Temporomandibular Joint Acoustic Emissions in Children With Juvenile Idiopathic Arthritis Differ From Those in Healthy Children.

PURPOSE: Articulation of the temporomandibular joint (TMJ) generates sounds with specific characteristics known as joint acoustic emissions (AEs). The purpose of this project was to determine if AEs as described by the joint health score (JHS) in children with juvenile idiopathic arthritis (JIA) differ from AEs in healthy children. METHODS: The investigators implemented a cross-sectional study with age- and sex-matched controls to compare AEs from 4 groups: (1) healthy subjects without TMJ sounds, (2) healthy subjects with TMJ sounds, (3) subjects with JIA without TMJ sounds, and (4) subjects with TMJ sounds. Predictor variables were JIA status (ie JIA/healthy) and joint sounds (present/absent). The outcome variable was AEs. Subjects wore a specialized headset and performed specific jaw movements that generated AEs. AEs were recorded and analyzed using an aggregated decision tree classification model that calculates a JHS for each group. JHSs were compared using a receiver operating characteristic curve and classification accuracies. The study team used a 2-tailed unpaired t-test to determine if score distributions were different. Significance was P < .05. RESULTS: A total of 51 subjects (102 TMJs; 37 females) with an average age of 13.1 years (range, 7 to 18) participated. Children with JIA and TMJ sounds had AEs with large repetitive clicks. Children with JIA without sounds had smaller repetitive clicks. Healthy children had grinding sounds with lower amplitude. The receiver operating characteristic curve had a classification accuracy of 71.6%. This accuracy compares against the gold standard clinical assessment for placing these patients into their groups (JIA vs healthy). JHSs of children with TMJ sounds and children with JIA and TMJ sounds were statistically significant (P < .0001). CONCLUSION: In our sample, the AE of TMJs in healthy children may be different than that in children with JIA. Assessment of an AE is a promising and noninvasive technique to determine involvement of TMJs in children with JIA.

Rural Hospital Designation Is Associated With Increased Complications and Resource Utilization After Primary Total Hip Arthroplasty: A Matched Case-Control Study.

BACKGROUND: As the prevalence of hip osteoarthritis increases, the demand for total hip arthroplasty (THA) has grown. It is known that patients in rural and urban geographic locations undergo THA at similar rates. This study explores the relationship between geographic location and postoperative outcomes. METHODS: In this retrospective cohort study, the Truven MarketScan database was used to identify patients who underwent primary THA between January 2010 and December 2018. Patients with prior hip fracture, infection, and/or avascular necrosis were excluded. Two cohorts were created based on geographic locations: urban vs rural (rural denotes any incorporated place with fewer than 2500 inhabitants). Age, gender, and obesity were used for one-to-one matching between cohorts. Patient demographics, medical comorbidities, postoperative complications, and resource utilization were statistically compared between the cohorts using multivariate conditional logistic regression. RESULTS: In total, 18,712 patients were included for analysis (9356 per cohort). After matching, there were no significant differences in comorbidities between cohorts. The following were more common in rural patients: dislocation within 1 year (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.08-1.41, P < .001), revision within 1 year (OR 1.17, 95% CI 1.05-1.32, P = .027), and prosthetic joint infection (OR 1.14, 95% CI 1.04-1.34, P = .033). Similarly, rural patients had higher odds of 30-day readmission (OR 1.31, 95% CI 1.09-1.56, P = .041), 90-day readmission (OR 1.41, 95% CI 1.26-1.71, P = .023), and extended length of stay (≥3 days; OR 1.52, 95% CI 1.22-1.81, P < .001). CONCLUSION: THA in rural patients is associated with increased cost, healthcare utilization, and complications compared to urban patients. Standardization between geographic areas could reduce this discrepancy.

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.

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation.

Sounds produced by the articulation of joints have been shown to contain information characteristic of underlying joint health, morphology, and loading. In this work, we explore the use of a novel form factor for non-invasively acquiring acoustic/vibrational signals from the knee joint: an instrumented glove with a fingertip-mounted accelerometer. We validated the glove-based approach by comparing it to conventional mounting techniques (tape and foam microphone pads) in an experimental framework previously shown to reliably alter healthy knee joint sounds (vertical leg press). Measurements from healthy subjects (N = 11) in this proof-of-concept study demonstrated a highly consistent, monotonic, and significant (p < 0.01) increase in low-frequency signal root-mean-squared (RMS) amplitude-a straightforward metric relating to joint grinding loudness-with increasing vertical load across all three techniques. This finding suggests that a glove-based approach is a suitable alternative for collecting joint sounds that eliminates the need for consumables like tape and the interface noise associated with them.

