WO2008074151A1 - Système et procédé destinés à évaluer l'état d'une articulation - Google Patents

Système et procédé destinés à évaluer l'état d'une articulation Download PDF

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Publication number
WO2008074151A1
WO2008074151A1 PCT/CA2007/002331 CA2007002331W WO2008074151A1 WO 2008074151 A1 WO2008074151 A1 WO 2008074151A1 CA 2007002331 W CA2007002331 W CA 2007002331W WO 2008074151 A1 WO2008074151 A1 WO 2008074151A1
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WIPO (PCT)
Prior art keywords
joint
feature vector
latent variables
health
data
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PCT/CA2007/002331
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English (en)
Inventor
John Macgregor
Francois Yacoub
Jonathan Derrick Jun Adachi
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Mcmaster University
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Publication of WO2008074151A1 publication Critical patent/WO2008074151A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/006—Detecting skeletal, cartilage or muscle noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4528—Joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4514—Cartilage
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0875—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of bone
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • This invention relates to a system for diagnosing the state of health of a joint.
  • the present invention relates to a system that measures joint vibrations and analyzes this data to determine the health of the joint.
  • Arthroscopy is considered to be the best method to assess the state of a joint. It is a procedure in which a camera or arthroscope is inserted into the joint through small incisions to allow the surgeon to visuedize the condition of the joint. The surgeon can also insert instruments through small incisions to make minor repairs or eliminate damaged tissue.
  • the arthroscope uses fiber optics to send pictures of the inside of the joint to a television monitor. The surgeon can then determine what the problems are, and at the same time may decide to insert other surgical instruments through small incisions in the joint to remove or repair damaged tissues. Although it is considered the most accurate approach, the risks and cost associated with this surgical diagnostic are significant
  • a second class of techniques in joint diagnosis is the use of radiological imaging such as X-rays, computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI).
  • Magnetic resonance imaging (MRI) is considered to be the gold standard in medical imaging of the joint. It is a reliable technique that has been growing in popularity over the past two decades and allows for non-invasive evaluation of meniscal abnormalities, cartilage lesions and other features of knee osteoarthritis (OA).
  • OA knee osteoarthritis
  • plain radiographs generated using X-ray limits the evaluation of OA to bony features such as joint space narrowing and osteophyte formation.
  • MRI magnetic resonance imaging
  • auscultation has been used by physicians for centuries and relies on their interpretation of the sounds and vibrations emitted when moving the joint.
  • Robert Hooke suggested that joint noise could be used as a diagnostic tool in patients suffering from painful joints.
  • the experiments of Rene Laexu ⁇ ec with a paper cylinder resulted in the stethoscope, a potential application of which was believed to be the clinical evaluation of joint disorder.
  • a system for assessing the health of a joint comprising: i) at least one device adapted to receive/capture a vibration signal from the joint; ii) data collection means adapted to extract time-frequency information from the vibration signal and generate a feature vector; ⁇ i) correlation means configured to correlate the feature vector with anatomical data corresponding to the joint to identify latent variables;, and iv) classification means configured to classify the vibration signal based on an input of said latent variables to produce an output relating to the health of the joint.
  • a method for assessing the health of a joint comprising the steps of:
  • capturing one or more vibration signals of the joint i) extracting time and frequency information from the captured vibration signal to generate a feature vector; iii) correlating the feature vector with anatomical data corresponding to the joint to identify latent variables; and iv) analyzing the latent variables of the feature vector as input to produce an output which, indicates at least one classification of the joint.
  • a method of quantitative determination of a characteristic of a joint comprising the steps of:
  • capturing one or more vibration signals of the joint ii) extracting time and -frequency information from the captured vibration signal to generate a feature vector; i ⁇ ) correlating the feature vector witb anatomical data corresponding to the joint characteristic to identify latent variables; and iv) analyzing the latent variables of the feature vector as input to produce an output which quantifies the characteristic of the joint.
