Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M1/00—Suction or pumping devices for medical purposes; Devices for carrying-off, for treatment of, or for carrying-over, body-liquids; Drainage systems
  • A61M1/36—Other treatment of blood in a by-pass of the natural circulatory system, e.g. temperature adaptation, irradiation ; Extra-corporeal blood circuits
  • A61M1/3621—Extra-corporeal blood circuits
  • A61M1/3653—Interfaces between patient blood circulation and extra-corporal blood circuit
  • A61M1/3656—Monitoring patency or flow at connection sites; Detecting disconnections
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/024—Measuring pulse rate or heart rate
  • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • 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
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M2205/00—General characteristics of the apparatus
  • A61M2205/33—Controlling, regulating or measuring
  • A61M2205/3306—Optical measuring means

Definitions

• the invention relates generally to the field of monitoring blood vessels in patients. Some aspects relate more particularly to early diagnosis of failure in blood vessel functionality, and even more particularly to early detection of failure of vascular access in patients undergoing hemodialysis treatments. Some aspects relate more particularly to measurements of fistulas.
• VA Dialysis vascular access
• VA vascular access
• AV arteriovenous fistula
• graft vascular endothelial graft
• intravenous catheter vascular access constructs, biological as well as synthetic, including, by way of some non-limiting examples, an arteriovenous fistula (AV), a synthetic graft, and an intravenous catheter.
• An AV fistula One type of long-term access is an AV fistula.
• a surgeon connects an artery to a vein, usually in an arm or leg, to create an AV fistula.
• the vein grows wider and thicker, making it easier to place needles for dialysis.
• the AV fistula also has a large diameter that allows blood to flow out and back into a body quickly.
• a goal of an AV fistula is to allow high blood flow so that a large amount of blood can pass through a dialyzer.
• VA function and patency are essential for optimal management of HD patients.
• Low VA flow and loss of patency limit hemodialysis delivery, extend treatment times, and may result in under-dialysis that leads to increased morbidity and mortality.
• thrombosis is the leading cause of loss of VA patency and increases healthcare expenditure.
• VA monitoring and surveillance The basic concept for VA monitoring and surveillance is that progressive stenoses develop over variable intervals in the great majority of VAs and, if detected and corrected (corrective procedure such as percutaneous transluminal angioplasty - PTA), under-dialysis can be minimized or avoided (dialysis dose protection) and the rate of thrombosis can be reduced.
• a number of monitoring and surveillance methods are available: sequential VA flow, sequential dynamic or static pressures, recirculation measurements, and physical examination.
• Monitoring is the examination and evaluation of the VA to diagnose VA dysfunction using physical examination, usually within the HD unit, in order to detect the presence of dysfunction and correctable lesions before VA loss.
• Physical examination can be used as a monitoring tool to exclude low flow associated with impending fistula and graft failures.
• VA examination there are 3 components to the VA examination: inspection, palpation, and auscultation.
• a simple inspection can reveal the presence of swelling, ischemic fingers, aneurysms, and rich collateral veins.
• a strong pulse and weak thrill in the vein central to the anastomosis indicates a draining vein stenosis.
• Strictures can be palpated, and the intensity and character of the Sons can suggest the location of stenoses.
• a local intensification of bruit over the graft or the venous anastomosis compared with the adjacent segment suggests a stricture or stenosis.
• Physical examination can also include the elevation test, which consists of the elevation of the extremity with the VA and examination of the normal collapse of the access. The test is considered normal when the fistula collapses after the organ is elevated above the heart level of the patient.
• the invention relates generally to automating monitoring of blood vessels in patients, more particularly to early diagnosis of failure in blood vessel functionality, and even more particularly to early detection of failure of vascular access in patients undergoing hemodialysis treatments.
• a system for monitoring blood vessel functionality including an illumination source, a detector, a display, a processor configured to identify a change in pulse wave velocity relative to a baseline measurement, identify a change in at least one parameter indicative of development of one or more collateral vessels relative to a baseline measurement, identify a change in the diameter of the blood vessel relative to a baseline measurement, identify a change in the blood vessel's spectroscopy analysis, correlate the identified changes, and determine the probability of failure of the blood vessel’s functionality failure based on the correlated identified changes.
• the at least one parameter indicative of development of one or more collateral vessels includes one or more of shape, density and distance from the blood vessel.
• the processor is configured to calculate the rate of change in at least one of pulse wave velocity, the at least one parameter indicative of development of one or more collateral vessels, the diameter of the blood vessel and the blood vessel's spectroscopy analysis.
• the blood vessel is in an arm of a patient
• the processor is further configured to identify a change in the collapse of the blood vessel when the patient’s arm or leg is elevated.
• the processor is further configured to calculate the rate of changes in the collapse of the blood vessel when the patient’s arm or leg is elevated.
• the processor is further configured to identify changes in the composition of the blood flowing within the blood vessel.
• a system for monitoring blood vessel functionality including an illumination source, a detector, a display, a processor configured to identify a change in at least one parameter indicative of development of one or more collateral vessels relative to baseline measurement, determine the probability of failure of the blood vessel’s functionality based on the identified change.
• the processor is further configured to identify changes relative to baseline measurements in one or more of pulse wave velocity, the diameter of the blood vessel and the blood vessel’s spectroscopy analysis, correlate the change identified in the one or more of pulse wave velocity, the blood vessel’s diameter and the blood vessel’s spectroscopy analysis with the change identified in the at least one parameter indicative of development of one or more collateral vessels, and determine the probability of failure of the blood vessel’s functionality based on the correlated changes.
• the blood vessel is positioned in an arm or leg of a patient
• the processor is further configured to identify a change in the collapse of the blood vessel when the patient’s arm or leg is elevated, correlate the change identified in the collapse of the blood vessel when the patient’s arm or leg is elevated with the change identified in the at least one parameter indicative of development of one or more collateral vessels, and determine the probability of failure of the blood vessel’s functionality based on the correlated changes.
• a method for monitoring blood vessel functionality including identifying changes in pulse wave velocity relative to baseline measurements, identifying changes in parameters indicative of collateral vessels development relative to baseline measurements, identifying changes in the blood vessel’s diameter relative to baseline measurement, correlating the identified changes, and determining the probability of failure of the blood vessel’s functionality based on the correlated identified changes.
• a method for monitoring blood vessel functionality including identifying changes in at least one parameter indicative of development of one or more collateral vessels relative to baseline measurement, and determining the probability of failure of the blood vessel’s functionality based on the identified changes.
• identifying changes relative to baseline measurements in one or more of pulse wave velocity, the blood vessel’s diameter and the bloods vessel's spectroscopy analysis correlating the changes identified in the one or more of pulse wave velocity, the diameter of the blood vessel the bloods vessel's spectroscopy analysis, with the change identified in the at least one parameter indicative of development of one or more collateral vessels, and determining the probability of failure of the blood vessel’s functionality based on the correlated changes.
• the blood vessel is in an arm or leg of a patient
• the method further includes identifying changes in the collapse of the blood vessel when the patient’s arm or leg is elevated, correlating the changes identified in the collapse of the blood vessel when the patient’s arm or leg is elevated with the change identified in the at least one parameter indicative of development of one or more collateral vessels, and determining the probability of failure of the blood vessel’s functionality based on the correlated changes.
• the method further includes the step of identifying changes in the composition of the blood flowing within the blood vessel.
• the blood vessel is in an arm or leg of a patient and the measurements are taken while the patient’s arm or leg is positioned approximately parallel to the ground.
• the blood vessel is in an arm or leg of a patient and the measurements are taken while the patient’s arm or leg is positioned approximately perpendicular to the ground.
• the blood vessel is in an arm or leg of a patient and the measurements are taken while the patient’s arm or leg is positioned lower than the patient’ s heart.
• the blood vessel is in an arm or leg of a patient and the measurements are taken while the patient’s arm or leg is positioned higher than the patient’ s heart.
• a method for monitoring blood vessel functionality including illuminating one or more blood vessels through a patient’s skin, capturing at least one image of the blood vessels, analyzing the at least one image, and calculating a parameter associated with blood vessel functionality based upon the image analysis.
• VA vascular access
• a fistula further including automatically detecting a location of a fistula in the at least one image.
• VA vascular access
• the capturing at least one image of the blood vessels is performed by a device configured to provide an image including at least one artery and at least one vein under the patient’s skin.
• the illuminating one or more blood vessels through a patient’s skin includes trans-illuminating the patient’s organ.
• the calculating a parameter associated with blood vessel functionality includes calculating at least one parameter selected from a group consisting of pulse wave velocity, a parameter indicative of development of one or more collateral vessels, a count of collateral vessels, a diameter of a blood vessel, the blood vessel's spectral analysis, a size of an arteriovenous fistula, and a size of a synthetic graft VA.
• the invention further including calculating a rate of change of one or more of the parameters based on performing several measurements, at different times, of the one or more of the parameters, some of the measurements based on historical data associated with the patient retrieved from a database.
• the automatically detecting a location of vascular access (VA) in the at least one image includes detect the location of the VA by detecting a meeting of a vein and an artery.
• the detecting a meeting of a vein and an artery is performed using a device capable of providing an image of both an artery and a vein under the patient’s skin.
• the automatically detecting a location of vascular access (VA) in the at least one image includes performing spectral analysis of the at least one image.
• a method for replacing a physical examination performed by medical staff for monitoring blood vessel functionality in dialysis patients including producing at least one image of a patient organ instead of manually manipulating the patient’s organ, analyzing the at least one image, and classifying the patient’s status to be one of suitable for dialysis or at risk for stenosis.
• analyzing the at least one image includes calculating a parameter associated with blood vessel functionality based upon the image analysis.
• a system for monitoring blood vessel functionality including an illuminator configured to illuminate a patient’s blood vessels through the patient’s skin, a camera configured to capture at least one image of the blood vessels through the patient’ skin, an image analyzer configured to process the at least one image, a calculator configured to calculate a parameter associated with blood vessel functionality based upon the image analysis, a classifier configured to classify the patient’s status to be one of suitable for dialysis or at risk for dialysis, and a display configured to provide a report of at least one of the patient’ status and a parameter associated with blood vessel functionality to a caregiver.
• a system which includes optical apparatus to acquire one or more images of the same patient’s fistula along a surveillance period.
• one or more measurements and/or features are optionally extracted from the image(s) - and their changes over time are optionally monitored.
• the features are timeline derivatives of parameters measured or estimated in the image(s), by way of a non-limiting example changes in number & size of collateral veins happening over a period of time, such as days/weeks/month.
• a feature of interest is derived from a graph representation of the identified blood vessels, and or changes over time of this representation, by way of a non-limiting example changes in the number of junctions, average or distribution of number of bifurcations in graph junctions.
