SYSTEM AND METHOD FOR AI-BASED 3D ASSESSMENT OF PULMONARY FUNCTIONS USING ELECTRICAL IMPEDANCE TOMOGRAPHY VEST

Abstract

The present disclosure relates to a system (300) for non-invasive lung imaging, the system includes a fabric structure defining a vest (200) configured to conform to body of one or more subjects. An array of electrodes (214) configured to be accommodated in predefined slots (218) on the fabric structure a controlling circuit (304) integrated within the fabric structure and configured to control excitation and switching of the array of electrodes, process a set of data acquired from the array of electrodes, transmit the processed data to a computing device. The computing device (306) is configured to generate two-dimensional (2D) or three-dimensional (3D) images of the lungs of the one or more subjects and analyze the generated images to classify corresponding subjects as healthy subjects and non-healthy subjects.

Specification

Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to Electrical Impedance Tomography (EIT) systems for non-invasive 3D imaging of air distribution inside the lungs and more specifically, relates to an IoT-based wireless system, method and an imaging biomarker to access the lung volume of the individuals.

BACKGROUND
[0002] Electrical Impedance Tomography (EIT) is an emerging non-invasive imaging modality that provides critical diagnostic information by assessing material properties, detecting cracks, and monitoring biological functions. This technique detects changes in electrical conductivity within a region of interest (ROI) by applying small electric currents, typically in the milliamperes range, and measuring voltage variations across a set of electrodes placed around the ROI.
[0003] A typical EIT system consists of three major components an array of electrodes, a data acquisition unit, and a post-processing unit. The electrically conducting electrodes are directly mounted on ROI. The electrodes, typically made of metals like copper, aluminum, or low-impedance material, are uniformly distributed around the ROI. Depending on the desired resolution and imaging requirements, the number of electrodes in the array may vary, with configurations of 16 or 32 electrodes being the most common, particularly in 3D imaging. When an alternating current (AC) is applied, the system detects slight changes in voltage at the remaining electrodes, allowing for the reconstruction of cross-sectional or volumetric images based on the impedance distribution within the ROI.
[0004] For non-destructive testing (NDT), EIT may play a role in detecting internal defects, material heterogeneities, and cracks in industrial components proportional to impedance distribution. The ROI in NDT applications is typically a material or structure that contains a conductive medium alongside the material being inspected. In clinical settings, EIT can be used for analyzing ventilation patterns. Mounted on patients' chests, the system can provide images of lung activity, capturing essential lung parameters. Functional Residual Capacity (FRC), Total Lung Capacity (TLC), Tidal Volume (TV), and other respiratory metrics are standard metrics. The EIT may especially be useful in diagnosing and managing conditions such as obstructive and restrictive lung diseases, where it offers a dynamic, non-invasive method to visualize regional lung function during various phases of breathing. Its application may be extended to critical care units, where EIT may provide real-time information about ventilation distribution, helping clinicians make informed decisions about patient management and treatment.
[0005] The probability of acceptance of a diagnostic system depends upon the flexibility and portability that it offers according to its intended environments. A wireless IoT-based system may offer a significant advancement by allowing real-time monitoring and data transmission without the constraints of wired connections. This wireless capability can be particularly beneficial in clinical settings where mobility is essential for continuous patient monitoring, enabling healthcare professionals to track lung function remotely or in dynamic conditions such as during patient movement. Similarly, a wireless EIT system in industrial applications can streamline non-destructive testing by allowing operators to monitor material integrity in real-time from remote locations. The integration of IoT can also enhance data acquisition and post-processing by enabling cloud-based storage and analysis, allowing for more efficient data management and advanced computational techniques such as AI-driven anomaly detection. This shift toward wireless, connected systems would significantly improve the scalability and usability of EIT technology across various fields.
[0006] Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that has gained prominence for its ability to visualize internal structures based on the electrical properties of tissues. The method involves placing electrodes around the body, applying small electrical currents, and measuring the resulting voltage differences to reconstruct an image of the tissue's impedance distribution. Various innovations have built upon this foundational concept, advancing its application in real-time imaging, as illustrated by several prior art documents.
[0007] A few examples of the system are recited in a patent WO1991019454A1 that outlines a pioneering method for real-time EIT imaging using a single array of surface electrodes arranged circumferentially around the body. This method uses constant current drivers and measures voltage differences between adjacent electrode pairs. Images are reconstructed via a non-iterative, back-projection algorithm. However, the limitation lies in its single-electrode array, which constrains spatial resolution and accuracy. While effective for basic imaging, this system lacks advanced wire management and IoT integration for modern healthcare applications, making it unsuitable for continuous remote monitoring.
[0008] Another example is recited in a patent JP2022149032A that introduces a system using a three-dimensional electrode array for EIT, applied specifically for gastrointestinal diagnostics. This design utilizes a Jacobian matrix to improve image accuracy and machine learning algorithms to estimate pH distributions in the stomach. While this system makes significant strides in abdominal imaging, it diverges from the needs of pulmonary function monitoring. Its application is limited to gastrointestinal health and does not offer comprehensive insights into lung function, particularly in the context of continuous remote monitoring through IoT technologies.
[0009] Another example is recited in a patent CN118218898B that presents a more advanced method of pulmonary EIT by integrating neural networks and mapping models for continuous lung monitoring. Although this invention provides a comprehensive monitoring system for pulmonary function, it is limited by its single-electrode belt design and lacks the real-time wireless data transfer capabilities necessary for seamless integration into IoT-based systems. Furthermore, the invention does not focus on enhancing spatial resolution with multi-electrode arrays, a crucial aspect for obtaining 3D volumetric images and more detailed lung function insights.