  • a computer program product having computer readable code embodied therein for execution by a processor of a computer system for configuring the computer to assess the health of a joint.
  • the computer program product comprises instructions and data for configuring the processor to:
  • a method for tracking the effectiveness of a treatment on the transient state of health of a joint comprising the steps of:
  • a method to quantify a joint characteristic comprising the steps of:
  • FIG. 1 is a flowchart generally illustrating a method in accordance with the invention
  • Figure 2 illustrates a typical placement of accelerometer sensors at sites around a knee joint
  • Figure 3 illustrates a typical time domain signal from three accelerometers (10 cycles) on a knee joint
  • Figure 4 shows a plot of the latent variable scores, from a PCA analysis, for patients exhibiting various states of joint health including healthy patients, OA patients, and patients with a meniscus tear;
  • Figure 5 shows the structure of X and Y data matrices
  • Figure 6 shows the wavelet decomposition of a vibration signal from an accelerometer placed on the patella of a healthy knee
  • Figure 7 shows the structure of the X-matrix after performing wavelet transform in which, for each sensor, Dl-9 and AP denote the features extracted from 9 wavelet detail scales and the approximation;
  • Figure 8 shows the structure of the X and Y matrix for the PLS model
  • FIG. 9 illustrates the structure of the SVM network
  • Figure 10 graphically illustrates the result of the SVM classification in the latent variable space between healthy knees and osteoarthritic knees
  • Figure 11 graphically illustrates the result of the S VM classification in the latent variable space between healthy knees and knees with meniscus tear
  • Figure 12 illustrates the ability of the method to provide quantitative prediction of cartilage thickness using signals obtained on pat cnts during external excitation of the patella
  • Figure 13 illustrates a joint assessment environinent including a diagnosis system in accordance with an aspect of the invention
  • Figure 14 illustrates a diagnosis system
  • Figure 15 illustrates a feature reduction system for generating latent variables from an X- matrix and a Y -matrix
  • Figure 16 generally illustrates a computing device useful to implement systems in accordance with aspects of the invention.
  • a method for diagnosing the health of a joint comprising the stops of: capturing one or more vibration signals of the joint ; pretreating or conditioning the signals; extracting time and frequency information from the vibration signal to generate a feature vector; correlating the feature vector with anatomical data to identify latent variables; and analyzing the latent variables of the feature vector as input to produce an output which provides a diagnosis of the health of the joint.
  • a system employing the present method is also provided. The present inven ion provides, thus, a method by which an accurate, non-invasive diagnosis of the state of health of a joint may be obtained.
  • a system employing the method can readily be provided in a portable package for use in an office or other non-hospital setting, thereby obviating disadvantages associated with other diagnostic methods such as invasive surgical methods and imaging diagnostic methods (MRI and X-ray) which can be expensive and generally entail long wait times.
  • diagnostic methods such as invasive surgical methods and imaging diagnostic methods (MRI and X-ray) which can be expensive and generally entail long wait times.
  • FIG. 1 A schematic flowchart generally illustrating the steps of the present method is shown in FIG. 1
  • a system for assessing the health of a joint comprises at least one device capable of capturing a vibration signal from the joint; data collection means adapted to extract time-frequency information from the vibration signal and generate a feature vector; means to correlate the feature vector with anatomical data to identify latent variables; and means to classify the signal based on an input of latent variables to produce an output that defines the health of the joint.
  • the first component of the system is a device, or aplurality of devices, capable of detecting a vibration signal(s) from the joint.
  • a device capable of detecting a vibration signal(s) from the joint.
  • Such device is used to carry out the first step of the present method.
  • suitable devices include accelerometers and microphones.
  • the system comprises at least one accelerometer, or optionally, an array of accelerometers, as shown in FIG. 2, for placement at different locations around a joint to be diagnosed.
  • the accelerometer also includes retaining means that applies a force against the accelerometer to retain it firmly against the skin at the contact site.
  • the retaining means applies a constant force on the accelerometer at the contact site to minimize damping of the signal through the skin and to improve reproducibility of the measurement.
