A blood vessel diagnosis method and system based on intelligent guide wire

Abstract

This disclosure relates to a vascular diagnostic method and system based on a smart guidewire. The smart guidewire has at least two electrodes at its tip, and its maximum diameter is less than or equal to 1.0 mm. The method includes: with the smart guidewire positioned at a target vascular location and at least two electrodes in contact with the tissue to be tested, responding to a measurement command, applying an excitation current to at least one of the at least two excitation electrodes at preset frequencies using a signal generator; measuring the voltage signal of at least one of the at least two excitation electrodes using a voltage measuring instrument, and processing the voltage signal to obtain a multi-frequency impedance spectrum; and processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be tested to obtain a vascular diagnostic result. Thus, by using a smart guidewire with integrated electrodes and multi-frequency impedance spectroscopy analysis, real-time and accurate acquisition of vascular diagnostic results such as thrombus type and blood flow velocity measurements is achieved.

Description

Technical Field

[0001] This disclosure relates to the field of medical device technology, and in particular to a vascular diagnostic method and system based on intelligent guidewires. Background Technology

[0002] Currently, acute ischemic stroke is a serious threat to human health, and intravenous thrombolysis and mechanical thrombectomy are its main treatment methods. Differences in thrombus type (such as red thrombus, white thrombus, and mixed thrombus) directly affect the choice of thrombectomy strategy, surgical success rate, and patient prognosis. Furthermore, during mechanical thrombectomy for acute ischemic stroke, real-time monitoring of blood flow velocity is of significant clinical value in assessing vascular recanalization effectiveness, predicting distal embolism risk, and guiding postoperative management. Therefore, vascular diagnostics such as thrombus type identification and blood flow velocity measurement are necessary.

[0003] In related technologies, imaging techniques such as computed tomography angiography (CTA), digital subtraction angiography (DSA), and magnetic resonance imaging, or histological and molecular detection techniques, or methods such as transcranial Doppler ultrasound (TCD) and thermodilution (TD) are used for thrombus type identification or blood flow velocity measurement. However, none of these methods can provide real-time and accurate identification of thrombus types or accurate blood flow velocity measurements for vascular diagnostic results. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a vascular diagnosis method and system based on intelligent guidewires.

[0005] This disclosure provides a vascular diagnostic method based on a smart guidewire, comprising: at least two electrodes disposed at the tip of the smart guidewire, wherein the maximum diameter of the smart guidewire is less than or equal to 1.0 mm; the method comprising: when the smart guidewire is at a target vascular location and the at least two electrodes are in contact with the tissue to be tested, in response to a measurement command, applying an excitation current to at least one of the at least two electrodes at preset frequencies based on a signal generator; measuring the voltage signal of at least one of the at least two excitation electrodes based on a voltage measuring instrument, and processing the voltage signal to obtain a multi-frequency impedance spectrum; and processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be tested to obtain a vascular diagnostic result.

[0006] Optionally, applying an excitation current to at least one of the at least two electrodes based on a signal generator at a preset plurality of frequencies includes: sequentially applying the excitation current to the at least one excitation electrode at the preset plurality of frequencies; or, superimposing the preset plurality of frequencies and applying the excitation current to the at least one excitation electrode at once.

[0007] Optionally, the step of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnosis result includes: comparing the multi-frequency impedance spectrum with a preset feature library of the tissue to be detected to obtain target features; classifying the target features based on a preset classification algorithm model to obtain the classification result as the vascular diagnosis result.

[0008] Optionally, the step of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnostic result includes: extracting feature parameters associated with the tissue to be detected based on the multi-frequency impedance spectrum; performing calculations based on the feature parameters and a preset calculation model to obtain an initial calculated value; and compensating the initial calculated value based on the temperature of the tissue to be detected to obtain a target calculated value as the vascular diagnostic result.

[0009] Optionally, the method further includes: applying a preset pulse to at least one of the at least two excitation electrodes to obtain a reflected signal, and processing the reflected signal to obtain signal features; and / or, performing fitting processing based on the voltage signal to obtain equivalent circuit parameters; the step of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnosis result includes: processing the multi-frequency impedance spectrum, the signal features, and / or the equivalent circuit parameters based on the preset diagnostic algorithm for the tissue to be detected to obtain the vascular diagnosis result.

[0010] Optionally, the method further includes: sending the vascular diagnosis results to a human-computer interaction module for display to prompt the user to select a target processing strategy based on the vascular diagnosis results.

[0011] This disclosure also provides a vascular diagnostic system based on a smart guidewire, comprising: a smart guidewire, a signal generator, a voltage measuring instrument, a signal amplifier, a signal filter, an analog-to-digital converter, and a processor; the signal generator is used to apply an excitation current to at least one of the at least two excitation electrodes at preset frequencies according to a measurement command when the smart guidewire is at a target vascular location and at least two electrodes at the guidewire tip of the smart guidewire are in contact with the tissue to be tested; wherein the maximum diameter of the smart guidewire is less than or equal to 1.0 mm; the voltage measuring instrument is used to measure the voltage signal of at least one of the at least two measuring electrodes; the signal amplifier is used to amplify the voltage signal; the signal filter is used to filter the amplified signal; the analog-to-digital converter is used to convert the filtered signal to a digital signal; the processor is used to process the digital signal to obtain a multi-frequency impedance spectrum, and to process the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be tested to obtain a vascular diagnostic result.

