Patent Description:
People suffer from chronic and compromised wounds with debilitating pain and reduced quality of life for those whose health is already compromised. Patients with this condition often present to a doctor at late stages of the disease, which leads to many amputations, which may be avoidable. Moreover, proper diagnostics requires specialized vascular labs, which precludes these types of tests from being performed outside major hospitals and in an expedited fashion.

The wound is considered chronic if it is not healed within four weeks. The tissue health and wound healing process can be compromised by various factors, including insufficient blood supply, edema, and the presence of bacteria. These factors (oxygenation/perfusion, subepidermal moisture, and bacteria presence) among others will be referred to as tissue health indicators.

Multispectral (hyperspectral) imaging is a promising non-invasive optical modality for early detection of problematic wounds.

Visualization of skin distribution of oxyhemoglobin and deoxyhemoglobin can give insight into perfusion and oxygenation of the tissue. It can be used for assessment of tissue health (for example, ischemia).

Such as elevated levels of subepidermal moisture are typical for pressure injuries, visualization of water distribution in tissue can be used for early (pre-ulcer) diagnostics of pressure injuries.

Fluorescence imaging is a promising non-invasive optical modality for detection of bacterial burden. Visualization of bacterial burden can be used to assess bacterial burden and guide swabbing and cleansing. <CIT> discloses a method and spectral light-based apparatus with an embedded (built-in) spectral calibration module for acquiring multi-spectral reflectance images from a digital camera are disclosed. <CIT> discloses Systems and methods for determining bacterial load in targets and tracking changes in bacterial load of targets over time.

The present invention is described in the appended claims. There is also disclosed a process for generating visualizations of tissue. The process captures measurement data by a user device (e.g., smartphone), and processes the measurement data using the visualization application. The process extracts indications of tissue health from the processed measurement data, and stores or transmits the underlying data. The process generates interface elements corresponding to the visualization tissue health indicators.

The process may involve calibrating the visualization application using a reference object.

A small self-reference can be used to position the device properly.

A small self-reference can be used to calibrate the measurement data based on an intensity of illumination.

An illumination unit independent of the mobile device can be used for calibration and capture measurements together with a camera, laptop, or tablet.

There is provided a tissue imaging system comprised of a user device with a visualization application, an image capturing unit, and an illumination unit. The illumination unit is configured to illuminate the target area; the image capturing unit captures measurement data, the visualization application extracts visualizations of tissue health indicators from the measurement data and generates an interface with one or more interface elements corresponding to the visualization of tissue health indicators.

There is also provided a tissue visualization system connected to a tissue imaging system (user device with a visualization application, an image capturing unit, and an illumination unit). The illumination unit illuminates the target area; the image capturing unit captures measurement data. The visualization application extracts visualization of tissue health indicators from the measurement data and transmits the visualization of tissue health indicators or underlying data to the tissue visualization system. The tissue visualization system processes and stores the visualization of tissue health indicators or underlying data, and displays them on user devices.

There is further provided a portable illumination apparatus for facilitating visualizations of tissue. The apparatus comprises: a portable housing for detachable attachment proximal to an image capturing unit; and an illumination unit comprising one or more narrow band light sources configured to shine m flashes at n predetermined wavelengths, wherein n/<NUM> ≤ m ≤ n.

The illumination unit may further comprise a lens covering the one or more light sources, and having a focal length that is <NUM>%-<NUM>% of a working distance between the illumination unit and a target area of tissue.

The one or more light sources may be configured to provide flashes that are at least one of: (i) <NUM>±<NUM> wavelength, and having at least one of (a) a long pass filter with a cut-on wavelength of <NUM>±<NUM> or (b) a bandpass filter with transmission in a <NUM>-<NUM> range; (ii) two wavelengths in a <NUM>-<NUM> range, at least one of which in the green range; (iii) three wavelengths in a <NUM>-<NUM> range, at least one of which in the green range; or (iv) <NUM>±<NUM> wavelength.

The illumination unit may further comprise at least one of (i) a controller to control illumination of the one or more light sources, and (ii) a rechargeable battery for powering the apparatus.

The one or more light sources may be arranged along a central aperture having a radius of <NUM>-<NUM>.

The one or more light sources may be arranged in a ring having a radius of <NUM>-<NUM>.

The portable housing may comprise a compression clip for mounting the apparatus on a mobile device along at least one edge of the mobile device and proximal to a camera of the mobile device.

The portable housing may comprise a spring clip for mounting the apparatus on a mobile device along at least one edge of the mobile device and proximal to a camera of the mobile device.

Further disclosed is a tissue imaging system for visualization of tissue health indicators comprising a portable computing device, an image capture unit, and an illumination unit. The illumination unit comprises one or more narrow band light sources configured to shine m flashes at n predetermined wavelengths, wherein n/<NUM> ≤ m ≤ n. The image capture unit and the illumination unit are configured to capture measurement data for a target area of tissue. The computing device comprises a processor configured to access and execute instrutions in accordance with a tissue visualization application stored in a non-transitory computer-readable memory of the computing device, for capturing measurement data, and pre-processing and processing the measurement data to generate tissue health indicators.

The computing device may comprise a mobile device and the image capture unit is a camera integrated with the mobile device.

The illumination unit of the tissue imaging system may comprise any of the embodiments of the illumination apparatus described above.

The portable illumination unit may further comprise a wireless communication module for receiving commands from the computing device.

There is also provided a tissue visualization system operatively connected to one or more tissue imaging systems (such as any of the tissue imaging systems described above), comprising a communications module for communicating with the one or more tissue imaging systems, a system processor, and system non-transitory computer-readable memory thereon, configured to receive measurement data and tissue health indicators from the one or more tissue imaging systems and to generate a visualization of tissue health indicators of tissue images received from the one or more tissue imaging systems, for display to a user display unit.

There is further provided a method for generating visualizations of tissue. The method comprises: positioning a computing device at a proper distance from a target area of the tissue for capturing an image of the target area, the computing device comprising a processor and a non-transitory computer-readable memory storing computer-executable instructions comprising a tissue visualization application; capturing measurement data using an image capturing unit and an illumination unit, the image capturing unit and the illumination unit communicatively coupled to the computing device and the illumination unit configured to shine m flashes at n predetermined wavelengths during capturing of the measurement data, wherein n/<NUM> ≤ m ≤ n; pre-processing the measurement data using the tissue visualization application to obtain normalized images; extracting indications of tissue health indicators from the pre-processed measurement data; generating interface elements corresponding to the visualization tissue health indicators; and storing and/or transmitting the indications of the tissue health indicators.

The method may further comprise, prior to capturing the measurement data: capturing a reference image, wherein the positioning the computing device for the reference image capturing comprises positioning the computing device using a reference object.

The illumination unit and the computing device may be configured to provide a working distance of <NUM>±<NUM> from the target area of tissue.

The positioning of the computing device for capturing the measurement data may comprise positioning the computing device using a self-reference object.

Pre-processing may comprise at least one of (i) registering images to avoid camera motion artifacts, (ii) subtracting images with no illumination from the illumination unit from images with illumination from the illumination unit to account for the presence of ambient light, (iii) recalibrating each measurement accordingly to control parameters related to intensity of illumination using a self-reference object positioned within the target area, (iv) dividing the intensity images on reference images to obtain normalized images, and/or (v) flattening the obtained images to account for reflections from curved surfaces.

Camera exposure time may be T and a flash time is T or any whole number multiple of T.

The measurement data may comprise wound-related data.

Many further features and combinations thereof will appear to those skilled in the art.

Reference is now made to the attached figures, wherein in the figures:.

Some clinical-grade tools can only be used in specialized medical establishments. They can be large, require special training, and are mostly suitable for the use in inpatient settings only. For example, they cannot be easily carried to a patient's home or remote communities. Thus, these solutions cannot be used as early diagnostic tools as a patient would have to be referred to a hospital having one of these tools.

