Noninvasive measurement of acceleration at the knee joint in patients with rheumatoid arthritis and spondyloarthropathy of the knee

Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood. Seven children and young people (CYP) with a diagnosis of JIA and suspected active arthritis of a single knee joint were recruited for this proof-of-concept study. The presence of active arthritis was confirmed by clinical examination. Four tri-axial accelerometers were integrated individually in elastic bands and placed above and below each knee. Participants performed ten periodic flexion-extensions of each knee joint while lying down, followed by walking ten meters in a straight path. The contralateral (non-inflamed) knee joint acted as a control. Accelerometry data were concordant with the results of clinical examination in six out of the seven patients recruited. There was a significant difference between the accelerometry measured range of movement (ROM, p-value = 0.032) of the knees with active arthritis and the healthy contralateral knees during flexion-extension. No statistically significant difference was identified between the ROM of the knee joints with active arthritis and healthy knee joints during the walking test. The study demonstrated that accelerometry may help in differentiating between healthy knee joints and those with active arthritis; however, further research is required to confirm these findings.

Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing-remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians' assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility.

Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures

BACKGROUND AND OBJECTIVE

Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint.

METHODS

The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications.

RESULTS

The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p<0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger (p<0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve.

CONCLUSIONS

The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology.

Return to clinical in contrast to serologically-based diagnoses

The future of rheumatology is predicated upon a return to basics. The advent and facile availability of laboratory testing led to reduction of emphasis on clinical skills. Recognition that immunologic abnormalities are not limited to individuals who clearly have related pathology provides new motivation for reorientation of training programs to assure that graduates have appropriate information gathering, diagnostic and procedural skills. Inadequate accessibility to rheumatologic care requires innovative approaches and especially training and educating those individuals who provide primary care. While the rheumatologist can elicit the patient’s history remotely, telerheumatology will be feasible only when the individual interacting physically with the patient has confidence in their examination skills and when those skills have been validated. Named syndromes or diseases will be modified to avoid impugning the individual or compromising their future access to health, disability and life insurance. Interventions will be pursued in a more cost-effective, evidence-based manner. The future of rheumatology is dependent upon the rheumatologist’s ability to amortize the inadequate reimbursement for direct patient interaction, depending on skills of interpretation of standard X-rays, ultrasound performance and results.

What qualifies as rheumatoid arthritis?

Expansion of diagnostic criteria for rheumatoid arthritis and deletion of exceptions increases sensitivity, but at the expense of specificity. Two decades later, modification of criteria included the caveat: “absence of an alternative diagnosis that better explains the synovitis.” That puts great faith in the diagnostic skills of the evaluating individual and their perspectives of disease. The major confounding factor appears to be spondyloarthropathy, which shares some characteristics with rheumatoid arthritis. Recognition of the latter on the basis of marginally distributed and symmetrical polyarticular erosions, in absence of axial (odontoid disease excepted) involvement requires modification to avoid failure to recognize a different disease, spondyloarthropathy. Skeletal distribution, pure expression of disease in natural animal models and biomechanical studies clearly rule out peripheral joint fusion (at least in the absence of corticosteroid therapy) as a manifestation of rheumatoid arthritis. Further, such studies identity predominant wrist and ankle involvement as characteristic of a different disease, spondyloarthropathy. It is important to separate the two diagnostic groups for epidemiologic study and for clinical diagnosis. They certainly differ in their pathophysiology.

ARTICULAR SOUND AND CLINICAL STAGES IN KNEE ARTHROPATHY

Objective: To clarify the pathophysiology of knee arthropathy, articular sound in the knee joint was recorded using an accelerometer, vibroarthrography (VAG), during standing-up and sitting-down movements in patients with osteoarthropathy (OA) of the knees. Methods: VAG signals and angular changes of the knee joint during standing-up and sitting-down movements were recorded in patients with OA, including 17 knees with OA at Kellgren–Lawrence stage I and II, 16 knees with OA at III and IV stages, and 20 knees of age-matched control subjects. Results: The level of VAG signals was greater in knees with a higher stage of OA at 50–99 and 100–149 Hz among the groups (ANOVA with Tukey–Kramer multiple comparisons test, p < 0.01). The VAG signals did not correlate with WOMAC-pain or physical scores. Conclusions: We considered that the increase in VAG signals in these ranges of frequency corresponded with pathological changes of OA, but not self-reported clinical symptoms. This method of VAG can be used by clinicians during interventions to obtain pathological information regarding structural changes of the knee joint.

