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Koker O, Sahin S, Yildiz M, Adrovic A, Kasapcopur O. The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence. Rheumatol Int 2024; 44:2315-2325. [PMID: 39012357 DOI: 10.1007/s00296-024-05661-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 07/05/2024] [Indexed: 07/17/2024]
Abstract
Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.
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Affiliation(s)
- Oya Koker
- Department of Pediatric Rheumatology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - Sezgin Sahin
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Mehmet Yildiz
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Amra Adrovic
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ozgur Kasapcopur
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.
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Proof-of-Concept Study of the Use of Accelerometry to Quantify Knee Joint Movement and Assist with the Diagnosis of Juvenile Idiopathic Arthritis. TECHNOLOGIES 2022. [DOI: 10.3390/technologies10040076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
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.
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Verma DK, Kumari P, Kanagaraj S. Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review. Ann Biomed Eng 2022; 50:237-252. [DOI: 10.1007/s10439-022-02913-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/01/2022] [Indexed: 12/14/2022]
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Ye Y, Wan Z, Liu B, Xu H, Wang Q, Ding T. Monitoring deterioration of knee osteoarthritis using vibration arthrography in daily activities. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106519. [PMID: 34826659 DOI: 10.1016/j.cmpb.2021.106519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Pathological recognition of knee joint using vibration arthrography (VAG) is increasingly becoming prevailed, due to the non-invasive and non-radiative benefits. However, knee joint health monitoring using VAG signals is a difficult problem, since VAG signals are contaminated by strong motion artifacts (MA) caused by knee movements during daily activities, such as squatting. So far few works have investigated this problem. Existing studies mainly focused on clinical diagnosis of knee disorders for 2-class (normal/abnormal) classification using VAG signals, which are less contaminated by MA in the scene when subjects perform knee extension and flexion movements in seated position. The purpose of this study is to propose a framework to monitor knee joint health during daily activities. METHODS In this paper, a general framework is designed to monitor knee joint health, which consists of VAG enhancement, feature extraction and fusion, and classification. VAG enhancement aims to remove MA and irrelevant components of knee joint pathologies in raw VAG signals. Distinctive features from enhanced VAG signals are obtained in feature extraction and fusion. Classification can not only distinguish whether the knee joint is normal or abnormal, but also distinguish the grade of deterioration of knee osteoarthritis. RESULTS 813 VAG signals from VAG-OA dataset, which is currently the largest VAG dataset, have been collected from medical cases in Xijing Hospital of the Fourth Military Medical University during daily activities. Experimental results on VAG-OA dataset showed that the accuracy of 2-class (normal/abnormal) classification was 95.9% with sensitivity 98.1% and specificity 93.3%. For 5-class classification based on deterioration grades of osteoarthritis (OA), we obtained accuracy 74.4%, sensitivity 52.6% and specificity 78.3%. CONCLUSION The VAG-OA dataset can be used not only for knee joint health monitoring but also for clinical diagnosis. The designed framework on VAG-OA dataset has high classification accuracy, which is of great value to monitor knee joint health using VAG signals during daily activities. The results also demonstrate that the designed framework significantly outperforms the baselines and several state-of-the-art methods.
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Affiliation(s)
- Yalan Ye
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhengyi Wan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benyuan Liu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, P. R. China
| | - Hu Xu
- Xijing Orthopaedics Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, P. R. China
| | - Qian Wang
- 705th Research Institute, China Shipbuilding Industry Corporation, Xi'an, 710065, Shaanxi, P. R. China
| | - Tan Ding
- Xijing Orthopaedics Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, P. R. China.
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Garner AJ, Saatchi R, Ward O, Hawley DP. Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies. Healthcare (Basel) 2021; 9:1683. [PMID: 34946409 PMCID: PMC8700900 DOI: 10.3390/healthcare9121683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/29/2022] Open
Abstract
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.
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Affiliation(s)
- Amelia J. Garner
- The Medical School, University of Sheffield, Sheffield S10 2TN, UK
| | - Reza Saatchi
- Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Oliver Ward
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
| | - Daniel P. Hawley
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
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Scherpereel KL, Bolus NB, Jeong HK, Inan OT, Young AJ. Estimating Knee Joint Load Using Acoustic Emissions During Ambulation. Ann Biomed Eng 2020; 49:1000-1011. [PMID: 33037511 DOI: 10.1007/s10439-020-02641-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/26/2020] [Indexed: 01/26/2023]
Abstract
Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p < 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.
