CN116392097A

CN116392097A - Non-invasive medical diagnosis using electrical impedance metrics and clinical predictors

Info

Publication number: CN116392097A
Application number: CN202211093574.9A
Authority: CN
Inventors: 迈克尔·A·加夫; 娜塔莎·安德烈亚森; 欧文·D·布里姆霍尔; 科里·J·凯利
Original assignee: Proron Corp Trading Ionic Scientific AS
Current assignee: Proron Corp Trading Ionic Scientific AS
Priority date: 2021-10-28
Filing date: 2022-09-08
Publication date: 2023-07-07

Abstract

Devices, systems, and methods for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors are disclosed. A method includes applying an electrical current to at least one interrogation electrode disposed on a surface of a human body within a sapet area of a human breast. The method includes measuring an electrical impedance of tissue of the person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. The method includes comparing the measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person.

Description

Non-invasive medical diagnosis using electrical impedance metrics and clinical predictors

Cross Reference to Related Applications

The present application claims the benefit of U.S. provisional patent application No. 63/273,146 entitled "NONINVASIVE BREAST CANCER DETECTION AND CLASSIFICATION MEASURING SKIN BIOIMPEDANCE IN LYMPHATIC hotps," filed by Michael a.garff et al at 2021, 10, 28, which provisional patent application is incorporated herein by reference.

FIELD

The subject matter disclosed herein relates to medical diagnostics, and more particularly to non-invasive medical diagnostics using electrical impedance metrics and clinical predictors.

Background

Although cancer is more prevalent in the elderly, they affect individuals of all ages. Those lost due to cancer not only inflict trauma to the human, but also social and significant economic losses to the home and the whole society. Thus, important research continues to focus on various complex diagnostic methods and treatment protocols for various cancer modalities. Diagnosis is made as early as possible, especially in a non-invasive, low risk way for the patient, with a high probability of success for cancer treatment.

SUMMARY

Devices, systems, and methods for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors (predictors) are disclosed. In one embodiment, a method includes applying an electrical current to at least one interrogation electrode disposed on a surface of a human body within a sapet area (Sappey Plexus region) of a human breast. In one embodiment, a method includes measuring electrical impedance of tissue of a person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. In one embodiment, a method includes comparing a measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in human tissue.

In one embodiment, an apparatus for performing noninvasive medical diagnostics using electrical impedance metrics and clinical predictors includes at least one interrogation electrode, a reference electrode, a processor, and a memory storing code executable by the processor. In one embodiment, code is executable by a processor to apply an electrical current to at least one interrogation electrode disposed on a surface of a human body within a saper region of a human breast. In one embodiment, the code is executable by the processor to measure electrical impedance of tissue of the person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. In one embodiment, code is executable by the processor to compare the measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person.

In one embodiment, an apparatus for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors includes means for applying an electrical current to at least one interrogation electrode placed on a surface of a human body within a sapet area of a human breast. In one embodiment, a method includes means for measuring electrical impedance of tissue of a person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. In one embodiment, a method includes means for comparing a measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in human tissue.

Brief Description of Drawings

In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1A is a schematic block diagram illustrating one embodiment of a system for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 1B is a schematic block diagram illustrating one embodiment of a system for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 1C is a schematic block diagram illustrating one embodiment of a probe system for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 1D is a schematic block diagram illustrating one embodiment of measurement results for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 2 is a schematic block diagram illustrating one embodiment of an apparatus for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 3 is a schematic block diagram illustrating one embodiment of an electrode garment for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 4 is a schematic flow chart diagram illustrating one embodiment of a method of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 6A illustrates one example of visual feedback for positioning a probe on a patient's body;

FIG. 6B illustrates another example of visual feedback for positioning a probe on a patient's body;

FIG. 7 is a schematic flow chart diagram illustrating one embodiment of a method of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

fig. 8 depicts one embodiment of an impedance measurement device for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors.

FIG. 9A is a perspective view of one embodiment of an interrogation electrode tip for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 9B is a perspective bottom view of one embodiment of an interrogation electrode tip for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 9C is a perspective bottom view of one embodiment of an interrogation electrode tip for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors;

FIG. 9D is a perspective cutaway view of one embodiment of an interrogation electrode tip for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors; and

fig. 10 is a schematic flow chart diagram illustrating one embodiment of a method of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors.

Detailed Description

Reference throughout this specification to "one embodiment," "an embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in one embodiment," "in an embodiment," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean "one or more but not all embodiments," unless expressly specified otherwise. The terms "include," "comprising," "having," and variants thereof mean "including but not limited to," unless expressly specified otherwise. The listing of items does not imply that any or all of the items are mutually exclusive and/or inclusive, unless expressly specified otherwise. The terms "a," "an," and "the" also mean "one or more," unless expressly specified otherwise.

Furthermore, the described features, advantages, and characteristics of the embodiments may be combined in any suitable manner. One skilled in the relevant art will recognize that an embodiment may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments.

These features and advantages of the embodiments will become more fully apparent from the following description and appended claims, or may be learned by the practice of the embodiments as set forth hereinafter. As will be appreciated by one of skill in the art, aspects of the present invention may be embodied as systems, methods, and/or computer program products. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," module, "or" system. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable media having program code embodied thereon.

Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

Modules may also be implemented in software for execution by various types of processors. An identified module of program code may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module.

Indeed, a module of program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein as modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices. In the case of a module or portion of a module implemented in software, the program code can be stored on and/or propagated on one or more computer-readable media.

The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to perform aspects of the present invention.

A computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: portable computer diskette, hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disc read-only memory (CD-ROM), digital Versatile Disc (DVD), memory stick, floppy disk, mechanical coding device (e.g., punch cards or embossed structures (tracks) on which instructions are recorded in grooves), and any suitable combination of the foregoing. A computer-readable storage medium as used herein should not itself be construed as a transitory signal, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a pulse of light passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a corresponding computing/processing device or to an external computer or external storage device via a network (e.g., the internet, a local area network, a wide area network, and/or a wireless network). The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for performing the operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server as a stand-alone software package. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, electronic circuitry, including, for example, programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having the instructions stored therein includes articles of manufacture including instructions which implement the aspects of the functions/acts specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

Modules may also be implemented in software for execution by various types of processors. An identified module of program instructions may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module.

The schematic flow chart diagrams and/or schematic block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the schematic flow diagrams and/or schematic block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).

It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated figure.

Although various arrow types and line types may be employed in the flow chart diagrams and/or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For example, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and program code.

As used herein, a list with "and/or" conjunctions includes any single item in the list or a combination of items in the list. For example, the list of A, B and/or C includes a only a, a only B, a only C, A, and B combinations, B and C combinations, a and C combinations, or A, B and C combinations. As used herein, a list using the term "one or more" includes any single item in the list or a combination of items in the list. For example, one or more of A, B and C include a combination of a only, B only, C, A only, and B only, B and C, a and C, or A, B and C. As used herein, a list using the term "one of … …" includes one of any single item in the list and only one of the items. For example, "one of A, B and C" includes only a, only B, or only C, and excludes a combination of A, B and C. As used herein, "one member selected from the group consisting of A, B and C" includes one and only one of A, B or C, and excludes a combination of A, B and C. As used herein, "one member selected from the group consisting of A, B and C, and combinations thereof" includes a alone, B alone, a combination of C, A and B alone, a combination of B and C, a combination of a and C, or a combination of A, B and C.

In one embodiment, a method includes applying an electrical current to at least one interrogation electrode disposed on a surface of a human body within a sapet area of a human breast. In one embodiment, a method includes measuring electrical impedance of tissue of a person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. In one embodiment, a method includes comparing a measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in human tissue.

In one embodiment, the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within the sapet area from a different person.

In one embodiment, the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within a sapet area from another breast of the person.

In one embodiment, a method includes providing measured electrical impedance to a machine learning model to calculate a risk score for the person, the machine learning model being trained based on previously measured electrical impedance, risk factors, and patient data of other persons who have been diagnosed with benign tumors and malignant tumors.

In one embodiment, a method includes receiving mammogram information associated with a person's breast, and in response to determining that the mammogram information indicates the presence of a nodule within the person's breast, inputting the mammogram information into machine learning to further calculate a risk score for the person based on the measured electrical impedance.

In one embodiment, the method includes periodically updating the risk score of the person based on the updated electrical impedance measurements and the changes in the risk factors and patient data to determine the effectiveness of the treatment in post-treatment monitoring.

In one embodiment, the method comprises heating at least one interrogation electrode to a temperature corresponding to a predefined conductance. In one embodiment, the method includes adjusting the temperature of the at least one electrode according to a controlled thermal profile until a steady current is detected between the at least one interrogation electrode and the reference electrode. In one embodiment, the method includes electrical impedance measurements at various temperatures at which controlled thermal distributions are captured.

In one embodiment, the interrogation electrode is an electrode tip of an electrode probe. In one embodiment, the electrode tip has a disk shape and a substantially smooth surface. In one embodiment, the electrode tip includes a textured surface, and the textured surface of the brass electrode tip includes a plurality of protrusions, each of the plurality of protrusions having a hexagonal shape.

In one embodiment, the electrode tip is made of a material selected from the group comprising: brass, silver-silver chloride, gold, and stainless steel. In one embodiment, the code is further executable by the processor to heat the at least one interrogation electrode to a temperature corresponding to the predefined conductance.