[0010] Another example is recited in a patent US7109933B2 that describes a wearable vest for continuous health monitoring, which features conductive sheets for data communication and sensors for monitoring vital signs. However, it is unrelated to EIT and does not address lung function monitoring. The vest is focused on integrating existing commercial sensors to assess parameters like temperature and heart rate, with no provision for advanced impedance-based imaging or IoT functionality.
[0011] Another example is recited in a patent CN218458103U that introduces an EIT-based monitoring system with wearable components that transmit health data to a cloud server. This system is not designed for pulmonary monitoring and lacks the capability to provide detailed lung function assessments. The EIT electrodes are arranged in belts rather than a vest, offering only 2D imaging. Additionally, the system does not address wire management complexities, a critical challenge when integrating multiple electrodes into wearable arrangement/garments for continuous use.
[0012] Similarly, CN211213147U and CN114847915A present EIT-based belts designed for impedance data collection, focusing on improving belt and electrode designs. However, these belts are limited in their application, offering only 2D imaging and lacking wireless IoT integration for remote monitoring. These designs also do not optimize electrode configurations based on individual user parameters such as BMI, which is vital for improving imaging accuracy in diverse populations.
[0013] Another example is recited in a patent KR2557846B1 that introduces a wearable EIT device using a series of active electrodes for 2D imaging, with a focus on improving signal-to-noise ratios. However, the device is not IoT-enabled, and the imaging is restricted to 2D, without any provision for 3D volumetric imaging of the lungs or real-time wireless data transfer. Finally, CN217365848U presents a wearable system for electrical impedance imaging, with a chest belt embedded in clothing. This system uses a Bluetooth module for signal transmission but is limited to 2D imaging and lacks IoT integration. The design does not incorporate mechanisms for optimizing electrode configurations based on individual physiological characteristics, limiting its ability to provide accurate lung function assessments. Further, some existing systems describe wearable vests for various health assessments using different types of sensors but lack EIT technology. Others focus on monitoring blood oxygen levels, ECG, and body temperature but do not feature wireless functionality or IoT integration.
[0014] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a system that integrates multiple electrode arrays, IoT functionality, enhanced wire management, and imaging biomarkers for lung function assessment.

OBJECTS OF THE PRESENT DISCLOSURE
[0015] An object of the present disclosure relates to Electrical Impedance Tomography (EIT) systems for non-invasive 3D imaging of air distribution inside the lungs, and more specifically, relates to a method and an imaging biomarker to access the lung volume of the individuals.
[0016] Another object of the present disclosure is to provide a wireless, IoT-based EIT system that is compact, portable, and cost-effective, offering an accessible and efficient healthcare diagnostic in the field of pulmonology.
[0017] Another object of the present disclosure is to provide a system to calculate lung functions and aims to provide a non-invasive, easy-to-perform, efficient alternative for assessing the critical respiratory parameters traditionally requiring more complex and less accessible methods.
[0018] Another object of the present disclosure is to provide a system that correlates the developed system with the gold standards.
[0019] Yet another object of the present disclosure is to offer an imaging biomarker that enables clinicians to determine an individual's health status without the need for conventional tests or harmful radiation-based procedures.

SUMMARY
[0020] The present disclosure relates to electrical impedance tomography (EIT) systems for non-invasive 3D imaging of air distribution inside the lungs, and more specifically, relates to a method and an imaging biomarker to access the lung volume of the individuals. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing system, by providing an Electrical Impedance Tomography (EIT) system for non-invasive 3D imaging of the lungs. The present disclosure in particular is related to the development of an IoT-based wireless, wearable EIT vest with multiple electrode arrays for 3D assessment of the lung function. The optimal number of arrays for a particular person is provided using the center limiting theorem according to the BMI. The vest is designed with a multi-layered construction, incorporating Faraday fabric i.e., anti-static fabric to shield against external signals and minimize interference, effectively reducing external noise. Its ergonomic design allows for a universal fit, adapting to different torso sizes, ensuring comfort and ease of use for all body types. The electronics have been enhanced to support simultaneous switching of multiple channels, optimizing performance. Additionally, a frequency-based imaging biomarker is integrated into the system, providing a methodology for assessing lung function.
[0021] A non-invasive, easily executable, and efficient alternative for assessing critical respiratory parameters that traditionally require more complex and less accessible methods. A method for calculating lung functions that were previously measurable only through plethysmography. A system for correlating the calculated lung functions with established gold standards and an imaging biomarker enabling clinicians to evaluate an individual's health status without the need for conventional tests or radiation-based procedures. Additionally, the system incorporates an artificial intelligence-driven process for image segmentation and feature extraction from reconstructed images, and utilizes the center limiting theorem to determine the optimal number of electrode arrays based on the individual's body mass index (BMI).
[0022] The present disclosure relates to a system for non-invasive lung imaging that includes a fabric structure defining a vest configured to conform to the body of one or more subjects, wherein the fabric structure incorporates one or more pouches to hold electronic components e.g., PCB. An array of electrodes is accommodated in predefined slots on the fabric structure, with the arrangement of the array determined based on a pre-calibrated relationship between the body mass index (BMI) of the one or more subjects and the optimal number of the array of electrodes. Thus, the system provides a wireless, IoT-based EIT system that is compact, portable, and cost-effective, offering an accessible and efficient healthcare diagnostic in the field of pulmonology.