• using a machine-leaming-derived method to identify a pattern within the above changes which may potentially lead to a significant clinical end point (e.g. Stenosis of the fistula) before there are clinical signs or symptoms which human nurses can identify.
• a system which measures parameters relating to a fistula by optical means.
• structured light is projected onto a patient’s body or limb, and the body is imaged.
• the structured light may include horizontal and/or vertical stripes of equal or different widths and/or various light patterns other than stripes.
• imaging the structured light is used to provide information about an extent of the fistula, by way of some non-limiting examples: length of a long axis of the fistula along the body; breadth of a short axis of the fistula along the body; shape of the fistula as it appears in the image; shape and/or segmentation of the fistula circumference; an eccentricity index and/or an aspect ratio of the fistula or each segment of the fistula, a smoothness and/or roughness index of the fistula outline.
• structured light patterns are projected onto a patient’s body or limb, and the body is imaged, providing information about a three-dimensional shape of the fistula or body organ, such as arm, leg.
• the system identifies changes in the shape of the fistula and/or body organ.
• a projector is used to project one or more light patterns (e.g. structured light).
• a method measures and/or estimates how the patterns deform on a patient’s organ to measure the organ’s shape and shape changes over time.
• structured light patterns are projected onto a patient’s body or limb, and the body is imaged, providing information about a three-dimensional shape of the fistula, for example one or more of: a volume of the whole fistula or of segments of the fistula (e.g. needle insertion points), characteristics and variance of curvature, changes in shape/volume of an underlying arm/organ section near the fistula, and three dimensional surface features, for example smoothness and/or roughness.
• LSI Laser Speckle Interferometry
• LSI is used to record and look at vibrations of the fistula surface that correlate with the blood flow and turbulence inside. Changes in the blood flow and turbulence are typically correlated with stenosis events, and potential development of clinical conditions.
• imaging the speckled light is used to provide information about dynamic effects in the fistula, for example heart pulse, blood flow turbulence, and optionally produce spectrograms of vibrations of a fistula.
• images of the body are taken some period of time apart, and differences between the images are optionally used to determine differences in the shape of the fistula.
• the images are taken days, weeks, months or years apart, and differences between the images is optionally used to measure and/or monitor changes in size or shape of the fistula.
• the images are taken seconds or minutes apart, for example with a limb such as a hand held horizontally followed by the hand held vertically, and differences between the images is optionally used to measure and/or monitor whether at least some of the blood in the fistula can evacuate the fistula, a rate of blood evacuation and/or degree of evacuation from the fistula or specific portions of the fistula, such as, for example, a collapse of needle insertion points.
• the images are taken fractions of a second apart, as a video clip or movie, and differences between image frames is optionally used to measure and/or monitor dynamic parameter related to the fistula, such as heart pulse, blood flow turbulence, and optionally produce spectrograms of vibrations of a fistula.
• an analysis is made of changes in the dynamic parameters relate to the fistula between imaging sessions, to monitor changes in the fistula and the patient’s conditions.
• performing the above together with Near IR imaging potentially enables collecting data that correlates with examinations required to be perform by nurses and/or physicians and that is already clinically proven to have predictive value to identify stenosis events.
• a system and methods for implementing and recording more than one technique or modality for example one or more of structured light; laser speckle interferometry; image analysis and Near IR imaging modalities, using one imaging device.
• the system includes a processor and an imaging device which includes a Digital Light Processing (DLP) projector and a Near IR camera.
• DLP Digital Light Processing
• a method for monitoring blood vessels including using a system for monitoring blood vessel functionality to look, listen and feel blood vessel functionality by imaging a patient’s body to obtain blood vessel geometry, imaging a patient body to obtain a shape of a location of the patient’s body using image analysis, and analyzing vibrations of the patient’s body at a location of the patient’s body which includes the blood vessels.
• obtaining the shape of the location of the patient’s body includes illuminating the location using structured lighting.
• the analyzing vibrations includes illuminating using Laser Speckle Interferometry (LSI).
• LSI Laser Speckle Interferometry
• the illuminating using LSI is performed at a location based on obtaining the shape of the location of the patient’s body using image analysis.
• the illuminating using LSI at the location is performed automatically by controlling a Digital Light Processing (DLP) projector.
• DLP Digital Light Processing
• the illuminating using LSI is performed by a physician guiding LSI illumination to a location of the patient’s body.
• obtaining the shape of the location of the patient’s body includes calculating a three-dimensional (3D) shape of the patient’s fistula.
• a rate of evacuation of the patient’s fistula is calculated based on changes in the 3D shape of the patient’s fistula.
• one or more parameters associated with blood vessel functionality are calculated based upon the image analysis.
• an estimation of a probability of failure of a blood vessel’s functionality is calculated based on the one or more parameters.
• an estimation when a blood vessel is likely to fail is calculated based on the one or more parameters.
• an estimation of a probability of failure of a blood vessel’s functionality is calculated based on the rate of change of the one or more parameters.
• an estimation of a maturity of a VA is calculated based on the rate of change of the one or more parameters.
• the calculating one or more parameters includes calculating a parameter indicative of development of one or more collateral vessels.
• the calculating one or more parameters includes calculating a count of collateral vessels.
• collateral veins are automatically detected by counting a number of veins in a specific image area in different images taken at different times.
• a location of vascular access is automatically detected by detecting a meeting of a vein and an artery.
• the image analysis includes automatically detecting a location of vascular access (VA) in at least one image.
• VA vascular access
• the image analysis includes automatically detecting a location of a fistula in at least one image.
• the looking at a patient’s blood vessel geometry includes capturing an image including at least one artery and at least one vein under the patient’s skin.
• the patient’s body is illuminated using Near Infra-Red wavelengths.
• the illuminating includes using a Digital Light Processing (DLP) projector.
• DLP Digital Light Processing
• the analyzing vibrations of the patient’s body includes analyzing intensity of light at a specific location in images of the location of the patient’s body which includes the blood vessels.
• the spectrum is produced by producing a vector of light intensity at the specific location and producing a spectrum of vibrations by transforming the vector of intensity to a vector of frequencies.
• the analyzing vibrations includes analyzing a spectrum of vibrations over a range of vibration frequencies.
• the spectrum of vibrations is in a range corresponding to human- audible frequencies.
• the spectrum of vibrations is in a range corresponding to frequencies below human-audible frequencies.
• the analyzing body vibrations is in a range of frequencies less than 1,000 Hz.
• analyzing vibrations of the patient’s body is done by analyzing images captured at a frame rate greater than 150 Frames Per Second (FPS).
• FPS Frames Per Second
• the analyzing vibrations of the patient’s body is done by analyzing images captured at a frame rate greater than 500 Frames Per Second (FPS). According to some embodiments of the disclosure, the analyzing vibrations of the patient’s body is done by analyzing selected pixels within captured images.
• FPS Frames Per Second
• a pulse wave parameter is measured by detecting a pulse wave location in two images taken at different times and comparing the pulse wave location in the two images.
• pulse wave velocity is measured by detecting a pulse wave location in two images and dividing a distance along a center line of a blood vessel in the two images by a time difference between capturing the two images.
• a method for replacing a physical examination performed by medical staff for monitoring blood vessel functionality including producing at least one image of a patient organ, analyzing the at least one image, and producing parameter values associated with blood vessel functionality.
• the analyzing the at least one image includes calculating a parameter associated with blood vessel functionality based upon the image analysis.
• the method is used instead of a medical practitioner performing a look listen and feel examination.
• the method is performed by a device without the device contacting a fistula of a patient.
• the method is performed by a device without the device contacting a body of a patient.
• a system for monitoring blood vessel functionality including an illuminator configured to provide both a laser spot for Laser Speckle Interferometry (LSI) and structured lighting, a camera configured to image a location where the illuminator is configured to illuminate, and a processor for processing images captured by the camera to extract data regarding shape from camera images obtained with structured lighting and data regarding vibration from camera images obtained with LSI.
• LSI Laser Speckle Interferometry
• a classifier configured to classify the patient’s status to be one of suitable for dialysis or at risk for stenosis.
• the illuminator includes a light source in Near Infra-Red wavelengths.
• the illuminator includes a Digital Light Processing (DLP) projector.
• DLP Digital Light Processing
• the camera includes a camera capable of capturing images at a frame rate greater than 150 Frames Per Second (FPS).
• FPS Frames Per Second
• the camera includes a camera capable of capturing images at less than maximum resolution of the camera and at a frame rate more than 500 FPS.
• a support for locating a patient’s limb where classifier configured to classify the patient’s status to be one of suitable for dialysis or at risk for stenosis.
• a method for calculating a count of collateral vessels including imaging a patient’s body to obtain blood vessel geometry, and calculating a count of collateral vessels.
• calculating a count of collateral vessels includes automatically detecting collateral veins by counting a number of veins in a specific image area in different images taken at different times.
• VA vascular access
• some embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.”
• some embodiments of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
• Implementation of the method and/or system of some embodiments of the invention can involve performing and/or completing selected tasks manually, automatically, or a combination thereof.
• several selected tasks could be implemented by hardware, by software or by firmware and/or by a combination thereof, e.g., using an operating system.
• a data processor such as a computing platform for executing a plurality of instructions.
• the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data.
• a network connection is provided as well.
• a display and/or a user input device such as a keyboard or mouse are optionally provided as well.
• the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
• a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
• a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
• a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
• a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
• Program code embodied on a computer readable medium and/or data used thereby may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
• Computer program code for carrying out operations for some embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
• the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
• the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
• LAN local area network
• WAN wide area network
• Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
• These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
• the computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
• Some of the methods described herein are generally designed only for use by a computer, and may not be feasible or practical for performing purely manually, by a human expert.
• a human expert who wanted to manually perform similar tasks, such as monitoring blood vessels in patients, might be expected to use completely different methods, e.g., making use of expert knowledge and/or the pattern recognition capabilities of the human brain, which would be vastly more efficient than manually going through the steps of the methods described herein.
• FIG. 1 is a graph showing probability of a vascular access thrombosis occurring within a 3-month period dependent on flow rate and on a change in flow rate, as reported by Besarab et al, “Access Monitoring is Worthwhile and Valuable”, Blood Purification”, February 2006;
• FIG. 2 is a simplified illustration of a system for measuring blood vessels according to an example embodiment of the invention
• FIG. 3 is a simplified block diagram of a system for measuring blood vessels according to an example embodiment of the invention
• FIGS. 4A-4E are simplified flow chart illustrations of algorithms according to example embodiments of the invention.
• FIGS. 5 A and 5B are simplified illustrations of a pulse wave travelling along a vein
• FIG. 6 is a simplified flow chart illustration of a classifier method according to an example embodiment of the invention.
• FIG. 7 is a simplified block diagram of a system for measuring blood vessels according to an example embodiment of the invention.