In one embodiment, the code is further executable by the processor to adjust the temperature of the at least one electrode according to the controlled thermal profile until a stable current is detected between the at least one interrogation electrode and the reference electrode.

In one embodiment, the code is further executable by the processor to capture electrical impedance measurements at various temperatures of the controlled thermal profile. In one embodiment, the previously captured electrical impedance measurements of the corresponding tissue include at least one of: an electrical impedance measurement of tissue in a region of a sapet plexus from a different person and an electrical impedance measurement of tissue in a region of a sapet plexus from another breast of the person.

In one embodiment, the code is further executable by the processor to provide the measured electrical impedance to a machine learning model that is trained based on previously measured electrical impedance, risk factors, and patient data of other people that have been diagnosed with benign and malignant tumors to calculate a risk score for the person.

In general, the subject matter disclosed herein relates to the use of noninvasive bioimpedance measurements to diagnose the presence of malignant or benign tumors in a patient's body, particularly in a patient's breast. Measuring the electrical characteristics associated with these physiological changes that occur when cancer occurs in vivo is likely to be a non-invasive technique that can provide early predictive diagnosis of patients with breast lesions. One particularly suitable technique for detecting these changes is electro-bioimpedance (EBI). EBI has been shown to provide signs of prognostic information (prognostic information) for the detection of many cancers, including skin, thyroid, liver, cervical and breast cancers.

Bioimpedance is particularly suited for detecting physiological and structural changes in tissue because it is affected by key parameters such as electrolyte concentration, pH, hydration status, and cell size and number. Thus, the bioimpedance may be used to distinguish between different tissue types or to detect pathological changes in tissue. The complexity of the bioimpedance instrument depends on how subtle one tries to detect the change. Where the relative change is large enough, a dual electrode, DC, or single frequency system may be sufficient. In other cases, it is necessary to apply more complex electrode systems to focus the measurement on a specific volume inside the body, or to make multi-frequency measurements within a specific frequency range to monitor certain dispersion mechanisms.

FIG. 1A is a schematic block diagram illustrating one embodiment of a system 100 for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, system 100 includes one or more information processing devices 102, one or more diagnostic equipment 104, one or more data networks 106, and one or more servers 108. In certain embodiments, although a particular number of information processing devices 102, diagnostic equipment 104, data networks 106, and servers 108 are depicted in FIG. 1A, one of ordinary skill in the art will recognize that any number of information processing devices 102, diagnostic equipment 104, data networks 106, and servers 108 may be included in system 100 in accordance with the present disclosure.

In one embodiment, system 100 includes one or more information processing devices 102. The information processing device 102 may be embodied as one or more of the following: desktop computers, laptop computers, tablet computers, smart phones, smart speakers (e.g., amazon

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) An internet of things device, a security system, a set top box, a game console, a smart television, a smart watch, a fitness strap or other wearable activity tracking device, an optical head mounted display (e.g., virtual reality head mounted equipment, smart glasses, headphones, etc.), a high definition multimedia interface ("HDMI") or other electronic display dongle, a personal digital assistant, a digital camera, a video camera, or another computing device that includes a processor (e.g., a central processing unit ("CPU"), a processor core, a field programmable gate array ("FPGA") or other programmable logic, an application specific integrated circuit ("ASIC"), a controller, a microcontroller, and/or another semiconductor integrated circuit device), volatile and/or nonvolatile storage media, a display, a connection to a display, etc.

Generally, in one embodiment, the diagnostic equipment 104 is configured to apply the electrical current non-invasively to tissue of the patient's body using the interrogation electrode of the probe. As shown in fig. 1B, the probe is configured to measure the electrical impedance of tissue between the interrogation electrode and the reference electrode. Furthermore, in one embodiment, the diagnostic equipment 104 is configured to measure the electrical impedance of the patient's body tissue between the interrogation electrode and the reference electrode of the probe, and detect the presence of malignancy in the patient's body tissue by inputting the measured electrical impedance of the tissue into machine learning, which may be trained based on patient data associated with the type of disease being diagnosed.

In this way, the diagnostic equipment 104 detects malignancy, such as malignancy associated with breast or lung cancer, in a non-invasive and non-radiative manner, and uses machine learning to detect, predict, forecast, etc., the presence of malignancy in the patient's body at the earliest stage of the tumor. The diagnostic equipment 104 uses a combination of bioimpedance measurements, biomarkers, symptoms, risks, and the like, along with artificial intelligence to provide early detection of malignancy in a patient, which can improve survival of a disease associated with the malignancy.

In one embodiment, at least a portion of the diagnostic equipment 104 is located on the information processing device 102, the probe system, the server 108, electrode clothing (described below), and the like. Diagnostic equipment 104, including its various sub-modules, may be located on one or more information processing devices 102 in system 100, on one or more servers 108, on one or more network devices, and so forth. The diagnostic equipment 104 is described in more detail below with reference to fig. 2.

In some embodiments, diagnostic equipment 104 may include hardware devices such as secure hardware dongles or other hardware device devices (e.g., set top boxes, network devices, etc.), which connect through a wired connection (e.g., universal serial bus ("USB") connection) or a wireless connection (e.g.,

Wi-Fi, near field communication ("NFC"), etc.) to devices such as head mounted displays, laptop computers, servers 108, tablet computers, smart phones, security systems, network routers or switches, etc.; attached to an electronic display device (e.g., attached to a television or monitor using an HDMI port, displayPort port, mini DisplayPort port, VGA port, DVI port, etc.); etc. The hardware devices of the diagnostic equipment 104 may include a power interface, a wired and/or wireless network interface, a graphical interface attached to a display, and/or a semiconductor integrated circuit device as described below that is configured to perform the functions described herein with respect to the diagnostic equipment 104.

In such embodiments, diagnostic equipment 104 may include a semiconductor integrated circuit device (e.g., one or more chips, dies, or other discrete logic hardware) or the like, such as a field programmable gate array ("FPGA") or other programmable logic, firmware for the FPGA or other programmable logic, microcode for execution on a microcontroller, an application-specific integrated circuit ("ASIC"), a processor core, or the like. In one embodiment, diagnostic equipment 104 may be mounted on a printed circuit board having one or more electrical lines or connections (e.g., lines or connections to volatile memory, non-volatile storage media, network interfaces, peripherals, graphics/display interfaces, etc.). The hardware devices may include one or more pins, pads, or other electrical connections configured to send and receive data (e.g., to communicate with one or more electrical lines of a printed circuit board, etc.), as well as one or more hardware circuits and/or other circuits configured to perform various functions of the diagnostic equipment 104.

In certain embodiments, the semiconductor integrated circuit devices or other hardware devices of diagnostic equipment 104 include and/or are communicatively coupled to one or more volatile memory media, which may include, but are not limited to, random access memory ("RAM"), dynamic RAM ("DRAM"), cache, and the like. In one embodiment, the semiconductor integrated circuit device or other hardware device of diagnostic equipment 104 includes and/or is communicatively coupled to one or more non-volatile memory media, which may include, but is not limited to: NAND flash memory, NOR flash memory, nano random access memory (nano RAM or "NRAM"), nano-wire based memory, silicon oxide based sub-10nano process memory (silicon-oxide based sub-10nanometer process memory), graphene memory, silicon-oxide-nitride-oxide-silicon ("SONOS"), resistive RAM ("RRAM"), programmable metallization cell ("PMC"), conductive bridging RAM ("CBRAM"), magnetoresistive RAM ("MRAM"), dynamic RAM ("DRAM"), phase change RAM ("PRAM" or "PCM"), magnetic storage media (e.g., hard disk, tape), optical storage media, and the like.

In one embodiment, the data network 106 comprises a digital communication network that transmits digital communications. The data network 106 may include a wireless network, such as a wireless cellular network, a local wireless network, such as a Wi-Fi network,

A network, a near field communication ("NFC") network, an ad hoc network, and the like. The data network 106 may include a wide area network ("WAN"), a storage area network ("SAN"), a local area network ("LAN") (e.g., home network), a fiber optic network, an interconnectionA network or other digital communications network. The data network 106 may include two or more networks. The data network 106 may include one or more servers, routers, switches, and/or other networking devices. The data network 106 may also include one or more computer-readable storage media, such as a hard disk drive, optical drive, non-volatile memory, RAM, and the like.

The wireless connection may be a mobile telephone network. The wireless connection may also employ Wi-Fi networks based on any of the institute of Electrical and electronics Engineers ("IEEE") 802.11 standards. Alternatively, the wireless connection may be

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Connection of defined infrared physical layer specification ("IrPHY"). Alternatively, the wireless connection may be a cellular telephone network communication. All standards and/or connection types include the latest version and revisions of the standard and/or connection type by the date of filing of the present application.

In one embodiment, one or more of the servers 108 may be embodied as blade servers, mainframe servers, tower servers, rack servers, or the like. One or more of the servers 108 may be configured as email servers, web servers, application servers, FTP servers, media servers, data servers, web servers, file servers, virtual servers, and so forth. One or more servers 108 may be communicatively coupled (e.g., networked) to the one or more information processing devices 102 via the data network 106, for example, as part of a healthcare information and patient data system.

