Systems and methods for mapping wound characteristics

Abstract

Systems and methods are provided for mapping wound characteristics of a wound bed and detecting degradation of electrode or connection performance. Electrical measurements are collected from an array of electrodes that apply electrical signals to peri-wound tissue outside the wound bed. The electrical measurements are processed to generate an impedance map of the wound bed and converted to a map conveying the spatial distribution of a clinical metric.

Description

[Technical field]

[0001] Smart wound dressings and other wearable sensors are used to monitor wound healing. The ability to monitor healing can lead to improved health care and improved patient outcomes. Monitoring healing may be used to determine whether a current treatment program is effective or whether changes to the current treatment program should be made. Summary of the Invention

[0002] It is desirable to non-invasively measure / monitor (e.g., via mapping and / or quantification) healing of a wound bed including open wound gaps and / or subcutaneous feature gaps. In one aspect, the present disclosure provides a method of acquiring wound characteristics of a wound bed. The method includes applying electrical signals to peri-wound tissue outside the wound bed via an array of sensors (hereinafter exemplified as electrodes) and collecting electrical measurements from the array of electrodes via circuitry operatively connected to the array of electrodes. The method further includes processing, via a processor, the collected electrical measurements to generate one or more impedance maps of the wound bed and converting the one or more impedance maps to one or more tissue property maps representative of the spatial distribution of clinical metrics of the wound bed.

[0003] In another aspect, the present disclosure provides a system for obtaining wound characteristics of a wound bed, the system including an array of electrodes configured to apply an electrical signal to peri-wound tissue exterior to the wound bed, circuitry operatively connected to the array of electrodes to collect electrical measurements from the array of electrodes, and a processor configured to process the collected electrical measurements to generate one or more impedance maps of the wound bed and convert the one or more impedance maps to one or more tissue property maps representative of a spatial distribution of clinical metrics of the wound bed.

[0004] In another aspect, the present disclosure provides a device for application to a wound bed, the device including an array of electrodes configured to be placed around the wound bed and to apply an electrical signal to peri-wound tissue exterior to the wound bed, circuitry operatively connected to the array of electrodes to collect electrical measurements therefrom, and a user interface to receive instructions from a user and display information based on the collected electrical measurements. In some cases, the device is a diagnostic or monitoring device. In some cases, the device is a dressing. [Brief description of the drawings]

[0005] [Figure 1] FIG. 1 is a schematic diagram illustrating an exemplary wound measurement system, according to one embodiment. [Figure 2A] 1 is a schematic top view of a tissue site being tested by a wound measurement system, according to one embodiment. [Figure 2B] FIG. 2B is a schematic side view of the tissue site of FIG. 2A. [Figure 2C] FIG. 2C is a schematic diagram showing a wound measurement system applied to the tissue site of FIGS. 2A-2B. [Figure 3A] 4 is a flow diagram of a method for generating a calibration model according to one embodiment. [Figure 3B] 1 is a plot of change in measured conductivity versus tissue depth in a calibration model, according to one embodiment. [Figure 4] 1 is a flow diagram of a method for generating a topographical map of a wound and a wound volume estimate according to one embodiment. [Diagram 5] 1 is a flow diagram of a method for generating wound healing data according to one embodiment. [Figure 6] FIG. 2 is a diagram of an eight-electrode device, according to one embodiment. [Figure 7A] FIG. 13 shows relative conductivity and topography maps of the wound progression for Example 1. [Figure 7B] FIG. 13 shows relative conductivity and topography maps of the wound progression for Example 1. [Figure 7C]FIG. 13 shows relative conductivity and topography maps of the wound progression for Example 1. [Figure 8A] 13 is a graph of wound volume measurements using EIT mapping and comparative methods for Example 3. [Figure 8B] FIG. 13 shows relative conductivity and topography maps of the wounds of Example 3. [Figure 9] Optical image of an 8-electrode device placed on a tissue-mimicking phantom (TMP) of a wound (A) and EIT maps of the TMP wound model using two different baseline estimation methods (B and C). [Figure 10A] Shown are the unwounded and wounded optical images (A1 and B1) of the TMP wound model, respectively, and the EIT maps (A2 and B2) of the TMP wound model using the baseline estimation method. [Figure 10B] Shown are the unwounded and wounded optical images (A1 and B1) of the TMP wound model, respectively, and the EIT maps (A2 and B2) of the TMP wound model using the baseline estimation method. [Figure 11] Shown are optical images of a TMP wound model (A), an 8-electrode device (B), an 8-electrode device placed on the TMP wound model (C), and an EIT map of the TMP wound model using the fdEIT method (D). [Figure 12] 1 shows images of an EIT map of a TMP wound model (A), an EIT map of a TMP wound model with simulated electrode failure (B), and an EIT map of a TMP wound model using an algorithm to correct for the simulated electrode failure (C).

[0006] In the following description of the illustrated embodiments, reference is made to the accompanying drawings, which show, by way of illustration, various embodiments in which the present disclosure may be practiced. It is understood that the embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. The drawings are not necessarily to scale. Like numbers used in the figures refer to like components. However, it will be understood that the use of a number to refer to a component in a given figure is not intended to limit the component in another figure labeled with the same number. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] As used herein, the term "impedance" refers to an electrical property that is a complex quantity that includes what are called "real" and "imaginary" quantities, e.g., Z=R+iX, where Z is the impedance, R is the so-called real component, resistance, and X is the so-called imaginary component, reactance. Additionally, the term "conductivity" is used herein, which is the mathematical inverse of resistance R. The term "relative conductivity" refers to the conductivity relative to some baseline value, measured or algorithmically estimated.

[0008] The term "electrical measurement" or "electrical measurements" refers to a measurement or measurements of an electrical property such as conductivity, resistivity, complex impedance, impedance magnitude, capacitance, inductance, admittance, impedance phase angle, reactance, etc., at one or more frequencies.

[0009] The term "impedance map" refers to a representation(s) of the spatial distribution of one or more electrical measurements, which may exist in the form of a map(s) or other suitable data structure.

[0010] The term "tissue characteristic map(s)" or "clinical metrics" refers to a representation(s) of the spatial distribution of one or more of tissue characteristics including, for example, wound edge / border, wound depth information (e.g., a topographical map of a wound with depth versus x and y coordinates), presence of granulation tissue, thickness of granulation tissue, wound healing stage (e.g., hemostasis, inflammation, proliferation, remodeling stage, etc.), epithelial coverage, epithelial layer thickness, biomass, bioburden, infection level, infection type, necrotic tissue, healing tissue, etc. The tissue characteristics may be in the form of a map(s) or other suitable data structure. In some cases, the tissue characteristic representation or map may include healing metrics.

[0011] The term "healing metrics" refers to a global assessment(s) of a wound that represents calculations performed on tissue characteristic data. Healing metrics may include wound length, wound width, wound depth (e.g., maximum, minimum, average, etc.), wound area, wound volume, granulation tissue thickness (e.g., maximum, minimum, average, etc.), total epithelial coverage (e.g., percent of wound bed covered with new epithelium), epithelial thickness (e.g., maximum, minimum, average, etc.), total bioburden, biofilm thickness (e.g., maximum, minimum, average, etc.), biofilm volume, etc.

