Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

Acoustical features as knee health biomarkers: A critical analysis

Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical features, which have presented promising diagnostic performances. However, these methods overlook the intricate multi-source nature of audio signals and the underlying mechanisms at play. By addressing this critical gap, the present paper introduces a novel causal framework for validating knee acoustical features. We argue that current machine learning methodologies for acoustical knee diagnosis lack the required assurances and thus cannot be used to classify acoustic features as biomarkers. Our framework establishes a set of essential theoretical guarantees necessary to validate this claim. We apply our methodology to three real-world experiments investigating the effect of researchers' expectations, the experimental protocol, and the wearable employed sensor. We reveal latent issues such as underlying shortcut learning and performance inflation. This study is the first independent result reproduction study in acoustical knee health evaluation. We conclude by offering actionable insights that address key limitations, providing valuable guidance for future research in knee health acoustics.

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models

Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.

Knee Osteoarthritis: Epidemiology, Pathogenesis, and Mesenchymal Stem Cells: What Else Is New? An Update

Osteoarthritis (OA) is a chronic disease and the most common orthopedic disorder. A vast majority of the social OA burden is related to hips and knees. The prevalence of knee OA varied across studies and such differences are reflected by the heterogeneity of data reported by studies conducted worldwide. A complete understanding of the pathogenetic mechanisms underlying this pathology is essential. The OA inflammatory process starts in the synovial membrane with the activation of the immune system, involving both humoral and cellular mediators. A crucial role in this process is played by the so-called “damage-associated molecular patterns” (DAMPs). Mesenchymal stem cells (MSCs) may be a promising option among all possible therapeutic options. However, many issues are still debated, such as the best cell source, their nature, and the right amount. Further studies are needed to clarify the remaining doubts. This review provides an overview of the most recent and relevant data on the molecular mechanism of cartilage damage in knee OA, including current therapeutic approaches in regenerative medicine.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

Advanced MR Imaging for Knee Osteoarthritis: A Review on Local and Brain Effects

Knee osteoarthritis is one of the leading causes of chronic disability worldwide and is a significant social and economic burden on healthcare systems; hence it has become essential to develop methods to identify patients at risk for developing knee osteoarthritis at an early stage. Standard morphological MRI sequences are focused mostly on alterations seen in advanced stages of osteoarthritis. However, they possess low sensitivity for early, subtle, and potentially reversible changes of the degenerative process. In this review, we have summarized the state of the art with regard to innovative quantitative MRI techniques that exploit objective and quantifiable biomarkers to identify subtle alterations that occur in early stages of osteoarthritis in knee cartilage before any morphological alteration occurs and to capture potential effects on the brain. These novel MRI imaging tools are believed to have great potential for improving the current standard of care, but further research is needed to address limitations before these compositional techniques can be robustly applied in research and clinical settings.

Acute effects of a single bout of exercise therapy on knee acoustic emissions in patients with osteoarthritis: a double-blinded, randomized controlled crossover trial

BACKGROUND

Knee osteoarthritis is associated with higher kinetic friction in the knee joint, hence increased acoustic emissions during motion. Decreases in compressive load and improvements in movement quality might reduce this friction and, thus, sound amplitude. We investigated if an exercise treatment acutely affects knee joint sounds during different activities of daily life.

METHODS

Eighteen participants with knee osteoarthritis (aged 51.8 ± 7.3 years; 14 females) were included in this randomized crossover trial. A neuromuscular exercise intervention and a placebo laser needle acupuncture treatment were performed. Before and after both interventions, knee joint sounds were measured during three different activities of daily living (standing up/sitting down, walking, descending stairs) by means of vibroarthrography. The mean amplitude (dB) and the median power frequency (MPF, Hz) were assessed at the medial tibial plateau and the patella. Differences in knee acoustic emissions between placebo and exercise interventions were calculated by analyses of covariance.