Fig. 1B depicts a system 110 for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. As described above, the subject matter herein describes using electrical impedance characteristics of biological regions, based on comparison of normal and abnormal levels, combining clinical predictors, and using artificial intelligence/machine learning algorithms to provide predictive screening and detection of malignancy (e.g., for various types of cancers or other diseases).

In general, when a malignancy itself is present in the body, the body will react, e.g., cancer changes the composition of components in the extracellular matrix. Diagnostic equipment 104 uses a probe system, interfaces with the probe system, communicates with the probe system, instructs the probe system, signals the probe system, etc. to introduce electrical current to a predetermined cancer specific point on the body, measures ionic current through the body, and provides the measurement to an artificial intelligence/machine learning engine that analyzes the measurement in conjunction with other biomarkers, biometrics, biological history, biopsy data, lifestyle data, symptoms, health risks, and other biological data associated with the patient to detect the presence of malignancy in the patient (e.g., in the lung or breast).

In one embodiment, the diagnostic equipment 104 measures the electrical conductance or resistance response of ionic current flowing primarily through interstitial fluid within the extracellular matrix. In certain embodiments, the diagnostic equipment 104 measures not only tissue at a specific location, but also changes in the volume resistance (bulk resistance) of the extracellular matrix and interstitial fluid within the lymphatic system in a critical target location on the body. Because of the presence of cancer in vivo, biological changes in the interstitium, extracellular matrix, and lymphatic system have been shown to be apparent and measurable by various analytical methods.

In general, the current may flow in different ways. The charged particles generate an electric current when they move. The electrons are charged particles. Most people are familiar with the flow of current in conductors such as copper. Common examples are electrical conductors in a home or computer. The current may also flow in vacuum or air, such as in a vacuum tube or in the form of sparks or lightning. It may also flow in the plasma, or in the form of electromagnetic waves such as radio waves, or in the form of ion streams. Most importantly, in biology and medicine, we are concerned with the current generated by the flow of ions. Ions are charged atoms, molecules or molecular structures, such as DNA, even larger proteins, etc., dissolved or suspended in water. The charge on these ions is due to an excess or deficiency of electrons. The current may flow through a liquid (e.g., in a human body). This current is constituted by the movement of ions. The ions may be negatively or positively charged and will flow in the opposite direction.

The current flowing through the body is due to the process of ion flow. This is true for either direct current ("DC") or alternating current ("AC"). When a voltage is applied to the body or tissue, current flows. The electricity will flow only when the charged ions move towards the oppositely charged electrode. This is commonly referred to as bioelectance. The movement of ions is resisted by many factors in the body. The resistance to the flow of current may be referred to as electrical impedance. The electrical impedance may be further characterized as a function of resistance and capacitance. The electrical impedance may be described by a set of complex variables that depend on the type of voltage signal applied. The impedance in the body is a function of the frequency of the applied voltage and the composition of the biological structure through which it flows.

The diagnostic equipment 104 improves upon conventional screening systems to increase the accuracy of cancer screening devices that use electrical impedance measurements to diagnose or screen for cancer or other disease states. In certain embodiments, the diagnostic equipment 104 is further configured to measure electrical impedance of tissue of interest, anatomical features, interstitial fluid, lymphatic system, etc. in vivo, and provide a comparison to data from known healthy subjects for proper diagnosis and/or screening of detection of disease states, including cancer screening. This includes interstitial fluid in the extracellular space, lymphatic capillaries, lymphatic channels, lymph nodes and electrical impedance properties in the anatomy of interest. The current induced and measured in biological systems is based on ion transport. These ion currents are complex in nature and depend on many variables, including ion type, ion concentration, the substrate through which the ions move, and so forth.

The lymphatic system is considered the body's immune system. In addition to its function as the interstitial fluid circulatory system, it also has the function of modulating mast cells, T cells, mirnas, electrolytes, biochemically important fragments, etc. associated with cancer, disease states and various systemic inflammations. The main lymphatic channel flows along a known anatomical region (usually corresponding to the intra-fascia plane). The location of lymph nodes is well known and includes cervical lymph nodes, axillary lymph nodes, and the like. The concentrations of electrolytes, proteins, mirnas, chemokines and cytokines in the interstitial fluid in the extracellular space, which are involved in immune reactions, were shown to be significantly higher than in normal blood.

Non-invasively measuring the electrical impedance of human tissue may be a rapid method of characterizing normal, healthy tissue relative to unhealthy or abnormal tissue. Clinical studies have shown the effectiveness of measuring bioimpedance and its relationship to disease states, including various cancers. By increasing the accuracy of the screening apparatus, detection capabilities can be increased, including higher sensitivity and increased specificity. Thus, facilitating earlier treatment for some diseases and reducing unnecessary treatment for other diseases.

Electrical impedance measurements of the human body and tissue provide important information that has been used to characterize the tissue and fluid of interest. Electrical impedance measurements are complex and provide non-linear functions related to voltage, frequency, path, electrodes and tissue. The first level of use of the data results in useful diagnostic information. The use of artificial intelligence/machine learning (e.g., deep learning) is increased for signal analysis and comparison to a set of known disease states that incorporate clinical predictors, such as biomarkers, e.g., age, gender, weight, height, race, genomic attributes, blood examination work, drugs, location, occupation, eating habits, alcohol intake, family history, income, biopsy results, and the like. The combined large dataset (which compares the status to known health or disease status) and electrical impedance metric provides additional predictive capability for a non-invasive electrical impedance screening device. Accurate predictions may be used to reduce unnecessary or risky surgical use or to expedite surgical use where appropriate, resulting in earlier intervention.

As shown in fig. 1B, the system 110 includes a device 111 for measuring electrical impedance over a particular region of a patient's body 124. Device 111 may be a computing device such as a desktop computer, a laptop computer, a mobile device, or any specially programmed or configured hardware and software device including a processor 116, memory, storage, network capabilities, display, etc. The device 111 may be a probe system (see fig. 1C), may be communicatively coupled to a probe system, and so on.

For example, the device 111 may include an input analog-to-digital converter 120 or be communicatively coupled to the input analog-to-digital converter 120 to process signals received from electrodes placed on the patient 124 in response to electrical signals applied to the patient's body via the probe. The device 111 may also include a signal generator 122 to generate or trigger signals for electrodes placed on the patient 124 at various dynamically determined or predefined voltages, the results of which are subsequently received by the input AD 120.

The electrodes may be placed on areas of the body associated with a disease state of interest. These diseases may include various cancers, including lung cancer, skin cancer, breast cancer, thyroid cancer, prostate cancer, and the like. The system 110 may include equipment that uses the signal generator 122 (e.g., using a probe) to apply an electrical signal as an input, as well as the ability to accurately measure and record a signal to a reference electrode (including complex impedance) using the input AD 120.

In addition, the system 110 may include a diagnostic apparatus 104, the diagnostic apparatus 104 utilizing an artificial intelligence/machine learning algorithm 128 to process measured signals from a particular location on the patient 124 and compare the signals to a database 116 of signals measured at the same body location of various subjects having known health conditions. The database 116 may include external patient data 112, the external patient data 112 including patient bioelectrical impedance metrics, clinical data such as predictors and medical history, clinical biopsy results, treatment data, medical informative information such as age, gender, race, weight, height, health status, medication, smoking history, eating habits, alcohol intake, income, geographic location, biopsy results, and the like. Database 116 may have the ability to grow because more data is included as more patients are diagnosed, treated, etc.

Database 116 may include any type of data store, such as a relational database, and may be stored locally or remotely, such as in the cloud. The external patient data 112 may be obtained from publicly available patient data (which may have personally identifiable information striped or compiled from the data), data provided by hospitals, doctors, clinics, patients, and the like. Database 116 may have means to control or protect the core diagnostic data set for diagnostic purposes, for example, using security measures such as encryption, requiring credentials (username/password, biometric information, etc.), etc.

In one embodiment, the diagnostic equipment 104 uses an artificial intelligence/machine learning algorithm 118 to pattern identify the data set, i.e., the patient data and the external patient data 112, combining the electrical impedance measurements to provide a higher confidence diagnostic score. As used herein, artificial intelligence may refer to the ability of a machine/computer to learn and simulate intelligent behavior over time. Further, as used herein, machine learning may refer to the application of Artificial Intelligence (AI) that provides the system with the ability to learn and improve automatically empirically without requiring explicit programming. Machine learning has focused on developing computer programs that can access data and learn themselves using the data.

The artificial intelligence/machine learning algorithms 118 may include various types of machine learning algorithms, such as supervised machine learning algorithms (e.g., nearest neighbors, naive bayes, decision trees, linear regression, support vector machines, neural networks, etc.), unsupervised machine learning algorithms (e.g., k-means clustering, association rules, etc.), semi-supervised machine learning algorithms, and/or enhanced machine learning algorithms (e.g., Q-learning, time differencing, deep challenge networks, etc.).

The diagnostic equipment 104 may train the artificial intelligence/machine learning 118 based on external patient data in the reference database 116. As the size of the training data set increases, the diagnostic equipment 104 may continually train and/or retrain the artificial intelligence/machine learning, which provides a statistically improved diagnostic score due to the increased accuracy of the artificial intelligence/machine learning 118. In some embodiments, results, predictions, estimates, forecasts, diagnostics, etc. from the artificial intelligence/machine learning 118 are provided, entered, or stored in the database 116 for future reference and training. In some embodiments, periodic clinical and regulatory reviews of data stored in database 116 may be performed, for example, by a third party, including suggested additions or modifications to database 116 to authorize upgrades to a core diagnostic dataset in database 116.