[0012] FIG. 1 is a schematic diagram illustrating an exemplary wound measurement system 100, according to one embodiment. In the illustrated embodiment, the wound measurement system 100 includes a device 102 and a computing device 106. In some cases, the device 102 is a diagnostic or monitoring device. In some cases, the device 102 is a dressing. The device 102 may be communicatively coupled to the computing device 106, for example, by a wired or wireless connection. The computing device 106 may include a processing circuit 216 coupled to a display 218, an output 221, and a user input 222 of a user interface 228. In some examples, the display 218 may include one or more display devices (e.g., a monitor, a PDA, a mobile phone, a tablet computer, any other suitable display device, or any combination thereof). For example, the display 218 may be configured to display information indicative of physiological information and epithelial tissue characteristics determined by the wound measurement system 100.

[0013] The device 102 may be of any type of construction. In some embodiments, the device 102 may include a bandage including a flexible backing, an adhesive for adhering to the skin of the patient 14, and the electrodes 130. In some embodiments, the device 102 may include a foam dressing including the electrodes 130. In some embodiments, the device 102 may include a material that is affixed to tissue via an adhesive or is physically held in place. In other embodiments, the device 102 may be a diagnostic patch, such as a material that includes any of the electrodes 130. In some embodiments, additional materials may be applied to the patient 14, such as sterile saline-containing gauze, gel, etc., disposed between the device 102 and the tissue site 150 for wound measurement / monitoring.

[0014] The device 102 includes an array of electrodes 130. When the device 102 is placed in the wound bed to be tested, the array of electrodes can apply an electrical signal from a signal generator to a peri-wound tissue site 152 outside the tissue site 150. The tissue site 150 can correspond to wound tissue or a wound bed, for example, tissue having a wound to the epithelial layer and / or subcutaneous tissue. The tissue site 150 may also correspond to tissue having a bruise, tissue having a rash, tissue having an infection, etc. The tissue site 150 can also correspond to recently healed or currently uninjured tissue that needs to be monitored for wound recurrence or development in the absence of an observable open wound (e.g., monitoring for general tissue health, preservation of healed tissue, viability of reconstructed or donor tissue such as flaps and grafts, edema, venous leg ulcers (VLU), or pressure ulcers (PU)). The peri-wound tissue site 152 may correspond to tissue within the peri-wound region, which may be defined as the area of ​​skin that extends beyond the wound bed to a certain distance (e.g., a few centimeters, such as 4 cm), or the surrounding skin that extends from the wound bed. In some examples, the additional material may include a treatment, such as a drug, and / or may be at least partially conductive to improve electrical conductivity between the electrode 130 and the peri-wound tissue site 152.

[0015] One or more signal generators may be electrically connected to the array of electrodes 130 and may be configured to generate alternating electrical signals (e.g., electrical waveforms). The electrical signals may be sine waves, square waves, pulse waves, triangular waves, sawtooth waves, and the like. The signal generators may be configured to generate electrical signals including one or more frequencies at any frequency including 1 kHz to 2 kHz, 2 kHz to 4 kHz, 4 kHz to 55 kHz, 55 kHz to 120 kHz. In some embodiments, the signal generators may be configured to generate electrical signals in a frequency range that may be greater or less than the exemplary ranges listed above. In some embodiments, the electrical signal generators may be configured to generate electrical signals at a predetermined frequency, such as about 85 kHz (e.g., 85 kHz ± 10 kHz). In the illustrated embodiment, the signal generator is configured to generate an electrical signal.

[0016] 1, device 102 further includes processing circuitry 116 and memory 124. In some embodiments, device 102 may process the electrical signal without transferring the electrical signal to computing device 106. For example, processing circuitry 116 may further include a signal monitor for detecting the electrical signal applied to periculum tissue side 152 proximate tissue site 150. In other embodiments, the electrical signal, or information corresponding to the electrical signal, may be transferred to computing device 106 for processing, e.g., by a wired or wireless connection between device 102 and computing device 106.

[0017] The memory 116 and the memory 224 may include any volatile or non-volatile media, such as random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, etc. The memory may be a storage device or other non-transitory medium. The memory may be used by the processing circuit 216 or the processing circuit 124 to store baseline or initialization information corresponding to physiological monitoring, such as wound monitoring, for example. In some examples, the processing circuit 216 or the processing circuit 124 may store previously received data from physiological measurements or electrical signals in the memory for later retrieval. In some examples, the processing circuit may store determined values, such as information indicative of epithelial tissue characteristics, or any other calculated values ​​in the memory for later retrieval.

[0018] FIG. 2A is a schematic top view of a tissue site tested by a wound measurement system 200, according to one embodiment. FIG. 2B is a schematic side view of the tissue site of FIG. 2A. FIG. 2C is a schematic diagram showing the wound measurement system 200 applied to the tissue site of FIG. 2A-2B. As shown in the example of FIG. 2A-2B, the tissue site 150 corresponds to the open wound tissue. The perianal tissue site 152 corresponds to the area surrounding the open wound tissue 150. Subcutaneous wound tissue 154 adjacent to or connected to the open wound tissue 150 is below the perianal tissue site 152 and therefore cannot be seen by the naked eye. The total volume of the wound bed includes both the visible open wound tissue 150 and the volume associated with the unseen subcutaneous features 154 (e.g., tunnels and subcutaneous tissue strips). It is difficult for conventional clinical methods (e.g., using ruler-based measurements or photo-based measurements) to accurately determine wound volume. Conventional clinical methods sometimes fail to account for these unseen subcutaneous volumes, which can comprise a significant portion of the wound volume.

[0019] The wound measurement system 200 includes an array of electrodes 210 disposed at the wound perimeter tissue site 152. One or more of the electrodes 210 may be disposed above the subcutaneous wound tissue 154. In the embodiment shown in FIG. 2C, electrode 210a is disposed directly above the subcutaneous wound tissue 154. The array of electrodes 210 is supported by a substrate 20. In the illustrated embodiment, the substrate 20 includes a central portion 202 that substantially covers the open wound tissue 150 and a periphery 204 of the central portion 202. The array of electrodes 210 is disposed on an inner surface of the dressing periphery 204.

[0020] It should be understood that in some embodiments, additional electrodes can be placed on the open wound. In some embodiments, electrodes can be present only in the peri-wound tissue, or in both the open wound and the peri-wound. In some embodiments, electrodes present within the peri-wound tissue are desirable because (i) they are less invasive since they do not need to contact sensitive open wound tissue, and (ii) the electrical interface with intact peri-wound tissue is more likely to be stable than open wound tissue, which may change over time as it heals.

[0021] In some embodiments, electrodes are provided for a four-probe measurement, with two electrodes for current sourcing and two electrodes for voltage measurement, and the minimum number of electrodes is four. In some embodiments, eight or more electrodes are provided to obtain mapping results. A greater number of electrodes may allow for greater resolution and accuracy.

[0022] It should be understood that any suitable type of electrode device can be used to allow placement of the electrode array in a wound-circumscribing pattern relative to the periwound tissue. In some embodiments, the electrode array can be placed on the periphery of a dressing, such as on the drape of a Negative Pressure Wound Therapy (NPWT) dressing. In some embodiments, the electrode array can be integrated into a non-dressing diagnostic device. In one embodiment, a flexible circuit board can be decorated with snap connectors to which multiple electrodes can be connected. In one embodiment, the device may include an inner array of metallic pin electrodes that can interact with the wound bed, or a flexible printed circuit board (PCB) with multiple metallic pin electrodes that interact with the periwound tissue surrounding the wound. Similar configurations can also be implemented in rigid PCB formats. These electrodes can be placed within the wound bed, outside the wound bed, or both.