• FIGS. 8 A and 8B are images of optical components in a system constructed according to an example embodiment of the invention.
• FIG. 9 is a simplified flow chart illustration of a segmentation method according to an example embodiment of the invention.
• FIG. 10 is a simplified flow chart illustration of a registration method according to an example embodiment of the invention.
• FIG. 11 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• FIG. 12 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• FIG. 13 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• FIG. 14 is a simplified flow chart illustration of a classifier method according to an example embodiment of the invention.
• FIGS. 15A-C show three different images of a same patient arm, according to an example embodiment of the invention.
• FIG. 16A is a table showing a procedure for a medical person to examine a patient with reference to vascular stenotic lesions or thrombosis;
• FIG. 16B is a simplified flow chart illustration of a method of examining a patient according to an example embodiment of the invention.
• FIG. 17 is a simplified block diagram illustration of a method for examining a patient according to an example embodiment of the invention.
• FIGS. 18A and 18B are images of a fistula of a patient taken at two different times
• FIGS. 19A and 19B are simplified drawings of a system for monitoring vascular access (VA) and/or fistulas according to two example embodiments of the invention
• FIG. 20 is an image of a system for monitoring vascular access (VA) and/or fistulas according to an example embodiment of the invention
• FIGS. 21A-C show three different images of a same patient’s arm
• Figure 22A is a graph showing power spectrum of vibrations measured by analysis of images produced by laser speckle imaging
• FIG. 22B is a simplified flow chart illustration of a method for transforming data from a stream of images to a frequency spectrum according to an example embodiment of the invention
• FIG. 23 is a simplified flow chart illustration of a method for monitoring blood vessel functionality according to an example embodiment of the invention.
• FIG. 24 is a simplified flow chart illustration of a method for replacing a physical examination performed by medical staff for monitoring blood vessel functionality according to an example embodiment of the invention.
• the invention relates generally to the field of monitoring blood vessels in patients. Some aspects relate more particularly to early diagnosis of failure in blood vessel functionality, and even more particularly to early detection of failure of vascular access in patients undergoing hemodialysis treatments. Some aspects relate more particularly to measurements of fistulas.
• VAs Monitoring by physical examination is cost-effective and a proven method to detect VA abnormalities.
• nephrologists and HD staff generally have limited availability and are not well informed.
• regular physical examinations of VAs are not generally carried out in HD units.
• VA flow and dynamic or static pressures surveillances were found to be inaccurate predictors of graft thrombosis and instead of preventing thrombosis yielded many unnecessary intervention procedures.
• PTA induces a mechanical trauma, accompanying neointimal hyperplasia (NIH), risk of stenosis and impaired VA survival.
• NASH neointimal hyperplasia
• Figure 1 is a graph showing probability of a vascular access thrombosis occurring within a 3-month period dependent on flow rate and on a change in flow rate, as reported by Besarab et al, “Access Monitoring is Worthwhile and Valuable”, Blood Purification”, February 2006.
• the graph of Figure 1 includes a Y-axis 101 showing probability of a vascular access thrombosis occurring within a 3-month period, various lines 103 showing flow rate in units of ml/min, and an X-axis 102 showing a change in flow rate per month, in units of ml/min.
• Figure 1 shows that a probability of a vascular access thrombosis occurring within a 3- month period is dependent not only on the absolute flow at any time but also on a rate of change in the flow, if there is a change in flow (Besarab et al, “Access Monitoring is Worthwhile and Valuable”, Blood Purification”, February 2006).
• An access with an initial flow of 600 ml/min and a 20-ml/min decrease in flow per month has a lower probability of thrombosis (22%) than an access with an initial flow of 1,200 ml/min and a decrease in flow of 100 ml/min (38%), even though the absolute flow is lower in the former (540 ml/min) than in the latter (900 ml/min) at the beginning of the observation period.
• Periodic measurements results may be influenced by unrelated hemodynamic events
• An aspect of some embodiments of the present invention relates to replacing or adding to physical examination performed by medical staff/ nurses.
• An aspect of some embodiments is related to performing look, listen and feel by instruments measurements and computerized analysis.
• systems as described herein perform a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• the systems teach how to predict fistula condition and potentially enable early prevention of failure.
• methods as described herein performs a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• the systems teach how to predict fistula condition and potentially enable early prevention of failure.
• blood flow is measured in a non-invasive manner, based on image processing of image of blood vessels in a human body.
• Physiological parameters which are known to affect vascular access (VA) are measured, and the measurements are optionally used to determine whether a patient should be scheduled for corrective procedure or proceed to undergo dialysis.
• An aspect of some embodiments is related to performing feel, as described herein, by instruments measurements and computerized analysis.
• the listen as described herein is performed by instruments, optionally the same instruments.
• the look as described herein is performed by instruments, optionally the same instruments.
• An aspect of some embodiments of the present invention relates to automatic detection and/or monitoring of an AV fistula in an images of blood vessels.
• an image of blood vessels is analyzed, and a location where an artery is connected to a vein is optionally determined to be a location of an AV fistula.
• an image of blood vessels is analyzed, and a location where an artery appears to be connected to a vein is optionally determined to be a location of an AV fistula.
• an image of blood vessels is analyzed, and an AV fistula is optionally measured to estimate geometric properties.
• An aspect of some embodiments of the present invention relates to automatic, non- invasive measurement of parameters associated with blood flow.
• the non-invasive measurement includes imaging blood vessels through skin, using reflected light and/or transmitted light.
• a probability of failure of vascular access is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a probability of occlusion formation is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a probability of thrombus formation is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a grade of stenosis is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a rate of stenosis formation is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a grade of VA maturation is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• a rate of VA maturation is optionally estimated. In some embodiments the estimation is based on one or more of the parameters measured.
• An aspect of some embodiments of the present invention relates to provide a visual report to a caregiver.
• One or more of patient-related parameters, including images, are readily available for measurement(s) in a way that is potentially cost-effective and/or non-invasive (optionally, non-contact), and/or integrated into routine dialysis appointments.
• An input to an algorithm described herein optionally includes one or more patient- related parameters in order to estimate probability of failure of vascular access, where each of the parameters can be available on a single-measurement basis or as multiple measurements along the time axis.
• Some of the patient-related parameters are obtained using objective measurements, potentially not requiring high competence from a user, such as a patient and/or a health care professional.
• Some of the patient-related parameters are optionally taken from the patient’s specific medical record and include elements such as demographics (e.g. age, gender, weight and height), lab tests, imaging tests (e.g. X-ray, MRI) and results of a physical exam.
• demographics e.g. age, gender, weight and height
• imaging tests e.g. X-ray, MRI
• results of a physical exam e.g. X-ray, MRI
• the parameters can be extracted in multiple ways, for example - directly typing exam results into a keyboard connected to a system as described herein, a computer process that accesses electronic medical records using a specific patient ID, speech-to-text conversion, voice recognition algorithms applied to verbal analysis of the staff and OCR of a printed/written documents.
• VA maturation The VA has a unique tissue structure when compared with veins and arteries. The structure changes during a VA maturation process, and during a stenotic process.
• Structural changes impact the mechanical and optical characteristics of the VA, thus monitoring of changes can potentially be measured, in some embodiments, by one or more of:
• Imaging by way of a non-limiting example by measuring changes in contrast or intensity of reflected light and/or transmitted light;
• Non imaging intensity of reflected light or transmitted light
• a system is configured to detect veins, monitoring VA during a maturation period potentially alters detection results.
• the response of the VA to light potentially changes over the maturation period.
• Monitoring of maturation is potentially beneficial to raise a success rate of VA maturation by suggesting a timely pre-emptive correction.
• Accuracy of estimating maturation (maturity level, stage, rate, completion) or probability of failure of vascular access, occlusion formation and probability of thrombus may be improved by using one or more parameters generated from non-invasive measurement.
• the parameters used can be directly measured or be a result of a pre-processing applied on the measurement.
• Such pre-processing can be application of various algorithms as well as combination of several parameters and utilization of multiple measurements over time.
• Pulse wave velocity detect reflection or absorption of optical radiation from at least two points in an image frame. In some embodiments changes in electrical impedance as measured by electrodes placed between and/or along the two points, along the blood vessel or tissue area.
• the two points include sections known to be more susceptible to develop stenosis. More generally, at least one point is used for measuring pulse wave shape (such as, for example, pulse wave amplitude, Full-Width Half Max (FWHM)).
• FWHM Full-Width Half Max
• pulse wave amplitude is optionally measured.
• An optional method for measuring pulse wave amplitude includes measuring a first measurement of an area of a location along a vein identified as a widening of a blood vessel due to a pulse wave. An area of the same location in a different image, when the pulse wave is not at that location, is also measured in a second measurement. A difference between the first measurement and the second measurement is optionally associated with the pulse wave amplitude.
• the pulse wave amplitude is taken as a feature which corresponds to mechanical properties of a vein all, and/or with maturity of an AV through which the pulse wave travels.
• a Pulse Wave Analysis is optionally performed to assess variance related to vascular stiffness which is associated with additional risk factors such as cardiovascular disease or atherosclerosis which in turn - may impact viability over time of the VA.
• a quality of the pulse is optionally scored, and changes over time and between different sections are optionally included in the analysis, in some embodiments.
• collateral veins and their characteristics such as: density, size, distance from the VA, orientation, filling etc. by image processing and/or other detection methods, e.g. measure contrast - by absorption of light in the visible or NIR wavelength; or emission at the far IR wavelength. Other measurement options include measuring an amount of change of absorption in the visible and near IR and amount of emission in the far IR.
• Another optional way to measure development of collaterals is optionally measuring temperature changes of the VA surrounding.
• detection of appearance and development of collateral veins optionally uses reference images or measurements taken from a prior examination.
• trend analysis of collateral vein development rate optionally uses frequent examinations. The examinations are optionally performed daily, every dialysis session, every week, bi-weekly, or monthly.
• collateral veins are detected by comparing a new image to a previous image and counting veins - an increase in the number of veins is optionally taken to mean that the new veins are collateral veins.
• appearance and/or development of collateral veins is detected by extracting features from one image or measurement. Rationale: detection of a collateral vessel potentially indicates a flow limiting (hemodynamic significant) lesion. Collateral vessels may develop and enlarge, dissipating the increased intra-access pressures in the setting of outflow stenosis.
• a blood vessel s smallest diameter by image processing (stenosis location).
• a blood vessel s largest diameter by image processing (appearance and size of aneurysms).
• NIR Near Infrared
• the measurements may be synched according to a detected breathing cycle and categorized for the detection algorithm in respect to their relative time along the breathing cycle.
• Such synching and categorization are potentially beneficial, for example, when evaluating changes in the oxygen mix over time, but can also improve accuracy of other measurements, such as pulse wave velocity.