In one example embodiment, the system 110 includes a device 111 for measuring electrical impedance over a particular region of the body located along lymphatic channels and lymph nodes. The test metric may be correlated with a disease state of interest as compared to a large statistically significant metric from known healthy subjects. These conditions may include various cancer types that can be identified by monitoring the lymphatic system, including lung, skin, breast, thyroid, etc. The system 110 includes a device that applies a controlled electrical signal as an input at a designated location along the lymphatic system, as well as the ability to accurately measure and record an accurate signal (including complex impedance) to a reference electrode. The electrodes may comprise a single point and an array of specific spaced apart electrodes to scan one or more regions, such as lymph nodes or vessels/channels.

Fig. 1C depicts one embodiment of an apparatus for generating a current and measuring impedance in a patient. In one embodiment, the apparatus includes a computer assembly, generally indicated at 150, and a probe system, generally indicated at 152. The computer assembly 150 typically includes a housing 154 to contain a processor and memory that communicate with a display device such as a monitor 156. One or more input devices (e.g., keyboard 158, mouse, etc., as illustrated) may also be included to operatively associate with the computer component. Similarly, output devices such as printers, USB ports, network connectors, media writers, and the like may be provided in operative relationship with computer system 150.

The probe system 152 generally includes an interrogation electrode 160. One example of an interrogation electrode that may be used is disclosed in U.S. patent application publication 2005/0015017, published 20/1/2005, the entire contents of which are incorporated herein by reference. Desirably, interrogation electrode 160 will be configured to allow application of electrode contact pressure to the subject's skin during a measurement sequence by computer control. Such computer control may include a feedback loop that includes real-time conductivity data measured by the probe itself.

The probe system 152 also includes a reference electrode, such as a cylinder 162 that may be held by a subject, or an optional point probe 164 that may be applied by a clinician. Ideally, the reference electrode is configured to contact a relatively large area of the skin of the subject under test and isolate the operator from the circuitry formed. The operable electrode 162 comprises a cylindrical block of conductive material (e.g., metal) having a diameter of about 1 inch and a length of about 3 inches. Brass is an operable metal that can be used to form the reference electrode, but other metals and conductive materials are also operable. The operable point probe 164 may be formed as a mass of blunt, generally mushroom-shaped, electrically conductive material (e.g., metal, including brass). The electrodes are placed in electrical communication with a conductivity measurement device, which may be conveniently contained in the housing 154 for transmitting conductivity data to the computer system 150.

Water is typically sprayed onto the subject's hand and a cylindrical reference electrode 162 is held in the wet palm of the hand of the clenched fist. Sometimes, a strap may be applied to ensure that the hand does not inadvertently disconnect the electrode 162 or lose contact with the electrode 162. The operator uses moisture to place the circular reference electrode 164 in a specific location on the back of the subject. Other electrodes may be placed on the patient's body (e.g., adhered to the patient's skin or placed against the body as part of a garment, as described below). The operator wears the glove steering electrode to maintain electrical isolation and apply uniform pressure during the measurement.

The data acquisition includes measuring a time-dependent conductivity between a reference electrode and an interrogation electrode over a period of time, the reference electrode being disposed at one or more reference points, the interrogation electrode typically being disposed at each of a plurality of interrogation points. Specific interrogation points operable to detect cancer (e.g., lung cancer or breast cancer) are located on the arm, upper arm, shoulder, chest and back. In some embodiments, during data acquisition for detecting lung cancer, reference electrode 162 will be held in the hand on the opposite side of the subject's body midline from the interrogation point.

In one embodiment, software running on computer system 150 and/or communicatively coupled to computer system 150, such as diagnostic equipment 104 located on computer system 150 and/or connected to computer system 150 through data network 106, is programmed to assist an operator during data acquisition using probe system 152. For example, the display 156 may present a visual anatomical schematic with a highlighted interrogation spot overlay that helps the device operator identify and place the interrogation probe 160.

During the series of data acquisitions, the screen image may be updated or changed to inform the operator of the desired interrogation point for each point of interest. A user-perceptible output (e.g., a low-level modulated tone) may be generated to provide real-time feedback to the device operator to verify the completion of an acceptable measurement. The conductivity measurement profile for each conductivity measurement may be visually displayed on monitor 156, for example, the conductivity value may be sampled 25 times per second during each conductivity measurement.

Further, the diagnostic equipment 104 may control the probe pressure to ensure accurate and consistent measurements. Thus, the pressure applied to the skin surface during operation of the probe is repeatable and independent of the operator's force. Diagnostic equipment 104 implements a threshold profile during interrogation electrode tip contact that adjusts probe pressure in real time to ensure accurate readings and to prevent false readings. After the measurement session is completed, the diagnostic equipment 104 may store the data for post-processing.

A representative plot of the data set obtained during the time-based measurement of conductivity at the interrogation point is presented in fig. 1D. In fig. 1D, the X-axis represents time, and the Y-axis represents the measured conductivity index. As used herein, conductivity index is defined as the conductance equivalent to a resistance measurement of 1K ohms to 999K ohms at nominal 1.2 or 2.4 volts. Firmware in the device 150 (e.g., the diagnostic equipment 104) stabilizes the current at, for example, 10 microamps, measures the voltage, and then calculates the conductance. The software/firmware of computer system 150 (e.g., diagnostic equipment 104) employs an algorithm to increase probe pressure until the conductivity index exhibits a zero slope.

The algorithm then commands the probe pressure to remain constant for a period of time (e.g., within five seconds). In one example use of the electrical interrogation probe, a reduced pulse width modulation ("PWM") rate variable of a computer algorithm is set to zero, which keeps the nominal pressure of the electrode tip constant after a zero slope is reached. The conductivity between the interrogation probe and the reference probe is periodically measured during the time interval and stored as a data set and this information is transmitted to the computer system 150. The measured conductance is plotted as normalized conductivity index on a scale of 0 to 100.

Eight attributes that can be resolved from a dataset (e.g., the dataset shown in fig. 1D) and that describe a particular portion of such a graph are defined as follows: the Base maximum (Base Max) is the maximum conductivity index value after zero slope is reached; the Base minimum (Base Min) is the minimum conductivity index value after zero slope is reached; the Rise (Rise) is the angle between the starting conductivity index and the conductivity index at zero slope; the drop (Fall) is the angle between the conductivity index at the zero slope point and the conductivity index at the end of the measurement; drop is the difference between the base maximum and the base minimum; the area under the curve to zero slope refers to the percentage of the area under the curve from the start point to zero slope compared to the total possible area from the start point to zero slope; the area under the curve starting from zero slope refers to the percentage of the area under the curve from zero slope to the end of measurement to the total possible area from zero slope to the end of measurement; and the sum of the areas under the curve is the percentage of the area under the curve from the beginning of the measurement to the end of the measurement to the total possible area from the beginning of the measurement to the end of the measurement.

The acceptability of the measurement results may be determined by the diagnostic equipment 104 of the system 150 and the clinician may receive perceptible feedback from the computer system 150 to confirm satisfactory completion of the data collection operation. Factors that may be evaluated to determine whether data was successfully collected include: 1) The conductivity rises to zero slope, controlled by the computer. 2) By maintaining a continuous measurement signal of the timeout value, inadvertent fluctuations are controlled by the computer and by the operator. 3) If no zero slope indication occurs within the first two seconds, the measurement should be repeated, controlled by the operator. 4) Excessive drop values are repeatedly confirmed and controlled by the operator.

The failed measurement may include: 1) Premature zero slope-controlled by the machine. 2) Excessive rise or fall after zero slope-controlled by the machine. 3) The low conductivity measurement is used as a first metric, particularly when no other low conductivity measurements are present, and is controlled by the operator. 4) There is no probe reset on the first contact-controlled by the operator.

Fig. 2 depicts one embodiment of an apparatus 200 for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, the apparatus 200 includes an embodiment of the diagnostic apparatus 104. In one embodiment, the diagnostic equipment 104 includes one or more of a current module 202, a measurement module 204, an ML module 206, and a monitoring module 208. In some embodiments, the probe system 210 and the electrode garment 212 are communicatively coupled to the diagnostic equipment 104.

In one embodiment, as described above, the current module 202 is configured to non-invasively apply current to tissue of the patient's body using the interrogation electrodes of the probes of the probe system 210. In one embodiment, the probe system 210 may be substantially similar to the probe system 152 described with reference to fig. 1C. The current module 202 may generate a voltage to apply an amount of current in response to an operator's instructions, based on predefined settings related to the type of disease being diagnosed, etc.

In one embodiment, the measurement module 204 is configured to measure the electrical impedance of the patient's body tissue between the interrogation electrode and the reference electrode of the probe. In certain embodiments, the probes of probe system 210 may be configured to detect and measure electrical impedance between the interrogation electrode and the reference electrode (or reference electrodes), and store the measurements for later analysis and processing.