[0023] 2C, system 200 further includes an electronics component 220 electrically connected to the array of electrodes 210. Electronics component 220 may include various control circuits, processors, memories, power sources, etc. For example, electronics component 220 may include one or more of processing circuit 116, memory 124, processing circuit 216, and memory 224 as shown in FIG.

[0024] The electronics component 220 is configured to apply electrical signals to the peri-wound tissue via the array of electrodes 210, collect electrical measurements from the array of electrodes 210, process the collected electrical measurements to generate one or more impedance maps of the wound bed (e.g., the open wound tissue 150 and the subcutaneous wound tissue 154), and convert the impedance maps into spatial maps of clinical metrics of the wound bed. The acquired wound bed information can be output via a user interface 230, which may include a display, user inputs, and outputs. As described in more detail below, the one or more impedance maps may also include a baseline map representing non-wound tissue.

[0025] Electrical impedance tomography (EIT) is used to measure and determine the spatial distribution of electrical impedance in a continuous two-dimensional (2D) or three-dimensional (3D) space. Typically, impedance measurements are obtained from electrical contacts sparsely distributed in the continuous 2D / 3D space, and the continuous 2D / 3D impedance map is reconstructed by solving a finite element model (FEM) inverse problem for the space. In the wound monitoring application described herein, an array of electrodes is placed around the wound bed on the peri-wound tissue to spatially map the resistance / conductivity profile of the wound and nearby tissue.

[0026] The electronics for performing EIT mapping of the wound bed may include electrodes 210, a microcontroller for control and data acquisition measurement, low noise precision current sources for power supply, analog to digital (ADC) preamplifiers for noise filtering and signal amplification, and an input / output multiplexer for switching electrodes for current supply and voltage measurement.

[0027] The impedance map may be acquired at a single frequency or multiple frequencies. In some embodiments, the impedance may be measured relative to different baselines established through different estimation schemes. The baseline may refer to a map of conductivity values ​​representing the tissue pre-wound. The relative conductivity may refer to the current conductivity map of the tissue subtracted from the baseline map.

[0028] In some examples, the baseline measurement of the non-wound tissue may include a homogeneous measurement that captures the background conductivity of the non-wound tissue and a heterogeneous measurement that captures the conductivity of the wound tissue. Methods for estimating the baseline may include, for example, frequency difference EIT (fdEIT), measurement scale feature (MSF), best homogeneous (BH) estimator, data-driven estimator, or combinations thereof. Data-driven estimators may include machine learning and deep causal learning methods using database lookups. Useful database lookups may be based on patient history or patient demographics.

[0029] Alternatively, it is also possible to reconstruct the conductivity map of wound tissue using a method that does not require a baseline measurement of non-wounded tissue. Since biological tissues may have unique frequency responses, the impedance distribution of the wound bed can be imaged by fdEIT reconstruction methods, where measurements are made under at least two frequencies on the wound tissue. The measurement at the first frequency (i.e., the reference frequency) can serve as a proxy for the baseline measurement, and the measurement at the second frequency (i.e., the measurement frequency) serves as a heterogeneous measurement.

[0030] Different methods can be used in reconstructing the conductivity map. Exemplary methods include the one-step Gauss-Newton (GN) method and the iterative total variation (TV) method, applying suitable hyperparameters in each case. The hyperparameters can be heuristically determined to optimize the degree of contrast between the anomaly and the background. It should be noted that this heuristic selection can be replaced by an automatic selection based on any predefined strategy. The one-step GN method can provide real-time reconstruction results with acceptable quality of the shape and size of the anomaly. The TV method, being an iterative method, tends to be computationally slower compared to the GN method, but can also provide higher resolution of phase features.

[0031] As a further alternative, a baseline measurement estimation technique can be applied to calculate the homogeneous conductivity distribution of non-wound tissue. TdEIT can then obtain the estimated baseline measurements and the heterogeneous measurements to reconstruct a conductivity map of the wound bed. For example, the baseline measurements can be estimated using either a best homogeneous approximation or a predefined MSF. E In an EIT system with electrodes, the baseline measurement U ベースライン is the n under the adjacent simulation pattern setting E (n E -3) represents a vector of voltage measurements.

[0032] In one embodiment, U ベースライン is estimated by the following steps: First, a finite element model (FEM) is generated that reflects the geometry of the wound bed and electrode placement. Second, a simulated baseline measurement U0 is obtained from the FEM with a uniform conductivity distribution, with a baseline conductivity σ0 = 1. Third, the non-uniform measurement U meas is obtained from the wound bed. Finally, U0 is scaled by a ratio parameter μ to estimate the baseline measurement. The baseline vector is then expressed as:

[0033]

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[0034]

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[0035]

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[0036]

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[0037] In some embodiments, new impedance maps may be mathematically calculated from the impedance maps at a given frequency and from maps generated from different baseline estimation schemes. The measured impedance may vary due to variations in electrical properties across tissue sites within an individual, e.g., across different tissue locations on the same patient and / or animal, variations in tissue properties at different times, and variations across individuals, e.g., across patients and / or animals. For example, the electrical properties of tissue may vary based on tissue constituents and thickness, tissue water content and / or tissue hydration, ambient relative humidity at the time of measurement, etc. In addition, the electrical properties of tissue may depend on which particular tissue type is present at the location. For example, variations are observed for muscle versus adipose tissue, and for cases of wounded tissue, non-wounded, well-epithelialized tissue versus open wound areas with various types and amounts of healing tissue within them (e.g., different amounts of granulation tissue fill in the wound bed and / or different degrees of epithelial coverage over the wound bed).

[0038] The resulting impedance map can be used to determine wound boundaries and other wound characteristics. In some embodiments, the map of wound tissue impedance can be converted into a quantitative metric of healing. In other words, the spatial map of impedance (e.g., conductance or resistance) acquired via EIT can be converted into a quantitative map of healing, such as, for example, wound shape, depth, size (e.g., area or volume), amount of granulation tissue, epithelial coverage, wound stage, etc.

[0039] In some embodiments, one or more impedance maps can be converted into spatial maps of clinical metrics by calibrating the impedance maps using a calibration model. The clinical metrics can include various wound information data relative to (x,y) coordinates in a Cartesian coordinate system (x,y,z) where the z-axis corresponds to the depth direction of the wound bed. Exemplary clinical metrics can include wound depth d(x,y), granulation tissue thickness t(x,y), epithelial coverage c(x,y), biofilm thickness t(x,y), bioburden (i.e., the number of contaminating organisms found in a given amount of material) b(x,y), infection level i(x,y), healing stage (e.g., inflammation, proliferation, or remodeling stage) index h(x,y), etc.

[0040] In some embodiments, a calibration model can be obtained by correlating the impedance map to physically measured wound data of clinical metrics. In general, a calibration model describes the relationship between impedance at various locations (x,y) and clinical metrics. For example, one calibration model can describe the relationship between impedance-related properties (e.g., conductivity) and wound depth d(x,y). After an impedance map is generated by correlating to physically measured clinical metrics, such as wound depth in this case, a calibration model can be used to convert the measured impedance map into a map of wound depth. Similarly, various calibration models can be generated by correlating impedance-related properties to corresponding clinical metrics including, for example, wound depth, wound length, wound width, wound area, wound volume, wound topography, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, healing stage (e.g., hemostasis, inflammation, proliferation, or remodeling stage), etc. The various calibration models generated can be used to convert the measured impedance maps into corresponding maps of clinical metrics.