[0023] A controlling circuit is integrated within the fabric structure, the array of electrodes and is configured to control the excitation of the array of electrodes and the switching of the array by selectively applying electrical signals to the array in a predefined sequence. The controlling circuit processes a set of data acquired from the array of electrodes, which pertains to electrical impedance measurements of the lungs of the one or more subjects, and transmits the processed data to a computing device operatively coupled to the controlling circuit. The computing device generates two-dimensional (2D) or three-dimensional (3D) images of the lungs based on the processed data, thereby providing visualization of lung function. The generated images are then analyzed to classify the corresponding subjects into categories based on lung function parameters, including total lung capacity, functional residual capacity, inspiratory capacity, and residual volume, enabling differentiation between healthy and non-healthy subjects. Thus, the system calculates lung functions and aims to provide a non-invasive, easy-to-perform, efficient alternative for assessing the critical respiratory parameters traditionally requiring more complex and less accessible methods.
[0024] The vest includes a multi-layer structure, wherein an external layer provides strength to maintain structural integrity, a middle layer made of electromagnetic shielding material shields the array of electrodes from external interference, and an internal layer integrates the array of electrodes, with the vest made of flexible material that conforms to the shape of the subjects. Each electrode in the array is of low impedance with a diameter of 2 mm and is mounted using detachable silicon rubber holders, allowing for repositioning to accommodate the anatomy and physiology of the subjects. The array is positioned with slots spaced 3 mm apart, maintaining a minimum distance of 5 mm between adjacent arrays to enable flexible distribution across the internal surface of the vest.
[0025] The controlling circuit features a wireless communication module that operates with Message Queuing Telemetry Transport (MQTT) protocol for secure wireless data transfer via the Internet of Things (IoT). The communication among the array of electrodes is facilitated through a single analog-to-digital converter (ADC). The controlling circuit is further configured to utilize a pre-calibrated relation between the height and weight of the subjects to activate the optimal number of electrodes, obtain the pre-calibrated relation of the optimal number of electrodes corresponding to the BMI through curve fitting, determine the optimal number of electrodes for subjects with known BMI using the central limit theorem, and recommend the optimal number based on a Gaussian fit of probability density estimated for the electrodes in the vest.
[0026] Additionally, the controlling circuit measures the set of data while subjects wear the vest and breathe naturally, directing them to breathe according to a controlled breath pattern. The computing device is configured to acquire the 2D or 3D images pertaining to cross sections and volumetric electrical impedance maps of the lungs, automatically segment air distribution within the lungs from the 3D images, utilize the segmented 3D images to estimate lung volume by multiplying lung area by lung length, analyze the frequency of specific pixel values within segmented portions of the images, declare certain pixel frequencies as a preliminary imaging biomarker, and establish specific frequency ranges to classify subjects as healthy or unhealthy. Thus, the system offers an imaging biomarker that enables clinicians to determine an individual's health status without the need for conventional tests or harmful radiation-based procedures.
[0027] The system also obtains pulmonary information related to lung function and volume, establishes a linear correlation of the obtained information with existing gold standard measurements, achieving a correlation of 93% for Functional Residual Capacity (FRC), 70% for Inspiratory Capacity (IC), 82% for Total Lung Capacity (TLC), and 89% for Residual Volume (RV). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) are calculated, yielding values of 86%, 78%, 86%, and 80%, respectively.
[0028] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.


BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0030] FIG. 1 illustrates a detailed flow chart for the wireless communication and data collection process utilizing Internet of Things (IoT) technology for an Electrical Impedance Tomography (EIT) system, in accordance with an embodiment of the present disclosure.
[0031] FIG. 2A illustrates an exemplary front section of the EIT vest, featuring multi-electrode arrays integrated into the vest, in accordance with an embodiment of the present disclosure.
[0032] FIG. 2B illustrates a back section of the EIT vest, in accordance with an embodiment of the present disclosure.
[0033] FIG. 2C illustrates an overview of the various layers of the vest, in accordance with an embodiment of the present disclosure.
[0034] FIG. 2D illustrates a perspective view of the various layers of the vest, in accordance with an embodiment of the present disclosure.
[0035] FIG. 2E illustrates the side view of the vest, showcasing the specially designed pouch where the associated electronics are securely housed, with a protective flap, in accordance with an embodiment of the present disclosure.
[0036] FIG. 2F illustrates the design of the electrode along with the silicon stopper, which is used to securely fix the electrodes in place within the vest, in accordance with an embodiment of the present disclosure.
[0037] FIG. 3 illustrates a circuit diagram of the EIT assembly, showcasing the integration of Wi-Fi modules with the EIT circuit for seamless communication, in accordance with an embodiment of the present disclosure.
[0038] FIG. 4 illustrates an exemplary view of circuit diagram of EIT vest, in accordance with an embodiment of the present disclosure.
[0039] FIG. 5 illustrates the reconstruction of the EIT data for four different breathing schemes: (a) normal inhale, (b) normal exhale, (c) forceful inhale, and (d) forceful exhale, in accordance with an embodiment of the present disclosure.
[0040] FIG. 6 illustrates the imaging biomarker based on the frequency of the pixel value, in accordance with an embodiment of the present disclosure.
[0041] FIG. 7 illustrates an exemplary flow chart of a method for non-invasive lung imaging, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature "may", "can", "could", or "might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0043] As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0044] The present disclosure pertains to an Electrical Impedance Tomography (EIT) system that diverges significantly from the existing system, particularly in its electrode arrangement, vest design, application focus, and assessment processes. The system described herein introduces multiple arrays of electrodes tailored specifically for pulmonary imaging, with electrode arrangements optimized based on the individual's body mass index (BMI). This enhancement in electrode configuration results in improved spatial resolution and imaging accuracy, making the system particularly suitable for continuous lung function monitoring.