In one embodiment, the ML module 206 is configured to detect the presence of malignancy in the patient's body tissue by inputting the measured electrical impedance of the tissue into machine learning. As described above, the ML module 206 can obtain, reference, find, retrieve, etc., the measured electrical impedance for the patient and the region of the patient's body under examination, and provide the measured electrical impedance to an artificial intelligence/machine learning engine for use in determining or detecting whether a malignancy is present in the tissue to which the electrical current is applied.

In such embodiments, machine learning is trained based on patient data associated with the type of disease being diagnosed (e.g., lung cancer, breast cancer, prostate cancer, etc.). For example, the machine learning model may be trained to detect lung cancer generally and/or at specific locations of the lung using historical training data that includes electrical impedance measured for other patients that have been diagnosed with lung cancer.

In one embodiment, the machine learning model may be trained based on other external data, such as biomarker data. As used herein, a biomarker may refer to a measurable indicator of a biological state or condition, such as, for example, age, gender, weight, height, race, genomic attributes, blood test work, medications, location, occupation, eating habits, alcohol intake, family history, income, previous biopsy results, and the like. Digital biomarkers, e.g., biomarkers focused on important parameters such as accelerometer data, heart rate, blood pressure, etc., are captured, recorded, sensed, detected, measured, etc., using a smart biosensor, e.g., a heart rate monitor on a smart phone or smart watch.

Other external data on which the machine learning module is trained may include risk factors associated with the type of disease being diagnosed (e.g., lung cancer or breast cancer). Risk factors may be related to a person's health, lifestyle, environmental conditions, socioeconomic status, employment, etc. For example, risk factors for lung cancer may include the age of the patient, personal and family cancer history, smoking history, size of nodules in tissue, number of nodules in tissue, characteristics of nodules in tissue, location of nodules in tissue, emphysema history, body mass index, employment type and history, where the patient lives, and so forth.

In another example embodiment, the risk factors for breast cancer may include age, genetic mutation, reproductive history, breast density, history of personal breast disease, family history of breast cancer, past radiation therapy treatment, administration of the drug diethylstilbestrol ("DES"), and the like.

Other external data on which the machine learning module trains may include symptoms associated with the type of disease being diagnosed (e.g., lung cancer or breast cancer). For example, risk factors for lung cancer may include recent weight loss, bloody sputum, chest pain, cough, shortness of breath, wheezing, fatigue, bone pain, and the like. In another example embodiment, the risk factors for breast cancer may include tumor size, tumor growth, breast portion thickening, breast skin sagging, flaky skin, nipple pain, nipple discharge, changes in breast size and/or shape, breast pain, and the like.

In certain embodiments, the ML module 206 interfaces with the data store or stores to obtain external patient data that is collected from other patients and/or contains information of the patient being diagnosed. For example, the data store may be stored locally at a hospital or clinic, may be stored in a cloud behind a security gateway that requires credentials to access, may be publicly accessible, and so on. In one embodiment, the ML module 206 periodically polls the data store to check for new data, receives notifications or signals that new data is available, and so on, the ML module 206 uses the new data to retrain and refine the machine learning model for the disease being diagnosed.

In one embodiment, the monitoring module 208 is configured to periodically measure the electrical impedance of the patient's body tissue between the interrogation electrode and the reference electrode of the probe over time and monitor the progress of the disease in the tissue and the effectiveness of the therapeutic treatment in treating the disease in the tissue. For example, the monitoring module 208 may track patient progress daily, weekly, monthly, etc. to determine whether a detected tumor is stable (indicating that the treatment is not effective), large (indicating that the treatment is not effective), small (indicating that the treatment is effective), and so forth.

The monitoring module 208 may use machine learning to generate suggestions, recommendations, etc. for treatments, lifestyle changes, etc. The monitoring module 208 may also generate reports including trends, forecasts, analyses, etc., based on machine learning, describing the patient's treatment, progress, etc., as if it were associated with other similar patients having similar biomarkers, risk factors, symptoms, disease, nodule/tumor location and size, etc.

In one embodiment, diagnostic equipment 104 may be communicatively coupled to electrode garment 212, as shown in FIG. 3. For example, electrical impedance tomography is commonly used to create a single layer image of electrical impedance across the chest. This can be achieved using a circular array of electrodes placed on or around the chest to characterize the entire chest. Electrical signals are induced through the electrodes and various mathematical algorithms are used to create the image.

As described herein, to facilitate efficient imaging and measurement of a patient using electrical impedance, in one embodiment, a garment 302 (which may be substantially similar to electrode garment 212 described above with reference to fig. 2) is configured with an array of electrodes 303 (e.g., reference electrodes), the electrodes 303 being placed on the garment 302 in a predefined or random pattern. For example, the electrodes 303 may include a reference electrode placed on the back side and a signal electrode placed on the abdomen side.

In some embodiments, garment 302 may be composed of two or more separate sheets or pieces of material, each including electrodes 303 in an array. The separate pieces may be stitched or otherwise fastened together so that the electrodes 303 may be used together or simultaneously. However, in some embodiments, the electrodes 303 may be used independently, e.g., in a specified pattern, e.g., every other electrode, one at a time, etc. The electrode 303 may be permanently fastened or integrated into the garment 302, or may be selectively/removably attached to the garment 302, which allows for changing or replacing the electrode 303 as desired.

In some embodiments, the electrode 303 may be pretreated with a conductive substance, such as an electrode gel. In further embodiments, the electrode 303 is located or positioned in a compliant pad of conductive polymer. In various embodiments, electrode 303 is a conductive element connected by a flexible wiring conductor. In some embodiments, the electrode 303 is embedded or integrated into a stretchable film or material.

Garment 302 may be a vest, as shown in fig. 3, but may also be embodied as a shirt, bra, hat, pants, undergarment, waistband, sock, headband, and/or other wearable material. Garment 302 may be made of various materials such as cotton, polyester, and the like. In certain embodiments, garment 302 is reusable and may be laundered with or without electrode 303. In other embodiments, garment 302 is a single-use garment, which may be discarded with or without electrode 303.

In one embodiment, garment 302 includes computing device 301, and computing device 301 includes signal generator 305 and analog-to-digital signal converter 306. Computing device 301 may be an off-the-shelf computing device, such as a mobile device, desktop or laptop computer, or a computing device specifically configured and/or programmed to perform the functions/steps described herein. The signal generator 305 may generate electrical signals, such as AC, DC, high frequency signals, at a desired voltage. In certain embodiments, the electrodes 303 are switchably connected to the signal generator 305 (on one side of the garment 302, e.g., the ventral side, and/or on both sides of the garment 302, e.g., the ventral and dorsal sides).

In some embodiments, the computing device 301 including the power source (e.g., battery), signal generator 305, analog-to-digital converter 306, and measurement equipment 304 is built into the garment 302 such that the garment 302 and the computing device 301 are modular without requiring additional connectors, wires, power sources, etc. to connect the garment 302 to an external computing device, such as the computing device 150 and/or probe system 152 described above with reference to fig. 1C. In such embodiments, the recorded or measured information is processed at the recording point, transmitted wirelessly to an external device for processing, or connected to the external device via a wired connection (e.g., USB connection) for processing.

In one embodiment, each electrode 303 is switchably connected to an analog-to-digital signal converter 306, and the analog-to-digital signal converter 306 is configured to detect and record complex properties of the signal including frequency, impedance, and phase. In embodiments where computing device 301 is an external device, signal generator 305 and analog-to-digital converter 306 may each be coupled to a controller or other hardware component on garment 302, which may be connected in parallel or in series to electrode 303, and transmit signals from computing device 301 to garment 302/from garment 302 to computing device 301.

In certain embodiments, the electrodes 303 may be used to monitor cardiac signals or other biometric information, or be switched to excite and measure bioimpedance of a target region. In various embodiments, all electrodes 303 are activated/energized simultaneously, or only a subset of electrodes 303 are activated to measure a particular target area, such as the lung or a portion of the lung, breast or a portion of the breast.

In one embodiment, measurement equipment 304 processes signals and measurements from electrodes 303 on garment 302 to determine information for diagnosing a subject or patient wearing garment 302 based on electrical impedance. For example, the measurement equipment 304 may calculate a diagnostic score based on a difference between normal and abnormal impedance measurements of the chest region (e.g., based on impedance measurements previously acquired from the patient or other patient). In one embodiment, the measurement equipment 304 dynamically determines and selects the signal nodes/electrodes 303 for excitation based on algorithmic calculations, e.g., based on previous readings or measurements.

In various embodiments, measurement equipment 304 may be embodied as hardware devices that may be installed or deployed on computing device 301, on apparel 302, or the like. In some embodiments, the measurement equipment 304 may include hardware devices such as secure hardware dongles or other hardware device equipment (e.g., set top boxes, network devices, etc.), which connect through a wired connection (e.g., universal serial bus ("USB") connection) or a wireless connection (e.g.,

Wi-Fi, near field communication ("NFC"), etc.) to a device such as computing device 301; attach to an electronic display device (e.g., using an HDMI port, a DisplayPort port, a Mini DisplayPort port,VGA ports, DVI ports, etc. are attached to a television or monitor); etc. The hardware devices of the measurement equipment 304 may include a power interface, a wired and/or wireless network interface, a graphical interface attached to a display, and/or a semiconductor integrated circuit device as described below configured to perform the functions described herein with respect to the measurement equipment 304.