Using Knee Acoustical Emissions for Sensing Joint Health in Patients with Juvenile Idiopathic Arthritis: A Pilot Study.

In this paper, we present a pilot study evaluating novel methods for assessing joint health in patients with Juvenile Idiopathic Arthritis (JIA) using wearable acoustical emission measurements from the knees. Measurements were taken from four control subjects with no known knee injuries, and from four subjects with JIA, before and after treatment. Time and frequency domain features were extracted from the acoustical emission signals and used to compute a knee audio score. The score was used to separate out the two groups of subjects based solely on the sounds their joints produce. It was created using a soft classifier based on gradient boosting trees. The knee audio scores ranged from 0-1 with 0 being a healthy knee and 1 being an involved joint with arthritis. Leave-one-subject-out cross-validation (LOSO-CV) was used to validate the algorithm. The average of the right and left knee audio scores was 0.085±0.099 and 0.89±0.012 for the control group and group with JIA, respectively (p<0.05). The average knee audio score for the subjects with JIA decreased from 0.89±0.012 to 0.25±0.20 following successful treatment (p<0.05). The knee audio score metric successfully distinguished between the control subjects and subjects with JIA. The scores calculated before and after treatment accurately reflected the observed clinical course of the subjects with JIA. After successful treatment, the subjects with JIA were classified as healthy by the algorithm. Knee acoustical emissions provide a novel and cost-effective method for monitoring JIA, and can be used as an objective guide for assessing treatment efficacy.

Acoustic Emissions From Loaded and Unloaded Knees to Assess Joint Health in Patients With Juvenile Idiopathic Arthritis.

OBJECTIVE: We studied and compared joint acoustical emissions (JAEs) in loaded and unloaded knees as digital biomarkers for evaluating knee health status during the course of treatment in patients with juvenile idiopathic arthritis (JIA). METHODS: JAEs were recorded from 38 participants, performing 10 repetitions of unloaded flexion/extension (FE) and loaded squat exercises. A novel algorithm was developed to detect and exclude rubbing noise and loose microphone artifacts from the signals, and then 72 features were extracted. These features were down-selected based on different criteria to train three logistic regression classifiers. The classifiers were trained with healthy and pre-treatment data and were used to predict the knee health scores of post-treatment data for the same patients with JIA who had a follow-up recording. This knee health score represents the probability of having JIA in a subject (0 for healthy and 1 for arthritis). RESULTS: Post-treatment knee health scores were lower than pre-treatment scores, agreeing with the clinical records of successful treatment. Regarding loaded versus unloaded knee scores, the squats achieved a higher score on average compared to FEs. CONCLUSION: In healthy subjects with smooth cartilage, the knee scores of squats and FEs were similar indicating that vibrations from the friction of articulating surfaces do not significantly change by the joint load. However, in subjects with JIA, the scores of squats were higher than the scores of FEs, revealing that these two exercises contain different, possibly clinically relevant, information that could be used to further improve this novel assessment modality in JIA.

Vibration Stimulation as a Non-Invasive Approach to Monitor the Severity of Meniscus Tears.

Musculoskeletal disorders and injuries are one of the most prevalent medical conditions across age groups. Due to a high load-bearing function, the knee is particularly susceptible to injuries such as meniscus tears. Imaging techniques are commonly used to assess meniscus injuries, though this approach suffers from limitations including high cost, need for skilled personnel, and confinement to laboratory or clinical settings. Vibration-based structural monitoring methods in the form of acoustic emission analysis and vibration stimulation have the potential to address the limits associated with current diagnostic technologies. In this study, an active vibration measurement technique is employed to investigate the presence and severity of meniscus tear in cadaver limbs. In a highly controlled ex vivo experimental design, a series of cadaver knees (n =6) were evaluated under an external vibration, and the frequency response of the joint was analyzed to differentiate the intact and affected samples. Four stages of knee integrity were considered: baseline, sham surgery, meniscus tear, and meniscectomy. Analyzing the frequency response of injured legs showed significant changes compared to the baseline and sham stages at selected frequency bandwidths. Furthermore, a qualitative analytical model of the knee was developed based on the Euler-Bernoulli beam theory representing the meniscus tear as a change in the local stiffness of the system. Similar trends in frequency response modulation were observed in the experimental results and analytical model. These findings serve as a foundation for further development of wearable devices for detection and grading of meniscus tear and for improving our understanding of the physiological effects of injuries on the vibration characteristics of the knee. Such systems can also aid in quantifying rehabilitation progress following reconstructive surgery and / or during physical therapy.

Detection of Meniscal Tear Effects on Tibial Vibration Using Passive Knee Sound Measurements.