  • suitable retaining means include a mechanical device which attaches to the knee and holds the accelerometers(s) in place throughout the flexing of the joint, and an air cuff filled with an amount of air that provides the desired force on the accelerometer.
  • the accelerometer(s) may also be manually held in position using an appropriate grip.
  • the accelerometer(s) may be embedded in the fingers of a glove and positioned appropriately on the joint by the wearer of the glove, e.g. a technician.
  • FIG. 2 illustrates the placement of multiple accelerometer sensors around a knee joint.
  • FIG.3 shows typical vibration signals captured at 3 locations during multiple knee flexing cycles, e.g. non-stationary cycles.
  • the vibration signals are fed into a data collection and processing means which is adapted to extract time-frequency information from the signal to generate a feature vector.
  • any one of several methods may be employed to transform the vibration signal from the time domain to the frequency domain including Fourier transform (FT), the Fast Fourier transform (FFT), and Wavelet Transform (WT) such as Discrete Wavelet Transform (DWT), the Continuous Wavelet transform (CWT) and Wavelet Packets (WP) which each provide good time-frequency resolution for signals that are non-stationary in intensity and frequency.
  • FT Fourier transform
  • FFT Fast Fourier transform
  • WT Wavelet Transform
  • DWT Discrete Wavelet Transform
  • CWT Continuous Wavelet transform
  • WP Wavelet Packets
  • wavelet transform provides a number of features.
  • wavelet analysis can reveal aspects like trends, breakdown points, discontinuities in higher derivatives, and self-sirnilarity not readily observed with other techniques, and can compress or de-noise a signal without appreciable signal degradation.
  • Wavelet analysis also offers a windowing technique with variable-sized regions, allowing the use of long time intervals for more precise low-frequency information and shorter regions for high- frequency information.
  • the wavelet transform provides wavelet coefficients relating to the signal strength for various times and frequencies. Various transformations of these coefficients provide features including the power or variance of the coefficients in each detail, and the histogram of the coefficients in each detail.
  • the X-matri ⁇ comprises the feature vector of the joint vibrations
  • the Y-matrix consists of anatomical data obtained from a joint using MRI and arthroscopy. Examples of anatomical data that may be obtained include:
  • the number of latent variables may be even further reduced to refine the information to the necessary or most important information using techniques well established in the art, including for example, principle component analysis (PCA) and partial least squares (PLS).
  • PCA principle component analysis
  • PLS partial least squares
  • uncorrelated noise may be reduced, for example, using orthogonal signal correctioxx (O-PLS) methods.
  • FIG. 4 shows the visual separation among patients provided by the latent vectors of PCA. Different patients with similar states of joint health (e.g. severe OA, mild OA, healthy or meniscus tear) will have latent variable values (scores) that fall in similar regions in the plot, while patients with different states of joint health will fall in different regions.
  • Classification of the data into defined classes of joint health may then be employed. Based on an input of latent variables processed as described above, the system produces an output which provides a classification of the input data relative to standard classifications.
  • Classification means may be any means capable of determining the placement of input data based on standards developed from the training data which is comprised of anatomical data obtained by imaging techniques and corresponding vibration data. Examples of classification means in accordance with the invention include feed-forward neural networks such as support vector machines (SVM) and radial basis function (RBF); recurrent neural networks; discriminant PLS; and partial least squares - discriminant analysis (PLS-DA).
  • SVM support vector machines
  • RBF radial basis function
  • PLS partial least squares - discriminant analysis
  • the data collection means, the correlation means and the classification means described above may execute within a computer system.
  • the computer system may include a visual display, a keyboard, and one or more auxiliary user interfaces, each of which is coupled to a computer processor.
  • the computer processor may include one or more general purpose processors and/or special purpose processors (e.g., ASICs, FPGAs, DSPs, etc.).
  • the computer system further includes a memory module which is coupled to the processor through a BUS or other suitable means.