In such embodiments, the measurement equipment 304 may include a semiconductor integrated circuit device (e.g., one or more chips, dies, or other discrete logic hardware) or the like, such as a field programmable gate array ("FPGA") or other programmable logic, firmware for the FPGA or other programmable logic, microcode for execution on a microcontroller, an application-specific integrated circuit ("ASIC"), a processor core, or the like. In one embodiment, measurement equipment 304 may be mounted on a printed circuit board having one or more electrical lines or connections (e.g., lines or connections to volatile memory, non-volatile storage media, network interfaces, peripherals, graphics/display interfaces, etc.). The hardware device may include one or more pins, pads, or other electrical connections configured to send and receive data (e.g., in communication with one or more electrical lines of a printed circuit board, etc.), as well as one or more hardware circuits and/or other circuits configured to perform multiple functions of the measurement equipment 304.

In certain embodiments, the semiconductor integrated circuit device or other hardware device of the measurement equipment 304 includes and/or is communicatively coupled to one or more volatile memory media, which may include, but is not limited to, random access memory ("RAM"), dynamic RAM ("DRAM"), cache, and the like. In one embodiment, the semiconductor integrated circuit device or other hardware device of the measurement equipment 304 includes and/or is communicatively coupled to one or more non-volatile memory media, which may include, but is not limited to: NAND flash memory, NOR flash memory, nano random access memory (nano RAM or "NRAM"), nano-crystalline wire based memory, silicon oxide sub-10 nanometer process memory, graphene memory, silicon-oxide-nitride-oxide-silicon ("SONOS"), resistive RAM ("RRAM"), programmable metallization cell ("PMC"), conductive bridging RAM ("CBRAM"), magnetoresistive RAM ("MRAM"), dynamic RAM ("DRAM"), phase change RAM ("PRAM" or "PCM"), magnetic storage media (e.g., hard disk, tape), optical storage media, and the like.

In one embodiment, the measurement equipment 304 interfaces with the diagnostic equipment 104, is communicatively coupled to the diagnostic equipment 104, and the like, to generate and apply current to the electrodes 303 (individually, all together, for a particular area, and the like) in the garment 302. In one embodiment, the interrogation electrode of probe 160 may be applied to the patient's body, such as on garment 302, and various electrical impedance measurements may be read, detected, acquired, etc. from various reference electrodes 303 on garment 302.

In some embodiments, the ML module 206 generates recommendations, suggestions, indications, etc. to select a reference electrode on the garment 302 for measuring electrical impedance. For example, the ML module 206 may receive the current location of the reference electrode 303 being used and its measured electrical impedance, and based on the measured electrical impedance, the disease being diagnosed, etc., the ML module 206 may calculate, generate, determine, etc., the amount of current or pressure to apply, the reference electrode 303 to use on the garment 302, etc., based on the trained ML model.

Fig. 4 depicts a schematic flow chart of one embodiment of a method 400 of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, the method 400 begins and uses an interrogation electrode of a probe to non-invasively apply 402 electrical current to tissue of a patient's body. The probe may be configured to measure an electrical impedance of tissue between the interrogation electrode and the reference electrode.

In further embodiments, the method 400 measures 404 electrical impedance of patient body tissue between an interrogation electrode and a reference electrode of the probe. In one embodiment, the method 400 detects 406 the presence of malignancy in the patient's body tissue by inputting the measured electrical impedance of the tissue into machine learning. Machine learning may be trained based on patient data associated with the type of disease being diagnosed, and method 400 ends. In some embodiments, the current module 202, the measurement module 204, and/or the ML module 206 perform the steps of the method 400.

Fig. 5 depicts one embodiment of an apparatus 500 for non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, the apparatus 500 includes an embodiment of the diagnostic apparatus 104. In one embodiment, the diagnostic equipment 104 includes one or more of a current module 202, a measurement module 204, an ML module 206, and a monitoring module 208, which may be substantially similar to the current module 202, the measurement module 204, the ML module 206, and the monitoring module 208 described above with reference to fig. 2. In further embodiments, the diagnostic equipment 104 includes instances of the adjustment module 502 and/or the temperature module 504. In some embodiments, the probe system 210 and the electrode garment 212 are communicatively coupled to the diagnostic equipment 104.

In one embodiment, the adjustment module 502 is configured to adjust the position of the probe on the patient's body according to feedback based on the measured tissue electrical impedance. For example, after applying a current to a location on the patient's body using the interrogation electrode of the probe and measuring the electrical impedance, the adjustment module 502 may provide feedback indicating whether the measurement is a good or bad reading for the tissue being examined and/or for the condition being diagnosed.

In one embodiment, the adjustment module 502 provides feedback to a user (e.g., a probe operator) that includes instructions for: the probe is moved to different positions (e.g., up, down, left, right between fingers 1 and 2 to position FML-8aR, etc.) to obtain different impedance measurements, the angle of the probe is adjusted (e.g., tilted more toward the patient, placed at a 45 degree angle, etc.), the pressure applied to the skin surface is adjusted (e.g., increased or decreased by an amount of pressure), the amount of current applied by the probe is adjusted (e.g., increased or decreased by an amount of voltage or current), etc.

In some embodiments, the adjustment module 502 provides feedback in real-time as the probe is moved around the user's skin surface and the current is applied to the patient's body using the probe. For example, the adjustment module 502 may provide a visual representation of the body part in which the probe is located and may display the probe or a graphical representation of the probe that is moving around the body part in real time on a display. The feedback may also display text instructions, videos, animations, etc. for adjusting the probe, the position of the probe, the settings of the probe, etc. to assist the user in determining the optimal location and/or settings for the disease or tissue of interest.

The adjustment module 502 may show a thermal map (hetmap) of the surrounding area of the body where the probe is located on a visual representation of the body, showing the different electrical impedances being measured, and showing other locations to which these different impedances are known to be located. For example, if the user is looking for a body part on the patient's hand having a low impedance, such as a lymphatic channel, the heat map may use, for example, a color gradient from red to green indicating a high impedance (red) region to a low impedance (green) region for the current impedance measurement and a known or pre-calculated predefined impedance (e.g., from previous measurements, from other patients, etc.).

Thus, the feedback may include visual feedback and/or auditory feedback as described above. The audible feedback may include voice commands, instructions, indications, tones (e.g., sounds indicating good measurements, positions, or angles, as opposed to bad measurements, positions, or angles), etc. for moving or adjusting the probe to place the probe in an optimal position to measure electrical impedance based on the type of tissue or disease the user wants to analyze. For example, the optimal location for locating a lymphatic channel may be different from the optimal location for locating a nodule or tumor in a lung or breast.

In one embodiment, the adjustment module 502 provides feedback that the probe is not generating usable data (e.g., does not obtain accurate, correct, defined, consistent, etc. electrical impedance measurements or readings). In such embodiments, adjustment module 502 may provide a visual or audible message that the probe did not generate the available data, and may provide visual and/or audible instructions or indications for adjusting the position, angle, pressure, voltage, current, and/or other settings of the probe to generate the available data.

In certain embodiments, the adjustment module 502 uses machine learning or other artificial intelligence to estimate or determine the optimal position, angle, pressure, current, or other setting of the probe to measure the patient's body based on the disease and/or tissue being analyzed using historical and current electrical impedance measurements. For example, the current probe settings and impedance measurements, diseases of interest, tissue types, etc. may be input into a machine learning model that is trained using historical probe settings, impedance measurements, etc. for diseases/tissues of interest to determine, calculate, predict, etc. the optimal settings/locations of the probe on the patient's body.

In one example embodiment, the diagnostic equipment 104 including the adjustment module 502 measures the lymphatic system by utilizing dielectric measurements on the skin surface. Dielectric measurements can provide valuable information by detecting diseases that occur within the body that were not detected and frequently occurred prior to symptoms. When disease is present, the lymphatic system acts as a network of tissues and organs, helping to remove toxins, waste and other unwanted materials from the body. The main function of the lymphatic system is to transport lymph throughout the body, which is a fluid containing anti-infective leukocytes. The lymphatic system also collects any cancer cells, if present. These lymph fluids then drain into the lymphatic vessels.

The diagnostic equipment 104 is configured to locate lymphatic system channels on the skin surface and also obtain biological conductivity measurements related to the disease. The human body is very complex, consisting of 11 systems including lymphoid muscles, bones, nerves, circulatory systems, etc. Access to the lymphatic system under the skin for bioelectrical measurements is invasive and locating lymphatic system channels on the skin surface is extremely challenging, as the different body shapes make it difficult to perform a uniform measurement method based on anatomical landmarks of the body.

For example, cancer is a life-threatening disease that is difficult to diagnose because it may exist in the body without symptomatic manifestation. However, if the cancer is found at an early stage, it can be treated, and an individual may be likely to cure the cancer. Conventional systems for measuring lymphatic systems have a number of drawbacks-performing invasive measurements under the skin, difficulty in locating lymphatic channels/vessels under the skin, difficulty in obtaining reliable and repeatable lymphatic system measurements that can provide valuable diagnostic information, and so forth.

The diagnostic equipment 104 described above, and more particularly the adjustment module 502, provides a solution to these drawbacks by non-invasively measuring the lymphatic system on the skin surface by identifying the correct location and angle on the skin surface, thereby performing the measurement by identifying the location of least resistance (in ohms) and by providing visual feedback and operator feedback as the operator scans the target area. In addition, the probe motor relieves the operator of the pressure and ceases inconsistent measurements. The diagnostic equipment 104 performs a plurality of measurements and identifies inaccurate measurement results based on average and/or outlier identifier techniques.