Many people suffer from diabetes. Diabetic foot ulcers (DFU) and the resulting lower extremity amputations are a frequent, disabling and costly complication of diabetes. Many diabetics can develop a foot ulcer. DFU is a cause of non-traumatic below knee amputation. In addition to the reduced quality of life, amputees might not survive for that long after amputation. Consequently, early detection of DFU can lead to better outcomes, thus saving limbs and lives.

Peripheral vascular disease (PVD) affects arteries (peripheral arterial disease, PAD) and veins (chronic venous insufficiency, CVI). PAD is of particular importance, as it affects about eight million Americans and is responsible for <NUM>% of all leg ulcers.

Pressure ulcers (PU) or pressure injuries represent a serious health problem to patients impacting up to <NUM>-<NUM>% of patients across acute and long-term care settings.

The cost of treatment of diabetic foot ulcer, pressure ulcer, and leg ulcer is high. Diagnosing these conditions at an earlier stage (e.g., before actual ulceration) might result in significant financial savings for healthcare systems and patients.

Other clinical indications associated with abnormal blood perfusion and/or oxygenation, such as skin cancer (angiogenesis), port-wine stains, and skin disorders, can benefit from a system for tissue imaging.

Subepidermal moisture, a measure of localized edema, is associated with erythema, Stage I and II PUs [Bates-Jensen <NUM>, Bates-Jensen <NUM>, Guihan <NUM>, Ching <NUM>], and can (ii) differentiate between healthy skin and skin with pressure-induced tissue damage [Harrow <NUM>] and (iii) serve as a predictor of imminent ulceration (PUs, sDTIs) in various populations [Bates-Jensen <NUM>, Bates-Jensen <NUM>, Bates-Jensen <NUM>]. Thus, changes in measures of subepidermal moisture could be utilized for both prevention and detection of PUs. Radiofrequency impedance measurement with spatially separated electrodes is a current standard way to measure skin moisture including subepidermal moisture. However, it is a contact single-point measurement technique, which may suffer from operator inconsistency.

Near-Infrared spectroscopy (NIR) reflectance can be used to determine water content in the skin. Water spectrum dominating NIR spectra with overtone bands of the O-H bonds with peak absorption at <NUM>, <NUM> (due to the second overtone of the O-H stretching band), <NUM> (the combination of the first overtone of the O-H stretching and the O-H bending band), <NUM> (first overtone of the OH-stretching band and a combination band), and <NUM> (combination of the O-H stretching band and the O-H bending band). [Luck <NUM>].

Water absorption at <NUM> is <NUM> times stronger than at <NUM>, which in turn is more than two times stronger than absorption at <NUM>. Thus, <NUM> and <NUM> wavelengths are suitable for imaging of water content in uppermost skin layers (stratum corneum), while <NUM> and <NUM> can be used for water content determination and imaging in deeper skin layers, including epidermis, dermis (<NUM>) and even subcutaneous tissues (<NUM>).

Bacteria presence can significantly impact tissue health and wound healing progress. Bacteria are always present in the wound. There are several distinct levels of bacterial burden in the wound: contamination, colonization, and infection.

Wound contamination is the presence of non-replicating organisms in the wound. All chronic wounds are contaminated. These contaminants come from the indigenous microflora and/or the environment.

Wound colonization is the presence of replicating microorganisms adherent to the wound in the absence of injury to the host. Most of these organisms are normal skin flora; such as Staphylococcus epidermidis, another coagulase negative Staph. , Corynebacterium sp. , Brevibacterium sp. , Proprionibacterium acnes, and Pityrosporum sp.

Wound Infection is the presence of replicating microorganisms within a wound that cause host injury. Primarily, pathogens are of concern here, such as Staphylococcus aureus, Beta-hemolytic Streptococcus (S. pyogenes, S. agalactiae), E. coli, Proteus, Klebsiella, anaerobes, Pseudomonas, Acinetobacter, and Stenotrophomonas (Xanthomonas).

Contamination and colonization by low concentrations of microbes are considered normal and are not believed to inhibit healing. However, critical colonization and infection are associated with a significant delay in wound healing.

Clinical testing for bacterial presence includes analysis of swabs from the tissue. In addition to long processing time (several days), these tests suffer from possible contamination during swabbing and randomness in the selection of swabbing sites. Thus, current clinical diagnostics techniques are sub-optimal.

Portable fluorescence imaging can be used for visualization of bacterial presence. It was found that while excited at <NUM>, S. epidermidis, Candida, S. marcescens, Viridans streptococci, Corynebacterium diphtheriae, S. pyogenes, Enterobacter, and Enterococcus produced red (<NUM>-<NUM>) fluorescence from porphyrin [Kjeldstad <NUM>] while P. aeruginosa produced a bluish-green (<NUM>-<NUM>) fluorescence from pyoverdin [Cody <NUM>]. Thus, fluorescence imaging can be used to assess bacterial burden and guide swabbing and wound cleansing.

Thus, multispectral/hyperspectral-based reflectance imaging, fluorescence imaging or their combination can provide valuable insights on tissue health and wound healing potential.

Embodiments described herein can provide a tool for tissue imaging.

<FIG> depicts a view of an example tissue visualization system <NUM> that connects to tissue imaging systems <NUM> via network <NUM>.

Tissue imaging system <NUM> is a device for visualization of abnormalities of blood circulation, moisture distribution, and bacterial burden in surface tissues (skin or mucosa). For example, the device can be used for identification of ischemic or angiogenic conditions. It can be used by primary care physicians, nurses, or even patients themselves in any type of settings: inpatient, outpatient, long-term facilities, patient's home, and so on, thus allowing earlier identification of problematic wounds. Tissue imaging system <NUM> may comprise a computing device <NUM> which may comprise a mobile device <NUM>, processor(s) 108a, non-transitory computer readable storage medium or memory 108b, image capturing unit <NUM>, and illumination unit <NUM>. Memory 108b may comprise computer executable instructions comprising tissue visualization app <NUM>.

Computing device <NUM> may be an off-the-shell computing device (for example, a mobile device, smartphone, tablet, laptop, a personal computer) or a custom-built computing device. In an example embodiment, computing device <NUM> comprises a smartphone.

Tissue visualization app <NUM> coordinates image capturing unit <NUM> and illumination unit <NUM> during data capturing, process images, display results on computing device <NUM>, and store and/or transmit data to tissue visualization system <NUM>.

Image capturing unit <NUM> may comprise an internal (built-in to computing device <NUM>) or external device capable of capturing images. In an example embodiment, image capturing unit <NUM> comprises a <NUM> channel (RGB) or <NUM> channel (RGB-NIR) camera.

Illumination unit <NUM> may comprise an internal (built-in to computing device <NUM>) or external device (e.g., multispectral flash) capable of illuminating a target area with required intensity, wavelengths, and duration.

Example tissue imaging system <NUM> architectures are presented on <FIG>. In some embodiments, the tissue imaging system <NUM> can be a single device. In some embodiments, the tissue imaging system <NUM> can have two separate parts (e.g., image capturing unit <NUM> built-in to computing device <NUM> and a separate illumination unit <NUM>, or illumination unit <NUM> built-in to computing device <NUM> (e.g., a mobile device <NUM>) and a separate image capturing unit <NUM>). In some embodiments, tissue imaging system <NUM> can have three separate parts (for example, a computing device <NUM>, a separate image capturing unit <NUM>, and a separate illumination unit <NUM>). The separate components of tissue imaging system <NUM> may communicate by known wired or wireless communications protocols.

In an example embodiment, illumination unit <NUM> can be a device attached (e.g., clip-on or by compression clip) to a computing device <NUM>, such as a mobile device or smartphone.

In some embodiments, illumination unit <NUM> can be connected or synchronized with the tissue visualization application <NUM> (installed on or otherwise accessible by computing device <NUM>) for example by known wireless connections (for example, Bluetooth™), optic or optoelectric coupling, or wired connection. In some embodiments, the illumination unit <NUM> can be triggered manually, and the visualization application <NUM> recognizes the light sequence and synchronizes image capturing.