  • Each of the data collection means, the correlation means and the classification means provide a set of computer instructions which are fetched by the processor from the memory for execution by the processor.
  • the processor also reads data stored on the memory and writes data to the memory.
  • the computer system may be coupled to an accelerometer which captures a vibration signal from the joint and said vibration signal is stored on the memory for subsequent access by the processor and execution of the computer instructions thereon.
  • the processor can further interface with the visual display to show received information, stored information, user inputs and the like in accordance with the computer instructions.
  • the memory described above can include any type of computer memory such as, but not limited to, random access memory (RAM), read-only memory (ROM), and computer readable mediums which can include hardware and/or software such as, by way of example only, magnetic disks, magnetic tape, optically readable medium such as CD/DVD ROMS, and memory cards.
  • RAM random access memory
  • ROM read-only memory
  • computer readable mediums which can include hardware and/or software such as, by way of example only, magnetic disks, magnetic tape, optically readable medium such as CD/DVD ROMS, and memory cards.
  • the data collection means, the correlation means and the classification means may execute in the same computer system, or can be distributed among separate computer systems. It will be understood that the system and methods described herein may be implemented by any hardware, software, or a combination of hardware and software having the above described functions. As described above, the software code, either in its entirety or a part thereof, may be stored in a computer readable memory,
  • the present system may also provide a visual display of the output in which the output for a given joint is compared with training data or standards, e.g. results obtained by imaging techniques.
  • This feature of the invention advantageously minimizes the subjectivity generally associated with analyzing a vibration signal.
  • the output, as well as the training data is transmitted to a display unit, such as a LED screen, for display in the conventional manner, thereby providing an immediate diagnosis of the health of a joint.
  • the present system and method may be applied to achieve, for example, determinations such as discriminating between healthy and non-healthy joints including knees, elbows, ankles, wrists and hips; assessing the degree of osteoarthritis in a joint, classifying menisci tears, evaluating the effectiveness of different treatments and tracking patients after treatment to determine the nature and timing of subsequent treatments, if required.
  • the system and method may also be used to predict quantitative features of a joint, for example, cartilage thickness and osteophyte length. As one of skill in the art will appreciate, other determinations may also be made using the present system and method.
  • a computer program product comprising computer readable code embodied therein for execution by a processor of a computer system which configures the system to assess the health of a joint according to the method herein described.
  • the computer program product comprises a general user interface (GUI) providing instructions and a set of algorithms to extract time and frequency information from received vibration signals of the joint to generate a feature vector; to calculate the latent variables from the feature vector; and then to analyze the latent variables of the feature vector as input to produce an output which indicates at least one classification of the joint
  • GUI general user interface
  • a joint assessment environment 10 has a diagnosis system 14 for receiving a plurality of vibration data (e.g. frequency data of the joint vibration signal amplitude or magnitude for a specific frequency) signals 12 from a vibration measurement and data capture device 13 (e.g. an accelerometer, microphone, etc.).
  • the signals 12 are processed by the diagnosis system 14 for producing the feature/frequency data X (e.g. the X matrix or the feature vector).
  • the feature/frequency data X could represent a temporally dependent sequence of discrete/continuous vibration data measurements for various physical positioning of a patient's joint (e.g.
  • the vibration data measurement captured from device 13 could be correlated to specific times that the frequency is detected by the device 13 throughout the range of motion of the joint and/or can be correlated to the relative position of the joint (e.g. the vibration data measurement is a function of position).
  • the vibration data measurement sequence is correlated with (e.g. is dependent upon) an independent variable (e.g. time and/or positioning).
  • the diagnosis system 14 also has access to raw anatomical data 16 (e.g. such as patient images and patient comments with respect to pain or other observations obtained from the patient and/or examiner during the joint positioning).
  • the anatomical data 16 e.g. MRJ of the patient's joint
  • the anatomical data 16 is recorded during the different joint positioning, such that the raw anatomical data measurements (also patient comments such as their indication of pain in a specific position) are correlated to the same independent variable (e.g. time and/or positioning) as the joint vibration data measurement, thus providing for synchronization between the anatomical data 16 and the feature/frequency data X.
