AE signals produced from complex structures like knee joints, a general statistical method frequently finds it difficult to differentiate between distinct types of AE signals from different kinds of damage. In such instances, machine learning (ML) and deep learning (DL) can be beneficial for differentiating these signals. In this study, the application of Gaussian mixture model (GMM) clustering with principal component analysis (PCA) for dimension reduction was proposed to detect knee osteoarthritis (OA) from AE data. Four groups of participants, Group A (age 20–39), Group B (age 40–59), and Group C (age 60+), along with diagnosed OA as Group D, were included in this study. Avoiding overlapping AE signal parameters from different groups of participants is a significant challenge. GMM was applied to identify and eliminate the coinciding data points to address this issue. Moreover, the impact of this approach on clustering was also investigated in this research, and by utilizing GMM and PCA, compact clustering of the AE data potentially ensured the efficiency and effectiveness of early diagnosis of OA using the AE Technique (AET).
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The electrical signal is amplified so that it can be recorded by a data acquisition system (Kapur, 2016). A threshold is defined to filter out undesired noise (Roques et al., 2004) so that signals whose amplitude is beyond the defined threshold are marked and identified as “hits” (Kapur, 2016). Then, the data acquisition board in a computer receives and displays these signals for interpretation (Davies and Harris, 1996).
Total hip replacements (THR) are becoming an common orthopedic surgucal procedure in the United States (332 K/year in 2017) to relieve pain and improve the mobility of those that are affected by osteoarthritis, ankylosing spondylitis, or injury. However, complications like tribocorrosion, or material degradation due to friction and corrosion, may result in THR failure. Unfortunately, few strategies to non-invasively diagnose early-stage complications are reported in literature, leading to implant complications being detected after irreversible damage. Therefore, the main objective of this study proposes the utilization of acoustic emission (AE) to continuously monitor implant materials, CoCrMo and Ti6Al4V, and identify degradations formed during cycles of sleeping, standing, and walking by correlating them to potential and friction coefficient behavior. AE activity detected from the study correlates with the friction coefficient and open-circuit potential observed during recreated in-vitro standing, walking, and sleeping cycles. It was found that the absolute energy level obtained from AE increased as the friction coefficient increased, potential decreased, and wear volume loss increased. Through the results, higher friction coefficient and AE activity were observed in Ti6Al4V alloys while there was also a significant drop in potential, indicating increased tribocorrosion activity. Therefore, AE can be utilized to predict material degradations as a non-invasive method based on the severity of abnormality of the absolute energy and hits emitted. The correlation between potential, friction coefficient, and AE activity was further confirmed through profilometry which showed more material degradation in Ti6Al4V than CoCrMo. Through these evaluations, it was demonstrated that AE could be utilized to identify the deformations and failure modes of implant materials caused by tribocorrosion.
Real time monitoring of screw insertion using acoustic emission can predict screw stripping in human cancellous bone
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To develop experience, orthopaedic surgeons train their own proprioception to detect torque during screw insertion. This experience is acquired over time and when implanting conventional/non-locked screws in osteopenic cancellous bone the experienced surgeon still strips between 38 and 45%. Technology needs to be investigated to reduce stripping rates. Acoustic-Emission technology has the ability to detect stress wave energy transmitted through a screw during insertion into synthetic bone. Our hypothesis is Acoustic-Emission waves can be detected through standard orthopaedic screwdrivers while advancing screws through purchase and overtightening in cancellous human bone with different bone mineral densities replicating the clinical state.
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Acoustic emission in orthopaedics: A state of the art review

Abstract

Aim

Introduction

Section snippets

AE in Orthopaedics

Discussion

Conclusion

Perspectives - The future of AE in orthopaedics

Conflict of Interest

J. Orthop. Res.

Med. Eng. Phys.

J. Arthroplast.

Bone

Biomaterials

J. Mater. Process. Technol.

Med. Eng. Phys.

Med. Eng. Phys.

J. Mech. Behav. Biomed. Mater.

Biomaterials

Med. Eng. Phys.

J. Biomech.

J. Orthop. Res.

Biomaterials

J. Biomech.

Acoustic emission based monitoring of the microdamage evolution during fatigue of human cortical bone

J Biomech. Eng.

Fracture of human femur tissue monitored by acoustic emission sensors

Sensors

Extended push-out test to characterize the failure of bone-implant interface

Biomed. Tech. Biomed. Eng.

Passive monitoring of knee joint condition using acoustic emission

Bone Jt. J.

Nondestructive evaluation of bone cement and bone cement/metal interface failure

J. Biomed. Mater. Res.; Part B - Appl. Biomater.

The acoustic emission technique in orthopaedics-a review

J. Strain Anal. Eng. Des.

Analysis of the effect of using two different strain rates on the acoustic emission in bone

J. Biomech.

Evaluation of the fixation of artificial hip joint by acoustic emission

Jpn. J. Appl. Phys.

An experimental study on the interface strength between titanium mesh cage and vertebra in reference to vertebral bone mineral density

Spine

Biomechanical monitoring of healing bone based on acoustic emission technology

Clin. Orthop. Relat. Res.