In some embodiments, the image capturing unit <NUM> can connect to the tissue visualization application <NUM> (installed on or otherwise accessible by computing device <NUM> (e.g., a mobile device <NUM>)) for example by known wireless connections (for example, Bluetooth™), optic or optoelectric coupling, or wired connection.

The tissue visualization application <NUM> can, in turn, be connected to tissue visualization system <NUM> (which may comprise, e.g., a backend server). The tissue visualization system <NUM> can collect data from tissue visualization applications <NUM> of tissue imaging systems <NUM>, via network <NUM>. The tissue visualization system <NUM> can transmit the data (or transformations and aggregations of the data) to user device <NUM>, which may comprise any device with computer processing capability (e.g., computer, laptop, tablet, or smartphone) for use by a user (e.g. a physician or other user). Thus, a qualified specialist may review the data collected by tissue visualization system <NUM> from one or more tissue imaging systems <NUM> used to capture image(s) in a different location by, e.g., a frontline health practitioner (e.g., nurse) or patient. This may facilitate early diagnostic by the physician.

Tissue imaging system <NUM> can capture measurement data as images of a patient's tissue. The visualization application <NUM> can extract visualizations of tissue health indicators from the measurement data. The visualization application <NUM> can generate one or more interface elements corresponding to the visualization of tissue health indicators. The interface elements populate an interface for display on the computing device <NUM> (e.g., a mobile device <NUM>).

In some embodiments, the computing device <NUM> can connect to a tissue visualization system <NUM> to transmit the measurement data and the visualization of tissue health indicators, for example. The tissue visualization system <NUM> can aggregate the measurement data and the visualization of tissue health indicators from multiple tissue imaging systems <NUM>. The tissue visualization system <NUM> can process and store the measurement data and the visualization of tissue health indicators.

In some embodiments, tissue imaging system <NUM> can connect to a user device <NUM>. In some embodiments, the computing device <NUM> (e.g., a mobile device <NUM>) with tissue visualization app <NUM> can receive and aggregate measurement data from multiple tissue imaging system(s) <NUM>, and generate the visualization of tissue health indicators for transmission to tissue visualization system <NUM>. The tissue visualization system <NUM> can aggregate the measurement data and the visualization of tissue health indicators from multiple tissue imaging systems <NUM>.

The tissue visualization system <NUM> receives imaging data from the tissue imaging system(s) <NUM> to generate a visualization of tissue and detect wounds and abnormalities. The tissue visualization system <NUM> and tissue imaging system(s) <NUM> connect to other components in various ways including directly coupled, and indirectly coupled via network <NUM>. Network <NUM> (which may comprise multiple communications networks) is capable of carrying data and can involve wired connections, wireless connections, or a combination thereof. Network <NUM> may involve different network communication technologies, standards, and protocols.

<FIG> depicts a view of an example tissue visualization system <NUM> according to some embodiments, interfaced with system components.

Tissue visualization system <NUM> receives imaging data from the tissue imaging system <NUM> via data I/O unit <NUM>. Data I/O unit <NUM> facilitates transmission of data to data processing unit <NUM>. Data processing unit <NUM> processes data received from the data I/O unit <NUM> or one or more databases <NUM>. For example, data processing unit <NUM> can apply one or more algorithms or extract data that may be used for, or that may facilitate the visualization or processing related to detection of problematic wounds or abnormalities of blood circulation, for example, in surface tissues. Data processing unit <NUM> can extract, create, and/or aggregate from that data a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacteria presence, and/or other indicia that may suggest abnormalities of tissue health, for example, in surface tissues.

Data processing unit <NUM> can receive, via data I/O unit <NUM> and network <NUM>, instructions for computation from one or more external systems <NUM>, user device <NUM>, tissue imaging system <NUM>, and/or tissue visualization app <NUM>. The instructions for computation can be used by data processing unit <NUM> to facilitate the extraction, creation, and/or aggregation of data providing a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health, for example, in surface tissues. In some embodiments, data processing unit <NUM> can process imaging data to prepare the data for presentation via the interface unit <NUM> in an appropriate form or to prepare the data for transmission to an external system <NUM>, user device <NUM>, and/or tissue imaging system <NUM> to be presented in an appropriate form.

Data processing unit <NUM> can receive data or processed data from aggregation unit <NUM> and may extract, create, and/or aggregate from that data, data providing a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health, for example, in surface tissues. The map, visualization, or other indication that can be extracted, created, and/or aggregated by data processing unit <NUM> can reflect imaging data or measurements corresponding to a plurality of patients. The data processed by data processing unit <NUM> may be imaging data collected at one or more tissue imaging systems <NUM> and/or one or more user devices <NUM>. The data processed by data processing unit <NUM> may be measurement data reflecting one or more images of a patient's tissue.

Aggregation unit <NUM> can receive via data I/O unit <NUM> and/or one or more databases <NUM> imaging data corresponding to a plurality of patients, tissue imaging systems <NUM>, or user devices <NUM>. Aggregation unit <NUM> can aggregate or modify the data by applying instructions for computation, and so may comprise one or more processors. Aggregation unit <NUM> can cause the aggregated or modified data to be transmitted to data processing unit <NUM> where the data can be processed to prepare the data for presentation via interface unit <NUM> in an appropriate form or to prepare the data for transmission to an external system <NUM>, user device <NUM>, and/or tissue imaging system <NUM> to be presented in an appropriate form.

Aggregation unit <NUM> can receive processed data from data processing unit <NUM> corresponding to a plurality of patients, tissue imaging systems <NUM>, or user devices <NUM>. Aggregation unit <NUM> can aggregate or modify the processed data by applying the instructions for computation. Aggregation unit <NUM> can cause the aggregated or modified data to be transmitted to data processing unit <NUM> where the data can be further processed to prepare the data for presentation via interface unit <NUM> in an appropriate form or to prepare the data for transmission to an external system <NUM>, user device <NUM>, and/or tissue imaging system <NUM> to be presented in an appropriate form.

Aggregation unit <NUM> can receive via data I/O unit <NUM> and instructions for computation from one or more external systems <NUM>, user device <NUM>, tissue imaging system <NUM>, and/or tissue visualization app <NUM>. The instructions for computation can be used by aggregation unit <NUM> to facilitate aggregation of imaging data corresponding to a plurality of patients.

Tissue visualization system <NUM> can receive imaging data, for example, aggregate imaging data, from computing device <NUM> (e.g., a mobile device <NUM>) via data I/O unit <NUM>. Tissue visualization system <NUM> can receive imaging data, for example, aggregate imaging data, from external systems <NUM> via data I/O unit <NUM>. Tissue visualization system <NUM> can receive computer instructions for processing or computation from external systems <NUM>. External systems <NUM> can store, cause to be stored, and/or receive data from one or more external databases <NUM>.

Aggregation unit <NUM> can receive via data I/O unit <NUM> and network <NUM> the instructions for computation from one or more external systems <NUM>, user device <NUM>, tissue imaging system <NUM>, and/or tissue visualization application <NUM>.

Tissue visualization system <NUM> can be associated with one or more databases or data storages <NUM>, for example, one or more local databases. The one or more databases <NUM> can store or process data received or transmitted by data I/O unit <NUM>, data processing unit <NUM>, and/or aggregation unit <NUM>. The data stored in the one or more databases <NUM> can be accessed by various units, including data I/O unit <NUM>, data processing unit <NUM>, and/or aggregation unit <NUM>. For example, data I/O unit <NUM> may cause database <NUM> to store data received via network <NUM> and/or from user device <NUM>, external systems <NUM>, tissue imaging system <NUM>, and/or tissue visualization app <NUM>. Data processing unit <NUM> and aggregation unit <NUM> can cause data to be retrieved from database <NUM>, for example, before processing or aggregating the data.