  • the anatomical data 16 has image characteristics extracted therefrom (either by the diagnosis system 14 or a third party processor - not shown) in order to produce a temporally dependent sequence of discrete/continuous anatomical characteristics Y (e.g. a matrix) fox various anatomical positioning of the patient' s j oint. It is recognised that the independent basis (e.g. as a function of time) for the feature/frequency data X is synchronized with the independent basis (e.g. same function of time) for the anatomical characteristics Y.
  • a temporally dependent sequence of discrete/continuous anatomical characteristics Y e.g. a matrix
  • the diagnosis system 14 then combines the feature/frequency data X and the anatomical characteristics Y in order to produce a resultant dataset 19 (e.g. LV ⁇ latent variables) that is then compared with one or more classification templates/standards 18, in order to produce output data 20 that is representative of the health of the patient's joint.
  • a resultant dataset 19 e.g. LV ⁇ latent variables
  • the dataset 19 is preferably dimensionally reduced in comparison to the data X, Y.
  • latent variables are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed and directly measured.
  • latent variables are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed and directly measured.
  • One advantage of using latent variables is that it reduces the dimensionality of data.
  • PLS-regression partial least squares regression
  • X and Y the fundamental relations between the two data sets
  • X and Y the fundamental relations between the two data sets
  • a latent variable approach is to model a covarianc ⁇ structures in the two spaces of an X matrix and a Y matrix.
  • the PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. It is recognised that an alternative long form for PLS is projection to latent structures.
  • the diagnosis system 14 contains a data collection and processing module 110 (e.g. data collection means), a correlation and extraction module 114 (e.g. correlation means), an image characteristic extractor module 112 for converting the anatomical image data 16 to the anatomical characteristics Y, a user interface 402 for monitoring/controllmg conversion operation of the characteristic extractor module 112 and monitoring/controlling classification operation of the module 114 for template (e.g. standard) generation, a classification module 116 (e.g. classification means), and one or more memories 410 for storing the data 16, 18, X, Y, as desired.
  • a data collection and processing module 110 e.g. data collection means
  • a correlation and extraction module 114 e.g. correlation means
  • an image characteristic extractor module 112 for converting the anatomical image data 16 to the anatomical characteristics Y
  • a user interface 402 for monitoring/controllmg conversion operation of the characteristic extractor module 112 and monitoring/controlling classification operation of the module 114 for template (e.g
  • the characteristic extractor module 112 can be part of or separate from the diagnosis system 14, and is configured so as to convert the anatomical image data 16 to the anatomical characteristics Y prior to sending the data Y to the correlation module 114. It is also recognised that the correlation module 114 can obtain the data Y directly from the memory 410. It is shown in Figure 14 that the various modules 110-116 can be used to process individual data X,Y,LV (for an individual joint of a patient) and/or group data X,Y,LV (for joints of a number of patients used for template generation and training).
  • the feature reduction system e.g. module 114 for generating latent variables from an X-raatrix and a Y-matrix (or other forms of the X, Y data other than matrix format).
  • the X-matrix is generated by obtaining one or more vibration signals 12 from a patient, the vibration signals 12 are processed by a data collection modules 110, which extracts vibration signal information at various times and frequency ranges.
  • the data collection module 110 may for example perform Wavelet analysis to extract the time frequency information and generate the X-matrix representing the vibration signal 12.
  • the Y-matrix represents the anatomical data such as that obtained from imaging techniques (X-Ray, MRI).
  • the Y-matrix has image characteristic information (e.g. shown as the variable o), which can be for example, various physical properties of the joint assessed from the image (including for example bone density information).
  • image characteristic information e.g. shown as the variable o
  • the Y- matrix is a function of time, which corresponds approximately to the time reference of the X-matrix. In this way, anatomical data (Y-matrix) and vibration signal data (X- tnatrix) occurring during approximately the same time period may be correlated via the correlation module 114. If there is no corresponding data (e.g.