[0041] FIG. 3A is a flow diagram of a method 300 for generating a calibration model according to one embodiment. At 310, a calibration wound bed or wound bed model is selected, which may include a variety of representations of the wound metric to be calibrated (e.g., wounds or wound models including different wound sizes are needed to generate a robust calibration, in order to calibrate the impedance map to wound size). At 320, a wound measurement system, such as the system 100 of FIG. 1 and the system 200 of FIG. 2, is used to collect electrical signals from the calibration wound bed. An exemplary electrical signal includes voltages (e.g., amplitude and / or phase) measured from an array of electrodes placed on the peri-wound tissue outside the wound bed, as shown in FIG. 2C. At 330, an impedance map is determined from the spatial distribution of electrical impedance based on the collected electrical signals under the method of electrical impedance tomography (EIT). In one example, the impedance map may be a map of relative conductivity reconstructed under the method of electrical impedance tomography (EIT) using the measured voltages. At 340, ground truth data can be measured that is related to one particular type of clinical metric to be determined, such as wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, healing stage (e.g., inflammation, proliferation, or remodeling stage), etc. For example, ground truth wound depth data for the calibration wound bed can be measured by any suitable means, such as, for example, a ruler or a camera-based device. At 350, a calibration model is created to correlate the impedance map at 330 and the measured ground truth data at 340. For example, as shown in FIG. 3B, a calibration curve 301 is created by correlating the change in measured conductivity to the measured tissue depth for the same calibration wound bed. It should be appreciated that when data of impedance-related characteristics and ground truth data of clinical metrics are obtained for the calibration wound samples, the relationship between them can be extracted through various calibration methods, such as, for example, machine learning. At 360, the calibration model is output and may be stored in the electronics component 220 for later retrieval.

[0042] FIG. 4 is a flow diagram of a method 400 for generating a topographical map of wound depth and wound volume estimates according to one embodiment. At 410, measurements begin by applying an electrical signal via an array of electrodes to peri-wound tissue at least partially surrounding the wound bed. At 420, a wound measurement system, such as system 100 of FIG. 1 and system 200 of FIG. 2, is used to collect an electrical signal from the wound bed. An exemplary electrical signal includes impedance-related characteristics such as voltage (e.g., amplitude and / or phase) measured from an array of electrodes placed on the peri-wound tissue outside the wound bed, as shown in FIG. 2C. At 430, an impedance map is determined from the spatial distribution of electrical impedance based on the collected electrical signal under the technique of electrical impedance tomography (EIT). In one embodiment, the impedance map may be a map of relative conductivity reconstructed under the technique of electrical impedance tomography (EIT) using the measured voltage. At 440, a spatial map for one or more specific types of clinical metrics is generated based on the calibration model and impedance map from 435. The specific types of clinical metrics may include, for example, wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, healing stage (e.g., inflammation, proliferation, or remodeling stage), etc. The calibration model used may be obtained by the method of 300 as shown in FIG. 3A. For example, the calibration curve 301 of FIG. 3B may be used to convert the relative conductivity map into a wound depth map. At 450, a wound volume estimate is obtained based on the determined wound depth map by integration of wound depth values ​​at various sites. At 460, the wound depth map and wound volume estimate may be output via a user interface, such as the user interface 230 of FIG. 2C.

[0043] FIG. 5 is a flow diagram of a method 500 for generating wound healing data according to one embodiment. At 510, measurements begin by applying an electrical signal via an array of electrodes to peri-wound tissue at least partially surrounding the wound bed. At 520, a wound measurement system, such as system 100 of FIG. 1 and system 200 of FIG. 2, is used to collect an electrical signal from the wound bed. An exemplary electrical signal includes a voltage (e.g., amplitude and / or phase) measured from an array of electrodes placed on the peri-wound tissue outside the wound bed, as shown in FIG. 2C. At 530, an impedance map is determined from the spatial distribution of electrical impedance based on the collected electrical signal under the mode of electrical impedance tomography (EIT). In one embodiment, the impedance map may be a map of relative conductivity reconstructed under the mode of electrical impedance tomography (EIT) using the measured voltage. At 540, a map for one or more specific types of clinical metrics is generated based on the calibration model and the impedance map from 545. Specific types of clinical metrics may include, for example, wound depth, granulation tissue thickness, percent epithelial coverage, biofilm thickness, bioburden, infection level, healing stage (e.g., hemostasis, inflammation, proliferation, or remodeling stage), etc. Other healing data besides wound volume that may be calculated based on the map of clinical metrics include, for example, calculation of wound cross-sectional area when the topographical map of the wound falls below a certain depth, total level of infection by spatially integrating bioburden and / or infection level, biofilm volume by integrating biofilm thickness, percent of wound in healing stage X by integrating the map of healing stage and dividing by total wound area. At 550, wound healing data is extracted by performing calculations on the map of clinical metrics. Exemplary wound healing data may include wound volume, wound volume vs. time, wound volume closure rate, wound area, wound area vs. time, wound area closure rate, amount of granulation tissue, granulation tissue proliferation rate, percent epithelial coverage, rate of epithelialization, infection level, infection risk assessment score, etc.At 560, the wound healing data may be output via a user interface, such as user interface 230 of FIG. 2C, and the wound healing data may be used herein as information indicative of the stage of wound healing. Other wound information may include wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, and infection level.

[0044] Electrodes used in EIT mapping can deteriorate or fail at any time. Even if all of the electrodes are nominally working, improper installation or adverse environmental conditions can degrade the adhesive connectivity used to establish the electrical / ionic conductive pathway from the tissue surface to the electrodes. Degradation of electrode or connection performance can generate inaccurate signal readings due to increased contact impedance, thus degrading the fidelity of the conductivity map reconstructed with EIT. Advantageously, the voltage-current reciprocity principle can identify these deteriorated or failed electrodes and provide a test to correct the EIT mapping results.

[0045] Voltage-current reciprocity provides that data acquired with an excitation and measurement pair of electrodes are comparable to data obtained by reversing the electrode pair under ideal conditions. In the case of a degraded or failed electrode, the affected measurements generally do not follow the voltage-current reciprocity principle. When the incorrect electrode is part of the excitation pair, less current flows through the electrode if the electrode contact impedance reaches the maximum external load of the constant current source. When the incorrect electrode is part of the measurement pair, inconsistent data is acquired because one input of the differential amplifier is floating. In these scenarios, the voltage-current reciprocity principle is violated.

[0046] The reciprocity error V for a voltage measurement is obtained by comparing the measured electrical measurement to a predicted electrical measurement based on voltage-current reciprocity. The reciprocity error e can be defined as: e 2 =(VV R ) 2 In the formula, V Rrepresents the reciprocal measurement of V.

[0047] In the formula, V is the voltage difference between electrodes a and b when current is supplied from electrodes c and d. Next, V R becomes the voltage difference between electrodes c and d when sourcing current from electrodes a and b. In order to properly reconstruct an EIT map using erroneous measurements from degraded or failed electrodes, a weighting parameter σ can be used to introduce an error into the EIT reconstruction algorithm based on this reciprocity, as follows:

[0048]

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[0049] Advantageously, the weighting parameter allows for algorithmic compensation for variations in electrode and / or connection performance. When the reciprocity error is zero, this weighting parameter is equal to 1, so no weighting effect is applied. In this manner, a large reciprocity error e 2 corresponds to a value smaller than 1 so that measurements from erroneous electrodes have less adverse effect on the EIT mapping results. To track the degradation state of each electrode, the average weight value σs of all measurements corresponding to each electrode can be calculated as follows:

[0050]

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[0051]

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[0052] Various embodiments are provided that are dressings, systems for acquiring wound characteristics of a wound bed, and methods for acquiring wound characteristics of a wound bed. It is understood that any of embodiments 1-10, 11-13, and 14-15 may be combined.