Data processing unit <NUM> can cause data to be stored in database or data storage <NUM> after it processes the data by applying instructions or extracting data that may be used for or facilitate the visualization or processing related to detection of problematic wounds or abnormalities of blood circulation in surface tissues. Data processing unit <NUM> can retrieve the processed data from database or data storage <NUM> and cause the processed data to be transmitted to the interface unit <NUM> or network <NUM>, for example, for presentation to a patient or physician using user device <NUM>, <NUM> or <NUM>, for example.

Data processing unit <NUM> may cause data to be stored in database or data storage <NUM> after it extracts, creates, and/or aggregates data providing a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health, for example, in surface tissues.

Data processing unit <NUM> may use Machine Learning (including supervised ML and unsupervised ML) to extract information from collected images and other data. In particular, data processing unit <NUM> can build and train models, which can discriminate between various conditions and provide users with additional information. In some embodiments, data processing unit <NUM> uses convolutional neural networks for automatic or semi-automatic detection and/or classification of the skin or wound conditions. In some embodiments, ML models built and trained using other tools may be deployed to data processing unit <NUM> for image/data detection/classification, such as from an external system <NUM>.

Aggregation unit <NUM> can cause data to be stored in database <NUM> after it aggregates imaging data or processed data that corresponds to a plurality of patients and/or user devices <NUM>. Aggregation unit <NUM> can retrieve the aggregated data from one or more databases <NUM> and cause the aggregated data to be transmitted to the interface unit <NUM> or network <NUM>, for example, for presentation to a patient or physician using user device <NUM>, <NUM> or <NUM>, for example.

Tissue visualization system <NUM> can cause data to be displayed on interface unit <NUM>, for example, aggregated and/or processed data providing a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health in surface tissues. Patients and physicians can engage with an interface unit to view or analyze the indicia.

Tissue visualization system <NUM> can cause data, for example, aggregated data, processed data, imaging data, and/or data providing a wound size and/or a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health in surface tissues, to be transmitted to one or more external systems <NUM>, such as via network <NUM>.

For example, tissue visualization system <NUM> can receive imaging data from a plurality of tissue imaging systems <NUM>, process and/or aggregate the data using data processing unit <NUM> and/or aggregation unit <NUM>, and cause the data to be routed, via one or more networks <NUM>, to, e.g. the appropriate physician (e.g., family doctor) for evaluation. The physician may be engaged with a user device <NUM>, an external system <NUM>, or a tissue imaging system <NUM>.

A user device <NUM> may receive, process, and/or aggregate data from a plurality of tissue imaging systems <NUM> and/or corresponding to a plurality of patients or tissue measurements. User device <NUM> may receive instructions for computation from one or more external systems <NUM> or tissue imaging systems <NUM>.

Tissue visualization system <NUM> can connect to various components, including user device <NUM>, tissue imaging system <NUM>, external systems <NUM>, external database <NUM>, in various ways including directly coupled and indirectly coupled via network <NUM> (which may comprise multiple networks). Each of these components can connect to each other in various ways including directly coupled and indirectly coupled via network <NUM> (or multiple networks).

<FIG> depicts a view of an example of tissue imaging system <NUM> comprised of the illumination unit <NUM> and computing device <NUM> (e.g., a mobile device <NUM>) comprising an internal image capturing unit <NUM> and installed tissue visualization app <NUM>, according to some embodiments.

A tissue imaging system <NUM> is associated with an image capture unit <NUM>. The image capture unit <NUM> may comprise a smartphone camera (front or back), for example.

A computing device <NUM> (e.g., a mobile device <NUM>) is associated with a display interface <NUM>. The display interface <NUM> can be a screen or viewfinder, for example. In some embodiments, a computing device <NUM> (e.g., a mobile device <NUM>) is associated with an app I/O unit <NUM> that may facilitate data transmission between an illumination unit <NUM> and the computing device <NUM>.

An illumination unit <NUM> may be associated with a computing device <NUM>, for example, through a physical connector <NUM> that attaches the illumination unit <NUM> to the computing device <NUM>, such as mobile device <NUM>. An illumination unit <NUM>, which acts as an external flash-generating device, is associated with a lighting unit <NUM>, which may include multiple light sources <NUM>. The light units <NUM> may be arranged in a circle on illumination unit <NUM>, for example. In an example embodiment, light units <NUM> are arranged in a circular configuration around a central aperture.

In some embodiments, an I/O unit <NUM> associated with the illumination unit <NUM> may facilitate data transmission between the illumination unit <NUM> and the computing device <NUM>. For example, I/O unit <NUM> may send and receive data from an app I/O unit <NUM>. I/O unit <NUM> and app I/O unit <NUM> may implement connectivity via Bluetooth, a cable (e.g., USB, lightning, audio jack), WiFi, near-field communication, optic or optoelectronic coupling, or other means. This communication can facilitate synchronization of the lighting unit <NUM> and the data capture by image capture unit <NUM>, for example, in accordance with an illumination schema that can account for various types of external illumination.

A controller <NUM> causes light sources to flash in a predetermined fashion. The controller <NUM> can receive commands from I/O unit <NUM> or be triggered manually (e.g., using a button). The controller <NUM> can be based on any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof. In an example embodiment, the controller <NUM> is based on a microcontroller.

In some embodiments, the lens <NUM> covering the light sources <NUM> can be used to homogenize the light distribution on the target area. In an example embodiment, the Fresnel lens is used. The focal length of the lens can be chosen in the range <NUM>-<NUM>% of the working distance between the illumination unit <NUM> and the target area. In the preferred embodiment, the focal length of the lens is equal to the working distance. Such a focal length tends to create a homogeneous illumination light distribution on the target area, which tends to result in more optimal use of dynamic range and higher accuracy of measurements on periphery of the target area.

For bacterial burden measurements, the emission filter <NUM> covers the image capturing unit <NUM> (e.g., the camera of a smartphone) to block the excitation illumination at <NUM>±<NUM>. In an example embodiment, the emission filter is attached to the illumination unit <NUM>. In some embodiments, the emission filter <NUM> is a long pass filter with cut-on wavelength <NUM>±<NUM>. In some embodiments, the emission filter is a band pass filter with the transmission in the <NUM>-<NUM> range, which has the lower cut-on wavelength in the <NUM>±<NUM> range.

Computing device <NUM> (e.g., a mobile device <NUM>) supports a tissue visualization application <NUM>. Computing device <NUM> may run on any suitable operating system such as iOS, Android, or Windows. The tissue visualization app <NUM> can help position the computing device, e.g. a smartphone, at a proper distance to a target area; can synchronize flashes from an illumination unit <NUM> with the image capturing unit <NUM>; can cause or coordinate the capture of a set of images; can cause or facilitate local processing of the images or of data captured; can cause capturing target area info (e.g., location, laterality, description, wound size, tissue type, patient ID, etc.); can cause or facilitate the extraction, creation, and/or aggregation of data providing a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacterial presence and/or other indicia that may suggest abnormalities of tissue health in surface tissues; can cause or facilitate storing data on computing device <NUM>; and can cause or facilitate data to be transmitted over one or more networks <NUM>.

The tissue visualization app <NUM> includes a positioning unit <NUM>, pre-processing unit <NUM>, calibration unit <NUM>, and app processing unit <NUM>.

Positioning unit <NUM> can cause or facilitate the positioning of the image capture unit <NUM> in relation to an area of patient tissue targeted for measurement.

For example, in some embodiments, positioning unit <NUM> can use a reference (or self-reference) object (e.g., a white circle, square, rectangle, or another shape, colour, or object) on the target area, where the reference (or self-reference) object and target area can be imaged through a viewfinder or screen associated with the, for example, mobile device <NUM>. In some embodiments, the positioning unit <NUM> can recognize the reference object and cause an overlay to be presented on the display interface <NUM>.