• Output of a system as described herein may be in the form of an audible alarm, visual alarm, image, sequence of images, or a video providing the medical personnel guidance for fast and accurate intervention (e.g. give a recommendation to the medical personnel regarding the best location(s) for intervention).
• the system may recommend treatment for a patient (PTA, not to intervene, thrombectomy).
• the recommendation is optionally based on information collected by the system.
• output of the system during a test is optionally analyzed and/or optionally used to guide a patient through a test in order to perform the test correctly.
• output of the system during a test is optionally analyzed and/or optionally used to guide a patient through a test in order to perform the test correctly.
• output of the system during a test is optionally analyzed and/or optionally used to guide a patient through a test in order to perform the test correctly.
• output of the system during a test is optionally analyzed and/or optionally used to guide a patient through a test in order to perform the test correctly.
• an elevation test verifying that the elevation/position of a limb is correct.
• the above-mentioned output is optionally used to support a remote physical examination to be performed by a patient while the system provides feedback on correct performance of the examination and/or alerts remote support personnel, such as a nurse or technician
• system output is optionally provided differently to different consumers of the data.
• a dialysis nurse is optionally provided with a general interpretation on a likelihood of clinically meaningful stenosis formation and an interventional radiologist is optionally provided with an alert with an annotated image and/or optionally a report highlighting parameters such as location, severity and rate of stenosis formation.
• a system which includes optical apparatus to acquire one or more images of the same patient’s fistula along a surveillance period.
• one or more measurements and/or features are optionally extracted from the image(s) - and their changes over time are optionally monitored.
• the features are timeline derivatives of parameters measured or estimated in the image(s), by way of a non-limiting example changes in number, branching & size of collateral veins happening over a period of time, such as days/weeks/months.
• using a machine-leaming-derived method to identify a pattern within the above changes which may potentially lead to a significant clinical end point (e.g. Stenosis of the fistula) before there are clinical signs or symptoms which human nurses can identify.
• a system which measures parameters relating to a fistula by optical means.
• structured light is projected onto a patient’s body or limb, and the body is imaged.
• the structured light may include horizontal and/or vertical stripes of equal or different widths and/or various light patterns other than stripes.
• imaging the structured light is used to provide information about an extent of the fistula, for example length of a long axis of the fistula along the body: breadth of a short axis of the fistula along the body: shape of the fistula as it appears in the image: segmentation of the fistula circumference, eccentricity index and/or aspect ratio of each segment, smoothness and/or roughness of a fistula outline
• structured light patterns are projected onto a patient’s body or limb, and the body is imaged, providing information about a three-dimensional shape of the fistula or organ.
• the system identifies changes in the shape of the fistula and/or an organ near the fistula.
• a projector is used to project one or more light patterns (e.g. structured light).
• a method measures and/or estimates how the patterns deform on a patient’s organ to measure the organ’s shape and shape changes over time.
• structured light patterns are projected onto a patient’s body or limb, and the body is imaged, providing information about a three-dimensional shape of the fistula, by way of some non-limiting examples volume of an entire fistula or segments of a fistula (e.g. needle insertion points); characteristics and/or variance of curvature; changes in shape and/or volume of an underlying arm/organ section near a fistula; and three-dimensional surface features such as smoothness and/or roughness.
• LSI Laser Speckle Interferometry
• LSI is used to record and look at vibrations of the fistula surface that correlate with the blood flow and turbulence inside. Changes in the blood flow and turbulence are typically correlated with stenosis events, and potential development of clinical conditions.
• imaging the speckled light is used to provide information about dynamic effects in the fistula, for example heart pulse, blood flow turbulence, and optionally produce spectrograms of vibrations of a fistula.
• images of the body are taken some period of time apart, and differences between the images are optionally used to determine differences in the shape of the fistula.
• the images are taken days, weeks, months or years apart, and differences between the images is optionally used to measure and/or monitor changes in size or shape of the fistula.
• the images are taken seconds or minutes apart, for example with a limb such as a hand held horizontally followed by the hand held vertically, and differences between the images is optionally used to measure and/or monitor one or more of: whether at least some of the blood in the fistula can evacuate the fistula; a rate of blood evacuation; a degree of blood evacuation from the fistula and/or specific portions of the fistula; and collapse of one or more needle insertion points.
• the images are taken fractions of a second apart, as a video clip or movie, and differences between image frames is optionally used to measure and/or monitor dynamic parameter related to the fistula, such as heart pulse, blood flow turbulence, and optionally produce spectrograms of vibrations of a fistula.
• the spectrogram is optionally produced by selecting one or more pixels in the image frames which show a large or even a maximal variation of intensity over time.
• the number of pixels selected is optionally in a range of 1-100 pixels.
• the values of pixel intensity of this or these pixels are used to compute a function of light intensity over time.
• a frequency spectrum of the light intensity is optionally produced by transforming from the time domain to the frequency domain, for example by a Fast Fourier Transform (FFT).
• FFT Fast Fourier Transform
• an analysis is made of changes in the dynamic parameters relate to the fistula between imaging sessions, to monitor changes in the fistula and the patient’s conditions.
• performing the above together with Near IR imaging potentially enables collecting data that correlates with examinations required to be perform by nurses and/or physicians and that is already clinically proven to have predictive value to identify stenosis events.
• a system and methods for implementing and recording more than one technique or modality for example one or more of structured light; laser speckle interferometry; image analysis and Near IR imaging modalities, using one imaging device.
• the system includes a processor and an imaging device which includes a Digital Light Processing (DLP) projector and a Near IR camera.
• DLP Digital Light Processing
• pulsatility of a heart is monitored.
• analyzing the pattern of vibrations caused by flow through or in vicinity to the fistula optionally detects full or partial occlusions of either the inflow or outflow pathways.
• analyzing the pattern of vibrations caused by flow through or in vicinity to the fistula while imposing local pressure to either inflow or outflow pathways optionally detects full or partial occlusions of either the inflow or outflow pathways. In some embodiments, analyzing the vibrations optionally detects onset of flow through the fistula related to normal heart activity, (the diastole or systole phases of the heart cycle).
• analyzing the vibrations optionally detects onset of flow through the fistula related to sudden release, (partially or full collapse or expansion of the fistula).
• analyzing the vibrations optionally detects a period of inflow of blood to a fistula, followed by a sudden opening of an obstacle which enables blood to flow out of the fistula. Such opening may happen during high pressure of a heart systole. In some instances the sudden opening is called hammering. In some embodiments, the hammering is detected by measuring amplitude of vibrations, optionally relative to the amplitude at other times, for example other times during a heartbeat.
• analyzing the vibrations related to onset of flow optionally measures a parameter value or a change in parameter value or a change in a characteristic parameter value, or a variance of the parameter value.
• the parameters may be one or more of: Intensity, Energy, Steepness of onset (derivative of value), Relaxation time, Temporal-width, Duty-cycle, Spectral-content, Spectral-width, or any combination of such.
• analyzing the vibrations related to onset of flow optionally measures a parameter value related to the time-delay or phase-delay between onsets related to sudden release, and onsets related to normal heart activity.
• analyzing the vibrations related to onset of flow optionally measures a parameter value related to the regularity or self- similarity of a series of onsets of the same source.
• Figure 2 is a simplified illustration of a system for measuring blood vessels according to an example embodiment of the invention.
• Figure 2 shows a top level set up configuration of an exemplary system 200 for measuring blood vessels.
• the system 200 may include at least one illumination source 202 and at least one detector 204, such as a camera.
• the system 200 may further include a control unit 206, which optionally activates the illumination source 202 and the camera 204, and an optional processor 208, which optionally receives and analyzes images generated by the camera 202.
• the generated images and/or the data generated following the analysis of the images may be displayed on an optional display 210 coupled to the processor 208, either wirelessly or via a wired connection.
• the processor 208 and the display 210 may be implemented in a single device, such as a laptop, tablet or smartphone.
• a scan system may be applied that optionally moves the detection unit (automatically or manually) and optionally scans an organ at more than one point.
• Figure 2 describes the system 200 applied to an arm 212.
• the system and method are capable of implementation with other organs, without limitation.
• Figure 3 is a simplified block diagram of a system for measuring blood vessels according to an example embodiment of the invention.
• Figure 3 describes the top-level block diagram of an exemplary system.
• the system may include at least two main units; a detection unit 302 and a software unit 306.
• the system may include additional units, such as a work station 304, optional cloud infrastructure 308, etc.
• the software unit 306 includes at least two sub-units, an embedded unit 330 and an algorithms unit 334.
• the software unit 306 may include additional blocks, such as a Graphical User Interface (GUI) unit 332, etc.
• GUI Graphical User Interface
• the detection unit 302 optionally uses:
• speckle imaging When an object is illuminated by laser light, the backscattered light forms an interference pattern consisting of dark and bright areas. This pattern is called a speckle pattern. If the illuminated object is static, the speckle pattern is stationary. When there is movement in the object, such as red blood cells in a tissue, the speckle pattern will change over time.
• the speckled images contain information related to changes in the blood vessels which is optionally analyzed and extracted by image processing.
• Transmitted illumination Illuminates the back surface of a sample.
• the sample is placed between the illumination source and the sensor device. Transmitted illumination potentially improves the image contrast and/or potentially increases the depth at which blood vessel can be imaged.
• Photo acoustic imaging potentially enhances contrast between different mediums because of differences in changes in the optical characteristic of the different mediums. Photo acoustic imaging potentially reduces scattering in tissue because of averaging of the refraction index gradient in tissue components, potentially resulting in a greater penetration depth of light.
• the detection unit 302 optionally includes one or more of the following components:
• One or more detectors/sensors/cameras 310 e.g., CCD or CMOS, InGaAs sensor, micro bolometer
• a sensor frame rate can range between single-frame to a high frame rate.
• Sensor frame rate are optionally in a range of, for example, 5, 10, 16, 24, 30, 50, 60, 100, 165, 200, and even up to 300- frames per second (fps).
• One or more lenses 312 zoom or fixed focal length
• filters 312 filters
• One or more illuminators 314 or emitters e.g., an illumination source that can be coherent or non-coherent, narrow spectra or broadband, UV, visible, SWIR, far IR, NIR - for example NIR led or green (532nm) laser. Emitters can be coaxial or in different angles relative to the detector 310 and a VA.
• an illumination source that can be coherent or non-coherent, narrow spectra or broadband, UV, visible, SWIR, far IR, NIR - for example NIR led or green (532nm) laser.
• Emitters can be coaxial or in different angles relative to the detector 310 and a VA.
• the operation mode can be stills or video.
• One or more polarization filters (elliptical and/or linear)
• the detection unit optionally includes a scan system or a moving bar scanner.
• the detection unit 302 optionally uses an audio/sound detection sensor 316, instead of, or in addition to, visual/optical detection, and the detection unit 302 may optionally include one or more audio sensors.