  • the latent variables (LV) generated either do not include the latent variables for the time period where the X-matrix is not correlated to the Y-matrix or a zero/negligible value is contained for that time period.
  • the latent variables may either be lacking the LV(X2) or LV(X2) may be equivalent to a null value.
  • the latent values may either be lacking the LV(XA) ox a null value for LV(X4).
  • Other methods such as the goodness of fit, and the sum of squares may be used to determine which values of the X and Y matrices are correlated such as to generate a latent variable set denoting the correlated values.
  • the latent variables provide a significant reduction in the dimension space. In this manner, a reduction in dimension of the LV values is realized in view of the operation of the module 114.
  • each of the above-described components of the environment 10, i.e. the signal collection device 13, the diagnosis system 14, and the anatomical data collector 18, can be implemented on one or more respective computing device ⁇ s) 101.
  • the devices 101 in general can include a network connection interface 400, such as a network interface card or a modem, coupled via connection 418 to a device infrastructure 404.
  • the connection interface 400 is connectable during operation of the devices 101 to the network 11 (e.g. an intranet/extranet for making available the anatomical data 16 and/or the signal data 12), which enables the devices 101 to communicate with each other as appropriate.
  • the network 11 can support the communication of the data signals 12 between the components of the environment 10.
  • the devices 101 can also have a user interface 402, coupled to the device infrastructure 404 by connection 422, to interact with a user (e.g. user diagnosis system 14).
  • the user interface 402 is used by the user of the device 101 to interact with the non-stationary frequency information contained within the signals 12 (e.g. the X matrix) and the anatomical data 17 (e.g. the Y matrix) to operate the diagnosis system 14.
  • the user interface 402 can include one or more user input devices such as but not limited to a QWERTY keyboard, a keypad, a trackwheel, a stylus, a mouse, a microphone and the user output device such as an LCD/LED screen display and/or a speaker. If the screen is touch sensitive, then the display can also be used as the user input device as controlled by the device infrastructure 404.
  • the user interface 402 is used to interact with the output results 20 of the diagnosis system 14, for example in graphical and/or textual form.
  • the device infrastructure 404 includes one or more computer processors 408 and can include an associated memory 410 (e.g. a random access memory).
  • the computer processor 408 facilitates performance of the device 101 configured for the intended task through operation of the network interface 400, the user interface 402 and other application programs/hardware 407 (eg. the diagnosis system 14) of the device 101 by executing task related instructions.
  • These task related instructions can be provided by an operating system, and/or software applications 407 located in tho memory 410, and/or by opc ⁇ ability that is configured into the electronic/digital circuitry of the processors) 408 designed to perform the specific task(s).
  • the device infrastructure 404 can include a computer readable storage medium 412 coupled to the processor 408 for providing instructions to the processor 408 and/or to load/update application programs 407.
  • the computer readable medium 412 can include hardware and/or software such as, by way of example only, magnetic disks, magnetic tape, optically readable medium such as CD/DVD ROMS, and memory cards.
  • the computer readable medium 412 may take the form of a small disk, floppy diskette, cassette, hard disk drive, solid-state memory card, or RAM provided in the memory module 410. It should be noted that the above listed examples of computer readable mediums 412 can be used either alone or in combination.
  • the device memory 410 and/or computer readable medium 412 can be used to store the protocols and associated plug-in identifications of the device 101.
  • the computing devices 101 can include the executable applications 407 comprising code or machine readable instructions for implementing predetermined functions/operations including those of an operating system and the diagnosis system 14, for example.
  • the processor 408 as used herein is a configured device and/or set of machine-readable instructions for performing operations as described by example above.
  • the processor 408 may comprise any one or combination of, hardware, firmware, and/or software (e.g. modules 110, 112, U 4, 116).
  • the processor 408 acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information with respect to an output device.