In some embodiments, the overlay can be marks, lines, arrows, shapes, and/or other attributes that can be used by a person engaged with the display interface <NUM> to move the computing device <NUM> (e.g. mobile device <NUM>), for example, forwards and/or backward to create appropriate positioning of the image capture unit <NUM>. The tissue visualization app <NUM> can adjust the presentation of the overlay on the display interface <NUM> in relation to the presentation of the reference object or tissue on the display interface <NUM>. This may help guide the user's movement of the image capture unit <NUM> or computing device <NUM> (e.g., a mobile device <NUM>) to achieve proper positioning of the image capture unit <NUM> or computing device <NUM> (e.g., mobile device <NUM>) in relation to the area of patient tissue targeted for measurement.

In some embodiments, the overlay presented on the display interface <NUM> can be of a predetermined size and presented at predetermined locations on the display interface <NUM>.

In some embodiments, positioning unit <NUM> can use the size of the reference object to trigger automatic data capturing when the computing device <NUM> (e.g. mobile device) or image capturing unit <NUM> is at a certain distance from the target area.

In some embodiments, positioning unit <NUM> can guide a user to move the computing device <NUM> (e.g. mobile device), for example, forwards and/or backward to create appropriate positioning of the image capture unit <NUM>, by graphical, text or voice commands.

In some embodiments, reference objects may be used to facilitate calculation of a distance from a wound and/or to rescale images or measurement data.

In some embodiments, image capture unit <NUM> may be positioned at a proper distance from a target area, for example, a wound, by other means such as using a rangefinder or ruler.

The tissue visualization app <NUM> may help control the illumination of the patient tissue targeted for measurement and/or the illumination of one or more images captured by image capture unit <NUM> to help ensure the illumination is stable and/or predictable. The intensity of illumination may depend on the distance of the image capture unit <NUM> to the target area and the stability of, for example, intensity of the light source, e.g. LED, which may degrade with time or within a battery cycle. Control of such factors may be facilitated by pre-processing unit <NUM>. For example, the tissue visualization app <NUM> may use a self-reference object (e.g., white or gray circle) that is placed within a target area to measure the intensity of each wavelength in each flash and recalibrate each measurement accordingly. A single measurement can include multiple flashes and wavelengths.

In some embodiments, the pre-processing unit <NUM> can compare the intensity of a self-reference object in the target image with the intensity of the same region in the reference image and uses the ratio between the two to scale the intensity of the target image pixel-by-pixel.

For reflectance images, app processing unit <NUM> can process image data captured by image capture unit <NUM> and pre-processed by pre-processing unit <NUM>. For example, the user or app processing unit <NUM> can compare one or more images or patient measurements of a suspicious area to one or more images or patient measurements of a non-affected area.

An observation may consist of one or more measurements on a patient. The one or more images or patient measurements of a non-affected area (control sites) can be used to establish a baseline for a particular patient. Ideally, one can select a control site as a spot with intact skin symmetrical with respect to the spinal cord (e.g. on another extremity) to the suspicious area (this may be another extremity; for example, if the left ankle of a person is affected, then the right ankle may be selected as the control site). However, if it is not possible (e.g., limb amputation or widespread ulcers), then other locations (e.g., antecubital fossa) can be used as a control site. In the case of a single measurement (e.g., suspicious area only), the suspicious area readings can be compared with an area on the same image distant from the suspicious area.

In some embodiments, tissue visualization app <NUM> can compare an image of a suspicious area to one or more images of control sites. The tissue visualization app <NUM> can process an image and can also operate in video mode to process a series of images or video frames.

App processing unit <NUM> can use the data captured by image capture unit <NUM> to facilitate the extraction, creation, and/or aggregation of data providing a map, visualization, or indication of oxygenation, oxyhemoglobin, deoxyhemoglobin, perfusion, water, bacteria presence, and/or other indicia that may suggest abnormalities of tissue health, for example, in surface tissues.

The outcome of the system can be a false color or grayscale 2D map of tissue health indicators. These maps can be presented via the display interface <NUM> and/or transmitted over one or more networks <NUM>, for example, to a tissue visualization system <NUM> or a user device <NUM>. For example, levels of oxygenation and perfusion can highlight areas with abnormal blood supply, namely ischemic (significantly reduced perfusion and oxygenation) and angiogenic (increased perfusion) areas. A trained physician will be able to interpret these 2D maps to assess the significance of findings and decide on next steps, for example, requesting further study, monitoring progress, or dismissing the matter.

App processing unit <NUM> can cause the processed data to be presented via display interface <NUM> and/or transmitted over a network <NUM>.

In some embodiments app processing unit <NUM> can use Machine Learning (ML)(including supervised ML and unsupervised ML) to extract information from collected images and other data. In particular, app processing unit <NUM> can build and train models, which can discriminate between various conditions and provide users with additional information. In some embodiments, the app processing unit <NUM> uses convolutional neural networks for automatic or semi-automatic detection and/or classification of the skin or wound conditions. In some embodiments, ML models can be built and trained using other tools (e.g., the data processing unit <NUM>) and deployed to app processing unit <NUM> for image/data detection/classification.

<FIG> is a flowchart of an example method for capturing measurements and visualizing tissue according to some embodiments.

At <NUM>, a computing device <NUM> (e.g. a mobile device <NUM>) is positioned at a proper distance (working distance) in relation to an area of tissue (e.g., using a positioning unit <NUM>). In some embodiments, the computing device <NUM> or image capturing unit <NUM> is positioned <NUM>-<NUM> from the tissue. In an embodiment, the image capturing unit <NUM> is positioned <NUM>-<NUM> from the tissue.

At <NUM>, image capture unit <NUM> in conjunction with illumination unit <NUM> captures a measurement of tissue according to an illumination schema.

At <NUM>, in some embodiments, pre-processing unit <NUM> preprocesses the measurement data by a) registering (aligning) images to avoid camera motion artifacts, b) subtracting image with no illumination from images with illumination to account for the presence of an ambient light.

In some embodiments, the step <NUM> may include any or all additional steps: c) recalibrating each measurement accordingly in order to control parameters related to the intensity of illumination, d) dividing the intensity images on reference images to obtain normalized images, e) flattening the images. In other embodiments, the filtering and frequency domain processing (e.g. fast Fourier transformation) may be used additionally for denoising.

Recalibration of each measurement using a self-reference object may take into account any possible drift or degradation of illumination light intensity, which will tend to improve the quality of results.

Dividing the intensity images on reference images may take into account the heterogeneity of illumination light distribution on a target area, resulting in normalized images that tend to improve quality of results.

Imaging of body parts with high curvature (for example, heels or toes) can pose a significant clinical challenge. Different parts of the target area are on different distance from the illumination unit and the camera, and since the camera registers light intensity that depends on that distance, the curvature(s) can negatively affects accuracy of measurements or may produce erroneous results. In an embodiment, the step of flattening images is to take into account the reflection of light from curved surfaces. This can be achieved by plurality of methods. In some embodiments, approximation of the shape of the body part and rescaling normalized image to compensate for these deviations from the working distance is used. In other embodiments, shape fitting (for example, spherical, ellipsoidal, or cylindrical) may be used.

In some embodiments, registration (alignment) of images can be done using phase correlation or block matching algorithms (e.g., using a self-reference object).

In some embodiments, recalibration can be done by pre-processing unit <NUM> using a self-reference object to measure the intensity of each wavelength in each flash.

In some embodiments, any or all steps after the step <NUM> can be skipped.

In some embodiments, app processing unit <NUM> can cause transmission over network <NUM> of data, such as pre-processed images after step <NUM>. In this case, upon receipt of the data, data processing unit <NUM> of tissue visualization system <NUM> may extract and visualize tissue health indicators.

At <NUM>, app processing unit <NUM> processes the images to extract information, such as concentrations of tissue chromophores. In some embodiments, app processing unit <NUM> extracts indications of oxyhemoglobin and deoxyhemoglobin. In some embodiments, in addition to oxy- and deoxyhemoglobin, app processing unit <NUM> extracts the indication of melanin. In some embodiments, app processing unit <NUM> additionally extracts water content indications.