• the detection unit 302 may include vital signs sensors.
• the software unit 306 may include one or more of the following components:
• GUI - graphic user interface / Application 332 for one or more of: operating a test procedure, displaying images and/or results and/or inserting or importing patient clinical information.
• Algorithms unit 334 - the algorithms unit optionally includes algorithms, or software modules, for:
• inputs for the ML algorithm are optionally images and/or data captured by the detection unit 302.
• the inputs may include also clinical information of the patient and/or vital signs.
• the work station 304 optionally includes a computer, a screen, a keyboard, one or more knob controls, a mechanical interface for the imaging unit, and an electric power supply or interface to electric power.
• the work station 304 may also include an “organ fixation surface”.
• the work station 304 optionally includes one or more of: a control unit 320, for controlling operation of the detection unit 302 and/or one or more of the components of the detection unit 302; a computer 320; a display 324; an optional organ fixation surface or device 326, for optionally placing an organ at a specific location relative to the illumination 314 and/or the detector 310; and a stand 328, for placing components of the system at a specific location relative to a patient’s organ.
• a control unit 320 for controlling operation of the detection unit 302 and/or one or more of the components of the detection unit 302
• a computer 320 for controlling operation of the detection unit 302 and/or one or more of the components of the detection unit 302
• a computer 320 for controlling operation of the detection unit 302 and/or one or more of the components of the detection unit 302
• a computer 320 for controlling operation of the detection unit 302 and/or one or more of the components of the detection unit 302
• a computer 320 for
• the cloud infrastructure 308 optionally includes one or more of the following cloud services a storage (database) server 340; a Web application server 336; a computing service for machine learning, such as refining algorithm(s) based on new data; and/or for analytics - to provide measures of function and metrics to a user; and/or insight - to provide metrics related to current or a predicted future clinical condition of the VA.
• cloud services a storage (database) server 340; a Web application server 336; a computing service for machine learning, such as refining algorithm(s) based on new data; and/or for analytics - to provide measures of function and metrics to a user; and/or insight - to provide metrics related to current or a predicted future clinical condition of the VA.
• a machine learning algorithm - may be supervised or unsupervised, learning based on database of images and/or of patient parameters produced by an embodiment of the invention, and/or of meta data such as a patient’s, disease, vital signs, parameters from a dialysis machine and/or other data available in a medical electronic record, optionally including previous interventions for this patient, additional risk factors, comorbidities, and so on.
• the steps include one or more of:
• an outcome of the ML is a statistical classifier model that distinguishes between less or more than 50% AV patency.
• Analytics and Insight run on the metadata and patient records, and calculate statistics of failure of the AV based on the patient profile (metadata and medical health record).
• analytics is optionally performed on a clinic’s performance, for example how many stenosis events per year.
• Figures 4A-4E are simplified flow chart illustrations of algorithms according to example embodiments of the invention.
• Figures 4A-4E show flow charts depicting exemplary algorithms which may be implemented, by way of a non-limiting example, in the system’s software unit 306 or in the cloud unit 308.
• Figure 4A illustrates a procedure flow.
• Figure 4A illustrates the procedure flow on a vascular access, as an example.
• a subject’s organ e.g. arm
• the organ is an arm or leg, and all measurements are taken when the organ is approximately perpendicular to the ground (pointing up or down). In some embodiments, some of the measurements are taken when the organ is approximately parallel to the ground, and some measurements are taken when the organ is perpendicular to the ground (pointing up or down). In some embodiments, some of the measurements are taken when the organ is lower than the patient’s heart, and some measurements are taken when the organ is higher than the patient’ s heart.
• a region of interest is detected (404).
• the ROI is the vascular access body and/or surroundings of the vascular access body.
• the detection can be done either automatically by the system or manually by a physician/user.
• a next step is taking one or more measurements (406), e.g. images, of the ROI.
• the images go through a processing algorithm (408), e.g., image processing algorithm, and are then optionally saved into a database 410).
• a processing algorithm e.g., image processing algorithm
• a next step is to extract features (414) from the current examination measurements, e.g., images, and from the previous examination measurements (412), e.g. images.
• the features are sent to a statistical model which may classify (416) between "Early detection failure” (418) and “Stable” state (420) of the vascular access body.
• Figure 4B illustrates an exemplary algorithm flow of extracting features of the "pulse wave velocity" phenomena.
• a first step is pre-processing (422), e.g., to detect the image scale, for example in units of mm.
• a second step is to subtract the first image from the second (424).
• the result includes two bright spots.
• a next step is to detect the centers of the bright spots (426) and to calculate a distance along a path along the blood vessel between the centers of the bright spots (428).
• a next step is dividing the calculated path by the time period between the two images (430), producing a result of a pulse wave velocity.
• Figure 4C shows an exemplary algorithm flow of extracting features of the collateral veins phenomena.
• a first step is pre-processing (434), e.g., to detect the image scale, for example in units of mm.
• a second step is to detect the vessel's route and/or branches (436).
• a next step is to calculate the length of each branch and its distance, along a vein route, from a fistula (438).
• VA vascular access
• a further step includes calculating parameters that describe the collateral veins phenomena (442), including one or more parameters such as:
• Figure 4D shows an exemplary algorithm flow of extracting features of the aneurysm and stenosis phenomena.
• a first step is pre-processing (446), e.g. to detect the image scale, for example in units if mm.
• a next step is to detect the vein and/or artery route (448).
• a next step is to and segment the vein and/or artery route (450).
• a next step is to find and calculate the narrowest and widest widths along the vein and/or artery routes (452).
• Figure 4E shows an exemplary algorithm flow of extracting features of an arm elevation examination. It is to be understood that the algorithm flow is applicable to other subject organs, and it is not limited to the arm.
• two images are obtained after an arm is elevated, to track changes in outflow, which translate to changes, over a short period of time, in the volume of a fistula.
• a normal outflow state the fistula contents “quickly” (over a few seconds) emptied, and a difference in shape/area between the two images is detected and/or measured.
• an obstructed outflow state the fistula contents do not evacuate fast enough, and a smaller change, if at all, is detected/measured in the shape/area of the fistula. Tracking such changes over time - enables tracking changes in patency of a fistula.
• arm elevation examination may be applied when the arm is elevated (pointing up or pointing down, perpendicular to the ground, and/or above the heart level).
• a first step is pre-processing (456), e.g., to detect the image scale, for example in units of mm.
• a next step is to detect the vascular access (fistula) in the image (458).
• a next step is to segment the vascular access (fistula) in the image (460), optionally segmenting the fistula from other portions of the image.
• a next step is to calculate the vascular access area in the image (462).
• the arm elevation examination starts by taking a first image when the arm is parallel to the ground, and a second image when the arm is perpendicular to the ground (pointing up or down) above heart level.
• a next step is pre-processing, e.g., to detect the image scale, for example in units of mm, in both images.
• a next step is to detect the vascular access in both images.
• a next step is to calculate the vascular access area in both images.
• a next step is to subtract the first vascular access area from the second vascular access area.
• the arm elevation examination starts by moving the arm (or any other subject organ) from a first position, where the arm is approximately parallel to the ground, to a second position, where the arm is approximately perpendicular to the ground (pointing up or down).
• a next step is taking two images of the elevated arm.
• a next step is pre-processing, e.g., to detect the image scale, for example in units of mm, in both images.
• a next step is detecting the vascular access in both images.
• a next step is to calculate the vascular access area in both images.
• a next step is subtracting the first vascular access area from the second vascular access area.
• a next step is dividing the calculated difference by the time period between the two images.
• FIGS. 5 A and 5B are simplified illustrations of a pulse wave travelling along a vein.
• Figure 5A shows a first image and Figure 5B shows a second image, taken a short time later.
• Figures 5A and 5B show an arm 502, a vein 504, an artery 506, and a fistula 508 where the vein 504 is connected to the artery 506.
• Figure 5A shows a first location 510 where the vein is enlarged by pressure of a pulse wave, at a time to.
• Figure 5A shows a second location 512 where the vein is enlarged by pressure of the pulse wave, at a time ti.
• the second location 512 is further along the vein 504 relative to the first location 510.
• Pulse wave velocity is optionally measured by measuring a distance between the first location 510 and the second location 512, divided by a time difference between the capture of the first image and the second image.
• the time difference is a fraction of a second.
• imaging a pressure wave progressing along a blood vessel is optionally done at a frame rate above 120 fps, for example at 165 fps.
• the time different is ⁇ 6ms. Such a time difference applies to all pulse wave velocities that are smaller than 20m/s.
• Figure 6 is a simplified flow chart illustration of a classifier method according to an example embodiment of the invention.
• Figure 6 shows input of one or more feature descriptors, such as: a collateral vein descriptor 602, a pulse wave velocity descriptor 604, an arm elevation descriptor 606, and an aneurysm and/or stenosis descriptor 608.
• a collateral vein descriptor 602 a pulse wave velocity descriptor 604
• an arm elevation descriptor 606 an aneurysm and/or stenosis descriptor 608.
• the inputs 602 604 606 608 are fed into a threshold calculation unit
• a trends calculation unit 614 optionally accepts input of a historical and/or trend descriptor 610, optionally from a local or a remote database.
• the trend calculation unit 614 produces a trend data output.
• the threshold calculation unit 612 produces a threshold data output.
• one or more of the above outputs are fed into a statistical unit 616.
• output of the statistical unit 616 is input to a decision unit 618.
• the decision unit 618 optionally produces a decision that the VA is determined to be “stable” 622, or that a failure is detected 620.
• Figure 6 describes an exemplary classifier algorithm, which may be based on machine learning (supervised or non-supervised) tools or on heuristic rules that execute the following steps:
• Data analysis such as image processing.
• Some examples of features include: smallest radius size of body of VA, pulse wave velocity, collateral veins sizes and density, distance of collateral veins from the AV or fistula etc.
• the features can also be a variation of the features between sequential images and/or a rate of variation of the features between sequential images.
• the classification can be base rule, threshold and/or statistical model.
• a statistical model can be based on a machine learning algorithm, such as: SVM (Support vector machine), logistic regression, neural network, decision tree, decision forest, “k means”, etc.
• the classification can be between two levels (intervention needed or not) or between more than two levels.
• a non-limiting example of methods used for predicting and/or classifying include one or more of:
• K Nearest Neighbors (KNN) as applied to parameter values
• SVM Support Vector Machine
• machine learning uses a training set.
• a non-limiting example of producing and using a training dataset includes measuring parameters as described herein, for N patients, repeatedly over a period of time.
• the parameters and the determination potentially produce a training dataset, which can be used to train the above-mentioned machine learning methods, or to produce KNN dataset.
• some or all of the parameters measured for the datasets described above are also measured for patients for prediction and/or classification purposes.