[0053] Embodiment 1 is a method of acquiring wound characteristics of a wound bed, the method comprising: applying one or more electrical signals to peri-wound tissue exterior to the wound bed via the array of electrodes; collecting electrical measurements from the array of electrodes via circuitry operatively connected to the array of electrodes; processing, via a processor, the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representative of a spatial distribution of a clinical metric of the wound bed.

[0054] Embodiment 2 is the method of embodiment 1, wherein converting the one or more impedance maps into one or more tissue property maps further comprises obtaining a calibration model by correlating the electrical measurements to physically measured wound data related to clinical metrics of the wound bed.

[0055] Embodiment 3 is the method of embodiment 2, wherein obtaining the calibration model further comprises obtaining a calibration curve by correlating the measured relative conductance values ​​to wound depth values.

[0056] Example 4 is the method of example 2 or example 3, further comprising converting the one or more impedance maps into one or more tissue property maps by using a calibration model.

[0057] Embodiment 5 is the method of any one of embodiments 1 to 4, wherein the one or more impedance maps include spatial maps of conductivity, resistivity, conductance, resistance, reactance, capacitance, inductance, impedance magnitude, impedance phase angle, complex impedance, or combinations thereof at one or more sampling frequencies.

[0058] Embodiment 6 is a method according to any one of embodiments 1 to 5, wherein the one or more tissue property maps comprise a spatial map of wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, or wound bed healing stage.

[0059] Example 7 is a method according to any one of Examples 1 to 6, further comprising displaying one or more tissue characteristic maps and a status of the system via a graphic user interface (GUI) and receiving system configuration parameters from a user via the GUI.

[0060] Embodiment 8 is the method of any one of embodiments 1 to 7, further comprising determining a volume of the wound bed from the one or more tissue property maps, the wound bed comprising one or more subcutaneous features.

[0061] Embodiment 9 is the method of any one of embodiments 1 to 8, further comprising determining at least one of the tissue properties including wound cross-sectional area, total infection level, biofilm volume, biofilm coverage area, total granulation tissue volume, average granulation tissue thickness, average wound depth, wound length, wound width, total epithelial coverage percentage, average epithelial thickness, or total epithelial volume based on the one or more tissue property maps.

[0062] Example 10 is the method of any one of Examples 1 to 9, further comprising outputting information indicative of tissue properties based on the one or more tissue property maps.

[0063]

[0023] Embodiment 11 is a system for acquiring wound characteristics of a wound bed, the system comprising: an array of electrodes configured to apply an electrical signal to peri-wound tissue exterior to the wound bed; a circuit operatively connected to the array of electrodes for collecting electrical measurements from the array of electrodes; A processor, the processor comprising: processing the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representative of a spatial distribution of a clinical metric of the wound bed.

[0064] Embodiment 12 is a system described in embodiment 11, wherein the processor is further configured to determine information indicative of a stage of wound healing based on the one or more tissue property maps.

[0065] Example 13 is a system described in Example 11 or 12, further comprising a graphic user interface (GUI) for displaying one or more tissue characteristic maps and a status of the system, and for receiving system configuration parameters from a user.

[0066] Embodiment 14 is a device for application to a wound bed, the device comprising: an array of electrodes disposed about the wound bed and configured to apply an electrical signal to peri-wound tissue exterior to the wound bed; a circuit operatively connected to the array of electrodes for collecting electrical measurements from the array of electrodes; and a user interface for receiving instructions from a user and displaying information based on the collected electrical measurements.

[0067] Embodiment 15 is the device of embodiment 14, wherein the circuit further comprises: processing the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representative of a spatial distribution of a clinical metric of the wound bed.

[0068] Embodiment 16 is a method according to any one of embodiments 1 to 10, wherein the one or more impedance maps of the wound bed are algorithmically estimated and do not require a baseline measurement of non-wound tissue, and the one or more impedance maps of the wound bed are algorithmically estimated using a frequency difference EIT (fdEIT), a measurement scale feature (MSF), a best homogeneous (BH) estimator, a data-driven estimator, or a combination thereof.

[0069] Embodiment 17 is a method as described in embodiment 16, wherein one or more impedance maps of the wound bed are algorithmically estimated using measurement scale features (MSFs) including arithmetic mean, range, mid-range, electrode-based average range, electrode-based average mid-range, or combinations thereof.

[0070] Embodiment 18 is a method of obtaining wound characteristics of a wound bed, the method comprising: applying one or more electrical signals to peri-wound tissue exterior to the wound bed via the array of electrodes; collecting electrical measurements from the array of electrodes via circuitry operatively connected to the array of electrodes; Quantifying one or more electrode or connection performances; processing, via a processor, the collected electrical measurements to generate one or more impedance maps of the wound bed, the processor algorithmically compensating for degradation of one or more electrodes or connection performance; and converting the one or more impedance maps into one or more tissue property maps representative of a spatial distribution of a clinical metric of the wound bed.

[0071] Example 19 is the method of example 18, wherein the performance of one or more electrodes or connections is detected by performing a voltage-current reciprocity based test.

[0072] Example 20 is the method of example 18 or example 19, wherein algorithmically compensating for degradation of one or more electrodes or connection performance includes applying weighting parameters to electrical measurements corresponding to the one or more electrodes or connection performance. EXAMPLES

[0073] These examples are for illustrative purposes only and are not meant to limit the scope of the appended claims.

[0074] Circumferential electrode device An eight-electrode device, shown in Figure 6, was fabricated on a soft, flexible substrate. The electrodes were 3M RED DOT 2360 electrodes (3M Company, St. Paul, Minn.). The device included a UV-curable silicone encapsulant protective layer and a urethane film that was laser etched and silver-bladed to act as electrical leads and contacts, with the electrodes embedded in the cured silicone encapsulant.

[0075] Electrical impedance tomography (EIT) hardware and methods The electronics used to implement the EIT mapping of the simulated wound environment included a microcontroller for control and measurement of data acquisition, a low-noise precision current source as a power source, an analog-to-digital converter (ADC) preamplifier for noise filtering and signal amplification, and an input / output multiplexer to switch electrodes for current delivery and voltage measurement. The switching of electrodes for current delivery and voltage measurement was controlled using a CD74HC4067 multiplexer (Texas Instruments, Dallas, TX). An alternating current with a frequency of 20 kHz (amplitude adjustable from 0.1 mA to 2 mA) was delivered to the simulated wound by a Keithley 6221 current source (Keithly Instruments, Solon, OH). Voltage measurements from the electrode pairs were amplified by approximately 17 times using an INA 128 instrumentation amplifier (Texas Instruments) with an appropriate resistor / potentiometer feedback arrangement. To attenuate ambient electromagnetic interference such as fluorescent lamp ballasts (e.g., 50 kHz) and power line noise (e.g., 60 Hz), the amplified signal was filtered by a low-pass filter with a cutoff frequency of 31.2 kHz, followed by a high-pass filter with a cutoff frequency of 4.8 kHz. The filtered signal was finally biased at 1.65 V (INA 111 AP amplifier, Texas Instruments) and amplified approximately 12 times by an INA 128 instrumentation amplifier before being fed into the 12-bit 3.3 V ADC input port of the microcontroller. The Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) v3.9 script was used in MATLAB® (Mathworks, Natlick, MA) to solve the EIT reconstruction map. A one-step Gauss-Newton inverse model was employed as the inverse solver to reconstruct the conductivity distribution from the measured voltages. The hyperparameters and background values ​​of the inverse model were manually set based on the conductivity of a tissue-mimicking phantom (TMP) model.