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Affiliation(s)
- Keaton L Scherpereel
- Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Nicholas B Bolus
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hyeon Ki Jeong
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Omer T Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Aaron J Young
- Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
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Athavale Y, Krishnan S. A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101580] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Knee joint vibroarthrography of asymptomatic subjects during loaded flexion-extension movements. Med Biol Eng Comput 2018; 56:2301-2312. [DOI: 10.1007/s11517-018-1856-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 06/01/2018] [Indexed: 10/28/2022]
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Bączkowicz D, Majorczyk E. Joint Motion Quality in Chondromalacia Progression Assessed by Vibroacoustic Signal Analysis. PM R 2016; 8:1065-1071. [PMID: 27060646 DOI: 10.1016/j.pmrj.2016.03.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 03/23/2016] [Accepted: 03/30/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Because of the specific biomechanical environment of the patellofemoral joint, chondral disorders, including chondromalacia, often are observed in this articulation. Chondromalacia via pathologic changes in cartilage may lead to qualitative impairment of knee joint motion. OBJECTIVE To determine the patellofemoral joint motion quality in particular chondromalacia stages and to compare with controls. DESIGN Retrospective, comparative study. SETTING Voivodship hospitals, university biomechanical laboratory. PATIENTS A total of 89 knees with chondromalacia (25 with stage I; 30 with stage II and 34 with stage III) from 50 patients and 64 control healthy knees (from 32 individuals). METHODS Vibroacoustic signal pattern analysis of joint motion quality. MAIN OUTCOME MEASUREMENTS For all knees vibroacoustic signals were recorded. Each obtained signal was described by variation of mean square, mean range (R4), and power spectral density for frequency of 50-250 Hz (P1) and 250-450 Hz (P2) parameters. RESULTS Differences between healthy controls and all chondromalacic knees as well as chondromalacia patellae groups were observed as an increase of analyzed parameters (P < .001) with only one exception. No statistically significant difference between control group and stage I of chondromalacia patellae was found. All chondromalacia groups were differentiated by the use of all analyzed parameters (P < .01), whose values correspond to the progress of chondromalacia. CONCLUSIONS Chondromalacia generates abnormal vibroacoustic signals, and there seems to be a relationship between the level of signal amplitude as well as frequency and cartilage destruction from the superficial layer to the subchondral bone. LEVEL OF EVIDENCE IV.
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Affiliation(s)
- Dawid Bączkowicz
- Institute of Physiotherapy, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland(∗).
| | - Edyta Majorczyk
- Institute of Physiotherapy, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole; and Laboratory of Immunogenetics and Tissue Immunology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland(†)
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Whitney GA, Mansour JM, Dennis JE. Coefficient of Friction Patterns Can Identify Damage in Native and Engineered Cartilage Subjected to Frictional-Shear Stress. Ann Biomed Eng 2015; 43:2056-68. [PMID: 25691395 DOI: 10.1007/s10439-015-1269-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 01/29/2015] [Indexed: 10/24/2022]
Abstract
The mechanical loading environment encountered by articular cartilage in situ makes frictional-shear testing an invaluable technique for assessing engineered cartilage. Despite the important information that is gained from this testing, it remains under-utilized, especially for determining damage behavior. Currently, extensive visual inspection is required to assess damage; this is cumbersome and subjective. Tools to simplify, automate, and remove subjectivity from the analysis may increase the accessibility and usefulness of frictional-shear testing as an evaluation method. The objective of this study was to determine if the friction signal could be used to detect damage that occurred during the testing. This study proceeded in two phases: first, a simplified model of biphasic lubrication that does not require knowledge of interstitial fluid pressure was developed. In the second phase, frictional-shear tests were performed on 74 cartilage samples, and the simplified model was used to extract characteristic features from the friction signals. Using support vector machine classifiers, the extracted features were able to detect damage with a median accuracy of approximately 90%. The accuracy remained high even in samples with minimal damage. In conclusion, the friction signal acquired during frictional-shear testing can be used to detect resultant damage to a high level of accuracy.