• parameters which are collected over time optionally include measured values and parameters calculated from measured values, first derivatives of the parameter values, and second derivatives of the parameter values.
• a nurse or physician performing a blood vessel test by the look, listen and feel method typically provide a decision or classification based on a value of one or two parameters, while systems and methods as described herein potentially use more parameters, and potentially arrive at more accurate decisions or classifications which are based on more data of the patient and/or of a group of patients used for producing the training set.
• a scaling algorithm calculates the image scale (for example scaling pixels to mm).
• the scaling may be used for calculating absolute or relative values of one or more of a vessel's radius, pulse wave velocity, size of collateral vessels, density of collateral vessels, and distance of collateral vessel from VA.
• a registration algorithm may perform automatic or semi-automatic registration between two or more sequential images.
• the registration algorithm may align and/or scale two or more images that contain the same object in different positions or angles of view or different fields of view.
• inputs to the registration algorithm include at least two images and in case of semi-automatic registration, optionally, one or more points that are marked by the user on the two images.
• the registration algorithm potentially enables the system to measure a variation between at least two examinations, no matter how the arm, or another examined organ, is positioned during the different examinations.
• registration of at least two images of the same patient that contain a VA object is optionally done by detection (e.g. segmentation) of the VA and fitting the VA image in a first image by geometrical transformation to the VA image in a second image.
• VA vascular access
• input for an algorithm for detection of a vascular access body includes at least one image that contains the vascular access body in the image frame.
• an optional input is a set of one or more points along a blood vessel which includes the VA body, optionally marked by a physician/nurse on an image which includes the VA body.
• the algorithm output may be a set of the vascular access body pixels in the image.
• computerized detection of the VA body is based on a unique VA shape, size, orientation, position and etc.
• a device such as, by way of a non-limiting example, an “ELY- 1000 vascular imaging instrument for Arterial puncture” as developed by ELYNNSH MEDICAL, is used.
• the device assists medical staff in identifying subcutaneous arteries during an arterial puncture, and can conveniently & quickly display the exact location of the arteries and direction.
• a location is detected in an image, where an artery and a vein are connected or appear to join.
• a blood vessel providing blood to a VA is elevated by surgery toward the skin surface. Because of depth differences of blood vessel segments, an image which cover a field-of-view (FOV) which includes a VA, the VA often appears as a closed contour centroid. Tissues surrounding the VA body are often deeper under the skin than the VA body.
• FOV field-of-view
• the difference in depth is optionally detected by the VA body potentially showing up as a darker area than native or surrounding vessels.
• the NIR light is absorb in the blood Hgb, and blood vessels closer to the surface appear darker than deeper vessels.
• Pulse wave velocity is also a common indicator of blood vessel stiffness and can be obtained by measurement of the distance and the pulse wave transit time between two points of vessels. Pulse wave velocity can be measured locally, regionally or systemically.
• the term locally is used to mean along a fistula and nearby related vessel structures.
• the pulse wave (caused by heartbeat) travels from the heart to the arteries, and from the veins back to the heart. When the pulse is traveling, it temporarily deforms the blood vessel (e.g., the vein) at a moving discrete point and time.
• the blood vessel e.g., the vein
• the vein radius may temporarily expand at a certain point along the vein. This point can be detected by measuring the absorption of light by the blood flowing in the vein - the location of the expanded vein shows as a darker or a lighter point along the vein (depending on a method of measurement, such as reflection or transmission).
• Pulse wave velocity equals the distance between two points divided by the time between capture of the two images.
• the system may measure one or more of the following example phenomena: vessel diameter, pulse wave velocity, NIR (e.g. 700-1000nm) reflected spectroscopy, appearance of collateral veins and their characteristics, such as: density, size, distance from the vascular access and oxygen concentration at the vascular access.
• NIR e.g. 700-1000nm
• the NIR spectral range is used for blood vessel imaging.
• a spectral window exists from approximately 700 nm to approximately 900 nm, where light can penetrate deep into tissues, and also more radiation is absorbed by venous blood vessels than by surrounding tissues.
• Figure 7 is a simplified block diagram of a system for measuring blood vessels according to an example embodiment of the invention.
• Figure 7 shows a top-level block diagram of an example embodiment system 700.
• the system 700 may include an imaging/detection unit 702 and a software/computation unit 706.
• the imaging/detection unit 702 optionally includes one or more sensor(s) 710, one or more lenses 712, one or more filter(s) 713, and one or more illuminator(s) 714716.
• the senor(s) 710 may be CMOS sensor(s).
• the senor(s) 710 may be a multispectral and/or hyperspectral camera(s).
• the sensor(s) 710 may be NIR sensor(s) or camera(s).
• the lens 712 may optionally be a fixed focal length lens.
• the lens 712 may optionally be a zoom lens.
• the filter(s) 713 may optionally include bandpass or long-pass filter(s).
• the illuminator(s) 714 716 may optionally include NIR LEDs, optionally in a spectral range of 700-1200 nm.
• the illuminator(s) 714 716 may optionally include broad band NIR
• the illuminator(s) 714 716 may optionally include one or more laser sources, optionally in Near IR spectral range of 850nm and 910nm.
• the illuminator(s) 714 716 may optionally include narrow band illumination, optionally in a spectral range of 900 nm
• the illuminator/ s) 714 716 may optionally include an array of illuminators.
• the software/computation unit 706 optionally includes one or more of a GUI 734, an image processing unit 735, a computer vision unit 736, and a machine learning algorithm unit 737.
• the algorithm unit 737 optionally includes one or more of: image processing algorithm(s), vein segmentation algorithm(s), collateral vein detection and/or segmentation algorithm(s), pulse wave detection algorithm(s), and classifier algorithm(s) - optionally machine learning algorithms.
• the system 700 may include additional units, such as a work station 704, optional cloud infrastructure 708, etc.
• the cloud infrastructure 708 optionally includes one or more of a web application 738, database(s) 740 (optionally including big data analytic capability), and analytic unit(s) 742.
• the work station 704 optionally includes one or more of: a control unit 720, for controlling operation of the imaging/detection unit 702 and/or one or more of the components of the imaging/detection unit 702; a computer 722; a display 724; an optional organ fixation surface or device 726, for optionally placing an organ at a specific location relative to the illumination 714716 and/or the sensors 710; and a stand 728, for placing components of the system at a specific location relative to a patient’s organ.
• a control unit 720 for controlling operation of the imaging/detection unit 702 and/or one or more of the components of the imaging/detection unit 702
• a computer 722 for controlling operation of the imaging/detection unit 702 and/or one or more of the components of the imaging/detection unit 702
• a computer 722 for a computer 722
• a display 724 for optionally placing an organ at a specific location relative to the illumination 714716 and/
• Figures 8 A and 8B are images of optical components in a system constructed according to an example embodiment of the invention.
• Figures 8A and 8B show some of the example system’s optical channel, which may include, as shown in Figure 8 A: a camera 802, optionally a hyper spectral sensor (camera); a lens 802, optionally a fixed focal length lens; an optional filter mount 806; a filter 808, in some embodiments an optical long pass filter, in some embodiments a filter with a cut off wavelength of 670 nm; and an illumination source 812.
• a camera 802 optionally a hyper spectral sensor (camera); a lens 802, optionally a fixed focal length lens; an optional filter mount 806; a filter 808, in some embodiments an optical long pass filter, in some embodiments a filter with a cut off wavelength of 670 nm; and an illumination source 812.
• a camera 802 optionally a hyper spectral sensor (camera); a lens 802, optionally a fixed focal length lens; an optional filter mount 806; a filter 808, in some embodiments an optical long pass filter, in some embodiments a filter
• the system includes an optional mechanical adaptor 810 to connect the illumination source 812 to the camera 802 body.
• Figure 8B show an assembled unit 814 including the components of Figure 8A.
• an example blood-vessel-status classifying algorithm may be divided to three blocks; image processing, feature extraction and statistical classifier.
• the example algorithm TOP level flow may be similar to that shown in Figure 4A.
• the image processing block may include several steps: • Image quality enhancement, such as contrast and illumination enhancement, sharpness, a combination of multiple polarization state images, multiple wavelength images (image of intensity ratios), multiple exposures, optionally High Dynamic Range (HDR), and contrast limited adaptive histogram equalization (CLAHE).
• Image quality enhancement such as contrast and illumination enhancement, sharpness, a combination of multiple polarization state images, multiple wavelength images (image of intensity ratios), multiple exposures, optionally High Dynamic Range (HDR), and contrast limited adaptive histogram equalization (CLAHE).
• the intensity ratio images show a pixel-wise ratio between images that were captured with different wavelengths, as described the following equation:
• Rif is the pixel at location (i, j) in the ratio image
• IMlij is the pixel at location (i, j) in the first image
• IM2ij is the pixel at location (i, j) in the second image.
• VA vascular access
• Figure 9 is a simplified flow chart illustration of a segmentation method according to an example embodiment of the invention.
• Figure 9 shows an exemplary segmentation flow, including input of a first image 902, segmentation 904 of the first image 902 producing a second image 906 with optional segmentation lines 907, optionally isolating 908 an organ which appears in the second image 906, producing a third image 910 containing just an image of the isolated organ.
• K-means algorithm K-means algorithm
• Histogram- based methods Edge detection
• Region-growing methods e.g., averaging of the following methods.
• Mumford and Shah Segmentation e.g., averaging of the following methods.
• the registration step optionally scales and/or aligns new image(s) to a reference image, optionally the image(s) from the earlier examination.
• Figure 10 is a simplified flow chart illustration of a registration method according to an example embodiment of the invention.
• Figure 10 shows a first image 1002A and a second image 1006A.
• a point detection operation 1004 is optionally performed on the two images.
• a point detection criterion is optionally one or more of: corner points, an intensity based criterion such as blob detection, SURF (speed up robust features), and so on.
• similarity of two points is measured by a feature metric difference between one or more feature metrics of each one of the two points.
• the first image 1002 A is marked by specific points detected in the first image 1002 A, producing a first new image 1002B with specific points marked thereon.
• the second image 1006 A is marked by specific points detected in the second image 1006 A optionally according to same criteria used for detecting points in the first image 1002A, producing a second new image 1006B with specific points marked thereon.
• Figure 10 shows some lines 1007 connecting corresponding specific points in the first new image 1002B and the second new image 1006B.
• first new image 1002B and the second new image 1006B are optionally transformed 1008, using the detection of corresponding marked points to perform the transformation, optionally producing a new combined image 1010.
• the transformation 1008 includes one or more of image standardization, image scaling, image rotation, and affine transform, performed on one or both of the first new image 1002B and the second new image 1006B.
• the registration is performed to align and/or scale a first image, for example a current examination image, to a second image, for example a prior examination image.