[0012] Optionally, the smart guidewire includes a guidewire substrate, a guidewire tip, and at least two electrodes; the at least two electrodes are a first excitation electrode, a second excitation electrode, a first measurement electrode, and a second measurement electrode, with an insulating layer separating the first excitation electrode, the second excitation electrode, the first measurement electrode, and the second measurement electrode, the insulating layer being represented by fine dotted shadows; the tip of the guidewire tip is conical, blunt, or arc-shaped; the guidewire substrate is cylindrical, and the first excitation electrode, the first measurement electrode, the second measurement electrode, and the second excitation electrode are arranged sequentially, each electrode being a uniformly wide annular or spiral structure, the width being 0.01 to 0.5 mm.

[0013] Optionally, the at least two electrodes may be more than four electrodes, and the more than four electrodes may be arranged in an electrode array of micro-dot electrodes at the front end of the guidewire.

[0014] Optionally, the system further includes: a human-computer interaction module, which includes a touch screen, indicator lights, and an audio-visual prompting unit; the touch screen is used to display the vascular diagnosis results and prompt the user to select a target processing strategy based on the vascular diagnosis results based on the indicator lights and the audio-visual prompting unit.

[0015] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the vascular diagnostic method based on smart guidewire as provided in this disclosure.

[0016] This disclosure also provides a computer-readable storage medium storing a computer program for executing the smart guidewire-based vascular diagnostic method provided in this disclosure.

[0017] This disclosure also provides a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the intelligent guidewire-based vascular diagnosis method described in one aspect above.

[0018] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: The vascular diagnostic solution based on a smart guidewire provided in this disclosure, when the smart guidewire is at the target vascular location and at least two electrodes are in contact with the tissue to be tested, responds to a measurement command by applying an excitation current to at least one of the at least two excitation electrodes at preset multiple frequencies using a signal generator; measuring the voltage signal of at least one of the at least two excitation electrodes using a voltage measuring instrument, and processing the voltage signal to obtain a multi-frequency impedance spectrum; processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be tested to obtain the vascular diagnostic result. Therefore, by using a smart guidewire with integrated electrodes and multi-frequency impedance spectrum analysis, real-time and accurate acquisition of vascular diagnostic results such as thrombus type and blood flow velocity measurements is achieved. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0020] Figure 1 A schematic diagram of a vascular diagnostic method based on a smart guidewire provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of the smart guidewire provided in an embodiment of this disclosure; Figure 3 This is an enlarged schematic diagram of the four-electrode structure at the front end of the smart guidewire provided in an embodiment of the present disclosure; Figure 4 A schematic diagram of a four-electrode measurement process provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the impedance analysis system provided in an embodiment of the present disclosure; Figure 6 A schematic flowchart of a vascular diagnosis method based on a smart guidewire provided in this embodiment of the present disclosure; Figure 7An example diagram illustrating a vascular diagnostic method based on a smart guidewire provided in this disclosure embodiment; Figure 8 Electrochemical Nyquist plots of blood samples at different red blood cell concentrations provided in embodiments of this disclosure; Figure 9 Electrochemical Bode plots of blood samples at different red blood cell concentrations provided in embodiments of this disclosure; Figure 10 An example diagram illustrating another vascular diagnostic method based on a smart guidewire provided in this disclosure; Figure 11 An example diagram illustrating the phase angle of simulated blood flow at different velocities in a peristaltic pump line, provided for embodiments of this disclosure. Detailed Implementation

[0021] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0024] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0025] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0026] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0027] Currently, vascular diagnosis, including thrombus type identification and blood flow velocity measurement, is required in existing acute ischemic stroke surgeries. Specifically, computed tomography angiography (CTA), digital subtraction angiography (DSA), and magnetic resonance imaging (MRI) can be used to assess preoperative and intraoperative vascular conditions, primarily for observing vascular morphology, determining occlusion location, and evaluating collateral circulation. However, these methods mainly reflect the anatomical morphology of the vessels, such as the location and extent of occlusion, but cannot directly distinguish the internal components and microstructure of the thrombus. Users cannot accurately determine the thrombus type (red, white, or mixed) based solely on imaging results, leading to a significant reliance on personal experience in the selection of thrombectomy strategies (such as stent thrombectomy, aspiration thrombectomy, or combined use), increasing surgical uncertainty, the risk of multiple thrombectomies, distal embolism, and vascular injury. Postoperative pathological analysis or molecular biological testing of the retrieved thrombus can accurately determine the thrombus type, but this method cannot provide real-time information during the operation.

[0028] Specifically, contrast agents can be injected to observe blood flow velocity and assess vascular patency, but this method is semi-quantitative, non-continuous, and requires the injection of contrast agents, posing certain risks. Doppler ultrasound can also be used, which is non-invasive and allows for continuous monitoring, but it is limited by the acoustic window of the skull and is highly dependent on the operator, making it difficult to integrate with the guidewire system in interventional procedures. Alternatively, blood flow velocity can be estimated by injecting cold saline and measuring temperature changes, but this method is invasive, discontinuous, and requires additional fluid injection.

[0029] Based on the above description, in order to address the limitations of intraoperative real-time and accurate thrombus type identification, the inability of existing imaging technologies to distinguish thrombus components, the inability to measure local blood flow velocity in real-time, continuously, and accurately, and the limitations of existing technologies (such as DSA and TCD) in interventional surgery (invasiveness, discontinuity, and operator dependence), this disclosure proposes a vascular diagnosis method based on intelligent guidewires. This method can achieve in-situ, real-time, and accurate identification of thrombus types during surgery without affecting the basic operational performance of the guidewire (pushing ability, flexibility, torsion control, etc.). Furthermore, it can be integrated into conventional neurointerventional intelligent guidewires, requires no contrast agents, and can continuously measure local blood flow velocity in real-time. In other words, the intelligent guidewire of this disclosure can achieve multi-functional integration of "thrombus identification plus blood flow monitoring," providing more comprehensive decision support for neurointerventional surgery.