In some embodiments, indications of oxyhemoglobin, deoxyhemoglobin, and water can be extracted directly from the obtained images using a Beer-Lambert or modified Beer-Lambert model.

In an exemplary embodiment, an additional step is taken to extract tissue absorption coefficients from the obtained images using a tissue optical model (or a tissue light propagation model). A tissue optical model would link the reflected signal with optical properties of the tissues, namely coefficients of absorption and scattering. Various light propagation models (for example, diffuse approximation model) can be used to extract such relationship. The appropriate model can be selected based on acceptable accuracy vs. computational intensity considerations.

In some embodiments, the least squares fitting, or LSF (with or without regularization) can be used to extract the concentration of each chromophore. In some embodiments, LSF extracts indications of chromophores directly from the obtained images. In an exemplary embodiment, LSF is applied after extraction of indication of absorption coefficient using the tissue light propagation model.

In other embodiments, other curve fitting methods (for example, least absolute deviations) may be used to extract indications of chromophores.

At <NUM>, app processing unit <NUM> extracts indicia that allows the tissue health indicators of the imaged tissue to be presented. For example, the indicia may allow the oxygenation and/or perfusion to be presented as a map.

In some embodiments, app processing unit <NUM> can send to the network <NUM> data from the step <NUM>. In this case, data processing unit <NUM> will visualize tissue health indicators.

At <NUM>, tissue visualization app <NUM> generates a visualization of the tissue health indicators of the imaged tissue and causes the visualization to be presented via the display interface <NUM>.

In some embodiments, computing device <NUM> comprises a graphical user interface displayed on display interface <NUM> by app processing unit <NUM>. At <NUM>, the tissue visualization app <NUM> collects data related to the image (e.g., patient ID, laterality, location, diagnosis, comments, measurements). In some embodiments, a speech-recognition system is used to collect data.

At <NUM>, tissue visualization app <NUM> causes a results file of the data or indicia to be stored and/or transmitted, for example, over a network <NUM> to a user device <NUM>, tissue visualization system <NUM>, and/or external system(s) <NUM>.

<FIG> is a view of an example interface for visualizing tissue according to some embodiments.

In some embodiments, a color bar <NUM> can be implemented to guide the user when viewing the image.

In some embodiments, the averaging tool <NUM> (which averages tissue health index within a defined area) can be implemented to assist the user. In some embodiments, the averaging tool <NUM> can be a small circle on a touchscreen, such as the relatively small area shown in <FIG>.

<FIG> is a schematic diagram of an exemplary embodiment of computing device <NUM>. As depicted, computing device <NUM> (e.g. mobile device <NUM>) includes at least one processor <NUM>, memory <NUM>, at least one I/O interface <NUM>, and at least one network interface <NUM>.

Each processor <NUM> may be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof.

Memory <NUM> may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM), or the like.

Each I/O interface <NUM> enables computing device <NUM> (e.g., a mobile device <NUM>) to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen, and a microphone, or with one or more output devices such as a display screen and a speaker.

Each network interface <NUM> enables computing device <NUM> (e.g., a mobile device <NUM>) to communicate with other components, to exchange data with other components, to access and connect to network resources, to serve applications, and perform other computing applications by connecting to a network (or multiple networks) capable of carrying data.

Computing device <NUM> is operable to register and authenticate users (using a login, unique identifier, and password, for example) prior to providing access to applications, a local network, network resources, other networks, and network security devices. Computing devices <NUM> may serve one user or multiple users.

<FIG> is an example of an illumination and image capturing schema according to some embodiments. Other illumination and image capturing schemas can be used. In order to account for external illumination, the example image capturing/illumination schema in <FIG> has been developed.

<FIG> plots the flash <NUM> coordinated by illumination unit <NUM>, as a function of time. Computing device <NUM> (e.g., a mobile device <NUM>) uses the synchronization of flash if the illumination unit <NUM> is used to provide the external flash. As shown at <NUM>, the illumination schema (cycle) consists of m flashes (with m=<NUM> in the example of <FIG>) and one period without flash, with n/<NUM>≤m≤n, where n is the number of wavelengths. Cycles can be repeated continuously during video mode capturing.

The exposure time (T) for each frame (in milliseconds) can be selected as T=k/<NUM>*f, where k is an integer, and f is the utility frequency for a particular country in Hz (e.g., <NUM> for North America, <NUM> for Europe). In a video mode, the framerate can be selected as fps=<NUM>*f/k (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> fps for North America and <NUM>, <NUM>, and <NUM> fps for Europe). The frame rate of 20fps (T=<NUM>) is an example selection. It can work without any configurations with external light sources connected to any electrical grid (<NUM> or <NUM>). Other frame rates can also be used.

The duration of each flash can be T or any whole number multiple of T. This arrangement facilitates easy optical synchronization between illumination unit and image capturing unit. For example, the cycle consists of m back to back flashes with duration 2T milliseconds each, followed by no lit period 2T milliseconds long, as shown in the plot <NUM>.

In some embodiments, computing device <NUM> (e.g. mobile device <NUM>) associated with an illumination unit <NUM> may use the same frame to capture an image illuminated at <NUM>, <NUM>, or <NUM> wavelengths, which can be captured by different color wavelengths of an RGB camera (e.g. <NUM> and <NUM>, which will be captured by blue and red wavelengths, respectively) or an RGB-NIR camera.

<FIG> is a view of example illumination units according to some embodiments.

An illumination unit <NUM> can be an external flash device that can be attached to a computing device <NUM>, for example, a smartphone. In some embodiments, it can be synchronized with a tissue visualization app <NUM> or computing device <NUM> (e.g., a mobile device <NUM>) using Bluetooth or other connectivity. In some embodiments, the illumination unit <NUM> can be built into a case for a computing device <NUM> (e.g., a mobile device <NUM>). In some embodiments, the illumination unit <NUM> receives power from the computing device <NUM> (e.g., a mobile device <NUM>) or an external source (e.g., wall charger).

In some embodiments, illumination unit <NUM> comprises a battery. The illumination unit <NUM> can also be chargeable using a standard micro USB port, wirelessly or by way of inductive charging.

The illumination unit <NUM> can be used with a front- or back camera of a mobile device <NUM>, for example. Illumination unit view <NUM> illustrates an illumination unit <NUM> used in conjunction with a front-facing camera of a user computing device <NUM>.

In some embodiments, the illumination unit <NUM> can be optimally designed to associate with a computing device <NUM> (e.g. mobile device <NUM>) by way of a clip or other means <NUM> that can be attached to the computing device <NUM> (e.g. mobile device <NUM>) with the thickness of up to <NUM>, as shown in views <NUM> and <NUM>.

In an example embodiment, illumination unit <NUM> uses a compression clip that can be attached to the computing device <NUM> (e.g. mobile device <NUM>), with the thickness up to <NUM>, as shown in view <NUM>. In some embodiments, the illumination unit <NUM> can be mounted using a spring clip, as shown in views <NUM> and <NUM>.

The illumination unit <NUM> can produce a sequence of flashes of predetermined length. A wavelength can refer to light sources shining at the same wavelength, or the possibility of multiple wavelengths shining in a single flash. Each of the flashes may shine at <NUM>-<NUM> particular wavelengths.

The illumination unit <NUM> can use narrow band high-efficiency light sources <NUM>, such as LEDs. The light source in the illumination unit <NUM> may contain single wavelength or multiwavelength LEDs.

As shown in view <NUM>, the light sources <NUM> can be arranged in a circle, with a center close to the center of a camera <NUM> of computing device <NUM> (e.g. mobile device <NUM>).

In some embodiments, each wavelength can consist of two or four light sources <NUM>, arranged in a symmetrical pattern on an illumination unit <NUM> (e.g., every <NUM> or <NUM> degrees on a circle).