[0076] Tissue-Mimicking Phantom (TMP) of a Simulated Subcutaneous Featureless Wound To simulate the electrical properties (i.e., dielectric and conductive properties) of intact skin tissue and granulation tissue in a wound, a tissue-mimicking phantom (TMP) wound model was prepared as a gelatin-based proxy tissue construct.

[0077] An artificial intact skin tissue was prepared as a mixture of water (230 g), gelatin (34.1 g), sodium chloride (1.4 g), vegetable oil (75.0 g), and detergent (40.0 g).

[0078] Artificial granulation tissue was prepared as a mixture of water (230 g), gelatin (34.1 g), sodium chloride (1.2 g), vegetable oil (15.0 g), and surfactant (40.0 g).

[0079] Reagent grade gelatin and sodium chloride (99% purity) were obtained from VWR USA, Radnor, PA. Vegetable oil was CRISCO Pure Vegetable Oil, obtained from B&G Foods, Parsippany-Troy Hills, NJ. Detergent was IVORY Ultra Concentrated Dish Washing Liquid Soap, obtained from Procter and Gamble Company, Cincinnati, OH. To prepare a given artificial tissue, gelatin was mixed with 100 g of deionized water and the mixture was heated to 80° C. in a water bath. The mixture was then cooled to 35° C. while stirring with a homogenizer. The remaining deionized water, sodium chloride, and detergent were then added. When the resulting mixture reached 28° C., the vegetable oil was added while mixing.

[0080] All TMP wound models were constructed by first pouring the artificial intact skin tissue into a 9x9 cm Petri dish to a depth of 1.2 cm immediately after mixing in vegetable oil to maintain an injectable temperature of approximately 28°C. The mixture was cooled overnight to harden the gelatin. The resulting product was used as the TMP of non-wounded tissue for baseline measurements and was designated TMP Model 0. For the TMP wound models, a 1.2 cm deep cavity was created in the center of the TMP Model 0 using a cylindrical punch / cutter (6 cm diameter). The base of the simulated wound was then created by pouring additional artificial intact skin tissue into the cavity to a depth of 0.3 cm (leaving an open-ended cavity 0.9 cm deep). The tissue was allowed to harden overnight. This construct was designated TMP Model A and was used as the TMP model of an open non-granulating wound.

[0081] A partially granulated simulated wound TMP model was prepared by the following procedure: the cavity of TMP model A was filled with artificial granulation tissue to a depth of 0.3 cm, and the tissue was allowed to harden overnight to obtain a partially granulated TMP wound model identified as TMP model B.

[0082] A fully granulated simulated wound TMP wound model was prepared by the following procedure: the cavity of TMP model A was filled with artificial granulation tissue to a depth of 0.6 cm and the tissue was allowed to harden overnight. This model served as a fully granulated TMP wound model identified as TMP model C and also served as a basis for preparing further TMP wound models with various degrees of epithelialization.

[0083] A TMP wound model of a partially epithelialized simulated wound was prepared by the following procedure: A circular counter mold (4 cm diameter) was placed over the central continuation of the granulation tissue surface of TMP Model C, leaving a portion of the peripheral granulation tissue surface exposed. Intact skin tissue mixture was added around the counter mold, filling the cavity up to the surface of the cavity open (i.e., approximately flush with the top surface of the phantom). After curing, the counter mold was removed, leaving a partially epithelialized TMP wound model identified as TMP Model D.

[0084] A TMP wound model of a fully epithelialized simulated wound was prepared by the following procedure: the remaining cavity of TMP Model C was filled with intact skin tissue mixture up to the top surface of the cavity (i.e., approximately flush with the top surface of the phantom) and the intact tissue was allowed to harden overnight to obtain a fully epithelialized wound model identified as TMP Model E.

[0085] A tissue-mimicking phantom (TMP) of a wound with simulated subcutaneous tunnel features To generate a wound with subcutaneous features, a non-granulating wound model (TMP Model A) was used. The phantom material was excavated from beneath the wound periphery area using a spoon-shaped spatula (Bel-Art SP Scienceware Stainless-steel Sampling Spoon and Spatula, ThermoFisher Scientific, Waltham, MA) to generate two simulated subcutaneous tunnel features that were beneath the top surface of the skin phantom and extended from the main cavity as shown in FIG. 8B. These tunnel features increased the total volume of the simulated wound by approximately 70% and were located near and / or at the surface of the Petri dish. This construct was designated TMP Model F.

[0086] Tissue-mimicking phantom (TMP) of an off-center wound without simulated subcutaneous features (TMP Model G) The off-center TMP wound models were constructed by first pouring the artificial intact skin tissue into a 9 × 9 cm Petri dish to a depth of 1.2 cm immediately after mixing in vegetable oil to maintain a pourable temperature of approximately 28 °C. The mixture was allowed to cool overnight to harden the gelatin. A cylindrical punch / cutter (6 cm diameter) was then used to create a 1.2 cm deep cavity a short distance from the center of each phantom, as shown in the photographic image in Figure 9A. The cavity was a hollow channel extending from the top to the bottom surface of the phantom. The gelatin in contact with the Petri dish formed the bottom surface of the phantom, and the exposed gelatin surface formed the top surface of the phantom. The cavity served as the simulated wound area. This structure was designated TMP Model G.

[0087] Tissue-Mimicking Phantom (TMP) of an Off-Center Wound with Simulated Subcutaneous Tunnel Features (TMP Model H) A subcutaneous tunnel feature was added to the TMP Model G (above) by excavating the intact phantom material from the bottom surface of the phantom using a spoon-shaped spatula (Bel-Art SP Scienceware Stainless-steel Sampling Spoon and Spatula). This resulted in a single large tunnel feature located below the top surface of the phantom and extending from the main cavity into the interior of the phantom. A photographic image of the bottom surface of the completed TMP is shown in Figure 10C. In this photographic image, a dotted line was superimposed on the image along the perimeter of the tunnel area to aid in visualization of the tunnel area. The original cavity + excavated tunnel area served as the simulated wound area. This construct was designated TMP Model H.

[0088] EIT mapping of TMP EIT maps were generated as impedance maps of the relative conductivity of the TMP Model A through TMP Model F. An eight-electrode device (described above) was used with a conventional rigid printed circuit board (PCB) and brass pin electrodes. A finite element model was generated based on the location and spacing of the eight electrodes and the geometry of the TMP. This electrode device was placed on the TMP Model 0 and TMP Model A through TMP Model F to obtain and generate spatial maps of the relative conductivity of the measured wound phantom. In the EIT maps, areas with void space had lower measured conductivity, visually represented by darker colors (black to gray), and areas filled with artificial tissue had higher measured conductivity, visually represented by lighter colors (light gray to white).