RESULTS

Controlled for participant's age, knee demanding activity level and osteoarthritis stage, the conditions significantly differed in their impact on the MPF (mean(± SD) pre-post-differences standing up: placebo: 9.55(± 29.15) Hz/ exercise: 13.01(± 56.06) Hz, F = 4.9, p < 0.05) and the amplitude (standing up: placebo:0.75(± 1.43) dB/ exercise: 0.51(± 4.68) dB, F = 5.0, p < 0.05; sitting down: placebo: 0.07(± 1.21) dB/ exercise: -0.16(± .36) dB, F = 4.7, p < 0.05) at the tibia. There were no differences in the MPF and amplitude during walking and descending stairs (p > 0.05). At the patella, we found significant differences in the MPF during walking (placebo 0.08(± 1.42) Hz/ exercise: 15.76(± 64.25) Hz, F = 4.8, p < .05) and in the amplitude during descending stairs (placebo: 0.02 (± 2.72) dB/ exercise: -0.73(± 2.84) dB, F = 4.9, p < 0.05). There were no differences in standing up/ sitting down for both parameters, nor in descending stairs for the MPF and walking for the amplitude (p > 0.05).

CONCLUSION

The MPF pre-post differences of the exercise intervention were higher compared to the MPF pre-post differences of the placebo treatment. The amplitude pre-post differences were lower in the exercise intervention. In particular, the sound amplitude might be an indicator for therapy effects in persons with knee osteoarthritis.

TRIAL REGISTRATION

The study was retrospectively registered in the German Clinical Trials Register ( DRKS00022936 , date of registry: 26/08/2020).

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint

Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.

Effects of Viscosupplementation on Quality of Knee Joint Arthrokinematic Motion Analyzed by Vibroarthrography

OBJECTIVE

To evaluate the influence of viscosupplementation on osteoarthritic knee arthrokinematics analyzed by VAG. It is considered that intra-articular hyaluronic acid injection may improve the function of synovial joints by recovery of friction-reducing properties of articular environment.

DESIGN

Thirty-five patients with knee osteoarthritis (grade II according to the Kellgren-Lawrence system) and 50 asymptomatic subjects were enrolled in the study. Patients were analyzed at 3 time points: 1 day before and 2 weeks and 4 weeks after single injection of 1.5% cross-linked hyaluronate. Control subjects were tested once. The vibroarthrographic signals were collected during knee flexion/extension motion using an accelerator and described by variation of mean square (VMS), mean range (R5), and power spectral density for frequency of 50 to 250 Hz (P1), and 250 to 450 Hz (P2).

RESULTS

Patients before viscosupplementation were characterized by about 2-fold higher values of vibroarthrographic parameters than controls. Two weeks after the procedure, the values of R5, P1, and P2 significantly decreased, in comparison to pre-injection. At 4 weeks post-injection, we noted a significant increase in R5, P1, and P2 values, when compared to 2 weeks post-injection. Finally, at 4 weeks post-injection, the level of VMS, R5, and P2 parameters did not differ from values obtained at pre-injection.

CONCLUSIONS

We showed that viscosupplementation may be effective in providing arthrokinematics improvement, but with a relatively short period of duration. This phenomenon is observed as decreased vibroacoustic emission, which reflects a more smooth movement in the joint.

Effect of warm-up and muscle fatiguing exercise on knee joint sounds in motion by vibroarthrography: A randomized crossover trial

Vibroarthrography measures joint sounds caused by sliding of the joint surfaces over each other. and can be affected by joint health, load and type of movement. Since both warm-up and muscle fatigue lead to local changes in the knee joint (e.g., temperature increase, lubrication of the joint, muscle activation), these may impact knee joint sounds. Therefore, this study investigates the effects of warm-up and muscle fatiguing exercise on knee joint sounds during an activity of daily living. Seventeen healthy, physically active volunteers (25.7 ± 2 years, 7 males) performed a control and an intervention session with a wash-out phase of one week. The control session consisted of sitting on a chair, while the intervention session contained a warm-up (walking on a treadmill) followed by a fatiguing exercise (modified sit-to-stand) protocol. Knee sounds were recorded by vibroarthrography (at the medial tibia plateau and at the patella) at three time points in each session during a sit-to-stand movement. The primary outcome was the mean signal amplitude (MSA, dB). Differences between sessions were determined by repeated measures ANOVA with intra-individual pre-post differences for the warm-up and for the muscle fatigue effect. We found a significant difference for MSA at the medial tibia plateau (intervention: mean 1.51 dB, standard deviation 2.51 dB; control: mean -1.28 dB, SD 2.61 dB; F = 9.5; p = .007; η² = .37) during extension (from sit to stand) after the warm-up. There was no significant difference for any parameter after the muscle fatiguing exercise (p > .05). The increase in MSA may mostly be explained by an increase in internal knee load and joint friction. However, neuromuscular changes may also have played a role. It appears that the muscle fatiguing exercise has no impact on knee joint sounds in young, active, symptom-free participants during sit to stand.