For oxygenation measurements, the illumination unit <NUM> can use two or more wavelengths in the range of <NUM>-<NUM>. For measurements of oxygenation and perfusion and the compensation of skin color (melanin), the illumination unit <NUM> can use three or more wavelengths in the range of <NUM>-<NUM>. In an example embodiment, <NUM>-<NUM> range is used.

Wavelengths can be selected from one or more of the following regions: a) biggest discrimination in light absorption between oxy- and deoxyhemoglobin: <NUM>-<NUM> and <NUM>-<NUM>, b) isobestic points (e.g., <NUM>±<NUM>, <NUM>±<NUM>, and <NUM>±<NUM>), c) largest absorption by oxy- and deoxyhemoglobin: <NUM>-<NUM>.

For water content measurement in addition to two or more wavelengths in <NUM>-<NUM> (or preferably <NUM>-<NUM>) range a wavelength of <NUM>±<NUM> is used.

For bacterial burden measurements, a wavelength of <NUM>±<NUM> is used. In some embodiments, it can be combined with two or more wavelengths in <NUM>-<NUM> (or preferably <NUM>-<NUM>) range, which captures reflectance images.

For bacterial burden measurements, the illumination unit <NUM> or image capture unit <NUM> may contain emission filter <NUM>. In an example embodiment, the emission filter is attached to the illumination unit <NUM>. In some embodiments, the emission filter <NUM> is a long pass filter with cut-on wavelength <NUM>±<NUM>. In some embodiments, the emission filter is a band pass filter with the transmission in the <NUM>-<NUM> range, which has the lower cut-on wavelength in the <NUM>±<NUM> range.

The illumination unit <NUM> can be synchronized with an image capture unit <NUM> of computing device <NUM> (e.g. mobile device <NUM>) to produce an illumination schema. The illumination unit <NUM> associated with an image capture unit <NUM> can follow an illumination schema where each wavelength shines sequentially (n=m, where n is the number of wavelengths, m is the number of flashes in one cycle).

In some embodiments, lighting unit <NUM> configured to engage with a computing device <NUM> (e.g. mobile device <NUM>) or image capture unit <NUM> may have the following example implementations:.

In some embodiments, illumination unit <NUM>, for example, including a multispectral external flash, can be operable with an image capture unit <NUM> or another recording device. For example, illumination unit <NUM> may be integrated with a personal computer, tablet, or otherwise.

The systems described tends to offer distinct advantages. For example: the flash design may be used with any computing device <NUM>, such as a smartphone (iOS, Android, etc.) of any shape; the flash/image capturing schema, may allow measurements in any type of ambient light and with any type of smartphone; self-calibration using a self-reference object increases accuracy; proper positioning of the camera (distance from the wound) is facilitated by use of a self-reference object (e.g. a circle); and the illumination schema produces reproducible and homogeneous illumination. The above-noted expected advantages are examples of advantages and may not comprise all advantages of the present systems/devices.

The system can also tend to overcome challenges, for example, of building the flash, in the case for a smartphone, such as a challenge that each smartphone can have its own form-factor and thus would require multiple cases to be built at least for the most popular models. Other challenges that the system may overcome, or benefits of the system, include:.

A tissue imaging system <NUM> can be used in a variety of applications, including in the following scenarios.

Use case <NUM>: A doctor at a hospital during a physical exam of a patient in acute care has found a suspicious wound on the leg. The patient has diabetes, so the MD has a suspicion that it can be a non-healing DFU. The current standard of care for this is angiography, which is not available in his community hospital. It will cost around $<NUM>,<NUM> for the procedure and arrangement of medical transportation to/from another hospital. However, using the device the doctor can screen the wound on the spot and see whether it is ischemic (and require angiography for proper assessment) or nonischemic (and will heal well without any extra efforts).

Use case <NUM>: A family doctor during an annual checkup has found a suspicious wound on a patient's leg. The patient has diabetes, so the MD has a suspicion that it can be a non-healing DFU. The current standard of care for this is angiography. However, it can be performed in major hospitals only. It is associated with $<NUM>,<NUM> per procedure (in the US) or waiting time (for example, <NUM> days in Ontario, Canada). Using the device, the doctor can screen the wound on the spot and see whether it is ischemic (and require angiography for proper assessment) or nonischemic (and will heal well without any extra efforts).

Use case <NUM>: A family doctor during an annual checkup has found a suspicious wound on a patient's leg. The patient has diabetes, so the MD has a suspicion that it can be a non-healing DFU. The current standard of care for this is angiography. It can be performed in major hospitals only. It is associated with $<NUM>,<NUM> per procedure (in the US) or waiting time (for example, <NUM> days in Ontario, Canada). Using the device, the doctor captures images of the wound on the spot. However, such as he does not have significant experience in wound care, he decides to send images to a podiatrist, who provides him with an assessment of whether it is ischemic (and requires angiography for proper assessment) or nonischemic (and will heal well without any extra efforts). The doctor accordingly refers the patient to the podiatrist.

Use case <NUM>: A nurse is attending a small rural community. During an exam of a patient, she has found a suspicious ulceration near the small toe. She has a suspicion that it can be a peripheral arterial disease. She uses the device to take a snapshot of the wound and sends images to a family physician (if the patient has one) or a podiatrist. The doctor reviews the images and provides guidance within a few hours. The nurse instructs the patient on further actions.

Use case <NUM>: A medical nurse is attending a small long-term residence. During an exam of a patient, she has found a suspicious ulceration near the big toe. She has a suspicion that it can be a peripheral arterial disease. She uses the device to take a snapshot of the wound and sends images to a family physician (if the patient has one) or a podiatrist. The doctor reviews the images and provides guidance within a few hours. The nurse instructs the patient on further actions.

Use case <NUM>: A senior with diabetes finds a suspicious cut on his heel. He is aware of the dreadful consequences of DFU, so he decides to buy the device in a drugstore. With the help of his wife he takes images of the wound and sends them to his family doctor. The doctor makes an assessment and advises the patient within a few hours.

Use case <NUM>: A senior with diabetes finds a suspicious cut on her forefoot. She is aware of the dreadful consequences of DFU, and tells her concerns to her daughter. Her daughter bought a flash attachment <NUM> in a drugstore, attaches it to her smartphone <NUM>, downloads the tissue visualization app <NUM>, and takes images of the wound. As her mother does not have a family doctor, she sends the images to a podiatrist. The doctor makes an assessment and sends a referral within a few hours.

Use case <NUM>: A family doctor during an annual checkup of a patient finds a suspicious mole. Using the device, he can screen the mole on the spot and see whether it is suspicious (has increased blood supply and requires additional study) or not suspicious.

Use case <NUM>: The nurse in a long-term care facility checks a bed-bound patient for potential pressure ulcers. Using the device, she can screen bony prominence areas to determine if any are suspicious.

Use case <NUM>: An advanced wound care nurse cleanses an existing wound. She uses the device to visualize bacterial presence and to guide debridement.

Use case <NUM>: A nurse takes a swab from an existing wound. She uses the device to visualize bacterial presence and to guide swabbing.

The accuracy of measurements can be improved if the light intensity distribution produced by illumination unit <NUM> is known. In an example embodiment, to capture light intensity distribution produced by illumination unit <NUM>, a reference image is used.

With reference to <FIG>, example features used for capturing reference images are depicted. In some embodiments, the reference image can be captured by calibration unit <NUM> of tissue visualization app <NUM> and then used by the tissue imaging system <NUM> or tissue visualization system <NUM> to obtain a processed measurement <NUM> (an example of which is shown in <FIG>).

The reference image is captured using a reference object <NUM>. Reference object refers to an object with known homogeneous optical properties (e.g., spectral dependence of reflectance). Reference object <NUM> can be various shapes, such as a circle or rectangle. In an example embodiment, reference object <NUM> is a rectangle with an aspect ratio of <NUM>:<NUM>. Various colors can be used for reference object <NUM>, such as white or gray (for example, an <NUM>% gray rectangle on a white background <NUM>, such as a white sheet of paper).