An acoustical evaluation of knee sound for non-invasive screening and early detection of articular pathology

Knee sound signals generated by knee movement are sometimes associated with degeneration of the knee joint surface and such sounds may be a useful index for early disease. In this study, we detected the acoustical parameters, such as the fundamental frequency (F0), mean amplitude of the pitches, and jitter and shimmer of knee sounds, and compared them according to the pathological conditions. Six normal subjects (4 males and 2 females, age: 28.3 ± 2.3 years) and 11 patients with knee problems were enrolled. The patients were divided into the 1st patient group (5 males and 1 female, age: 30.2 ± 10.3 years) with ruptured wounds of the meniscus and 2nd patient group (2 males and 3 females, age: 42.1 ± 16.2 years) with osteoarthritis. The mean values of F0, jitter and shimmer of the 2nd patient group were larger than those of the normal group, whereas those of the 1st patient group were smaller (p < 0.05). Also, the F0 and jitter in the standing position were larger than those in the sitting position in both the 1st and 2nd patient groups (p < 0.05). These results showed good potential for the non-invasive diagnosis and early detection of articular pathologies via an auscultation.

Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions

Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose parameters related to signal variability for the analysis of VAG signals, including an adaptive turns count and the variance of the mean-squared value computed during extension, flexion, and a full swing cycle of the leg, for the purpose of classification as normal or abnormal, that is, screening. With a database of 89 VAG signals, screening efficiency of up to 0.8570 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial-basis functions, with all of the six proposed features. Using techniques for feature selection, the turns counts for the flexion and extension parts of the VAG signals were chosen as the top two features, leading to an improved screening efficiency of 0.9174. The proposed methods could lead to objective criteria for improved selection of patients for clinical procedures and reduce healthcare costs.

Modeling and classification of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Diagnostic information related to the articular cartilage surfaces of knee-joints may be derived from vibro-arthrographic (VAG) signals. Although several studies have proposed many different types of parameters for the analysis and classification of VAG signals, no statistical modeling methods have been explored to represent the fundamental distinctions between normal and abnormal VAG signals. In the present work, we derive models of probability density functions (PDFs), using the Parzen-window approach, to represent the basic statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance (KLD) is then computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. A classification accuracy of 73.03% was obtained with a database of 89 VAG signals. The screening efficiency was derived to be 0.6724, in terms of the area under the receiver operating characteristics curve.

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions.

Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint

Arthritis is one of the leading causes of disability and affects a major segment of the population. Consequently, accurate diagnosis of arthritis is important. Arthritis due to calcium pyrophosphate deposition disease (CPPD), rheumatoid arthritis, and spondyloarthropathy, induce complex changes in the cartilage and the articular surface. The fractal dimension provides a measure of the complexity of a signal. Recently, we have developed non-invasive acceleration measurements to characterize the arthritic patients. The question remains if the fractal dimension of the acceleration signal is different for different arthritis conditions. The purpose of this study was to distinguish between different types of arthritis of the finger joint using the fractal dimension of the acceleration signal obtained from the finger joint of the arthritic patients. Acceleration signals were obtained from the finger joint of arthritis patients with rheumatoid arthritis, spondyloarthropathy, and calcium pyrophosphate deposition disease of the finger joint. ANOVA results showed that there were significant differences between the fractal dimension of acceleration signals from patients having calcium pyrophosphate deposition disease and rheumatoid arthritis and spondyloarthropathy. Fractal dimension of acceleration signals, in concert with other clinical symptoms, can be used to classify different types of arthritis.