Quantifying Signal Quality for Joint Acoustic Emissions Using Graph-Based Spectral Embedding

We present a new method for quantifying signal quality of joint acoustic emissions (JAEs) from the knee during unloaded flexion/extension (F/E) exercises. For ten F/E cycles, JAEs were recorded, in a clinical setting, from 34 healthy knees and 13 with a meniscus tear (n=24 subjects). The recordings were first segmented by F/E cycle and described using time and frequency domain features. Using these features, a symmetric k-nearest neighbor graph was created and described using a spectral embedding. We show how the underlying community structure of JAEs was comparable across joint health levels and was highly affected by artifacts. Each F/E cycle was scored by its distance from a diverse set of manually annotated, clean templates and removed if above the artifact threshold. We validate this methodology by showing an improvement in the distinction between the JAEs of healthy and injured knees. Graph community factor (GCF) was used to detect the number of communities in each recording and describe the heterogeneity of JAEs from each knee. Before artifact removal, there was no significant difference between the healthy and injured groups due to the impact of artifacts on the community construction. Following implementation of artifact removal, we observed improvement in knee health classification. The GCF value for the meniscus tear group was significantly higher than the healthy group (p<0.01). With more JAE recordings being taken in the clinic and at home, this paper addresses the need for a robust artifact removal method which is necessary for an accurate description of joint health.

Reprint of "The detection of knee joint sounds at defined loads by means of vibroarthrography"

BACKGROUND

Crepitus of the knee may mirror structural and functional changes in the joint during motion. Although the magnitude of these sounds increases with greater cartilage damage, it is unclear whether knee joint sounds also reflect joint loading.

METHODS

Twelve healthy volunteers (mean 26 (SD 3.6) years, 7 females) participated in the randomized-balanced crossover study. Knee joint sounds were recorded (linear sampling, 5512 Hz) by means of two microphones, one placed on the medial tibial plateau and one on the patella. Two activities of daily living (standing up from/sitting down on a bench; descending stairs) and three open kinetic chain knee extension-flexion cycles (passive movement, 10% and 40% loading of the individual one repetition maximum) were performed. Each participant carried out three sets of five repetitions and three sets of 15 steps downwards (stairs), respectively. For data analysis, the mean sound amplitude and the median power frequency for each loading condition were determined. Friedman test and Bonferroni-Holm adjusted post-hoc test were performed to detect differences between conditions.

FINDINGS

We obtained significant differences between joint sound amplitudes for all movements, both measured at the medial tibial plateau (Chi² = 20.7, p < 0.001) and at the patella (Chi² = 27.6, p < 0.001). We showed a significant difference in the median power frequency of the patella between all movements (Chi² = 17.8, p < 0.5).

INTERPRETATION

Overall, the larger the supposed knee joint loading was, the louder was the recorded knee crepitus. Consequently, vibroarthrographically assessed knee joint sounds can differ across knee joint loading conditions.

Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.

Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Background

The patellofemoral joint (PFJ) provides extremely low kinetic friction, which results in optimal arthrokinematic motion quality. Previous research showed that these friction-reducing properties may be diminished due to the increase in articular contact forces. However, this phenomenon has not been analyzed in vivo during functional daily-living activities. The aim of this study was the vibroarthrographic assessment of changes in PFJ arthrokinematics during squats with variated loads.

Methods

114 knees from 57 asymptomatic subjects (23 females and 34 males) whose ages ranged from 19 to 26 years were enrolled in this study. Participants were asked to perform 3 trials: 4 repetitions of bodyweight squats (L0), 4 repetitions of 10 kg barbell back loaded squats (L10), 4 repetitions of 20 kg barbell back loaded squats (L20). During the unloaded and loaded (L10, L20) squats, vibroarthrographic signals were collected using an accelerometer placed on the patella and were described by the following parameters: variation of mean square (VMS), mean range (R4), and power spectral density for frequency of 50–250 Hz (P1) and 250–450 Hz (P2).

Results

Obtained results showed that the lowest values were noted in the unloaded condition and that the increased applied loads had a significant concomitant increase in all the aforementioned parameters bilaterally (p < 0.05).