  • the processor 408 may use or comprise the capabilities of a controller or microprocessor, for example. Accordingly, any of the functionality of the diagnosis system 14 (e.g. modules 110-116, and subset/submodules thereof) may be implemented in hardware, software or a combination of both. Accordingly, the use of a processor 408 as a device and/or as a set of machine-readable instructions is hereafter referred to genericaliy as a processor/module for sake of simplicity. Further, it is recognised that the diagnosis system 14 can include one or more of the computing devices 101 (comprising hardware and/or software) for implementing the modules 110-116 or functionality subset thereof, as desired.
  • the computing devices 101 of the measurement device 13 and the diagnosis system 14 may be, for example, personal computers, personal digital assistants and the like. Further, it is recognised that the measurement device 13 may only comprise an electronic data collection mechanism, such as a sensor (e.g. accelerometer).
  • a sensor e.g. accelerometer
  • the modules 110-116 can be configured to operate interactively as shown, the operations/functionality of the selected modules 110-116 can be combined or the operations/functionality of the selected modules 110-116 can be further subdivided, as desired. Further, it is recognised that the modules 110-116 can communicate or otherwise obtain their calculated results from one another or can store their respective calculated results in the storage 410 for subsequent retrieval by another module 110-116 therefrom. It is also recognised that there may be more than one memory 410 used by associated modules 110-116, as desired. Further, it is recognised that the extractor module 112 may be implemented external to the diagnosis system 14 and thereby only supply completed training sets/templates to the diagnosis system 14 for use by the correlation module 114.
  • the objective was to build a database consisting of subjects (observations) belonging to three different groups: (i) Healthy individuals, (ii) Patients with known history of knee problems, and (iii) Patients that received treatments. For each subject, X-ray, MRI, and vibration measurement were taken on the non-dominant knee.
  • the objective was to build a supervised classification model that defined classes based on observed results from the MRI and X-rays using vibration measurements. This was accomplished in three steps: 1) wavelet analysis applied to the time-domain signals from the accelerometers to construct sets of parameters that describe the vibration pattern in terms of time and frequencies; 2) the vector containing features computed from, the coefficients from the wavelet decomposition was further processed using multivariate projection methods (PCA or PLS) to reduce its dimensions to few latent variables that contained most of the significant information in the signal. In a sense, this step can be seen to de-noise the data and improve the computation; and 3) Support Vector Machine (SVM) was employed to design a classifier.
  • PCA or PLS multivariate projection methods
  • Figure 5 is a schematic showing the structure of the database based on this X-matrix and Y-matrix.
  • Group 3 Patients with advanced of OA that had received a treatment in the form of injection were also recruited Measurements were performed on the subject before and after the treatment (n-1)
  • the X-matrix [0054]The vibration of the knee was measured in the time domain on patients in the above identified groups by using three accelerometers placed on the patella and the femur (medial & lateral) as illustrated in Figure 2, to generate a time domain signal as shown, in Fig. 3. These measurements were acquired under two different conditions. Io the first condition, the subject is seated and swinging their leg in a repetitive motion. On a small subset of group 1, active vibration was applied while the patient was asked to go up and down three steps.
  • Periodic MRI MR scans were acquired using a Tesla pMRI system. Subjects were positioned such that their non-dominant knee was centered within the iso-centre of the magnet. A complete description of this particular MR system can be found in a review by Shellock and Hollister (2002), the contents of which at pages 261-287 are incorporated herein by reference. The following measurements were evaluated from the scan:
  • the formulation of the classification problem includes three distinct steps; (i) feature extraction (ii) feature reduction, and (iii) classifier development
  • the data collection means included discrete wavelet transform (DWT) analysis to extract the appropriate features from the signal since it exhibited a non- stationary behavior.
  • DWT discrete wavelet transform
  • WT Wavelet Transform
  • the mother wavelet used in this example was the Daubechies; however, any one of several may be used including the Daubechies, Symlets, and Haar.
  • the wavelet decomposition obtained (from a healthy knee) is shown in Figure 6.
  • the structure of the X-matrix after performing the wavelet transform is illustrated in Figure 7.