Fig. 6A and 6B illustrate an embodiment of visual feedback provided by the adjustment module 502 on a display. In one example embodiment, during operation, the adjustment module 502 displays an anatomical schematic on a display to guide an operator to a correct anatomical location 604 on the subject 602. For example, to locate a lymphatic channel on a patient's hand, the operator first follows a screen prompt on the display that provides a visual display with anatomical references in the description for placement of the interrogation and/or reference electrodes at the following locations:

FML-8aR

point location-this point is located between the radius on the ulnar side of the extensor longus tendon and the navicular bone.

Electrode cable position-left hand

FML-8bR

Point location-the point is located distal to the diaphysis of the proximal phalanx on the radial side of the thumb. It is measured at a 45 degree angle with the probe pointing distally.

Electrode cable position-left hand

In another example shown in fig. 6B, to locate a lymphatic channel on a patient's chest, the operator first follows a screen prompt on the display that provides a visual display with anatomical references in the description for placement of interrogation and/or reference electrodes at the following locations:

FML-1aTR

Point location-this point is located on the second rib, approximately 2 1/2 thumb wide laterally from the midline of the sternum or the depression point.

Electrode cable position-upper right back

FML-1bTR

Point location-this point is located in the second intercostal space on the line between the lateral insertion of the sternocleidomastoid muscle and the nipple. With about 3-3 1/2 thumbs wide from the midline.

Electrode cable position-upper right back

FML-1cTR

Point location-this point is located in the third rib space, approximately 3 1/2 thumb wide laterally from the chest middle.

Electrode cable position-upper right back

FML-2aTR

Point location-this point is located in a depression on the lower collarbone edge, 2 thumbs wide laterally from the midline. The 2 thumb wide line is located midway between the midline and the nipple line.

Electrode cable position-lower right back

FML-2aR

Point location-this point is located in the depression in the lower collarbone margin, 2 thumbs wide laterally from the midline. The 2 thumb wide line is located midway between the midline and the nipple line.

Electrode cable position-left hand

FML-2bR

Point location-this point is located on the chest side, in the first intercostal space, 6 thumbs wide laterally from the midline, 1 thumb wide lower than FML-2 cR.

Electrode cable position-left hand

To ensure that the angle and position are correct, the device operator can press a button on the probe to activate the lymphatic channel locator mode. The operator applies moisture to the measurement target area and then slides the probe tip back and forth, such as interrogating an electrode, listens for an audible tone, or may follow a visual display (e.g., a heat map) that identifies when the probe tip is in the correct position and/or angle with the least amount of resistance/impedance (e.g., the least amount of measured ohms).

The following table illustrates the importance of identifying the correct location. The table shows measurements at four different measurement locations, each showing the minimum and maximum ohms obtained for a particular location. The adjustment module 502 allows the operator to identify the exact location where the measured impedance has the least amount of resistance (column 2). This function is important because the resistance can vary depending on the operator placement of the probe position and probe tip angle, for example by about 224%. The adjustment module 502 may provide feedback to the operator in real-time (either through audible tones or visually on a display) based on the information in the table, for example, as raw data, as a heat map, etc.

Table 1 shows data of impedance differences at four measurement positions.

Diagnostic equipment 104 obtains a bioelectrical conductance measurement by passing a current of, for example, less than 25 microamps between a reference electrode placed on the subject's body (e.g., on the back or hand of the subject) and a probe (interrogation electrode) that may be placed on the chest, shoulder and/or arm of the subject. The adjustment module 502 provides real-time visual and audible feedback to the operator for quality assurance purposes, e.g., for moving the probe to different positions, for adjusting the angle, pressure, current, etc. of the probe, etc. The conductance measurement profile may be visually displayed on a display.

The diagnostic equipment 104 may sample the conductance value 25 times per second and monitor the probe pressure to obtain accurate and consistent measurements. Diagnostic equipment 104 monitors and controls probe pressure during measurement. After the measurement session is completed, the diagnostic equipment 104 stores the data for processing by classifier algorithms, machine learning algorithms, and the like. The device classifier algorithm combines the measured data into a composite risk score that corresponds to a high or low likelihood phase of malignancy based on a predetermined score cutoff or threshold. After processing the data using the algorithm, a report is generated indicating the health status of the patient.

In a practical exemplary embodiment, the use of the probe is non-invasive, not exposed to radiation, and may typically be accomplished within 20-40 minutes. When a subject (typically wearing hospital apparel) is in a seated position, an operator enters relevant user and patient information. The operator opens the disposable test kit. The disposable sweat electrodes are applied to specific locations on the back and hands of the subject according to the description in the operator manual and demonstration during operator training. Following the on-screen prompts provided by the adjustment module 502, the operator uses the probe to collect measurement data from body areas (e.g., chest, shoulders, and arms of the subject) associated with the disease (e.g., cancer) or tissue being analyzed.

In use, the adjustment module 502 provides written descriptions, images, prompts, instructions, etc. on the screen in real-time for each measurement point as the test is performed. The operator observes real-time monitoring, verification and logging for each measurement. If a re-measurement is required, the adjustment module 502 provides a visual and/or audible notification that it has not received the available data, whereby the operator can re-measure. The diagnostic equipment 104 stores data for further use and analysis, e.g., as part of a machine learning algorithm, engine, model, training, etc.

Fig. 7 depicts a schematic flow chart diagram of one embodiment of a method 700 of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, the method 700 begins and an interrogation electrode of a probe used at a location on a patient's body applies 702 a current to tissue of the patient to locate and measure electrical impedance of the tissue between the interrogation electrode and a reference electrode.

In further embodiments, method 700 measures 704 electrical impedance of tissue between an interrogation electrode and a reference electrode of the probe. In one embodiment, the method 700 adjusts 706 the position of the probe on the patient's body in accordance with feedback based on the measured tissue electrical impedance, and the method 700 ends. In some embodiments, the current module 202, the measurement module 204, and/or the adjustment module 502 perform the steps of the method 700.

Fig. 8 depicts one embodiment of an impedance measurement device 800 according to the subject matter described herein. In one embodiment, the device 800 includes an operator-held probe housing 802 through which a linear voice coil motor 804 controls the position and force applied by an interrogation electrode tip 806. In one embodiment, interrogation electrode tip 806 is centrally located, coaxial with probe tip 810, and may be selectively coupled to probe tip 810, for example, using a threaded connection, using a magnet, using a snap fit, using a friction fit, and so forth. In one embodiment, interrogation electrode tip 806 is disposable. In such an embodiment, the interrogation electrode tip 806 is surrounded by an annular shield and is configured to extend from the probe tip 810 to apply a force to a surface of the patient's body.

In one embodiment, interrogation electrode tip 806 has a textured surface comprising a plurality of protrusions 808. In one embodiment, the protrusions 808 may be any size (e.g., length and/or width) and shape. In one embodiment, the protrusions 808 have a hexagonal shape.

In one embodiment, interrogation electrode tip 806 has a disk-like shape, as shown in FIGS. 9A-9D, with a substantially smooth surface. In some embodiments, the interrogation electrode has a diameter in the range of 4.0mm to 5.0 mm. In certain embodiments, the diameter of the protrusions 808 is in the range of 0.5mm and 0.6 mm. In one embodiment, interrogation electrode tip 806 is made of brass, silver-silver chloride, gold, stainless steel, and/or the like.

In one embodiment, the interrogation electrode tip 806 may be heated or cooled to a predetermined temperature corresponding to a predefined level of conductance as described below. In such embodiments, the probe tip 810 may include heating elements and/or cooling elements for controlling the temperature of the interrogation electrode tip 806.

In one embodiment, the impedance measurement device 800 is part of a probe system (e.g., probe system 152 described above with reference to fig. 1C). Accordingly, the impedance measurement device 800 may be connected to, or otherwise in communication with, a reference electrode and computer component (e.g., the reference electrode 162 and computer component 150 described above with reference to fig. 1C).

In an exemplary embodiment, the impedance measurement device 800, such as a handheld probe, has a total length of about 18.5cm, a maximum diameter of 4cm, and a weight of 280g. In one embodiment, inside the probe handle are a conductive shaft driven by a voice coil linear motor and a cooling fan. In one embodiment, the shaft is threaded and is attached by an operator to a textured disposable tip, such as interrogation electrode tip 806. During operation, in one embodiment, device 800 applies a nominal force of 5.5N to interrogation electrode tip 806 on the skin. In one embodiment, the operator pushes the interrogation electrode tip 806 against the skin with a force that must exceed the probe force. In one embodiment, the interrogation electrode tip 806 is surrounded by a small annular shield. The operator pushes and holds the outer annular tip flush with the skin while the coaxially positioned electrodes automatically extend and increase the force to a set level.