[0030] The intelligent guidewire-based vascular diagnostic method disclosed herein can be applied to, for example... Figure 1The application environment shown is illustrated. This smart guidewire-based vascular diagnostic method is applied to a smart guidewire-based vascular diagnostic system. This smart guidewire-based vascular diagnostic system includes: a smart guidewire 10 and an impedance analysis system 20.

[0031] In some embodiments, such as Figure 2 As shown, the intelligent guidewire 10 includes a guidewire substrate 100, a guidewire tip 108, and at least two electrodes; the at least two electrodes are a first excitation electrode 101, a second excitation electrode 104, a first measurement electrode 102, and a second measurement electrode 103, which are separated by an insulating layer 105, which is represented by fine dotted shadows; the tip of the guidewire tip 108 is conical, blunt, or arc-shaped; the guidewire substrate 100 is cylindrical, and the first excitation electrode 101, the first measurement electrode 102, the second measurement electrode 103, and the second excitation electrode 104 are arranged sequentially, each electrode being a uniformly wide annular or spiral structure with a width of 0.2 to 0.5 mm.

[0032] Specifically, the guidewire substrate 100 uses a nickel-titanium alloy or stainless steel core wire, consistent with clinically routine guidewires, and is coated with a polymer material to ensure good pushability, flexibility and torsion control; the total length and maximum diameter of the guidewire substrate 100 meet the preset standards.

[0033] Specifically, a sensing unit consisting of four ring electrodes is integrated at the tip 108 of the guidewire (e.g., approximately 2-5 mm from the tip of the guidewire tip 108); such as Figure 2 As shown, four electrodes (E1, E2, E3, E4) are arranged in a longitudinal ring along the guidewire axis. Each electrode is an independent platinum-iridium alloy or gold ring structure with a width between 0.01 and 0.5 mm. Adjacent electrodes are separated by an insulating layer 105. This arrangement ensures high-sensitivity detection of local electrical characteristics such as thrombi without affecting the guidewire's bending performance. The two outer electrodes (E1 and E4) are the excitation electrodes, and the two inner electrodes (E2 and E3) are the measurement electrodes, forming a standard four-electrode measurement configuration that effectively eliminates the influence of contact impedance.

[0034] Specifically, such as Figure 2As shown, the guidewire tip (left side) is magnified and integrates four ring electrodes arranged longitudinally along the axial direction: the first excitation electrode 101 is closest to the tip, the second measurement electrode 102, the third measurement electrode 103, and the fourth excitation electrode 104. The four electrodes are separated by an insulating layer 105, which is represented by fine dotted shading. The guidewire rear end (right side) is connected to a connector 106, which is a small cylindrical plug. A signal cable 107 is led out from the rear of the connector 106, and the end of the signal cable 107 is connected to the impedance analysis system 20. The tip of the guidewire tip 108 is tapered or blunt, which facilitates intravascular advancement.

[0035] To describe in more detail the setup of the four-electrode structure at the front end of the smart guidewire, such as Figure 3 As shown, the guidewire tip is drawn at an enlarged scale, approximately 5-8 mm in length. The guidewire base 100 is cylindrical, with its outer contour represented by two parallel lines. Four annular electrodes (101, 102, 103, 104) are arranged sequentially, each electrode being a uniformly wide annular structure, with a width marked as 0.01-0.5 mm. The electrodes are separated by an insulating layer 105, with a width marked as 0.1-0.2 mm. The tip of the guidewire tip 108 is arc-shaped or conical. A very fine wire, i.e., a signal transmission line 109, is led out from each electrode, extending along the inside of the guidewire, represented by a dashed line. Dimension lines are added below the electrodes: the maximum diameter of the intelligent guidewire is marked as ≤1.0 mm. The signal transmission line 109 includes, for example, four very fine insulated wires (typically with a diameter less than or equal to 0.03 mm) connected to the four electrodes respectively, and spirally wound along the inside or surface of the guidewire to the rear end of the guidewire, connecting to the external connector 106.

[0036] For example, to understand the four-electrode measurement process in detail, such as Figure 4 As shown, the system includes a first excitation electrode 101, a second measuring electrode 102, a third measuring electrode 103, and a fourth excitation electrode 104; thrombus tissue 30; a signal generator 206 representing an AC current source; a voltage measuring instrument 201; a processor 205 serving as an impedance calculation module; Rc contact resistance; and Cc contact capacitance. Specifically, as... Figure 3The left side shows the four-electrode structure (101-104) at the tip of the guidewire 108, surrounded by wavy lines or dotted areas to represent thrombus tissue 300. Arrows from the first excitation electrode 101 and the fourth excitation electrode 104 point to an AC current source, i.e., a signal generator 206, indicating the application of excitation current. Arrows from the second measuring electrode 102 and the third measuring electrode (103) point to a voltage measuring instrument icon, i.e., a voltage measuring instrument 201, indicating the measurement of voltage. The voltage measuring instrument is connected to a complex impedance calculation module, i.e., a processor 205, whose output is the impedance magnitude |Z| and phase angle θ. At the electrode-thrombus contact interface, an equivalent circuit diagram is added, using small resistance and capacitance symbols to represent the contact impedance (labeled as Rc and Cc), indicating that the four-electrode method can eliminate its influence.

[0037] It should be noted that the four electrodes at the guidewire tip 108 can be designed as a spiral structure, rather than a simple ring; this design can increase the contact area between the electrodes and the thrombus and may have less impact on the flexibility of the guidewire tip; the spiral electrodes can be achieved by laser cutting of conductive tubing or precision winding of metal wire.