[0045] In the disclosed system the use of multiple electrode arrays is disclosed, which departs from the single electrode belt approach commonly seen in prior systems. This advancement significantly enhances imaging precision and is critical for obtaining detailed pulmonary diagnostics. Moreover, the proposed system integrates Internet of Things (IoT) technology, enabling real-time, remote monitoring of lung function. This feature is absent in previous systems, which limits their capacity for continuous patient monitoring and real-time data accessibility.
[0046] The enhanced wire management system simplifies and reduces the complexity associated with electrode connections. This streamlining results in a more user-friendly, wireless setup, making the system more practical and efficient. Additionally, the integration of Faraday (anti-static) fabric further enhances the system's performance by reducing interference. The developed system introduces the use of mean frequency analysis of pixel intensities in EIT images as a biomarker for assessing lung function. This method provides a unique approach to pulmonary diagnostics that is not addressed in the existing system. The existing system does not include specific processes for pulmonary function evaluation nor utilize imaging biomarkers in this context, highlighting the distinctiveness of the present invention.
[0047] Additionally, EIT belt designs in prior patents rely on metallic electrodes and clamping mechanisms and are limited to single electrode arrays, restricting their applicability for advanced pulmonary diagnostics. The present disclosure overcomes these limitations by offering a system that integrates multiple electrode arrays, IoT functionality, enhanced wire management, and innovative imaging biomarkers for lung function assessment. This combination of features provides a comprehensive, non-invasive solution for continuous pulmonary function monitoring, representing a significant advancement over the prior art.
[0048] Electrical Impedance Tomography (EIT) systems for non-invasive three-dimensional (3D) imaging of air distribution within the lungs, and more particularly, to a wireless, Internet of Things (IoT)-based EIT system that is compact, portable, and cost-effective, providing an innovative solution for accessible and efficient healthcare diagnostics in pulmonology. The present disclosure provides a system offering a non-invasive, easy-to-perform, and efficient alternative for assessing critical respiratory parameters that traditionally require complex and less accessible methods. The present disclosure further provides a method for calculating lung functions, previously measurable only through plethysmography, and correlates the developed system with existing gold standards. Additionally, the present disclosure provides an imaging biomarker enabling clinicians to assess an individual's health status without the need for conventional tests or radiation-based procedures. Moreover, an artificial intelligence (AI)-driven step is integrated for image segmentation and feature extraction from the reconstructed images, and the optimal number of arrays for an individual is determined based on the Body Mass Index (BMI) using the central limit theorem. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0049] The advantages achieved by the system of the present disclosure can be clear from the embodiments provided herein. The system enables non-invasive lung imaging, significantly reducing the need for traditional diagnostic methods that may involve radiation exposure. The system is portable and cost-effective, making advanced healthcare diagnostics accessible to a wider population, including those in remote or underserved areas. Additionally, the system with automated processes that streamline data collection and analysis, facilitating quicker interpretation of lung function and health status. The system employs artificial intelligence for image processing and feature extraction, enhancing the accuracy of lung disease classification and improving clinical decision-making. Moreover, the system intelligently adjusts the number of active electrode arrays based on the user's body mass index (BMI), optimizing the performance and accuracy of the imaging without manual intervention. The system integrates real-time data transfer and monitoring via IoT, allowing healthcare professionals to access patient information remotely and make timely interventions. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0050] FIG. 1 illustrates a detailed flow chart for the wireless communication and data collection process utilizing Internet of Things (IoT) technology for an Electrical Impedance Tomography (EIT) system, in accordance with an embodiment of the present disclosure.
[0051] Referring to FIG. 1, method 100 involves, at block 102, ensuring the physical setup is properly connected. At block 104, the method includes starting the local broker and flow-editor to ensure the proper establishment of the flow. At block 106, the setup is powered on, and at block 108, the microcontroller unit i.e., ESP32 board is checked for Wireless Fidelity (Wi-Fi) and Message Queuing Telemetry Transport (MQTT) connection. If the ESP32 board is not connected, at block 110, a retry is performed to establish the connection. If connected, at block 112, the Wi-Fi and MQTT connection status is sent to esp32/status. At block 114, the method checks if the START! message is received from the flow-editor. If the message is not received, at block 116, a resend process is initiated. Upon successful receipt of the message, at block 118, the data is received from the Electrical Impedance Tomography (EIT) system. At block 120, the data is saved, completing the method.
[0052] The system 300 (as depicted in FIG. 3 and explained in detail below) for wireless communication begins by verifying that the physical setup is properly connected. Once the connection is confirmed, the local broker i.e., host computer is started to ensure the establishment of proper flow. The EIT setup is powered on and connected to the Wi-Fi network via the MQTT protocol. The status of the connection is then sent to the Wi-Fi module. Upon receiving the "start" message from the flow editor, data is wirelessly transmitted to the local host computer and automatically saved as text files. The process shown in FIG. 1 uses IEEE 802.11 standard security Wi-Fi components with MQTT protocol for wireless data transfer via IoT. All the electrode arrays communicate with the help of a single analog-to-digital converter (ADC). Data measurement and wireless data transfer take place simultaneously. Any Wi-Fi-enabled PC or smartphone can be used to communicate with the EIT vest via an AI-based process control and data processing interface.
[0053] FIG. 2A illustrates a graphical representation of the circuit diagram, showcasing the integration of Wi-Fi modules with the EIT circuit for seamless communication, in accordance with an embodiment of the present disclosure.