[0089] Example 1. EIT mapping of a TMP model progressing through the stages of simulated healing Wound phantoms prepared as TMP Model A to TMP Model E were used to simulate the progression of wound healing from a relatively fresh wound without significant healing (TMP Model A, no granulation tissue, wound inflammation stage), to TMP Model B and TMP Model C with partial (some) and complete granulation tissue, respectively (wound proliferation stage), and finally to Model D and Model E with partial (some) and complete epithelial coverage, respectively (wound remodeling stage). The method described in "Electrical Impedance Tomography (EIT) Hardware and Methods" was followed. EIT maps for TMP Model A to TMP Model E were constructed using TMP Model 0 for baseline measurements. The EIT maps of TMP Model A to TMP Model E (Figures 7A to 7C, "EIT Map Rows") showed a decrease in dark (black, gray) areas progressing continuously from the phantom without granulation tissue (TMP Model A) to the fully epithelialized phantom (TMP Model E). In Figures 7A-C, the EIT maps of the phantoms without granulation tissue had large black areas representing cavities, while the maps of the fully epithelialized phantoms were white to light gray shades with no black areas. This demonstrated that EIT maps can be used to visually represent changes in wound healing, including different stages of wound healing.

[0090] Example 2. Estimation of wound size and wound closure based on EIT data The EIT maps of relative conductivity in Figures 7A-7C were empirically correlated to physically measured wound depth values. To create the calibration model / curve, EIT map data from dozens of wound phantoms (with different cavity diameters and depths) were collected. This resulted in 136,488 data points (for each wound phantom, every pixel in each EIT map served as a data point). The data points were plotted on the x / y axis, with wound depth on the x-axis and change in conductivity on the y-axis (see, e.g., Figure 3B). The calibration curve was generated by least-squares linear fitting. The calibration curve was used to correlate the relative conductivity at each site to wound depth, for example, by taking a pixel value on the EIT map and converting that pixel to a depth value.

[0091] The calibration curves were applied directly to the EIT maps (in this example, the relative conductivity vs. (x,y) coordinate values ​​in the "EIT Map" row of Figures 7-7C) to convert them into contour maps of the wound at different stages of simulated healing (in this example, the tissue or wound depth vs. (x,y) coordinate values ​​in the "EIT-based Wound Contour Map" row of Figures 7A-7C). These contour plots captured the volumetric morphology of the wound phantom.

[0092] In addition, the EIT-based wound contour maps were used to extract data for use by clinicians when monitoring patient wounds. For example, the EIT-based wound contour maps were used to calculate the void volume of simulated wounds by integrating the depth versus x- and y-coordinate values ​​(Table 1). The "true phantom wound volume" was determined sequentially gravimetrically by a) weighing the TMP model, filling the void volume of the TMP model with deionized water, c) reweighing the TMP model to determine the added weight, and d) converting the added weight to volume based on the density of water. The EIT-calculated volumes of TMP Model A through TMP Model E closely matched the corresponding measured "true phantom wound volumes."

[0093] [Table 1] N / A = not applicable, initial wound volume on which calculation was based

[0094] Example 3. Mapping and volume estimation of a wound phantom with tunnel features A wound phantom (TMP Model F) with two excavated subcutaneous tunnel features was mapped using EIT as described in Example 1 (FIGS. 8A and 8B). The locations of the subcutaneous tunnel features were identified by the EIT map as areas of lower conductivity, as indicated by darker colors (dark gray and black) in the EIT map. In the "volume map" image (FIG. 8B) (calculated using the same calibration method as described in Example 2), elongated regions of void volume (simulating subcutaneous wound components) extend from the top and bottom of the phantom's opening in the tunnel area. This wound contour topography map very well superimposed the physical contour of the entire phantom wound space, including both the open wound volume and the covered subcutaneous volume.

[0095] The void volume of the phantom was determined by three different methods. In method 1, a ruler was used to measure the depth and diameter of the open void and, based on the measurements, the void volume was calculated assuming a cylindrical void space (i.e., volume = depth x pi x radius). 2 ). This method is often used by clinicians to measure wound volume and has the drawback of not measuring the volume of subcutaneous features. In method 2, true volume was measured using water according to the method described in Example 2. The true volume method involves measuring the volume of subcutaneous features. In method 3, EIT-based volume calculations were performed. The void volume of the TMP Model F was determined to be 21.4 cm by EIT-based mapping. 3 , 21.2 cm by true volume measurement 3 , and 12.6 cm by ruler measurement. 3 It was determined that.

[0096] Example 4. Mapping of a wound phantom using algorithmically calculated baseline estimates. The method described in "Electrical Impedance Tomography (EIT) Hardware and Methods" was followed. An eight-electrode device was prepared from a KAPTON polyimide flexible printed circuit board (PCB). The device was equipped with a snap connector to which electronic leads and 3M RED DOT 2560 electrodes (3M Company) were attached. A finite element model was generated based on the location and spacing of the eight electrodes and the geometry of the TMP. The electrode device was placed on the top surface of the TMP model G (Figure 9A) to obtain and create a spatial map of the relative conductivity of the wound phantom to be measured. The conductivity map was reconstructed using a one-step Gauss-Newton method, with the hyperparameter set to 0.3.

[0097] Instead of using the actual baseline measurements, the BH and MSF3 baseline measurement estimation methods were applied independently to calculate the conductivity distribution of the simulated non-wounded regions. The Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) v3.9 script in MATLAB® was used for the estimation. The off-center simulated wound locations with lower conductivity were accurately displayed in the reconstructed maps (Figures 9A-9C). In the individual EIT maps (Figure 9B using the BH method and Figure 9C using the MSF3 method), the cavity regions had lower measured conductivity, visually represented by darker colors (black to gray), and the regions filled with artificial tissue had higher measured conductivity, visually represented by lighter colors (light gray to white). Note that the eight small dark regions around the periphery of the EIT map images resulted from the positional placement of the electrodes.

[0098] Example 5. Mapping of wound phantoms (with and without subcutaneous tunnel features) using algorithmically calculated baseline estimates. The procedure of Example 4 was followed, except that TMP Model G (i.e., TMP model without simulated subcutaneous tunnel feature) and TMP Model H (i.e., TMP model with simulated subcutaneous tunnel feature) were used with the MSF4 baseline measurement estimation method. The off-center simulated wound locations with lower conductivity were accurately displayed in the reconstructed maps (FIGS. 10B and 10D).

[0099] In the EIT map of TMP Model G, the hollow regions had lower measured conductivity, visually represented by darker colors (black to gray), and the regions filled with artificial tissue had higher measured conductivity, visually represented by lighter colors (light gray to white). Image A1 of FIG. 10A is a photographic image of the top surface of TMP Model G, and Image A2 of FIG. 10A is an EIT map of TMP Model G.

[0100] For the TMP Model H, the EIT map showed significantly larger areas of dark color (black to gray) encompassing both the cavity and subcutaneous tunnel feature regions (i.e., areas with lower measured conductivity), while the areas filled with artificial tissue (i.e., areas with higher measured conductivity) had lighter colors (light gray to white). Image B1 in FIG. 10B is a photographic image of the bottom surface of the TMP Model H, and Image B2 in FIG. 10B is the EIT map of the TMP-H. Note that the eight small dark regions on the periphery of the EIT map image result from the positional placement of the electrodes. Additionally, note that the dashed lines in FIG. 10A and FIG. 10B were added after imaging to aid in visualization of the perimeter of the cavity and tunnel regions of the TMP model.