[0038] In some embodiments, the four electrodes can be transferred from the guidewire tip 108 to the inner wall or tip of the thrombectomy microcatheter. Before thrombectomy or stent release, the microcatheter can be used to directly contact the thrombus for identification, which can also achieve the purpose of guiding the operation, and may have lower design requirements for the smart guidewire 10.

[0039] In some embodiments, the front-end portion, which includes four electrodes and part of the signal conditioning circuitry, can be designed as a disposable sterile consumable, while the back-end signal processing, control, and display module can be designed as a reusable independent host. This can reduce the cost per use and simplify the disinfection and sterilization process, which is more in line with actual clinical application habits.

[0040] In some embodiments, at least two electrodes are used instead of more than four electrodes, and the more than four electrodes are arranged in an electrode array of micro-dot electrodes at the front end 108 of the guidewire.

[0041] Therefore, multiple (more than four) micro-point electrodes are set at the tip of the guidewire 108 to form an array. By selecting different electrode pairs for excitation and measurement, the electrical property distribution information of the tissue to be tested, such as the local area of ​​the thrombus, can be obtained, thereby more precisely assessing the heterogeneity of the tissue to be tested (such as the distribution of different components in mixed thrombi).

[0042] It is understood that the embodiments of this disclosure provide at least two electrodes at the front end of the smart guidewire, which can meet the needs of different scenarios. For example, a two-electrode scheme (one for excitation and measurement, and one for reference) can be adopted to further simplify the structure, reduce costs, and partially compensate for accuracy loss through calibration and algorithm compensation, thereby further meeting the needs of different processing scenarios.

[0043] It should be noted that a miniature Bluetooth or Near Field Communication (NFC) module can be integrated into the connector at the back end of the guidewire to wirelessly transmit the collected raw signals or processed results to an external tablet or surgical navigation system. This simplifies device connection, reduces indoor cables, and improves operational convenience.

[0044] For example, such as Figure 5 As shown, the impedance analysis system 20 includes: a voltage measuring instrument 201, a signal amplifier 202, a signal filter 203, an analog-to-digital converter (ADC) 204, a processor 205, a signal generator 206, a human-computer interaction module 207, and a power supply module 208; wherein, the processor 205 includes an impedance spectrum calculation unit 2051, a preset classification algorithm model 2052, and a feature library 2053, and the human-computer interaction module 207 includes a touch screen 2071, an indicator light 2072, and an audio-visual prompt unit 2073.

[0045] Specifically, the signal generator 206 is used to apply an excitation current to at least one of the at least two excitation electrodes at preset frequencies when the smart guidewire 10 is at the target vascular location and at least two electrodes at the tip of the smart guidewire 10 are in contact with the tissue to be detected, in response to a measurement command; wherein the maximum diameter of the smart guidewire 10 is less than or equal to 0.5 mm; the voltage measuring instrument 201 is used to measure the voltage signal of at least one of the at least two measuring electrodes; the signal amplifier 202 is used to amplify the voltage signal; the signal filter 203 is used to filter the amplified signal; the analog-to-digital converter 204 is used to convert the filtered signal to a digital signal; and the processor 205 is used to process the digital signal to obtain a multi-frequency impedance spectrum, and to process the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnostic result.

[0046] In some embodiments, such as Figure 5As shown, the vascular diagnostic system based on intelligent guidewire also includes a human-computer interaction module 207, which includes a touch screen 2071, an indicator light 2072, and an audio-visual prompting unit 2073. The touch screen 2071 is used to display the vascular diagnostic results and prompts the user to select a target processing strategy based on the vascular diagnostic results using the indicator light 2072 and the audio-visual prompting unit 2073.

[0047] Understandably, the vascular diagnostic system based on smart guidewires also includes a power supply module 208 for powering the impedance analysis system 20.

[0048] Specifically, such as Figure 5 As shown, the impedance analysis system 20 includes a signal generator 206, a voltage measuring instrument 201, a signal amplifier 202, a signal filter 203, and an analog-to-digital converter 204 as signal generation and acquisition modules, electrically connected to the guidewire substrate 100 via a connector 106. The processor 205, which can be an embedded microprocessor or a digital signal processor, is responsible for controlling signal generation, data acquisition, and algorithm execution. The power supply module 208 supplies power to the entire analysis system. The human-machine interface module 207 includes a touch screen 2071, indicator lights 2072, and an audio-visual prompt unit 2073, used to display real-time impedance spectra, thrombus type identification results (such as "red thrombus," "white thrombus," "mixed thrombus," or thrombus component content), or blood flow velocity measurements, or real-time blood flow velocity values ​​and waveforms, and provides audio-visual prompts. A data storage module can also be set up to store pre-calibrated model parameters and historical measurement data.

[0049] It is understood that the intelligent guidewire-based vascular diagnostic system of this disclosure can be used for vascular diagnosis such as thrombus type identification and blood flow velocity measurement. The following detailed description will take thrombus type identification and blood flow velocity measurement as examples.