OBJECTIVE: To evaluate whether non-invasive knee sound measurements can provide information related to the underlying structural changes in the knee following meniscal tear. These changes are explained using an equivalent vibrational model of the knee-tibia structure. METHODS: First, we formed an analytical model by modeling the tibia as a cantilever beam with the fixed end being the knee. The knee end was supported by three lumped components with features corresponding with tibial stiffnesses, and meniscal damping effect. Second, we recorded knee sounds from 46 healthy legs and 9 legs with acute meniscal tears (n = 34 subjects). We developed an acoustic event ("click") detection algorithm to find patterns in the recordings, and used the instrumental variable continuous-time transfer function estimation algorithm to model them. RESULTS: The knee sound measurements yielded consistently lower fundamental mode decay rate in legs with meniscal tears ( 16 ±13 s - 1) compared to healthy legs ( 182 ±128 s - 1), p < 0.05. When we performed an intra-subject analysis of the injured versus contralateral legs for the 9 subjects with meniscus tears, we observed significantly lower natural frequency and damping ratio (first mode results for healthy: [Formula: see text]injured: [Formula: see text]) for the first three vibration modes (p < 0.05). These results agreed with the theoretical expectations gleaned from the vibrational model. SIGNIFICANCE: This combined analytical and experimental method improves our understanding of how vibrations can describe the underlying structural changes in the knee following meniscal tear, and supports their use as a tool for future efforts in non-invasively diagnosing meniscal tear injuries.

An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions.

The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While these methods are based on JAEs measured in controlled settings, we anticipate that JAE measurements could enable accessible and affordable diagnosis of acute knee injuries also in field-deployable settings. However, in such settings, the noise and interference would be greater than in sterile, laboratory environments, which could decrease the performance of existing knee health classification methods using JAEs. To address the need for an objective noise and interference detection method for JAE measurements as a step towards field-deployable settings, we propose a novel experimental data augmentation method to locate and then, remove the corrupted parts of JAEs measured in clinical settings. In the clinic, we recruited 30 participants, and collected data from both knees, totaling 60 knees (36 healthy and 24 injured knees) to be used subsequently for knee health classification. We also recruited 10 healthy participants to collect artifact and joint sounds (JS) click templates, which are audible, short duration and high amplitude JAEs from the knee. Spectral and temporal features were extracted, and clinical data was augmented in five-dimensional subspace by fusing the existing clinical dataset into experimentally collected templates. Then knee scores were calculated by training and testing a linear soft classifier utilizing leave-one-subject-out cross-validation (LOSO-CV). The area under the curve (AUC) was 0.76 for baseline performance without any window removal with a logistic regression classifier (sensitivity = 0.75, specificity = 0.78). We obtained an AUC of 0.86 with the proposed algorithm (sensitivity = 0.80, specificity = 0.89), and on average, 95% of all clinical data was used to achieve this performance. The proposed algorithm improved knee health classification performance by the added information through identification and collection of common artifact sources in JAE measurements. This method when combined with wearable systems could provide clinically relevant supplementary information for both underserved populations and individuals requiring point-of-injury diagnosis in field-deployable settings.

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis.

In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA-10 of whom were recorded a second time 3-6 months later-and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA.

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee.

The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics. Using our custom joint sound recording system, we recorded the AEs from nine cadaver legs in four stages: at baseline, after a sham surgery, after a meniscus tear, and post-meniscectomy. We compare the resulting AEs using their b-values. We then compare joint anatomy/kinematics to the AEs using the X-ray reconstruction of moving morphology (XROMM) technique. After the meniscus tear the number and amplitude of the AE peaks greatly increased from baseline and sham (b-value = 1.33 ± 0.15; p < 0.05). The XROMM analysis showed a close correlation between the minimal inter-joint distances (0.251 ± 0.082 cm during extension, 0.265 ± .003 during flexion, at 145°) and a large increase in the AEs. This work provides key insight into the nature of joint AEs, and details a novel technique and analysis for recording and interpreting these biosignals.

Template-Based Statistical Modeling and Synthesis for Noise Analysis of Ballistocardiogram Signals: A Cycle-Averaged Approach.