[0101] Example 6. Mapping a wound phantom using the fdEIT method The method described in "Electrical Impedance Tomography (EIT) Hardware and Methods" was followed using the TMP Model A (photograph image, FIG. 11A) and the electrode device described in Example 4. The electrode device (photograph image, FIG. 11B) was placed on top of the TMP Model A (photograph image, FIG. 11C) to acquire and create a spatial map of the relative conductivity of the measured wound phantom. The conductivity map was reconstructed using a one-step Gauss-Newton method, with the hyperparameter set to 0.8. The fdEIT method was used, utilizing two measurements captured with an AC current source at frequencies of 2.5 kHz and 40 kHz. A uniform baseline measurement was captured at 2.5 kHz and a non-uniform measurement was captured at 40 kHz. In the reconstructed EIT map (image in FIG. 11D), the central hollow region of TMP Model A had a lower measured conductivity, visually represented by darker colors (black to gray), and the region of TMP Model A filled with artificial tissue had a higher measured conductivity, visually represented by lighter colors (light gray to white).

[0102] Example 7. Detection of electrode connectivity degradation and algorithmic compensation for degraded electrode performance by application of electrode weighting parameters A saline-filled tank phantom was used as an artificial proxy structure to simulate the electrical properties and responses of tissue. The tank phantom consisted of a cylindrical plastic tank with an inner diameter of 90 mm, a wall height of 14 mm, and a wall thickness of 2 mm. Eight metal alligator clips (BU-30 Series, Mueller Electric Co., Akron, OH) were fixed to the vertical walls and uniformly distributed along the circumference of the cylindrical tank. The tank was filled with water until a portion of each alligator clip was partially submerged in the tank. Salt (NaCl) was then added to generate saline, which served as an ionically conductive medium. Each alligator clip served as an electrode. A cylindrical non-conductive plastic disk, 20 mm in diameter and 16 mm in height, was partially submerged in the saline at a position slightly offset from the center of the tank phantom. Using the method described in "Electrical Impedance Tomography (EIT) Hardware and Methods," an EIT map of the phantom was generated as a benchmark reference map (Figure 12A of the image). The area in the map corresponding to the location of the non-conductive plastic disk was black to dark grey, while the remaining area of ​​saline surrounding the disk was light grey.

[0103] The following method was used to simulate an erroneous signal from a failed electrode. To reconstruct a single EIT map, 40 voltage measurements were taken based on adjacent simulated patterns (8-electrode system). The first five voltage measurements were manually reset to 0 voltage, and an EIT map from the simulated error signal was generated (Figure 12B in the image). The introduction of an erroneous electrode measurement resulted in the appearance of a bright white artifact region located in the upper right periphery of the EIT map. This artifact region in the EIT map did not faithfully represent the phantom being mapped, since the upper right periphery of the phantom did not contain highly conductive material. Correction of the EIT map for simulated electrode failure was performed by algorithmically calculating the reciprocal error (e 2), a weighting parameter (σ) was applied to each electrode measurement as described in the above formula and methods. In the calculations, τ (tau) was set to 0.03. A corrected EIT map (image FIG. 12C) was created from the weight-adjusted electrode measurements. In the corrected map, the artifact areas observed in FIG. 12B were removed, but the black to dark gray areas corresponding to the plastic disc were maintained.

Claims

1. 1. A method of obtaining wound characteristics of a wound bed, the method comprising: applying one or more electrical signals to peri-wound tissue outside the wound bed via an array of electrodes; collecting electrical measurements from the array of electrodes via circuitry operatively connected to the array of electrodes; processing, via a processor, the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representing the spatial distribution of clinical metrics of the wound bed.

2. converting the one or more impedance maps into one or more tissue property maps; obtaining a calibration model by correlating the electrical measurements to physically measured wound data related to the clinical metrics of the wound bed; converting the one or more impedance maps to the one or more tissue property maps by using the calibration model; The method of claim 1 , comprising:

3. The method of claim 2 , wherein obtaining the calibration model includes obtaining a calibration curve by correlating measured relative conductance values ​​to wound depth values.

4. 10. The method of claim 1, wherein the one or more impedance maps comprise a spatial map of measurements selected from the group consisting of conductivity, resistivity, conductance, resistance, reactance, capacitance, inductance, impedance magnitude, impedance phase angle, complex impedance, and combinations thereof at one or more sampling frequencies.

5. 10. The method of claim 1, wherein the one or more tissue property maps comprise a spatial map of measurements selected from the group consisting of wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, and wound bed healing stage.

6. 10. The method of claim 1, further comprising: displaying the one or more tissue characteristic maps and system status via a graphic user interface (GUI); and receiving system configuration parameters from a user via the GUI.

7. The method of claim 1 , further comprising determining a volume of the wound bed from the one or more tissue property maps, the wound bed including one or more subcutaneous features.

8. 2. The method of claim 1, further comprising determining at least one tissue property selected from the group consisting of wound cross-sectional area, total infection level, biofilm volume, biofilm coverage area, total granulation tissue volume, average granulation tissue thickness, average wound depth, wound length, wound width, total epithelial coverage percentage, average epithelial thickness, and total epithelial volume based on the one or more tissue property maps.

9. The method of claim 1 , further comprising outputting information indicative of one or more tissue properties based on the one or more tissue property maps.

10. 1. A system for acquiring wound characteristics of a wound bed, the system comprising: an array of electrodes configured to apply one or more electrical signals to peri-wound tissue exterior to the wound bed; a circuit operatively connected to the array of electrodes for collecting electrical measurements from the array of electrodes; a processor within or connected to the circuitry, the processor comprising: processing the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representing the spatial distribution of clinical metrics of the wound bed.

11. 11. The system of claim 10, wherein the processor is further configured to determine information indicative of wound information based on the one or more tissue property maps, the wound information being selected from the group consisting of wound depth, granulation tissue thickness, epithelial coverage, biofilm thickness, bioburden, infection level, and healing stage of the wound bed.

12. 1. A device for application to a wound bed, said device comprising: an array of electrodes disposed about the wound bed and configured to apply one or more electrical signals to peri-wound tissue about the wound bed; a circuit operatively connected to the array of electrodes for collecting electrical measurements from the array of electrodes; a user interface for receiving instructions from a user and displaying information based on the collected electrical measurements.

13. The circuit further comprises: processing the collected electrical measurements to generate one or more impedance maps of the wound bed; and converting the one or more impedance maps into one or more tissue property maps representing the spatial distribution of clinical metrics of the wound bed.

14. 10. The method of claim 1, wherein the one or more impedance maps of the wound bed are estimated algorithmically and do not require a baseline measurement of non-wound tissue, and the one or more impedance maps of the wound bed are estimated algorithmically using a method selected from the group consisting of frequency difference EIT, measurement scale features, best homogeneous estimator, data-driven estimator, and combinations thereof.

15. 15. The method of claim 14, wherein the one or more impedance maps of the wound bed are algorithmically estimated using a measurement scale feature comprising a value selected from the group consisting of arithmetic mean, range, mid-range, electrode-based average range, electrode-based average mid-range, and combinations thereof.

16. 1. A method of obtaining wound characteristics of a wound bed, the method comprising: applying one or more electrical signals to peri-wound tissue outside the wound bed via an array of electrodes; collecting electrical measurements from the array of electrodes via circuitry operatively connected to the array of electrodes; Quantifying one or more electrode or connection performances; processing, via a processor, the collected electrical measurements to generate one or more impedance maps of the wound bed, wherein the processor algorithmically compensates for degradation of the one or more electrodes or connection performance; and converting the one or more impedance maps into one or more tissue property maps representing the spatial distribution of clinical metrics of the wound bed.

17. 17. The method of claim 16, wherein the one or more electrode or connection performance is detected by performing a voltage-current reciprocity based test.

18. The method described in claim 17, wherein algorithmically compensating for degradation of the one or more electrodes or connection performance includes applying weighting parameters to electrical measurements corresponding to the one or more electrodes or connection performance.

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