[0050] Example 1: Under image guidance, the smart guidewire 10 is pushed to the site of vascular occlusion. When the four electrodes of the guidewire tip 108 of the smart guidewire 10 contact the thrombus, the user initiates the measurement via a foot pedal or device panel. The impedance analysis system 20, through the signal generator 206, applies an alternating current signal containing multiple frequencies (e.g., 10Hz, 100Hz, 1kHz, 10kHz, 100kHz, 1MHz) to the excitation electrodes (E1, E4). The measuring electrodes (E2, E3) detect the excitation current in the blood. The voltage drop generated in the thrombus tissue is collected by voltage measuring instrument 201, and the voltage signal is conditioned by analog front-end circuit (including signal amplifier 202, signal filter 203, etc.). Finally, processor 205 calculates the impedance magnitude |Z| and phase angle θ at multiple frequency points in real time according to Ohm's law (Z=V / I), thereby obtaining an impedance spectrum covering low to high frequencies. Processor 205 has a pre-trained thrombus impedance feature database and classification algorithm model (such as support vector machine SVM, convolutional neural network CNN) built in. Finally, the impedance spectrum obtained in real time is compared and calculated with the standard features in the database. The algorithm model quickly determines the type of thrombus currently encountered. The identification result (e.g., red thrombus, 95% confidence) is displayed on the screen in real time to guide the user to select the most appropriate thrombus removal strategy (e.g., red thrombus is preferred for aspiration thrombus removal, white thrombus is preferred for stent thrombus removal). It can also automatically record the impedance spectrum, identification result and timestamp of each measurement for postoperative analysis and model optimization.

[0051] It is understandable that the core mechanism by which the phase angle of blood impedance changes with flow velocity lies in the fact that flow velocity regulates the aggregation and orientation of red blood cells through shear force. At low flow velocities, red blood cells tend to form rouleaux, increasing the contribution of the equivalent capacitance in the low-frequency band and thus increasing the absolute value of the phase angle. At high flow velocities, the aggregates dissociate and the long axis of the cells tends to align along the streamlines, changing the polarization anisotropy of the cell membrane, resulting in a drift in the relaxation characteristic frequency and a decrease in the phase angle. Therefore, there is a calibrable monotonic mapping relationship between the phase angle and the flow velocity, which can be used for real-time measurement of blood flow velocity.

[0052] Example 2: Intraoperative placement and positioning. Under image guidance, the intelligent guidewire 10 is pushed to the target blood vessel (such as the internal carotid artery or middle cerebral artery). When the four electrodes of the guidewire tip 108 of the intelligent guidewire 10 are completely immersed in the blood flow, the user starts the measurement via foot pedal or device panel. The impedance analysis system 20 applies an alternating current signal containing multiple frequencies to the excitation electrodes (E1, E4) through the signal generator 201. The measuring electrodes (E2, E3) detect the voltage signal generated in the blood by the excitation current. The voltage signal is collected by the voltage measuring instrument 201, conditioned by the analog front-end circuit (including signal amplifier 202, signal filter 203, etc.), and converted into a digital signal by the analog-to-digital converter 204. The processor 205 calculates the impedance value at multiple frequency points in real time based on the measured voltage and known excitation current, thereby obtaining the electrical impedance spectrum and extracting characteristic parameters related to blood flow velocity from the electrical impedance spectrum. Among them, changes in blood flow velocity will cause regular changes in specific characteristic phase angles of the electrical impedance spectrum. The processor 205 has a built-in flow velocity calculation model calibrated through a large number of in vitro and animal experiments. The extracted characteristic parameters are substituted into the model, and the current blood flow velocity value is quickly calculated through a preset algorithm. It can also combine the blood temperature measured by the temperature probe to automatically compensate for the calculation results. Finally, the real-time blood flow velocity (unit: cm / s) is displayed on the screen in digital and waveform form, and audio-visual prompts can be provided.

[0053] Figure 6 This is a flowchart illustrating a vascular diagnostic method based on a smart guidewire, provided as an embodiment of this disclosure. This method can be executed by a vascular diagnostic system based on a smart guidewire, wherein the device can be implemented using software and / or hardware, and is generally integrated into an electronic device. Figure 6 As shown, the method includes: Step 601: When the smart guidewire is in the target position of the blood vessel and at least two electrodes are in contact with the tissue to be detected, in response to the measurement command, an excitation current is applied to at least one of the excitation electrodes at a preset multiple frequencies based on the signal generator.

[0054] In this embodiment of the disclosure, the target location of the blood vessel and the tissue to be detected are different in different application scenarios. For example, in the thrombus type identification scenario, the target location of the blood vessel can be the occlusion site of the blood vessel, and the tissue to be detected is the thrombus; in another example, in the blood flow velocity measurement scenario, the target location of the blood vessel can be the internal location of the blood vessel, and the tissue to be detected is blood.

[0055] In the embodiments of this disclosure, there are many ways to apply excitation current to at least one of the at least two electrodes based on a signal generator at a preset multiple frequencies. In some embodiments, excitation current is applied to at least one excitation electrode sequentially at the preset multiple frequencies; in other embodiments, the preset multiple frequencies are superimposed and then the excitation current is applied to at least one excitation electrode at once.

[0056] Understandably, by using multi-frequency synchronous excitation technology, multiple frequency current signals are superimposed and applied at once. Impedance information at each frequency can be extracted simultaneously through fast Fourier transform, which can shorten the measurement time to the millisecond level, achieve near real-time dynamic monitoring, and further improve processing efficiency.

[0057] Step 602: Measure the voltage signal of at least one excitation electrode among at least two electrodes using a voltage measuring instrument, and process the voltage signal to obtain a multi-frequency impedance spectrum.

[0058] Step 603: Process the multi-frequency impedance spectrum based on the preset diagnostic algorithm for the tissue to be detected to obtain the vascular diagnosis result.

[0059] In this embodiment of the disclosure, the voltage signal of at least one excitation electrode among at least two electrodes can be measured based on a voltage measuring instrument. Then, the voltage signal is amplified, the amplified signal is filtered, the filtered signal is converted from analog to digital to obtain a digital signal, and the digital signal is processed to obtain a multi-frequency impedance spectrum.