[0054] The wireless, IoT-based EIT system 300 (also referred to as system 300, herein) is compact, portable, and cost-effective, offering a solution for accessible and efficient healthcare diagnostics in the field of pulmonology. It provides a method to extract the lung volumes from the reconstructed images. Referring to FIG. 2A, EIT vest 200 involves a front zipper 202 with a pull tab 204 for easy fastening and removal, wherein the vest 200 incorporates an array of electrodes 214. In an exemplary embodiment, the array of electrodes 214 present in the example can be sixteen electrodes in a single array. The vest 200 can include three layers of clothing including the outermost layer 206, a middle Faraday (anti-static) layer 208, and an innermost layer 210 that supports and holds the electrode arrays 214. The external layer 206 provides structural strength, supporting the assembly of side pouches (212-1, 212-2 (which are collectively referred to as pouches 212, herein)) and maintaining the integrity of the vest 200. The side pouches 212 are depicted along with slots 218 for holding the electrodes.
[0055] The array of electrodes 214 mounted in the slots 218 is present in the vest, with the distance 216-1 between each slot 218 in the array being 3mm. The vest 200 includes multiple arrays of these slots 218, wherein the distance 216-2 between two electrode array slots 218 is 5mm. Two side pouches 212 are assembled in the system 300 to house the associated electronics. The vest 200 is further configured to intelligently activate an optimal number of electrode arrays 214 based on a pre-calibrated relationship between the height and weight of the user, determined through curve fitting of data from a statistically significant sample of individuals, correlating the optimal number of arrays with the body mass index (BMI). The optimal number of arrays for a given individual with a known BMI is calculated using the central limit theorem, wherein the most suitable number of arrays is identified by selecting the best Gaussian fit from the probability density function estimated for 1 to N arrays integrated within the vest 200.
[0056] FIG. 2B illustrates a back section of the EIT vest, in accordance with an embodiment of the present disclosure. The back portion of the vest 200, over which the array of electrodes 214 are mounted, wherein the side pouches 212 are depicted along with the slots 218 for holding the electrodes.
[0057] FIG. 2C illustrates an overview of the various layers of the vest, in accordance with an embodiment of the present disclosure. FIG. 2C illustrates the layers of the EIT vest 200, wherein the vest includes three layers of clothing, the outermost layer 206 (also interchangeably referred to as external layer) providing structural strength, supporting the assembly of side pouches 212 and maintaining the integrity of the vest 200. The middle layer 208 consists of Faraday (anti-static) fabric to shield the electrode arrays from external signal interference; and the innermost layer 210 configured to support, mount, and hold the electrode arrays.
[0058] FIG. 2D illustrates a perspective view of the various layers of the vest, in accordance with an embodiment of the present disclosure. FIG. 2D illustrates a perspective view of the vest, showing all three layers, including the outermost layer 206, the middle Faraday (anti-static) layer 208, and the innermost layer 210, which supports and holds the electrode arrays.
[0059] FIG. 2E illustrates the side view of the vest, showcasing the specially designed pouch where the associated electronics are securely housed, with a protective flap, in accordance with an embodiment of the present disclosure. FIG. 2E illustrates a side view of the vest 200, showing all three layers of clothing and the pouch 212. The pouch 212 is equipped with a flap 226 that allows for easy access to control and monitor the system from the outside. The number of active electrode arrays can be adjusted externally, and troubleshooting can be performed if necessary. The electronics housed in the pouches 212 include a mechanism for simultaneous switching of all the 3D electrode arrays 214, controlled by a single power source and a single signal generator, utilizing a circuit design. The controlling circuit 304 is depicted in FIG. 3 is integrated on a flexible PCB 302, allowing for automatic activation of specific sections of the EIT vest 200. Additionally, the calculated specific absorption rate (SAR) values ensure the safety of patients during operation within the defined parameters.
[0060] FIG. 2F illustrates the design of the electrode along with the silicon stopper, which is used to securely fix the electrodes in place within the vest, in accordance with an embodiment of the present disclosure. FIG. 2F shows a low-impedance metallic electrode utilized in the vest, wherein the diameter of the head 220 of the electrode measures 2 mm, and the length of the tail 222 is 4 mm with a diameter of 1 mm. A silicon rubber stopper 224 is employed to secure the electrodes within the distributed slots 218 of the vest. The sidewalls of the array of electrodes 214 are coated with a high-impedance material to prevent short-circuiting in the event of contact between the electrodes.
[0061] FIG. 3 illustrates a circuit diagram of the EIT assembly, showcasing the integration of Wi-Fi modules with the EIT circuit for seamless communication, in accordance with an embodiment of the present disclosure. FIG. 3 provides a circuit diagram of the complete EIT assembly, wherein EIT system 200 represents the custom-designed PCB 302, where the PCB 302 can include a controlling circuit 304. In an exemplary embodiment, the controlling circuit 304 can be ESB board. The controlling circuit 304 can include wireless modules. FIG. 3 illustrates the connection between the EIT vest 200 and its associated electronics in the PCB 302, along with the link to the wireless modules. Additionally, it depicts how the wireless module connects to the local host computer 306 for data collection using Wi-Fi.
[0062] The system 300 for non-invasive lung imaging can include a fabric structure defining a vest 200 configured to conform to the body of one or more subjects (also referred to as subjects, herein). The fabric structure incorporates one or more pouches 212 (also referred to as pouches, herein). An array of electrodes 214 is accommodated in predefined slots on the fabric structure, with the arrangement of the array determined based on a pre-calibrated relationship between the body mass index (BMI) of the subjects and the optimal number of the array of electrodes.