OBJECTIVE: Ballistocardiogram (BCG) can be recorded using inexpensive and non-invasive hardware to estimate physiological changes in the heart. In this paper, a methodology is developed to evaluate the impact of additive noise on the BCG signal. METHODS: A statistical model is built that incorporates subject-specific BCG morphology. BCG signals segmented by electrocardiogram RR intervals (BCG heartbeats) are averaged to estimate a parent template and subtemplates leveraging the quasi-periodic nature of the heart. Noise statistics are obtained for subtemplates with respect to the parent template. Then, a synthesis algorithm with adjustable additive noise is devised to generate subtemplates based on the individual's parent template and statistics. For the example use of the synthesis algorithm, the average correlation coefficient between subtemplates and the parent template (subtemplate versus parent template approach) is tested as a signal quality index. RESULTS: A BCG heartbeat synthesis framework that incorporates an individual's BCG morphology and physiological variability was developed to quantify variations in the BCG signal against additive noise. The signal quality assessment of a person's BCG recording can be performed without requiring any a priori knowledge of the person's BCG morphology. A data-driven constraint on the required minimum number of heartbeats for a reliable template estimation was provided. CONCLUSION: The impact of additive noise on BCG morphology and estimated physiological parameters can be analyzed using the developed methodology without requiring prior statistics. SIGNIFICANCE: This paper can facilitate the performance evaluation of BCG analysis algorithms against additive noise.

Quantifying the Effects of Increasing Mechanical Stress on Knee Acoustical Emissions Using Unsupervised Graph Mining.

In this paper, we investigate the effects of increasing mechanical stress on the knee joints by recording knee acoustical emissions and analyze them using an unsupervised graph mining algorithm. We placed miniature contact microphones on four different locations: on the lateral and medial sides of the patella and superficial to the lateral and medial meniscus. We extracted audio features in both time and frequency domains from the acoustical signals and calculated the graph community factor (GCF): an index of heterogeneity (variation) in the sounds due to different loading conditions enforced on the knee. To determine the GCF, a k-nearest neighbor graph was constructed and an Infomap community detection algorithm was used to extract all potential clusters within the graph-the number of detected communities were then quantified with GCF. Measurements from 12 healthy subjects showed that the GCF increased monotonically and significantly with vertical loading forces (mean GCF for no load = 30 and mean GCF for maximum load [body weight] = 39). This suggests that the increased complexity of the emitted sounds is related to the increased forces on the joint. In addition, microphones placed on the medial side of the patella and superficial to the lateral meniscus produced the most variation in the joint sounds. This information can be used to determine the optimal location for the microphones to obtain acoustical emissions with greatest sensitivity to loading. In future work, joint loading quantification based on acoustical emissions and derived GCF can be used for assessing cumulative knee usage and loading during activities, for example for patients rehabilitating knee injuries.

Wearable knee health system employing novel physiological biomarkers.

Knee injuries and chronic disorders, such as arthritis, affect millions of Americans, leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive subclinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions. This review describes our progress in developing wearable sensing technologies that enable quantitative physiological measurements and interpretation of knee health status. Our sensing system enables longitudinal quantitative measurements of knee sounds, swelling, and activity context during clinical and field situations. Importantly, we leverage machine-learning algorithms to fuse the low-level signal and feature data of the measured time series waveforms into higher level metrics of joint health. This paper summarizes the engineering validation, baseline physiological experiments, and human subject studies-both cross-sectional and longitudinal-that demonstrate the efficacy of using such systems for robust knee joint health assessment. We envision our sensor system complementing and advancing present-day practices to reduce joint reinjury risk, to optimize rehabilitation recovery time for a quicker return to activity, and to reduce health care costs.

High-strength, surface-porous polyether-ether-ketone for load-bearing orthopedic implants.

Despite its widespread clinical use in load-bearing orthopedic implants, polyether-ether-ketone (PEEK) is often associated with poor osseointegration. In this study, a surface-porous PEEK material (PEEK-SP) was created using a melt extrusion technique. The porous layer was 399.6±63.3 µm thick and possessed a mean pore size of 279.9±31.6 µm, strut spacing of 186.8±55.5 µm, porosity of 67.3±3.1% and interconnectivity of 99.9±0.1%. Monotonic tensile tests showed that PEEK-SP preserved 73.9% of the strength (71.06±2.17 MPa) and 73.4% of the elastic modulus (2.45±0.31 GPa) of as-received, injection-molded PEEK. PEEK-SP further demonstrated a fatigue strength of 60.0 MPa at one million cycles, preserving 73.4% of the fatigue resistance of injection-molded PEEK. Interfacial shear testing showed the pore layer shear strength to be 23.96±2.26 MPa. An osseointegration model in the rat revealed substantial bone formation within the pore layer at 6 and 12 weeks via microcomputed tomography and histological evaluation. Ingrown bone was more closely apposed to the pore wall and fibrous tissue growth was reduced in PEEK-SP when compared to non-porous PEEK controls. These results indicate that PEEK-SP could provide improved osseointegration while maintaining the structural integrity necessary for load-bearing orthopedic applications.