[0060] Furthermore, the multi-frequency impedance spectrum is processed based on a preset diagnostic algorithm for the tissue to be detected to obtain vascular diagnostic results. In some embodiments, the multi-frequency impedance spectrum is compared with a preset feature library of the tissue to be detected to obtain target features. The target features are then classified based on a preset classification algorithm model, and the classification result is used as the vascular diagnostic result. In other embodiments, feature parameters associated with the tissue to be detected are extracted based on the multi-frequency impedance spectrum. The initial calculated value is obtained based on the feature parameters and a preset calculation model. The initial calculated value is then compensated based on the temperature of the tissue to be detected to obtain the target calculated value as the vascular diagnostic result.

[0061] In some embodiments, a preset pulse is applied to at least one of the at least two excitation electrodes to acquire a reflected signal, and the reflected signal is processed to obtain signal features; and / or, a fitting process is performed based on a voltage signal to obtain equivalent circuit parameters; the multi-frequency impedance spectrum, signal features, and / or equivalent circuit parameters are processed based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnostic result.

[0062] Specifically, it is not limited to measuring the impedance modulus between electrodes. Time-domain reflectometry can be used, by applying a fast pulse to the excitation electrode and measuring the signal reflected back from the thrombus interface. The amplitude, rise time and attenuation characteristics of the reflected signal also contain electrical and physical structural information of the thrombus, which can be used as a complementary identification feature.

[0063] Specifically, based on the traditional electrical impedance spectrum, an electrochemical model (such as the Randle circuit model) can be introduced to fit the measurement data; the equivalent circuit parameters of the model (such as solution resistance, charge transfer resistance, double layer capacitance, etc.) can be extracted as new feature vectors; these parameters have a more direct correspondence with the physicochemical properties of thrombi, such as interface characteristics and microstructure, further improving the recognition accuracy and robustness.

[0064] In some embodiments, raw multi-frequency impedance spectrum data (complex form) can be used as input to construct a convolutional neural network (CNN) or recurrent neural network (RNN) model; this model can automatically learn deep, nonlinear features related to thrombus type, and may have a higher recognition upper limit than traditional machine learning models (such as SVM); or a large amount of calibration data can be used to train the neural network model to directly establish the mapping from impedance spectrum to flow velocity, which may have higher accuracy and robustness than traditional models.

[0065] In some embodiments, prior knowledge based on physical models (such as parameters obtained by fitting equivalent circuits) can be fused with data-driven deep learning features to form a hybrid model. This "physical plus data" dual-driven approach ensures both the interpretability of the model and leverages the powerful fitting capabilities of deep learning, thus balancing robustness and accuracy.

[0066] In some embodiments, the vascular diagnosis results are sent to the human-computer interaction module for display to prompt the user to select a target treatment strategy based on the vascular diagnosis results, such as guiding the user to select the most appropriate thrombectomy strategy (e.g., red thrombus is preferentially selected for aspiration and thrombectomy, and white thrombus is preferentially selected for stent thrombectomy).

[0067] Based on the above description, the vascular diagnosis method based on smart guidewires can be applied to different scenarios. Examples are given below for detailed explanation.

[0068] As an example, such as Figure 7As shown, the process includes: Step 701, pushing the smart guidewire to the occluded vascular site; Step 702, the four electrodes at the tip of the guidewire contacting the thrombus; Step 703, starting the measurement, with the signal generator applying an excitation current (frequency range, e.g., 10Hz-1MHz); Step 704, the measuring electrodes acquiring the response voltage signal; Step 705, signal conditioning (amplification, filtering) and analog-to-digital conversion; Step 706, calculating the multi-frequency impedance spectrum (|Z| and θ); Step 707, comparing with the thrombus feature database, and identifying the classification algorithm model; Step 708, determining whether the identification is successful; if successful, proceeding to Step 709, if unsuccessful, returning to Step 703; Step 709, outputting the thrombus type identification result (red thrombus / white thrombus / mixed thrombus); Step 710, displaying the result and prompting the user to select a thrombectomy strategy, thus demonstrating the complete workflow from guidewire introduction to thrombus type output.

[0069] As an example scenario, six in vitro thrombus models with different red blood cell contents were prepared. Thrombus type identification was performed using the methods described in the previous embodiments. Electrical impedance spectroscopy measurements were conducted using the prepared smart guidewire in the range of 1 Hz to 10 MHz, establishing a quantitative spectrum between composition and electrical characteristics. Based on this, characteristic frequency pairs were selected for fixed-frequency impedance testing. Specifically, Figure 8 Electrochemical Nyquist plots of blood samples at different red blood cell concentrations were shown. Figure 9 The electrochemical Bode plots of blood samples at different red blood cell concentrations are shown, meaning that... Figure 8 and Figure 9 The electrochemical impedance spectroscopy characteristics of blood samples at different red blood cell (RBC) concentrations were demonstrated. Figure 8 In the Nyquist plot (impedance complex plane plot), the horizontal axis represents the real part of the impedance (Z), and the vertical axis represents the negative value of the imaginary part of the impedance (Z). Z Each curve corresponds to a sample (from plasma to 100% RBC). As the RBC concentration increases, the curve shifts to the upper right, indicating that the resistivity and capacitance effects of the system are enhanced and the charge transfer resistance is increased. Figure 9 In the Bode plot (impedance magnitude-frequency plot), the horizontal axis represents frequency (logarithmic scale, 1 to 10). 8  The vertical axis represents the impedance magnitude (|Z|, logarithmic scale). The results show that as the RBC concentration increases, the impedance magnitude increases at all frequencies, especially in the low-frequency region (<10 Hz). 6 The impedance is relatively high and changes slowly in the Hz region, but decreases rapidly in the high-frequency region (>106Hz), consistent with the trend of the Nyquist plot. This indicates that the higher the RBC concentration, the greater the overall blood impedance. This may be related to the fact that RBC increases blood viscosity and changes the complexity of conductivity.