[0063] The controlling circuit 304 is integrated within the fabric structure, the array of electrodes and is configured to control the excitation of the array of electrodes 214 and the switching of the array by selectively applying electrical signals to the array in a predefined sequence. The controlling circuit 304 processes a set of data acquired from the array of electrodes, 214 which pertains to electrical impedance measurements of the lungs of the one or more subjects, and transmits the processed data to a computing device 306 operatively coupled to the controlling circuit. The computing device 306 generates two-dimensional (2D) or three-dimensional (3D) images of the lungs based on the processed data, thereby providing visualization of lung function. The generated images are then analyzed to classify the corresponding subjects into categories based on lung function parameters, including total lung capacity, functional residual capacity, inspiratory capacity, and residual volume, enabling differentiation between healthy and non-healthy subjects.
[0064] The vest 200 includes a multi-layer structure, wherein an external layer provides strength to maintain structural integrity, a middle layer made of electromagnetic shielding material shields the array of electrodes from external interference, and an internal layer integrates the array of electrodes, with the vest made of flexible material that conforms to the shape of the subjects. Each electrode in the array is of low impedance with a diameter of 2 mm and is mounted using detachable silicon rubber holders 224, allowing for repositioning to accommodate the anatomy and physiology of the subjects. The array is positioned with slots spaced 3 mm apart, maintaining a minimum distance of 5 mm between adjacent arrays to enable flexible distribution across the internal surface of the vest 200.
[0065] The controlling circuit 304 features a wireless communication module that operates with Message Queuing Telemetry Transport (MQTT) protocol for secure wireless data transfer via the Internet of Things (IoT). The communication among the array of electrodes is facilitated through a single analog-to-digital converter (ADC). The controlling circuit 304 is further configured to utilize a pre-calibrated relation between the height and weight of the subjects to activate the optimal number of electrodes, obtain the pre-calibrated relation of the optimal number of electrodes corresponding to the BMI through curve fitting, determine the optimal number of electrodes for subjects with known BMI using the central limit theorem, and recommend the optimal number based on a Gaussian fit of probability density estimated for the electrodes in the vest.
[0066] In addition, the controlling circuit 304 measures the set of data while subjects wear the vest and breathe naturally, directing them to breathe according to a controlled breath pattern. The computing device 306 is configured to acquire the 2D or 3D images pertaining to cross sections and volumetric electrical impedance maps of the lungs, automatically segment air distribution within the lungs from the 3D images, utilize the segmented 3D images to estimate lung volume by multiplying lung area by lung length, analyze the frequency of specific pixel values within segmented portions of the images, declare certain pixel frequencies as a preliminary imaging biomarker, and establish specific frequency ranges to classify subjects as healthy or unhealthy.
[0067] The system also obtains pulmonary information related to lung function and volume, and establishes a linear correlation of the obtained information with existing gold standard measurements, achieving a correlation of 93% for Functional Residual Capacity (FRC), 70% for Inspiratory Capacity (IC), 82% for Total Lung Capacity (TLC), and 89% for Residual Volume (RV). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) are calculated, yielding values of 86%, 78%, 86%, and 80%, respectively.
[0068] In an implementation of an embodiment, consider a hospital deploying the non-invasive EIT system to monitor the lung function of a patient with chronic obstructive pulmonary disease (COPD). The patient wears the EIT vest, which is carefully fitted to conform to their body. The vest's fabric ensures minimal radiation exposure during monitoring. Once the vest is in place, the array of electrodes in the vest begins measuring the electrical impedance across different sections of the patient's lungs. The controlling circuit, housed within the vest, selectively excites the electrodes in a predefined sequence and simultaneously collects electrical impedance data. This data is processed by the embedded controlling circuit and wirelessly transmitted to a local computing device via Wi-Fi. The computing device, following pre-programmed algorithms, generates a real-time two-dimensional (2D) or three-dimensional (3D) image of the patient's lungs. These images show detailed cross-sectional views of the lung tissue, mapping air distribution as the patient breathes naturally.
[0069] Further, the healthcare provider reviews the generated 3D lung images to assess lung function parameters such as total lung capacity (TLC), functional residual capacity (FRC), inspiratory capacity (IC), and residual volume (RV). Based on the data, the system classifies the patient's lung function as impaired, using the pre-established frequency ranges derived from the impedance values. These ranges help differentiate between healthy and unhealthy lung conditions.
[0070] FIG. 4 illustrates an exemplary view of circuit diagram of EIT vest, in accordance with an embodiment of the present disclosure. The circuit diagram 400 includes various components such as impedance measurement electrodes, data acquisition units, and a signal processing module configured to capture and analyze electrical signals from the vest. These components work together to monitor and map the impedance changes in the user's body, enabling real-time analysis of physiological conditions, which may assist in diagnosing or tracking specific health parameters.
[0071] FIG. 5 illustrates the reconstruction of the EIT data for four different breathing schemes (a) normal inhale, (b) normal exhale, (c) forceful inhale, and (d) forceful exhale, in accordance with an embodiment of the present disclosure.
[0072] The system 300 illustrates the reconstructions of lung volumes under four different conditions, namely normal inhale, normal exhale, forceful inhale, and forceful exhale, wherein normal inhale provides information regarding the tidal volume (TV), normal exhale yields the forced residual capacities (FRC), forceful inhale reveals the inspiratory capacities (IC), and air remaining in the lungs after a forceful exhalation indicates the residual volume (RV). The method of lung volume extraction from images comprises the following steps:
• performing EIT scans under the four specific conditions to assess various lung volumes, where the first scan establishes a baseline value during normal inhalation, the second captures the Functional Residual Capacity (FRC) during normal exhalation, the third measures the Inspiratory Capacity (IC) during forceful inhalation, and the fourth provides the Residual Volume (RV) during forceful exhalation.