• SIFT Scale Invariant Feature Transform
• SURF speeded up robust features
• a features extraction block may include several sub-blocks that analyze data and extract features from images.
• the feature extraction optionally produces a feature vector.
• the feature extraction is optionally performed after an image processing step which produces a standardized image.
• the features vector is a mathematical representation used to characterize data such as an image. There are several ways to characterize the data, some of which are listed below:
• One method includes passing an image through a neural network that was trained on a large image data set and use its descriptors layer. Another way is to develop specific descriptors for each phenomenon.
• Figure 11 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• Figure 11 illustrates a method producing a descriptor for a blood vessel's length and/or smallest diameter.
• Figure 11 shows: a first image 1102 as an input; a conversion 1104 of the first image 1102 to a binary image 1106; a location 1114 of a narrowest passage in the organ (blood vessel); and a tracing 1108 of a center line of the organ (blood vessel) appearing in the binary image 1106, producing a third image 1110 with a center line 1112 of the organ (blood vessel) marked on the third image 1110.
• a similar method can also optionally be used for producing a descriptor for "pulse wave velocity”, “collateral vessels development”, and “aneurysm and stenosis”.
• “Distance transform” and “local maxima” methods may be used on a binary image for detecting the center line of the blood vessel and the diameter.
• Path finding algorithm -Dijkstra's algorithm A* search algorithm.
• Figure 12 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• Figure 12 illustrates a method producing a descriptor for arterial and/or venous oxygen concentrations in the VA.
• Figure 12 shows: a first image 1202 as an input; a histogram unit 1204 for producing a histogram 1206 of the first image 1202; and a calculation unit 1210 for producing a feature(s) vector 1212 associated with the first image 1202.
• deoxy Hb is higher than Oxy Hb at the range of 740nm to 760nm, so at this range, veins absorb the light radiation and arteries become relatively more transparent.
• the blood in the VA is a mixture of arterial and venous blood, especially when stenosis occurs, resulting in recirculation of blood.
• the system can create a features vector that describes a change in the blood mixture in the VA, or a rate of change in the blood mixture in the VA.
• Figure 13 is a simplified flow chart illustration of a method according to an example embodiment of the invention.
• Figure 13 illustrates a method for calculation of pulse wave velocity.
• Figure 13 shows: a first image 1302 obtained at a time to as an input; a second image 1304 obtained at a time ti as an input; and a calculation unit 1306 for producing a third image 1308.
• Figure 13 illustrates an exemplary method for features extraction of pulse wave velocity.
• two consecutive image frames such as the images 1302 1304 of Figure 13, optionally each image frame after registration and/or segmentation (standardized images), are fused, producing a fused image such as the third image 1308.
• the fused image is produced by subtraction of one of the images from the other.
• the fused image is produced by adding one of the images to the other.
• centers of mass of the two brightest spots 1312 1314 are calculated, and a length of a path 1312 between the centers of mass of the two brightest spots 1312 1314 along the path 1312 is measured.
• the path 1312 is optionally a center line of the blood vessel.
• Figure 14 is a simplified flow chart illustration of a classifier method according to an example embodiment of the invention.
• Figure 14 shows input of one or more feature descriptors, such as: a collateral vein descriptor 1402, a pulse wave velocity descriptor 1404, an aneurysm and/or stenosis descriptor 1406, and an arterial and venous blood mix descriptor 1408.
• the inputs 1402 1404 1406 1408 are fed into a trend calculation unit 1412.
• the trend calculation unit 1412 optionally accepts input of a historical and/or trend descriptor 1410, optionally from a local or a remote database.
• the trend calculation unit 1412 produces a trend features vector
• trend features vector 1414 is optionally stored in the (local or remote) database.
• the trend features vector 1414 is input to a classifier 1416.
• a result of the classifier 1416 is optionally input to a decision unit 1418, which produces a decision that the VA is determined to be “stable” 1420, or that a failure is detected 1422.
• the detecting a failure may include estimating a high probability of imminent failure of the VA.
• Classification to a "stable” or an “Early failure detection” can be done by a statistical classifier model, such as SVM, logistic regression, Neural network, etc.
• extracted features 1402 1404 1406 1408 of every phenomenon are optionally collected to one "features" vector 1414.
• the features vector 1414 is optionally stored in a data base.
• the features vector 1414 and a "history features vectors" 1410 are optionally sent to a "Trends calculation" unit 1412.
• the output of the "Trends calculation" unit 1412 is a "new trend features vector" 1414, which is optionally stored in the database and/or sent to a classifier unit 1416.
• output from the classifier unit 1416 can be detected to be "Early failure detection” or “Stable”.
• classification is made to a maturity level or rate of maturation after VA surgery can be done.
• the rate of maturation of a fistula may be expressed as X% maturation after Y number of days.
• Figures 15A-C show three different images of a same patient arm, according to an example embodiment of the invention.
• Figure 15A shows an image of a patient’s arm in human- visible wavelengths, taken at a distance of approximately 40 centimeters from the arm.
• Figure 15B shows an image of a patient’s arm in Near IR wavelengths.
• Figure 15B shows that using Near IR imaging improve visibility of blood vessels such as superficial veins 1512.
• Figure 15C shows an image of a patient’s arm, with points-of-interest 1522 which were automatically (by image analysis) generated at locations of the blood vessels.
• systems as described herein perform a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• methods as described herein performs a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• Figure 16 A is a table showing a procedure for a medical person to examine a patient with reference to vascular stenotic lesions or thrombosis.
• Figure 16A is intended to show what a human is instructed to do. However, it is known that differences between humans is expected to affect such examinations.
• Figure 16B is a simplified flow chart illustration of a method of examining a patient according to an example embodiment of the invention.
• the method of Figure 16 includes: a device looking (1622) at a patient’s body by capturing one or more images of the body, and using image analysis on the image(s); the device listening (1624) to a patient’s body by capturing vibrations of the body, and analyzing the vibrations at human-audible frequencies; and the device feeling (1624) the patient’s body by analyzing vibrations of the body, at frequencies below human-audible frequencies.
• the capturing one or more images of the body is optionally performed by capturing images at Near IR wavelengths.
• the capturing vibrations of the body is optionally performed by laser speckle imaging, as described elsewhere herein.
• the capturing vibrations of the body is optionally performed by a microphone touching the patient’s body, and/or by a microphone attached to a stethoscope touching the patient’s body.
• automatic examination in some embodiments, is potentially able to provide such an examination without a human touching the patient, potentially usable in conditions where medical distancing is desired, such as, for example, when the patient may carry a contagious disease.
• the systems and methods described herein optionally “look”, that is, analyze images of blood vessels, “listen”, that is, analyze vibration of the patient’s body at human hearing frequencies, and “feel”, that is, analyze vibration of the patient’s body at low frequencies, reaching lower than typical audio frequencies.
• a no-contact surveillance tool is provided, to complement and/or replace physical examination of vascular access (VA).
• VA vascular access
• the surveillance tool does not contact a patient’s fistula, and/or a patient’s limb, even while the limb is optionally positioned in a device which enables position the fistula in a field of view of the device.
• recording and monitoring parameters measure by the surveillance potentially enables the early detection and/or prediction of stenosis, potentially earlier than human examination.
• surveillance is enabled without human touch, for example at distances greater than 10, 20, 30, 40, 50 centimeters from a location of VA.
• system and methods optionally enable acquiring all parameters typically acquire by a human physical examination by look, feel, and listen.
• Using embodiments as described herein potentially enable pre- and/or post- session examination in a clinic with no physical contact.
• Using embodiments as described herein potentially enable care in a home setup, possibly operated by a patient.
• Figure 17 is a simplified block diagram illustration of a method for examining a patient according to an example embodiment of the invention.
• Figure 17 shows a method including: accepting a patient for examination (1702); measuring the patient, using an embodiment of the invention (1704); collecting data from sensors (1706); analyzing the data (1708); and optionally providing a decision (1710) regarding a status of the patient’s fistula.
• the status of the patient’s fistula optionally includes a determination of a medical condition and/or patency of the patient fistula.
• the medical condition is optionally determined to be healthy and/or functioning or having a probability of deterioration.
• a probability of deterioration above a certain threshold optionally produces a recommendation to send the patient to additional tests such as Doppler ultra- sonography or X-ray angiography.
• Figures 18A and 18B are images of a fistula of a patient taken at two different times.
• Figure 18A shows an ink marking 1802 of an outline of the fistula.
• Figure 18A also shows a physical feature 1804 visible on the skin of the patient.
• Figure 18B is an image of the fistula taken at a different time.
• Figure 18B shows that the ink marking 1802 change shape due to a change in the shape and/or size of the fistula.
• Figure 18B also shows that the physical feature 1804 appears to have moved, relative to an outline of the fistula, or to the ink marking 1802.
• Figures 18A and 18B are images of a 36-year-old man with a right brachiocephalic fistula created in 2004.
• the fistula has several aneurysms 1801A 1801B.
• FIGS 19A and 19B are simplified drawings of a system for monitoring vascular access (VA) and/or fistulas according to two example embodiments of the invention.
• Figure 19A shows a system 1900 including a head 1902 and a base 1906.
• the head 1902 optionally includes a light projector and an imaging system.
• the base 1906 optionally includes a shape configured to support an arm or leg in a specific position relative to the head 1902.
• the base 1906 optionally includes a strap configured to support an arm or leg in a specific position relative to the head 1902.
• Figure 19A shows a system 1910 including a projector 1912 and an imager 1914.
• the projector 1912 includes an optional cover 1916.
• the cover 1916 may be desired and/or required for safety.
• the imager 1914 is optionally capable of imaging frame at a rate above standard video rate, optionally at a rate of 60 Frames Per Seconds (FPS), above 60 FPS, above 100 FPS, above 150, 200, 300, 400, 500 and 600 FPS.
• FPS Frames Per Seconds
• a high frame rate enables detecting vibrations of a patient’s body at high frequencies, as is known in the art - Shannon’s Law.
• a NIR fast Camera optionally at frame rates of 150 FPS or greater, is optionally used.
• an off-the-shelf camera is optionally used, for example a FLIR FL3 U3 camera, capable of imaging at a frame rate of 150 FPS at a full frame size of 1.3 megapixels.
• the camera is used to capture a frame rate of more than 160 FPS, up to 600 FPS, 620 FPS and more.
• an off-the-shelf camera is optionally used, capable of imaging at a frame size in a range of 1.3-2 mega-pixels and more.
• an off-the-shelf camera is optionally used, capable of imaging at higher frame rates when imaging at a lower frame size.
• the camera optionally images at a size of 10x20 pixels, 10x10 pixels, and so on.
• the imager captures a small frame, less than maximum frame size and optionally down to the above-mentioned small frame sizes, of a specific location of interest on the patient’s body at a location of the fistula or location of a VA point of interest.