Conclusion

This phenomenon indicates that the application of increasing knee loads during squats corresponds to higher intensity of vibroacoustic emission, which might be related to higher contact stress and kinetic friction as well as diminished arthrokinematic motion quality.

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist-another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz-20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen-Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems.

Validity of and recommendations for knee joint acoustic assessments during different movement conditions

Knee joint sounds contain information on joint health, morphology and loading. These acoustic signals may be elicited by further, as yet unknown factors. By assessing potential elicitors and their relative contributions to the acoustic signal, we investigated the validity of vibroarthrographic assessments during different movement conditions with the aim to derive recommendations for their practical usage. Cross-sectional study. Nineteen healthy participants (24.7 ± 2.8 yrs, 7 females) performed five movements: level walking, descending stairs, standing up, sitting down, and forward lunge. Knee joint sounds were recorded by two microphones (medial tibial plateau, patella). Knee joint kinematics and ground reaction forces were recorded synchronously to calculate knee joint moments (Nm/Kg). The mean amplitude (dB) and the median power frequency (Hz) were determined. A repeated measures mixed model investigated the impact of potential predictors (sagittal, frontal, transverse plane and total knee joint moments, knee angular velocity, age, sex, body mass index (BMI) and Tegner Activity Score (TAS)). Most of the amplitudes variance is explained by between-subject differences (tibia: 66.6%; patella: 75.8%), and of the median power frequencies variance by the movement condition (tibia: 97.6%; patella: 98.9%). The final model revealed several predictor variables for both sensors (tibia: sagittal plane, frontal plane, and total knee joint moments, age, and TAS; patella: sagittal plane knee moments, knee angular velocity, TAS). The standardization of the execution of the activities, a between-group matching of variables and the inclusion of co-variates are recommended to increase the validity of vibroarthrographic measurements during different movement conditions of the knee joint.

Effect of whole body vibration on HIF-2α expression in SD rats with early knee osteoarthritis

INTRODUCTION

To investigate the effect of different frequencies of whole body vibration (WBV) on articular cartilage of early knee osteoarthritis (OA) rats and determine whether WBV would influence the pathway of hypoxia-inducible factor-2α (HIF-2α) regulation-related genes after 8 weeks of treatment.

MATERIALS AND METHODS

Forty 8-week-old OA rats were divided into five groups: sham control (SC); high frequency 60 Hz (HV₁); high frequency 40 Hz (HV₂); middle frequency 20 Hz (MV) and low frequency 10 Hz (LV). WBV (0.3 g) treatment was given 40 min/day and 5 days/week. After 8 weeks, rats were killed and knees were harvested. OA grading score: Osteoarthritis Research Society International (OARSI), and the expression of related genes: interleukin-1β (IL-1β), HIF-2α, matrix metalloproteinases-13 (MMP-13), and collagen type II alpha 1 (COL2A1), at both mRNA and protein levels were analyzed.

RESULTS

After 8 weeks of WBV, our data showed that lower frequency (10 Hz) was more effective than the higher ones, yet they all suggested that WBV alleviates the erosion of knee articular cartilage in early OA. The expression of IL-1β, HIF-2α and MMP-13 decreased with frequency and reached the lowest level at 10 Hz, the expression of COL2A1 increased with frequency and reached the highest level at 10 Hz.

CONCLUSIONS

This study demonstrates that WBV could alleviate the degeneration of knee joints in an early OA rat model. WBV regulates related gene expression at both mRNA and protein levels. HIF-2α could be a therapeutic target. The effect of WBV is frequency dependent; the lower frequency shows better effects.

Acoustic signal analysis of instrument-tissue interaction for minimally invasive interventions

Purpose

Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception.

Methods

In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction.

Results

The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%.