In one embodiment, screen markers <NUM> displayed on a screen <NUM> of the computing device <NUM> (e.g. mobile device <NUM>) can define a target area <NUM> which can be used to position the device an optimal distance away from the reference object <NUM>. The computing device <NUM> should be positioned such that screen markers <NUM> line up with the reference object <NUM> to ensure an optimal image-capturing distance is achieved. Other distance measuring devices, such as a rangefinder or ruler, can be used to position device at the optimal distance. In an example embodiment, object recognition by tissue visualization app <NUM> can be used to position the device at the optimal image capturing distance.

In an example embodiment, the computing device <NUM> (e.g., a mobile device <NUM>) can take the required reference image automatically upon proper placement of the device. In other embodiments, the computing device <NUM> takes the image upon manual user initiation. In an embodiment, upon activation of the image capture unit <NUM>, the computing device <NUM> takes several images. In an example embodiment, one or more images are taken with flash, and one is taken without. Alternatively, images can be taken only with flash.

The computing device <NUM> (e.g., a mobile device <NUM>) can pre-process the reference image to improve the image quality. The pre-processing may comprise the following steps: a) image registration, b) image subtraction.

In some embodiments, computing device <NUM> (e.g., a mobile device <NUM>) uses image registration to reduce shake during image capturing. This can be accomplished using phase correlation or block matching algorithms.

In some embodiments, computing device <NUM> (e.g., a mobile device <NUM>) uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (no flash) is subtracted from images with external illumination (with flash). Image subtraction is not required if only images with flash are used.

The reference image can be stored locally on the computing device <NUM> (e.g. mobile device <NUM>) or remotely, for future use.

The reference image can be captured before the first measurement and at any time thereafter. There is no need to capture reference images before every measurement.

Steps for producing measurement map <NUM> are now discussed, with respect to <FIG>.

Computing device <NUM> (e.g. mobile device <NUM>) is held at a specific distance away from the subject, for example a human body, in order to optimally image the area of interest.

In some embodiments, a self-reference object <NUM> is used to ensure the proper distance from the human body. A self-reference object <NUM> is placed within the device target area <NUM> imaged by the computing device <NUM> (e.g., a mobile device <NUM>). In an example embodiment, the self-reference object <NUM> comprises an <NUM>% gray circle <NUM>-<NUM> in diameter.

In some embodiments, the computing device <NUM> (e.g. mobile device <NUM>) or image capturing unit <NUM> is moved so that a predefined screen marker <NUM> is shown as the same size as self-reference object <NUM> on the device target area <NUM>, so as to guide the user to the optimal image capturing distance.

In an example embodiment, computing device <NUM> (e.g., a mobile device <NUM>) uses object recognition to trigger automatic image capturing upon a certain screen size of the self-reference object, in pixels, being achieved.

Alternatively, other means of measuring a distance, such as a rangefinder or a ruler, can be used to position the device at the proper distance from the area of interest.

Once the optimal distance from the human body is determined, computing device <NUM> (e.g., a mobile device <NUM>) can take the required images. In an example embodiment, the device takes the required image automatically upon the proper placement of the computing device <NUM> (e.g., a mobile device <NUM>) or image capturing unit <NUM>. The device may take several images. In an example embodiment, one or more images will be taken with flash, and one will be taken without flash.

The device pre-processes the image in order to improve the quality of the image and measurement map <NUM>. The pre-processing may contain the following steps: a) image registration, b) image subtraction.

In some embodiments, the device uses image registration to reduce shake. This can be accomplished through phase correlation or block matching.

In some embodiments, the device uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (flash) is subtracted from images with external illumination (flash).

To further increase the quality of results, the self-calibration of each measurement using a self-reference object <NUM> can be implemented. In this case the pre-processing may contain the following steps: a) image registration, b) image subtraction, c) self-calibration, and d) division on the reference image, e) flattening the images.

If the embodiment utilizes self-reference object <NUM>, the intensity of the image is adjusted using the self-reference object to account for any imperfections or changes in intensity. In an example embodiment, pre-processing unit <NUM> can compare the intensity of a self- reference object in the target image with the intensity of the same region in the reference image and use the ratio between the two to scale the intensity of the target image pixel-by-pixel.

If the embodiment utilizes previously taken reference images, the device finds the normalized image by dividing pixel-by-pixel image onto the reference image and multiplying by a known reflectance of the reference object.

In some embodiments, the tissue imaging system <NUM> can perform the processing of the image to obtain measurements. This can be achieved through all or some of the following steps: a) the absorption coefficient is determined from reflectance (e.g., using Beer-Lambert, or modified Beer-Lambert law); b) the chromophore concentration is determined from the absorption coefficient (e.g., using least square fitting); c) the perfusion and oxygenation is determined from the chromophore concentration (oxygenation = oxyhemoglobin/(oxyhemoglobin+deoxyhemoglobin), perfusion= oxyhemoglobin + deoxyhemoglobin).

In some embodiments, the pre-processed measurement (normalized image) is taken on computing device <NUM> (e.g., a mobile device <NUM>) and then sent through network <NUM> to the tissue visualization system <NUM>.

Bacterial burden indicator can be used stand-alone or in combination with reflectance images. Porphyrin and pyoverdine have an absorption peak in the Soret band, where oxyhemoglobin and deoxyhemoglobin have absorption peaks as well. Thus, the presence of a blood component may significantly impact porphyrin/pyoverdine emission. With reference to <FIG>, true fluorescence intensity can be deconvoluted using known oxyhemoglobin and deoxyhemoglobin concentrations found in step <NUM>. In an example embodiment, a light source with the center wavelength of <NUM>±<NUM> is used in combination with <NUM> or <NUM> wavelengths from the <NUM>-<NUM> range.

Once tissue health indicators levels are found, the color or grayscale maps is presented through processing via tissue visualization system <NUM> or tissue imaging system <NUM>. These results can be stored locally on the device or remotely. The pre-processed normalized image and the processed tissue health indicators maps can all be stored in local or remote storage.

The embodiments of the devices, systems, and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions described herein and to generate output information. The output information is applied to one or more output devices. In some embodiments, the communication interface may be a network communication interface. In embodiments in which elements may be combined, the communication interface may be a software communication interface, such as those for inter-process communication. In still other embodiments, there may be a combination of communication interfaces implemented as hardware, software, and combination thereof.

Throughout the foregoing discussion, references have been made to servers, devices, systems, units, or computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or another type of computer server in a manner to fulfill described roles, responsibilities, or functions.

Various example embodiments are described herein. Although each embodiment represents a single combination of inventive elements, all possible combinations of the disclosed elements include the inventive subject-matter. Thus, if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject-matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

Part of the technical solution of embodiments may be in the form of a software product (while other required aspects, for example the image capture unit <NUM> and illumination unit <NUM>, necessitate hardware).

Claim 1:
A portable illumination apparatus (<NUM>) for facilitating visualizations of tissue, the apparatus comprising:
a portable housing defining an aperture extending through the portable housing,
wherein the portable housing is for detachable attachment proximal to an image capturing unit (<NUM>);
an emission filter (<NUM>) covering the aperture, wherein the emission filter (<NUM>) is selected from:
a long pass filter with a cut-on wavelength of <NUM>±<NUM>; or
a bandpass filter allowing transmission through the emission filter in the <NUM>-<NUM> wavelength range and having a lower cut-on wavelength of <NUM>±<NUM>; and
an illumination unit (<NUM>) comprising one or more narrow band light sources positioned around the aperture and configured to shine m flashes at n predetermined wavelengths, wherein;
n/<NUM> ≤ m ≤ n;
each of the m flashes has a duration equal to xT, wherein x is a whole number and T is equal to <NUM>; and
each of the m flashes are performed consecutively.