  • Figure 10 shows graphically the result of the SVM classification in the latent variable space (t 1 ..t 2 ) between healthy knee and knee diagnosed with OA, while Figure 11 shows the separation between healthy knee and knees diagnosed with meniscus tear. The separation between different types of tear was not clear due to the small sample size.
  • Figure 10 also shows the projection of one patient diagnosed with OA before and after the treatment by a synovial fluid injection. This sequence of measurements shows that this tool may be used to assess the effect of treatments.
  • the methodology can also be used to predict quantitative features (e.g. those listed in Table 1) obtained from the analysis MRI images or from observations made during surgical or arthroscopic examinations.
  • the analysis of the signal was done in two steps. In a first step, feature extraction was performed followed, in a second step, by a linear PLS.
  • the X-matrix consisted of features extracted from the wavelet coefficients, while cartilage thickness and volume were used in the Y-matrix. Y-observed versus Y-predicted showed good correlation with a prediction error of 8% as shown in Figure 12,

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Abstract

L'invention concerne un système destiné à diagnostiquer l'état d'une articulation. Ce système comprend (i) au moins un dispositif permettant de capturer un signal vibratoire en provenance de l'articulation, (ii) des moyens de collecte de données permettant d'extraire des informations temps-fréquence à partir du signal vibratoire et de générer un vecteur d'attributs, (iii) des moyens pour la mise en corrélation du vecteur d'attributs avec des données anatomiques en vue de l'identification de variables latentes, et (iv) des moyens destinés à classifier le signal vibratoire sur la base d'une entrée de variables latentes en vue de la production d'une sortie définissant l'état de l'articulation.
PCT/CA2007/002331 2006-12-20 2007-12-20 Système et procédé destinés à évaluer l'état d'une articulation WO2008074151A1 (fr)

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JP2015109972A (ja) * 2009-02-02 2015-06-18 ジョイントヴュー・エルエルシー 非侵襲性診断システム及び方法
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Cited By (12)

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Publication number Priority date Publication date Assignee Title
JP2015109972A (ja) * 2009-02-02 2015-06-18 ジョイントヴュー・エルエルシー 非侵襲性診断システム及び方法
EP2391971B1 (fr) * 2009-02-02 2021-11-10 JointVue, LLC Système diagnostic non invasif
EP2566383A4 (fr) * 2010-05-03 2017-07-12 Emovi Inc. Procédé et système pour l'aide à l'évaluation et au diagnostic d'une pathologie de l'articulation du genou
WO2014150780A3 (fr) * 2013-03-15 2014-11-20 Jointvue, Llc Détermination de l'état d'une articulation basée sur l'analyse de vibrations
CN105193454A (zh) * 2015-10-19 2015-12-30 北京悦琦创通科技有限公司 骨检测设备和骨检测方法
CN105193454B (zh) * 2015-10-19 2017-12-12 北京悦琦创通科技有限公司 骨检测设备和骨检测方法
WO2017100322A1 (fr) 2015-12-08 2017-06-15 Kneevoice, Inc. Évaluation de l'état d'articulations à l'aide de capteurs acoustiques
EP3386393A4 (fr) * 2015-12-08 2019-12-18 Kneevoice, Inc. Évaluation de l'état d'articulations à l'aide de capteurs acoustiques
EP3878367A1 (fr) * 2015-12-08 2021-09-15 Kneevoice, Inc. Évaluation de l'état d'articulations à l'aide de capteurs acoustiques
US11844643B2 (en) 2015-12-08 2023-12-19 Kneevoice, Inc. Assessing joint condition using acoustic sensors
US20200410674A1 (en) * 2018-03-02 2020-12-31 The Medical College Of Wisconsin, Inc. Neural Network Classification of Osteolysis and Synovitis Near Metal Implants
US11969265B2 (en) * 2018-03-02 2024-04-30 The Medical College Of Wisconsin, Inc. Neural network classification of osteolysis and synovitis near metal implants

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