Conclusion

Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

Reliability of Vibroarthrography to Assess Knee Joint Sounds in Motion

Knee acoustic emissions provide information about joint health and loading in motion. As the reproducibility of knee acoustic emissions by vibroarthrography is yet unknown, we evaluated the intrasession and interday reliability of knee joint sounds. In 19 volunteers (25.6 ± 2.0 years, 11 female), knee joint sounds were recorded by two acoustic sensors (16,000 Hz; medial tibial plateau, patella). All participants performed four sets standing up/sitting down (five repetitions each). For measuring intrasession reliability, we used a washout phase of 30 min between the first three sets, and for interday reliability we used a washout phase of one week between sets 3 and 4. The mean amplitude (dB) and median power frequency (Hz, MPF) were analyzed for each set. Intraclass correlation coefficients (ICCs (2,1)), standard errors of measurement (SEMs), and coefficients of variability (CVs) were calculated. The intrasession ICCs ranged from 0.85 to 0.95 (tibia) and from 0.73 to 0.87 (patella). The corresponding SEMs for the amplitude were ≤1.44 dB (tibia) and ≤2.38 dB (patella); for the MPF, SEMs were ≤13.78 Hz (tibia) and ≤14.47 Hz (patella). The intrasession CVs were ≤0.06 (tibia) and ≤0.07 (patella) (p < 0.05). The interday ICCs ranged from 0.24 to 0.33 (tibia) and from 0 to 0.82 (patella) for both the MPF and amplitude. The interday SEMs were ≤4.39 dB (tibia) and ≤6.85 dB (patella) for the amplitude and ≤35.39 Hz (tibia) and ≤15.64 Hz (patella) for the MPF. The CVs were ≤0.14 (tibia) and ≤0.08 (patella). Knee joint sounds were highly repeatable within a single session but yielded inconsistent results for the interday reliability.

The detection of knee joint sounds at defined loads by means of vibroarthrography

BACKGROUND

Crepitus of the knee may mirror structural and functional changes in the joint during motion. Although the magnitude of these sounds increases with greater cartilage damage, it is unclear whether knee joint sounds also reflect joint loading.

METHODS

Twelve healthy volunteers (mean 26 (SD 3.6) years, 7 females) participated in the randomized-balanced crossover study. Knee joint sounds were recorded (linear sampling, 5512 Hz) by means of two microphones, one placed on the medial tibial plateau and one on the patella. Two activities of daily living (standing up from/sitting down on a bench; descending stairs) and three open kinetic chain knee extension-flexion cycles (passive movement, 10% and 40% loading of the individual one repetition maximum) were performed. Each participant carried out three sets of five repetitions and three sets of 15 steps downwards (stairs), respectively. For data analysis, the mean sound amplitude and the median power frequency for each loading condition were determined. Friedman test and Bonferroni-Holm adjusted post-hoc test were performed to detect differences between conditions.

FINDINGS

We obtained significant differences between joint sound amplitudes for all movements, both measured at the medial tibial plateau (Chi² = 20.7, p < 0.001) and at the patella (Chi² = 27.6, p < 0.001). We showed a significant difference in the median power frequency of the patella between all movements (Chi² = 17.8, p < 0.5).

INTERPRETATION

Overall, the larger the supposed knee joint loading was, the louder was the recorded knee crepitus. Consequently, vibroarthrographically assessed knee joint sounds can differ across knee joint loading conditions.

Wireless multichannel vibroarthrographic recordings for the assessment of knee osteoarthritis during three activities of daily living

BACKGROUND

Variations in the internal pressure distribution applied to cartilage and synovial fluid explain the spatial dependencies of the knee vibroarthrographic signals. These spatial dependencies were assessed by multi-channel recordings during activities of daily living in patients with painful knee osteoarthrosis.

METHODS

Knee vibroarthrographic signals were detected using eight miniature accelerometers, and vibroarthrographic maps were calculated for the most affected knee of 20 osteoarthritis patients and 20 asymptomatic participants during three activities: (i) sit to stand, (ii) stairs descent, and (iii) stairs ascent in real life conditions. Vibroarthrographic maps of average rectified value, variance of means squared, form factor, mean power frequency, % of recurrence and, % of determinism were obtained from the eight VAG recordings.

FINDINGS

Higher average rectified value and lower % of recurrence were found in knee osteoarthritis patients compared with asymptomatic participants. All vibroarthrographic parameters, except for % of recurrence, differentiated the type of activity. Average rectified value, variance of means squared, form factor, and % of determinism were lowest while mean power frequency was highest during sit-to-stand compared with stairs ascent and descent.

INTERPRETATION

Distinct topographical vibroarthrographic maps underlined that the computed parameters represent unique features. The present study demonstrated that wireless multichannel vibroarthrographic recordings and the associated topographical maps highlighted differences between (i) knee osteoarthritis patients and asymptomatic participants, (ii) sit to stand, stairs descent and ascent and (iii) knee locations. The technique offers new perspectives for biomechanical assessments of physical functions of the knee joint in ecological environment.