[0070] Therefore, by integrating a four-electrode microsensor unit and multi-frequency impedance spectroscopy analysis technology, the limitations of traditional imaging in distinguishing thrombus components are overcome, providing objective and quantitative decision-making basis and effectively avoiding the risks associated with empirically selected surgical procedures. By setting up the four-electrode structure through longitudinal ring arrangement, sensing functionality is achieved without increasing the guidewire tip diameter or affecting its bending performance and flexibility. Compared to potential issues with electrode protrusion or maneuverability in existing technologies, this ensures that the guidewire's basic pushability, flexibility, and torsional control meet or exceed the levels of conventional clinical guidewires. The four-electrode measurement method separates the excitation and measurement circuits, effectively eliminating the risk of electrode-tissue contact issues. The influence of contact impedance and polarization effect on measurement results is reduced, improving the signal-to-noise ratio and repeatability of measurement data. Through a high-performance backend signal processing module and optimized classification algorithm model, millisecond-level response is achieved, ensuring high recognition accuracy of ≥90% and ≥85% in in vitro and animal experiments, respectively, providing reliable support for rapid intraoperative decision-making. The guidewire, signal acquisition, data processing, and human-computer interaction are integrated into one unit, with an intuitive interface that allows for clear recognition results without additional complex operations, facilitating clinical promotion and application. The guidewire material and coating meet biocompatibility requirements, exhibiting no significant cytotoxicity, hemolytic reaction, or tissue irritation, ensuring its safety for in vivo use.

[0071] In summary, the intelligent guidewire achieves highly sensitive and low contact impedance detection of the local electrical characteristics of thrombi without affecting the routine operation performance of the guidewire (especially the tip bending performance). By acquiring impedance spectra in a wide frequency domain and comparing them with a pre-trained database, it enables rapid and accurate classification of red thrombi, white thrombi, and mixed thrombi. It achieves full-process integration from signal excitation, acquisition, processing to result output, and constructs a complete technical solution to solve the problem of intraoperative thrombus identification.

[0072] As an example, such as Figure 10 As shown, the process includes: Step 1001, pushing the smart guidewire to the target blood vessel; Step 1002, the four electrodes at the tip of the smart guidewire contacting the blood; Step 1003, applying multi-frequency excitation current; Step 1004, acquiring voltage signals; Step 1005, signal conditioning and analog-to-digital conversion; Step 1006, calculating the electrical impedance spectrum; Step 1007, extracting the characteristic parameter phase angle; Step 1008, substituting into the inversion model (linear / logarithmic / joint inversion); Step 1009, temperature compensation; Step 1010, outputting real-time blood flow velocity.

[0073] As an example of a scenario, such as Figure 11 As shown, the phase angle of blood at different flow rates in the peristaltic pump tubing is simulated.

[0074] This enables real-time, continuous, and non-invasive intraoperative blood flow velocity measurement, overcoming the limitations of existing technologies (DSA, TCD, TD) in interventional surgery. It seamlessly integrates blood flow monitoring into the guidewire, eliminating the need for contrast agents and achieving a leap from "qualitative / semi-quantitative" to "precise quantification." Maintaining excellent clinical operability, the "longitudinal ring arrangement" four-electrode structure achieves sensing functionality without increasing the guidewire tip diameter or affecting its bending performance and flexibility. Compared to potential issues with electrode protrusion or maneuverability in existing technologies, it ensures that the guidewire's basic pushability, flexibility, and torsional control meet or exceed the levels of conventional clinical guidewires.

[0075] Furthermore, the system boasts high measurement accuracy and strong anti-interference capabilities. Employing a four-electrode measurement method, it separates the excitation and measurement circuits, effectively eliminating the influence of contact impedance between the electrodes and tissue, as well as polarization effects, on the measurement results. This improves the signal-to-noise ratio and repeatability of the measurement data. In addition to the existing thrombus identification function (optional), it adds a blood flow velocity measurement function, enabling a single guidewire to simultaneously provide two key pieces of information: "thrombus components" and "hemodynamics." This provides a more comprehensive and objective basis for formulating thrombectomy strategies, assessing recanalization effects, and predicting prognosis. The system is also highly scalable and easy to operate. By establishing a model parameter library under different conditions, the system can adapt to the physiological parameters of different patients, reducing the impact of individual differences on the measurement results. The back-end analysis system has an intuitive interface, one-click start, and real-time result display, allowing doctors to use it without additional training.

[0076] In summary, without affecting the routine operation performance of the guidewire (especially the bending performance of the tip), stable and high-precision acquisition of intravascular blood flow impedance spectrum was achieved; by extracting flow velocity-related characteristic parameters from the blood impedance spectrum, real-time blood flow velocity was calculated using a pre-calibrated mathematical model; and the entire process from signal excitation, acquisition, feature extraction, model calculation to result output was integrated, constructing a complete technical solution to solve the problem of real-time intraoperative blood flow monitoring.

[0077] The vascular diagnosis method based on intelligent guidewire provided in this disclosure can execute the vascular diagnosis system based on intelligent guidewire provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the execution system.

[0078] This disclosure also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the smart guidewire-based vascular diagnosis method provided in any embodiment of this disclosure.