• the projector optionally projects light onto the location of interest to enable a user to locate the patient’s body correctly.
• the location of interest is a patient’s fistula.
• more than one spot is illuminated simultaneously.
• one location of interest where a spot is illuminated is a patient’s fistula
• another location of interest where a spot is illuminated is a location neighboring the patient’ s fistula, but not at the fistula.
• one location of interest where a spot is illuminated is a fistula aneurism
• another location of interest where a spot is illuminated is a location neighboring the fistula aneurism, but not at the fistula aneurism.
• the projector is a Digital Light Processing (DLP) projector.
• DLP Digital Light Processing
• the projector is a laser projector.
• a location of interest for example a fistula, or an aneurysm, or a bloated area of a body, is optionally identified by using structured lighting and image analysis, and the projector is controlled, optionally automatically controlled, to illuminate the location of interest.
• the DLP and/or the laser projector are optionally controlled to illuminate the location of interest.
• a physician or nurse controls the illumination to the location of interest.
• a physician or nurse controls laser illumination to the location of interest.
• the projector is optionally capable of projecting light in multiple modes.
• the modes include two or more of: projecting uniform (or approximately uniform) lighting on an area, or a limited spot, on a patient’s body, potentially sufficient for imaging collateral veins; projecting structured lighting, optionally including stripes of specific widths, equal widths or unequal widths as programmed or other patterns; and projecting one or more spots of coherent laser light, potentially useful for measuring one or more of vibration, micro vibration, and pulses, for example by Laser Speckle Interferometry.
• the projector is capable of switching between any one of three different lighting modes: uniform, structured and spot.
• the projector is capable of providing a spot size in a range of diameters between 0.5 mm and 5 mm on a patient’s limb. For example, a spot size of approximately 1 mm.
• the projector includes one or more LEDs and/or laser light sources, optionally at Near IR wavelengths.
• the projector is optionally a Digital Light Processing (DLP) projector.
• DLP Digital Light Processing
• the projector optionally includes nano-mirrors to shape light.
• the projector optionally includes Micro-Electro-Mechanical System (MEMS) mirrors to shape light.
• MEMS Micro-Electro-Mechanical System
• the projector optionally includes a Digital Mirror Driver (DMD).
• DMD Digital Mirror Driver
• the projector and the camera are packaged in one package.
• Figure 20 is an image of a system for monitoring vascular access (VA) and/or fistulas according to an example embodiment of the invention.
• VA vascular access
• Figure 20 shows a system including a projector 2004, an imager 2006 and an optional processor 2002.
• the image of Figure 21 A shows the patient’s arm held below the patient’s heart level.
• Two inflated needle insertion points21022104 are shown on the patient’s arm fistula.
• the image of Figure 2 IB shows the patient’s arm held above the patient’s heart level, the image captured just after the patient raised the arm to the elevated position.
• a first 2102 one of the insertion points is shown deflated, and a second 2104 of the insertion points is still inflated.
• FIG. 21C shows the patient’s arm held elevated, the image captured a little later than the image of Figure 2 IB. Both of the needle insertion points 21022104 are shown deflated and collapsed.
• systems and methods described herein are optionally used to measure and quantify in an elevation test, that is, the measurement and quantification are performed once or more with a body or limb held below the heart level, and once or more with the body or limb held at an elevated position above heart level.
• a fistula may not drain when held at one position, and drain when held at one or more other positions. In some instances a fistula may not drain when held at one position, and also not drain when held at one or more other positions.
• Differences between the positions correlate with medical condition of the fistula such as a ratio between inflow and outflow rates and/or pressure).
• drainage rate and/or pattern are measured, optionally by generating a 3D shape and/or one or more 3D curves depicting the outer shape of the fistula and/or tracing changes between curves along the set.
• the drainage rate and pattern are optionally estimated by evaluating the volume encapsulated by the 3D shape and/or by one or more curve(s) and tracing the change in total volume over time, potentially providing a level and rate of draining.
• a spatial curvature of a curve(s) can be estimated, and drainage pattern can be characterized by analyzing the changes in curvature over time.
• the smoothness of curvature of each curve and changes in curvature smoothness over time during drainage can be used for estimation of a drainage pattern.
• correlation between any flow related parameter estimated from analysis of measured vibrations and any parameter estimated from the drainage pattern or rate based on 3D shape or curve shape(s) is optionally used for estimating fistula health.
• Figure 22A is a graph showing power spectrum of vibrations measured by analysis of images produced by laser speckle imaging.
• Figure 22A shows a graph 220, with a X-axis 2202 showing frequency ranges or bins, and a Y-axis 2204 showing relative power spectrum in the units in which it was measured.
• FV blood flow velocity
• the graph 2200 shows us that the maximum in the power spectrum is located approximately at approximately 140 Hz for both groups. This leads us to suspect that listening to the pitch of the blood flow in both groups might not be a good method to differentiate among them. However, analyzing the power spectrum of both groups shows differences:
• the first group 2206 appears to have a higher amplitude at the maximum than the second group 2208;
• the second group 2208 appears to have a flatter, or broader, curve than the first group
• the vibrations analyzed in the power spectrum are caused by blood flow and/or turbulence through a blood vessel.
• Flow and turbulence change over time and are affected by local physical conditions in and around the vessels through which the flow occurs.
• the physical conditions potentially include a pressure gradient, vessel diameter, vessel wall compliance, vessel inner surface characteristics, and so on.
• the power spectrum of blood flow measured at VA / fistula locations is potentially related to physical and/or clinical flow conditions at these locations. Changes in the features of such power spectra over time potentially correlate to degradation in fistula health. Analyzing the changes in the power spectrum obtained from the VA / fistula location are potentially early stage predictive of fistula deterioration.
• the power spectrum is measured by measuring an intensity of light reflected off a patient’s body.
• the intensity is expected to change at a frequency related to frequency of vibration of the body.
• the power spectrum is measured by measuring an intensity of light reflected off an illumination spot on the patient’s body. In such embodiments the vibration is practically measured specifically at the illuminated spot. In some embodiments, the power spectrum is measured by measuring differences between successive images of the body, for example small shifts of a pattern on the body.
• the pattern may be a mole on the skin, structured lighting, movement of a spot of light, movement of laser speckles, and similar movements.
• Figure 22B is a simplified flow chart illustration of a method for transforming data from a stream of images to a frequency spectrum according to an example embodiment of the invention.
• the method of figure 22B includes: receiving a stream of images imaging a patient’s body (2222); optionally selecting one or more pixels with high variance of intensity over duration of the stream of images (2224); producing a vector of intensity over the duration (2226); transforming the vector of intensity to a vector of a frequency spectrum (2228).
• the transforming is performed by a Fast Fourier Transform.
• the power spectrum before analyzing the power spectrum, is optionally normalized.
• a normalization factor is optionally calculated from: total spectrum energy, peak value, peak to baseline ratio, energy in a specific band width, and so on.
• a reference spectrum measured at a remote location is used as a reference. Both spectrums may or may not be normalized and the measured spectrum replaces by a difference between the spectra at the different locations.
• skewness or kurtosis of the measured power spectrum or the difference power spectrum are optionally used for estimating flow.
• a measured power spectrum is first fitted to a model, in some embodiments assuming one or more hidden model mixtures, by way of a non-limiting example a Poisson-Gaussian mixture, and model parameters are used as correlators to flow.
• a model in some embodiments assuming one or more hidden model mixtures, by way of a non-limiting example a Poisson-Gaussian mixture, and model parameters are used as correlators to flow.
• energy in a specific frequency range is used for estimating flow.
• a “Look, Listen and Feel” procedure is optionally performed by embodiments of the system described herein.
• systems as described herein perform a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• methods as described herein performs a look, listen and feel based on illuminating and imaging a patient’s limb and analyzing the data collected from the imaging.
• a fistula bruit also called a vascular murmur, is an indicator of how dialysis access is functioning.
• An arteriovenous fistula is one access type that is created by connecting an artery to a vein under the skin, usually in the upper or lower arm or leg. (i) The high blood flow from the artery through the vein allows the fistula to grow larger and stronger.
• a healthy AV fistula has a bruit (a rumbling sound that a human can hear), a thrill (a rumbling sensation that a human can feel), and good blood flow rate.
• the “Look” aspect is optionally performed by imaging a body or limb and analyzing an image or images to quantify blood vessel structure and/or fistula structure.
• the “Look” aspect is optionally performed by imaging a body or limb using structured light, and producing a 3D shape of a fistula.
• the “Listen” aspect is optionally performed by measuring vibrations of a body or limb and analyzing the vibrations to quantify parameter values relating to a medical condition of a fistula. In some embodiments, the “Listen” aspect includes analyzing vibrations in a frequency range within the human audible range.
• the “Feel” aspect is optionally performed by measuring vibrations of a body or limb and analyzing the vibrations to quantify parameter values relating to a medical condition of a fistula. In some embodiments, the “Feel” aspect includes analyzing vibrations optionally in a frequency range extending even beyond and/or below the human audible range.
• analyzing vibrations is optionally performed in a frequency range of less than 1,000 Hz. In some embodiments, analyzing vibrations is optionally performed in a frequency range of less than a typical human speech, for example less than 4,000 Hz.
• the “Feel” aspect includes one or more of:
• Measuring human pulse which is typically in a range of 40 beats per minute and above. Such measurement needs analyzing vibrations at a frequency of 1 Hertz and even somewhat less. When such analyzing is performed by analyzing image frames of a video sequence, it is sufficient to analyze image frames at approximately double the rate of the frequency being measured, that is, for example, approximately 2 frames per second or above.
• Measuring thrill which is typically in a range of 50-250 Hertz or 50-750 Hertz.
• analyzing is performed by analyzing image frames of a video sequence, it is sufficient to analyze image frames at approximately double the rate of the frequency being measured, that is, for example, approximately 100 frames per second or above.
• the “Look, Listen and Feel” is performed without physically touching the patient, by image analysis and/or by using a specific mode of lighting.
• Figure 23 is a simplified flow chart illustration of a method for monitoring blood vessel functionality according to an example embodiment of the invention.
• the method of Figure 23 includes: illuminating one or more blood vessels through a patient’s skin (2302); capturing at least one image of the blood vessels (2304); analyzing the at least one image (2306); and calculating a parameter associated with blood vessel functionality based upon the image analysis (2308).
• Figure 24 is a simplified flow chart illustration of a method for replacing a physical examination performed by medical staff for monitoring blood vessel functionality according to an example embodiment of the invention.
• the method of Figure 24 includes: producing at least one image of a patient organ (2402); analyzing the at least one image (2404); and producing parameter values associated with blood vessel functionality (2406).
• compositions, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
• a unit or “at least one unit” may include a plurality of units, including combinations thereof.
• range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

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