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Affiliation(s)
- G A Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Wickenden, Room 319, 2071 Martin Luther King Jr. Drive, Cleveland, OH, 44106, USA
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Bączkowicz D, Majorczyk E. Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders. BMC Musculoskelet Disord 2014; 15:426. [PMID: 25496721 PMCID: PMC4295352 DOI: 10.1186/1471-2474-15-426] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 12/05/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chondromalacia, lateral patellar compression syndrome and osteoarthritis are common patellofemoral joint disorders leading to functional and/or structural disturbances in articular surfaces. The objective of the study was to evaluate their impact on joint motion quality via the vibroacoustic signal generated during joint movement analysis. METHODS Seventy-three patients (30 with chondromalacia, 21 with lateral patellar compression syndrome, and 22 with osteoarthritis) and 32 healthy controls were tested during flexion/extension knee motion for vibroacoustic signals using an acceleration sensor. Estimated parameters: variation of mean square (VMS), difference between mean of four maximum and mean of four minimum values (R4), power spectral density for frequency of 50-250 Hz (P1) and 250-450 Hz (P2) were analyzed. RESULTS Vibroacoustic signals recorded for particular disorders were characterized by significantly higher values of parameters in comparison to the control group. Moreover, differences were found among the various types of patellofemoral joint disturbances. Chondromalacia and osteoarthritis groups showed differences in all parameters examined. In addition, osteoarthritis patients exhibited differences in VMS, P1 and P2 values in comparison to lateral patellar compression syndrome patients. However, only the value of R4 was found to differ between knees with lateral patellar compression syndrome and those with chondromalacia. CONCLUSION Our results suggest that particular disorders are characterized by specific vibroacoustic patterns of waveforms as well as values of analyzed parameters.
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Affiliation(s)
- Dawid Bączkowicz
- Institute of Physiotherapy, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Prószkowska Street 76, PL-45-758 Opole, Poland.
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12
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Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.05.004] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
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.
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Affiliation(s)
- Noriyuki Tanaka
- Department of Rehabilitation Sciences, Postgraduate School of Health Sciences, Nagoya University, 1-1-20, Daiko-minami, Higashi-ku, Nagoya 461-8673, Japan
- Division of Rehabilitation, Syutaikai Hospital, 8-1 Shirokita-cho, Yokkaichi, Mie 510-0823, Japan
| | - Minoru Hoshiyama
- Department of Rehabilitation Sciences, Postgraduate School of Health Sciences, Nagoya University, 1-1-20, Daiko-minami, Higashi-ku, Nagoya 461-8673, Japan
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Rangayyan RM, Wu Y. Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows. Biomed Signal Process Control 2010. [DOI: 10.1016/j.bspc.2009.03.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Rangayyan RM, Wu Y. Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann Biomed Eng 2008; 37:156-63. [PMID: 19015987 DOI: 10.1007/s10439-008-9601-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2007] [Accepted: 11/04/2008] [Indexed: 10/21/2022]
Abstract
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.
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Affiliation(s)
- Rangaraj M Rangayyan
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
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Glaser D, Komistek RD, Cates HE, Mahfouz MR. Clicking and squeaking: in vivo correlation of sound and separation for different bearing surfaces. J Bone Joint Surg Am 2008; 90 Suppl 4:112-20. [PMID: 18984724 DOI: 10.2106/jbjs.h.00627] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Diana Glaser
- Center for Musculoskeletal Research, Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, 301 Perkins Hall, Knoxville, TN 37996, USA.
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Screening of knee-joint vibroarthrographic signals using parameters of activity and radial-basis functions. ACTA ACUST UNITED AC 2008. [DOI: 10.1109/ccece.2008.4564495] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Mu T, Nandi AK, Rangayyan RM. Strict 2-Surface Proximal Classification of Knee-joint Vibroarthrographic Signals. ACTA ACUST UNITED AC 2008; 2007:4911-4. [PMID: 18003107 DOI: 10.1109/iembs.2007.4353441] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Externally detected vibroarthrographic (VAG) signals contain information that can be used to characterize certain pathological aspects of the knee joint. To classify VAG signals as normal or abnormal, we propose to apply both the linear and nonlinear strict 2-surface proximal (S2SP) classifiers based on statistical parameters derived from VAG signals and selected by using a genetic algorithm (GA). A database of VAG signals of 89 human knee joints (51 normal and 38 abnormal) was studied. The classification performance of the linear S2SP classifier reached 0.82 in terms of the area under the receiver operating characteristics curve (Az) and 74.2% in average classification accuracy with the leave-one-out (LOO) procedure. The classification performance of the nonlinear S2SP classifier reached 0.95 in Az value and 91.0% in average classification accuracy using the Gaussian kernel with the LOO procedure, and possessed good robustness around the selected kernel parameter.
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Affiliation(s)
- Tingting Mu
- Department of Electrical Engineering and Electronics, the University of Liverpool, Brownlow Hill, Liverpool, UK, L69 3GJ
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Rangayyan RM, Wu Y. Modeling and classification of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2008; 2008:2099-2102. [PMID: 19163110 DOI: 10.1109/iembs.2008.4649607] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
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.