In one embodiment, the system measures the resistance between the location on the body where the operator places the interrogation electrode tip 806 and a hand-held brass electrode (e.g., reference electrode 162 depicted in FIG. 1C). In one embodiment, the device 800 begins recording once the interrogation electrode tip 806 contacts the skin. Meanwhile, in one embodiment, the voice coil motor algorithm is activated or triggered and the force of the interrogation electrode tip 806 increases in a controlled rising manner to a control level of 5.5N. Device 800 monitors the signal resistance and holds interrogation electrode tip 806 in place for a controlled period of time based on the stability of the signal. For example, this time typically takes 7 to 10 seconds. In one embodiment, at the end of the signal acquisition cycle, the probe tip motor is deactivated and signal recording is terminated, and then the operator moves the interrogation electrode tip 806 to measure the next predefined anatomical location, moisturize the skin as detailed in the protocol, and make the next measurement.

Referring to fig. 5, in one embodiment, the current module 202 is configured to apply a current to at least one interrogation electrode tip 806 placed on a surface of a human body within a sapet area of a human breast. As used herein, the sapet area of a human breast includes the area of the breast that includes the lymphatic network in the nipple areola.

In one embodiment, the measurement module 204 is configured to measure the electrical impedance of the tissue of the person between at least one interrogation electrode 160 and a reference electrode 162 placed within the sapet area of the person's breast.

In one embodiment, the measurement module 204 is further configured to compare the measured electrical impedance with previously captured electrical impedance measurements of the corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person.

In one embodiment, the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within the sapet area from a different person, e.g., data from other patients without a tumor, with a benign tumor, and/or with a malignant tumor. In further embodiments, the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within a sapet area from another breast of the person that may be tumor-free, have benign tumors, and/or have malignant tumors.

In one embodiment, ML module 206 is configured to provide the measured electrical impedance to a machine learning model that is trained based on previously measured electrical impedance, risk factors, and/or patient data of other people who have been diagnosed with benign tumors and malignant tumors to calculate a risk score for the person. In one embodiment, the risk score may include a grade, value, ratio, percentage, probability, likelihood, etc. that the patient has a malignancy, benign tumor, or no tumor within a measurement region of the patient's body (e.g., a sapet region).

In one embodiment, the ML module 206 is configured to receive mammogram information associated with a human breast. In such embodiments, the mammogram information is analyzed, for example by the ML module 206, to determine whether the mammogram information indicates the presence of a nodule within a person's breast. If so, in one embodiment, the ML module 206 inputs mammogram information into a machine learning model to further calculate a person's risk score based on the measured electrical impedance.

In one embodiment, the ML module 206 is configured to periodically update the person's risk score based on new, updated, revised, changed, adjusted, modified, etc. electrical impedance measurements and changes in risk factors and/or patient data (as described above), such as the patient starting or stopping smoking or drinking, determining that the patient's family has a history of breast cancer, that the patient's age or weight has changed, etc. The ML module 206 may provide or input new information, which in one embodiment may be input or provided to a machine learning model to determine the effectiveness of the treatment in post-treatment monitoring, e.g., to determine whether a patient's tumor grows or shrinks in response to a drug, dose, duration, etc., to determine whether a patient's symptoms decrease or increase in response to a drug, dose, duration, etc., and so forth.

In one embodiment, temperature module 504 is configured to heat interrogation electrode tip 806 to a temperature corresponding to a predefined conductance. In such an embodiment, the temperature module 504 adjusts the temperature of the interrogation electrode tip 806 according to the controlled thermal profile until a steady current is detected between the at least one interrogation electrode and the reference electrode. The controlled thermal distribution may be, for example, a mapping of temperature to current and/or electrical impedance of a particular region of the patient's body (e.g., a sapet region). In such embodiments, the measurement module 204 is configured to capture electrical impedance measurements at various temperatures of the controlled thermal distribution. In one embodiment, temperature module 504 heats interrogation electrode tip 806 to the body temperature of the patient.

Fig. 9A-9D depict one embodiment of an interrogation electrode tip 806 in accordance with the subject matter described herein. In the depicted embodiment, the interrogation electrode tip 806 is disk-shaped or circular in shape with a smooth surface. However, in some embodiments, the surface is a textured surface having a plurality of protrusions 808, as described above. In certain embodiments, interrogation electrode tip 806 is selectively connected to probe tip 810, for example, using a screw fit, snap fit, friction fit, clamp fit, or the like. In this way, different disposable tips may be used, different tip types (e.g., different materials, shapes, textures, etc.), and so forth.

Fig. 10 is a schematic flow chart diagram illustrating one embodiment of a method 1000 of non-invasive medical diagnosis using electrical impedance metrics and clinical predictors. In one embodiment, the method 1000 begins and applies 1002 a current to at least one interrogation electrode placed on a surface of a human body within a sapet area of a human breast.

In further embodiments, the method 1000 measures 1004 electrical impedance of tissue of the person between at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast. In some embodiments, method 1000 compares 1006 the measured electrical impedance with previously captured electrical impedance measurements of the corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person, and method 1000 ends. In one embodiment, the current module 202 and the measurement module 204 perform the steps of the method 1000.

Means for applying an electrical current to at least one interrogation electrode placed on a surface of a human body within the sapet area of a human breast may include the diagnostic module 104, the current module 202, the impedance measurement device 800, the electrode probe, and the like.

The means for measuring the electrical impedance of the tissue of the person between the at least one interrogation electrode and the reference electrode placed within the sapet area of the person's breast may comprise a diagnostic module 104, a measurement module 204, an impedance measurement device 800, a computing device, a processor, a memory, or the like.

Means for comparing the measured electrical impedance to previously captured electrical impedance measurements of the corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person may include the diagnostic module 104, the measurement module 204, the impedance measurement device 800, the computing device, the processor, the memory, or the like.

The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. A method, comprising:

applying an electrical current to at least one interrogation electrode placed on a surface of a human body within a sapet area of a human breast;

measuring an electrical impedance of tissue of the person between the at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast; and

the measured electrical impedance is compared to previously captured electrical impedance measurements of the corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person.

2. The method of claim 1, wherein the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within a sapet area from a different person.

3. The method of claim 1, wherein the previously captured electrical impedance measurements of the corresponding tissue include electrical impedance measurements of tissue within a sapet region from another breast of the person.

4. The method of claim 1, further comprising providing the measured electrical impedance to a machine learning model to calculate a risk score for the person, the machine learning model being trained based on previously measured electrical impedance, risk factors, and patient data of other persons who have been diagnosed with benign tumors and malignant tumors.

5. The method of claim 4, further comprising:

receiving mammogram information associated with the person's breast; and

in response to determining that the mammogram information indicates the presence of a nodule within the person's breast, the mammogram information is input into the machine learning to further calculate a risk score for the person based on the measured electrical impedance.

6. The method of claim 4, further comprising periodically updating the person's risk score based on updated electrical impedance measurements and changes in risk factors and patient data to determine the effectiveness of treatment in post-treatment monitoring.

7. The method of claim 1, further comprising heating the at least one interrogation electrode to a temperature corresponding to a predefined conductance.

8. The method of claim 7, further comprising adjusting the temperature of the at least one electrode according to a controlled thermal profile until a steady current is detected between the at least one interrogation electrode and the reference electrode.

9. The method of claim 8, further comprising capturing electrical impedance measurements at various temperatures of the controlled thermal distribution.

10. An apparatus, comprising:

at least one interrogation electrode;

a reference electrode;

a processor; and

a memory storing code executable by the processor to:

measuring an electrical impedance of tissue of the person between the at least one interrogation electrode and the reference electrode placed within a sapet area of the person's breast; and

11. The kit of claim 10, wherein the interrogation electrode is an electrode tip of an electrode probe.

12. The apparatus of claim 11, wherein the electrode tip has a disk shape and a substantially smooth surface.

13. The apparatus of claim 11, wherein the electrode tip comprises a textured surface, the textured surface of the brass electrode tip comprising a plurality of protrusions, each of the plurality of protrusions having a hexagonal shape.

14. The apparatus of claim 11, wherein the electrode tip is made of a material selected from the group consisting of: brass, silver-silver chloride, gold, and stainless steel.

15. The apparatus of claim 10, wherein the code is further executable by the processor to heat the at least one interrogation electrode to a temperature corresponding to a predefined conductance.

16. The apparatus of claim 15, wherein the code is further executable by the processor to adjust the temperature of the at least one electrode according to a controlled thermal profile until a stable current is detected between the at least one interrogation electrode and the reference electrode.

17. The apparatus of claim 15, wherein the code is further executable by the processor to capture electrical impedance measurements at various temperatures of the controlled thermal profile.

18. The apparatus of claim 10, wherein the previously captured electrical impedance measurements of the corresponding tissue comprise at least one of: an electrical impedance measurement of tissue in a region of a sapet plexus from a different person and an electrical impedance measurement of tissue in a region of a sapet plexus from another breast of the person.

19. The apparatus of claim 10, wherein the code is further executable by the processor to provide the measured electrical impedance to a machine learning model to calculate a risk score for the person, the machine learning model being trained based on previously measured electrical impedance, risk factors, and patient data of other persons who have been diagnosed with benign tumors and malignant tumors.

20. An apparatus, comprising:

means for applying an electrical current to at least one interrogation electrode placed on a surface of a human body within a sapet area of a human breast;

means for measuring electrical impedance of tissue of the person between the at least one interrogation electrode and a reference electrode placed within a sapet area of the person's breast; and

Means for comparing the measured electrical impedance with previously captured electrical impedance measurements of corresponding tissue to determine an indication of the presence of malignancy in the tissue of the person.