• calculating lung volume for each individual by integrating imaging data with physiological parameters, specifically by multiplying the area of the lung, as determined from segmented images, by a lung height value based on the individual's Body Mass Index (BMI), thereby accounting for variations in lung dimensions and body composition.
• normalizing the volume extracted from each of the four images with respect to the reference value by dividing the volume measurements by the reference value to standardize the data, thereby facilitating clearer observation of relative changes in lung volumes and
• normalizing the volume calculations derived from the four images using the reference value for 23 healthy volunteers to determine the ranges for FRC, IC, and RV, followed by developing a range conversion relationship to align these normalized volumes with established reference ranges for FRC (2-4 liters), TLC (6-7 liters), and RV (1.2-1.6 liters), thereby providing a framework for interpreting the EIT-derived volumes in the context of standard lung function measurements.
[0073] FIG. 6 illustrates the imaging biomarker based on the frequency of the pixel value, in accordance with an embodiment of the present disclosure. A comprehensive comparison between healthy individuals and patients based on various breathing schemes, indicates that the frequency patterns for patients with obstructive and restrictive lung diseases significantly differ from those of healthy individuals across all breathing schemes. The distinct frequency ranges observed for healthy volunteers, patients with obstructive lung disease, and patients with restrictive lung disease demonstrate substantial variations in respiratory function, wherein healthy individuals typically exhibit lower and more consistent mean frequencies compared to patients, whose frequencies vary widely depending on the specific type of lung disease.
[0074] FIG. 7 illustrates an exemplary flow chart of a method for non-invasive lung imaging, in accordance with an embodiment of the present disclosure.
[0075] The method 700 involves block 702, which provide a fabric structure defining a vest configured to conform to the body of one or more subjects, wherein the fabric structure incorporates one or more pouches of to hold electronic components. At block 704, arrange an array of electrodes in predefined slots on the fabric structure, wherein the arrangement of the array of electrodes is determined based on a pre-calibrated relationship between the body mass index (BMI) of one or more subjects and the optimal number of the array of electrodes.
[0076] At block 706, control, by a controlling circuit, the excitation of the array of electrodes and the switching of the array of electrodes by selectively applying electrical signals to the array of electrodes in a predefined sequence. At block 708, Process, by the controlling circuit, a set of data acquired from the array of electrodes, wherein the set of data pertains to electrical impedance measurements of the lungs of the one or more subjects.
[0077] At block 710, transmit the processed data from the controlling circuit to a computing device. At block 712, generate, by the computing device, two-dimensional (2D) or three-dimensional (3D) images of the lungs of the one or more subjects based on the processed data, thereby providing visualization of lung function. At block 714, analyze, by the computing device, the generated images to classify the corresponding subjects into categories based on lung function parameters, wherein the lung function parameters pertain to total lung capacity, functional residual capacity, inspiratory capacity, and residual volume, enabling differentiation between healthy subjects and non-healthy subjects.
[0078] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a system that enables non-invasive lung imaging, significantly reducing the need for traditional diagnostic methods that may involve radiation exposure. The system is portable and cost-effective, making advanced healthcare diagnostics accessible to a wider population, including those in remote or underserved areas. Additionally, the system with automated processes that streamline data collection and analysis, facilitating quicker interpretation of lung function and health status. The system employs artificial intelligence for image processing and feature extraction, enhancing the accuracy of lung disease classification and improving clinical decision-making. Moreover, the system intelligently adjusts the number of active electrode arrays based on the user's body mass index (BMI), optimizing the performance and accuracy of the imaging without manual intervention. The system integrates real-time data transfer and monitoring via IoT, allowing healthcare professionals to access patient information remotely and make timely interventions.
[0079] It will be apparent to those skilled in the art that the system 300 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0080] The present disclosure provides a system that enables non-invasive lung imaging, significantly reducing the need for traditional diagnostic methods that may involve radiation exposure.
[0081] The present disclosure provides a system that is portable and cost-effective, making advanced healthcare diagnostics accessible to a wider population, including those in remote or underserved areas.
[0082] The present disclosure provides a system with automated processes that streamline data collection and analysis, facilitating quicker interpretation of lung function and health status.
[0083] The present disclosure provides a system that employs artificial intelligence for image processing and feature extraction, enhancing the accuracy of lung disease classification and improving clinical decision-making.
[0084] The present disclosure provides a system that intelligently adjusts the number of active electrode arrays based on the user's body mass index (BMI), optimizing the performance and accuracy of the imaging without manual intervention.
[0085] The present disclosure provides a system that integrates real-time data transfer and monitoring via IoT, allowing healthcare professionals to access patient information remotely and make timely interventions.
, Claims:1. A system (300) for non-invasive lung imaging, the system comprising:
a fabric structure defining a vest (200) configured to conform to body of one or more subjects, the fabric structure incorporates one or more pouches (212) to hold electronic components;
an array of electrodes (214) configured to be accommodated in predefined slots (218) on the fabric structure, wherein arrangement of the array of electrodes is determined based on a pre-calibrated relationship between body mass index (BMI) of the one or more subjects and optimal number of the array of electrodes; and
a controlling circuit (304) integrated within the fabric structure, the controlling circuit configured to:
control excitation of the array of electrodes and switching of the array of electrodes by selectively applying electrical signals to the array of electrodes in a predefined sequence;
process a set of data acquired from the array of electrodes, the set of data pertaining to electrical impedance measurements of lungs of the one or more subjects;
transmit the processed data from the vest to a computing device, the computing device (306) wirelessly coupled to the controlling circuit, the computing device configured to:
generate two-dimensional (2D) or three-dimensional (3D) images of the lungs of the one or more subjects based on the processed data, so as to provide visualization of lung function; and
analyze the generated images to classify corresponding subjects into different categories based on lung function parameters, the lung function parameters pertain to total lung capacity, functional residual capacity, inspiratory capacity, and residual volume enabling differentiation between healthy subjects and non-healthy subjects.