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Affiliation(s)
- Rangaraj M Rangayyan
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, AB, Canada.
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Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med Biol Eng Comput 2007; 46:223-32. [PMID: 17960443 DOI: 10.1007/s11517-007-0278-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2007] [Accepted: 10/04/2007] [Indexed: 10/22/2022]
Abstract
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.
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Shah EN, Reddy NP, Rothschild BM. Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2005; 77:233-239. [PMID: 15721651 DOI: 10.1016/j.cmpb.2004.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2003] [Revised: 09/07/2004] [Accepted: 10/07/2004] [Indexed: 05/24/2023]
Abstract
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.
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Affiliation(s)
- Ekta N Shah
- Human Interface Laboratory, Biomedical Engineering Department, University of Akron, Akron, OH 44325-0302, USA.
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Krishnan S, Rangayyan RM, Bell GD, Frank CB. Auditory display of knee-joint vibration signals. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2001; 110:3292-3304. [PMID: 11785830 DOI: 10.1121/1.1413995] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Sounds generated due to rubbing of knee-joint surfaces may lead to a potential tool for noninvasive assessment of articular cartilage degeneration. In the work reported in the present paper, an attempt is made to perform computer-assisted auscultation of knee joints by auditory display (AD) of vibration signals (also known as vibroarthrographic or VAG signals) emitted during active movement of the leg. Two types of AD methods are considered: audification and sonification. In audification, the VAG signals are scaled in time and frequency using a time-frequency distribution to facilitate aural analysis. In sonification, the instantaneous mean frequency and envelope of the VAG signals are derived and used to synthesize sounds that are expected to facilitate more accurate diagnosis than the original signals by improving their aural quality. Auditory classification experiments were performed by two orthopedic surgeons with 37 VAG signals including 19 normal and 18 abnormal cases. Sensitivity values (correct detection of abnormality) of 31%, 44%, and 83%, and overall classification accuracies of 53%, 40%, and 57% were obtained with the direct playback, audification, and sonification methods, respectively. The corresponding d' scores were estimated to be 1.10. -0.36, and 0.55. The high sensitivity of the sonification method indicates that the technique could lead to improved detection of knee-joint abnormalities; however, additional work is required to improve its specificity and achieve better overall performance.
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Affiliation(s)
- S Krishnan
- Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada
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Krishnan S, Rangayyan RM, Bell GD, Frank CB. Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology. IEEE Trans Biomed Eng 2000; 47:773-83. [PMID: 10833852 DOI: 10.1109/10.844228] [Citation(s) in RCA: 102] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's of VAG signals suitable for feature extraction. An adaptive TFD was constructed by minimum cross-entropy optimization of the TFD obtained by the matching pursuit decomposition algorithm. Parameters of VAG signals such as energy, energy spread, frequency, and frequency spread were extracted from their adaptive TFD's. The parameters carry information about the combined TF dynamics of the signals. The mean and standard deviation of the parameters were computed, and each VAG signal was represented by a set of just six features. Statistical pattern classification experiments based on logistic regression analysis of the parameters showed an overall normal/abnormal screening accuracy of 68.9% with 90 VAG signals (51 normals and 39 abnormals), and a higher accuracy of 77.5% with a database of 71 signals with 51 normals and 20 abnormals of a specific type of patellofemoral disorder. The proposed method of VAG signal analysis is independent of joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of patellofemoral disorders such as chondromalacia patella.
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Affiliation(s)
- S Krishnan
- Department of Electrical and Computer Engineering, Ryerson Polytechnic University, Toronto, ON, Canada
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Krishnan S, Rangayyan RM. Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations. Med Biol Eng Comput 2000; 38:2-8. [PMID: 10829383 DOI: 10.1007/bf02344681] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
A novel de-noising method for improving the signal-to-noise ratio of knee-joint vibration signals (also known as vibro-arthrographic (VAG) signals) is proposed. The de-noising methods considered are based on signal decomposition techniques, such as wavelets, wavelet packets and the matching pursuit (MP) method. Performance evaluation with synthetic signals simulated with the characteristics expected of VAG signals indicates good de-noising results with the MP method. Statistical pattern classification of non-stationary signal features extracted from time-frequency distributions of 37 (19 normal and 18 abnormal) MP method-de-noised VAG signals shows a sensitivity of 83.3%, a specificity of 84.2% and an overall accuracy of 83.8%.
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Affiliation(s)
- S Krishnan
- Department of Electrical & Computer Engineering, Ryerson Polytechnic University, Toronto, Ontario, Canada
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