[0079] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0080] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0081] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0082] The aforementioned computer-readable medium carries one or more programs. When the electronic device executes the aforementioned one or more programs, the electronic device causes the electronic device to: acquire an image to be recognized; process the image to be recognized based on a pre-trained spacecraft attitude recognition model to obtain a feature map, and perform deconvolution on the feature map to generate a heat map; classify each pixel in the heat map based on the classification function of the spacecraft attitude recognition model to determine the probability of each structural point in the heat map; determine the target structural point and the coordinate position of the target structural point based on the probability of each structural point in the heat map and a preset probability threshold, and perform coordinate compensation on the coordinate position of the target structural point to obtain the target coordinate position corresponding to the target spacecraft.

[0083] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0085] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0086] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] According to one or more embodiments of this disclosure, this disclosure provides an electronic device, including: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the smart guidewire-based vascular diagnostic method as described in any of the present disclosure.

[0089] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium storing a computer program for performing a smart guidewire-based vascular diagnostic method as described in any of the present disclosure.

[0090] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0091] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0092] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for diagnosing a blood vessel based on a smart guide wire, characterized by, The method includes: a smart guidewire with at least two electrodes at its tip and a maximum guidewire diameter of less than or equal to 1.0 mm; and the method further includes: When the smart guidewire is at the target location of the blood vessel and the at least two electrodes are in contact with the tissue to be detected, in response to the measurement command, an excitation current is applied to at least one of the at least two excitation electrodes at preset multiple frequencies based on the signal generator. The voltage signal of at least one excitation electrode among the at least two electrodes is measured using a voltage measuring instrument, and the voltage signal is processed to obtain a multi-frequency impedance spectrum. The multi-frequency impedance spectrum is processed based on a preset diagnostic algorithm for the tissue to be detected to obtain vascular diagnostic results.

2. The method of claim 1, wherein, The method of applying excitation current to at least one of the at least two electrodes according to a preset multiple frequencies based on a signal generator includes: The excitation current is applied sequentially to the at least one excitation electrode according to the preset plurality of frequencies; or... The excitation current is applied to at least one excitation electrode at a time by superimposing the preset multiple frequencies.

3. The method of claim 1, wherein, The process of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain vascular diagnostic results includes: The multi-frequency electrical impedance spectrum is compared with a preset database of tissue features to be detected to obtain target features; The target features are classified based on a preset classification algorithm model, and the classification result is used as the vascular diagnosis result.

4. The method of claim 1, wherein, The process of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain vascular diagnostic results includes: Based on the multi-frequency electrical impedance spectroscopy, feature parameters associated with the tissue to be detected are extracted; Calculations are performed based on the aforementioned feature parameters and a preset calculation model to obtain initial calculated values; The initial calculated value is compensated based on the temperature of the tissue to be tested, and the target calculated value is obtained as the vascular diagnosis result.

5. The method of claim 1, wherein, The method further includes: A preset pulse is applied to at least one of the at least two excitation electrodes to acquire a reflected signal, and the reflected signal is processed to obtain signal characteristics; and / or, The equivalent circuit parameters are obtained by fitting the voltage signal. The process of processing the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain vascular diagnostic results includes: The multi-frequency impedance spectrum, signal characteristics, and / or equivalent circuit parameters are processed based on the preset diagnostic algorithm for the tissue to be detected to obtain the vascular diagnostic result.

6. The method according to claim 1, characterized in that, The method further includes: The vascular diagnosis results are sent to the human-computer interaction module for display, prompting the user to select a target processing strategy based on the vascular diagnosis results.

7. A vascular diagnostic system based on intelligent guidewire, characterized in that, The system includes: a smart guidewire, a signal generator, a voltage measuring instrument, a signal amplifier, a signal filter, an analog-to-digital converter, and a processor; The signal generator is used to apply an excitation current to at least one of the at least two excitation electrodes at preset frequencies in response to a measurement command when the smart guidewire is at the target vascular location and at least two electrodes at the guidewire tip of the smart guidewire are in contact with the tissue to be detected; wherein the maximum diameter of the smart guidewire is less than or equal to 1.0 mm. The voltage measuring instrument is used to measure the voltage signal of at least one of the at least two electrodes; The signal amplifier is used to amplify the voltage signal; The signal filter is used to filter the amplified signal. The analog-to-digital converter is used to convert the filtered signal into a digital signal. The processor is used to process the digital signal to obtain a multi-frequency impedance spectrum, and to process the multi-frequency impedance spectrum based on a preset diagnostic algorithm for the tissue to be detected to obtain a vascular diagnostic result.

8. The system according to claim 7, characterized in that, The smart guidewire includes a guidewire substrate, a guidewire tip, and at least two electrodes; The at least two electrodes are a first excitation electrode, a second excitation electrode, a first measurement electrode, and a second measurement electrode. The first excitation electrode, the second excitation electrode, the first measurement electrode, and the second measurement electrode are separated by an insulating layer, which is represented by fine dotted shadows. The tip of the guidewire can be tapered, blunt, or arc-shaped. The guidewire substrate is cylindrical, and the first excitation electrode, the first measurement electrode, the second measurement electrode, and the second excitation electrode are arranged sequentially. Each electrode is a ring-shaped structure or a spiral structure with a uniform width of 0.01 to 0.5 mm.

9. The system according to claim 8, characterized in that, The at least two electrodes are more than four electrodes, and the more than four electrodes are arranged in an electrode array of micro-dot electrodes at the front end of the guidewire.

10. The system according to claim 7, characterized in that, The system further includes: a human-computer interaction module, which includes a touch screen, indicator lights, and an audio-visual prompt unit; The touch screen is used to display the vascular diagnosis results and prompt the user to select a target processing strategy based on the vascular diagnosis results using the indicator lights and the audio-visual prompt unit.

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