2. The system as claimed in claim 1, wherein the vest (200) comprises a multi-layer structure, the multi-layer structure comprises:
an external layer (206) configured to provide strength to maintain structural integrity of the vest;
a middle layer (208) made of anti-static fabric to shield the array of electrodes from external interference, wherein the middle layer comprises one or more pouches (212) located at side portions of the vest, configured to hold electronic components of a printer circuit board (PCB) (302), with the one or more pouches (212) made of non-anti-static fabric; and
an internal layer (210) integrated with the array of electrodes, wherein the vest made of a flexible material configured to conform to shape of the corresponding subjects, wherein the vest comprises a strap with a Velcro band at one end for adjusting and tightening the vest to fit securely on the body of the corresponding subjects.
3. The system as claimed in claim 1, wherein the array of electrodes (214) is of low impedance, each having a diameter of 2 mm and mounted using detachable silicon rubber holders (224), allowing for repositioning of the array of electrodes to accommodate anatomy and physiology of the corresponding subjects.
4. The system as claimed in claim 1, wherein the array of electrodes (214) comprises slots (218) positioned on the surface of the vest, with each slot spaced at a distance (216-1) of 3 mm apart for mounting the array of electrodes (214), wherein the array of electrodes (214) mounted on the vest, with a minimum distance (216-2) of 5 mm between adjacent arrays, enabling flexible distribution of the array of electrodes across internal surface of the vest.
5. The system as claimed in claim 1, wherein the controlling circuit (304) comprises wireless communication module that is configured to:
operate with Message Queuing Telemetry Transport. (MQTT) protocol for secure wireless data transfer through an Internet of Things (IoT), wherein a single analog-to-digital converter (ADC) communication is facilitated among the array of electrodes; and
enable an interface communication between the vest and the computing device through a learning engine (308).
6. The system as claimed in claim 1, wherein the controlling circuit (304) is configured to:
utilize the pre-calibrated relation between height and weight of the corresponding subjects to activate the optimal number of the array of electrodes;
obtain, through curve fitting approach, the pre-calibrated relation of the optimal number of array of electrodes corresponding to the Body Mass Index (BMI);
determine the optimal number of the array of electrodes for the corresponding subjects with a known BMI by applying central limit theorem; and
recommend the optimal number of array of electrodes based on Gaussian fit of probability density estimated for the array of electrodes in the vest.
7. The system as claimed in claim 1, wherein the controlling circuit (304) is configured to:
measure the set of data while the corresponding subjects wearing the vest breathes according to a natural breathing pattern pertaining to normal inhalation, and normal exhalation; and
direct the corresponding subjects to breathe according to a controlled breath pattern, the controlled breath pattern pertains to forceful inhalation followed by holding the breath, and forceful exhalation followed by holding the breath.
8. The system as claimed in claim 1, wherein the computing device (306) is configured to:
acquire the 2D or 3D images of the lungs to process the acquired images, wherein the 2D or 3D images pertain to cross sections and volumetric electrical impedance map of the lungs;
automatically segment, through the learning engine (308), air distribution within the lungs from the 3D images;
utilize the segmented 3D images of the air distribution to estimate lung volume of the corresponding subjects by multiplying lung area by lung length of the corresponding subjects;
analyze frequency of specific pixel values within segmented portions of the 3D images;
declare frequency of certain pixel values as a preliminary imaging biomarker; and
establish specific frequency ranges to classify the corresponding subjects as healthy or unhealthy.
9. The system as claimed in claim 1, wherein the computing device (306) is configured to:
obtain pulmonary information related to lung function and volume;
establish a linear correlation of the obtained pulmonary information with the existing gold standard measurements, obtaining a high correlation for functional residual capacity (FRC), inspiratory capacity (IC), total lung capacity (TLC), and residual volume (RV); and
calculates specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV), yielding values indicative of accuracy and reliability.
10. A method (700) or non-invasive lung imaging, the method comprising:
providing (702) a fabric structure defining a vest configured to conform to a body of one or more subjects, wherein the fabric structure incorporates one or more pouches to hold electronic components;
arranging (704) an array of electrodes in predefined slots on the fabric structure, wherein the arrangement of the array of electrodes is determined based on a pre-calibrated relationship between body mass index (BMI) of the one or more subjects and optimal number of the array of electrodes;
controlling (706), by a controlling circuit, an excitation of the array of electrodes and switching of the array of electrodes by selectively applying electrical signals to the array of electrodes in a predefined sequence;
processing (708), by the controlling circuit, a set of data acquired from the array of electrodes, wherein the set of data pertains to electrical impedance measurements of the lungs of the one or more subjects;
transmitting (710) the processed data from to a computing device;
generating (712), by the computing device, two-dimensional (2D) or three-dimensional (3D) images of the lungs of the one or more subjects based on the processed data, thereby providing visualization of lung function;
analyzing (714), by the computing device, the generated images to classify the corresponding subjects into categories based on lung function parameters, wherein the lung function parameters pertain to total lung capacity, functional residual capacity, inspiratory capacity, and residual volume, enabling differentiation between healthy subjects and non-healthy subjects.

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