Tissue state evaluation method and tissue elasticity detection equipment

Abstract

The invention relates to a tissue state evaluation method and tissue elasticity detection device.The method comprises the steps that measurement data of to-be-detected tissue are obtained, and measurement parameters of the to-be-detected tissue are obtained through measurement of the tissue elasticity detection device and comprise elasticity detection parameters, blood flow detection parameters and / or ultrasonic detection parameters; determining comprehensive evaluation parameters according to the measurement data of the to-be-measured tissue; inputting the comprehensive evaluation parameters into a trained tissue inflammation degree evaluation model to obtain the inflammation degree of the to-be-tested tissue; the measurement data of the to-be-tested tissue and the inflammation degree of the to-be-tested tissue are input into a trained tissue state evaluation model, state information of the to-be-tested tissue is obtained, and the state information is one of a pathological change and a non-pathological change.

Description

Technical Field

[0001] The present disclosure relates to artificial intelligence technology, and more specifically, to a tissue state assessment method and a tissue elasticity detection device. Background Art

[0002] In recent years, researchers have conducted extensive and in-depth research on the application of transient elastography (TE) in the assessment of liver disease. However, after obtaining measurement data from TE, researchers still need to manually annotate the disease results, which is inefficient.

[0003] Public content

[0004] One object of the present disclosure is to provide a new technical solution for tissue status assessment methods.

[0005] According to a first aspect of the present disclosure, a method for assessing tissue status is provided, comprising:

[0006] Acquiring measurement data of the tissue to be measured, wherein the measurement parameters of the tissue to be measured are obtained by measuring tissue elasticity detection equipment, including elasticity detection parameters, blood flow detection parameters and / or ultrasonic detection parameters;

[0007] Determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured;

[0008] Inputting the comprehensive evaluation parameters into the trained tissue inflammation degree evaluation model to obtain the inflammation degree of the tissue to be tested;

[0009] The measurement data of the tissue to be measured and the degree of inflammation of the tissue to be measured are input into the trained tissue state assessment model to obtain state information of the tissue to be measured, wherein the state information is one of the occurrence of lesions and the absence of lesions.

[0010] Optionally, determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured includes:

[0011] In a case where the measurement data of the tissue to be measured includes multiple parameters, obtaining a weight coefficient of each parameter in the measurement data of the tissue to be measured;

[0012] The comprehensive evaluation parameter is obtained by weighted summation based on the weight coefficient of each parameter in the measurement data of the tissue to be measured and the corresponding parameter.

[0013] Optionally, determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured includes:

[0014] The measurement data of the tissue to be measured is input into the trained neural network model to obtain comprehensive evaluation parameters.

[0015] Optionally, when the tissue to be tested is the liver, the status information of the tissue to be tested includes fibrosis, fatty change, inflammatory cell infiltration, and / or bleb-like change obtained based on pathological interpretation.

[0016] Optionally, the tissue elasticity detection device includes a processor, a control device, an ultrasonic transceiver circuit, a motor drive circuit, a motor, an ultrasonic transducer, and a pressure sensor, wherein the control device is respectively connected to the processor, the ultrasonic transceiver circuit, the motor drive circuit, and the pressure sensor, the motor drive circuit is connected to the motor, the ultrasonic transducer is respectively connected to the ultrasonic transceiver circuit and the motor, and the ultrasonic transducer and the pressure sensor are arranged on the probe of the device; wherein,

[0017] The measurement parameters of the tissue to be tested are measured by the tissue elasticity detection device in the following manner:

[0018] The pressure sensor is used to collect the pressure applied by the probe to the tissue to be measured, and send the collected pressure value to the control device;

[0019] The control device is used to control the ultrasonic transceiver circuit to transmit and receive ultrasonic waves, and control the motor drive circuit to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer when it is determined that the pressure value meets the preset requirements;

[0020] The control device is also used to obtain measurement data collected by the ultrasonic transceiver circuit.

[0021] Optionally, the control device sends a square wave excitation signal to control the motor drive circuit to drive the motor to vibrate.

[0022] Optionally, the control device is also used to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit based on the tissue type of the tissue to be tested and the detection object information; wherein the ultrasonic control parameters include at least one of the ultrasonic frequency and the ultrasonic amplitude, and the vibration control parameters include at least one of the vibration mode, vibration frequency and vibration amplitude.

[0023] Optionally, the detection object information includes one or more parameters of the detection object's age, height, weight, BMI parameter, and subcutaneous fat thickness, and the control device is also used to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit based on the tissue type of the tissue to be tested and one or more parameters of the detection object's age, height, weight, BMI parameter, and subcutaneous fat thickness.

[0024] Optionally, the control device is further configured to determine an excitation mode for driving the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information, including continuous excitation, multi-pulse excitation, or complex pulse excitation mode.

[0025] Optionally, the control device is used to control the ultrasonic transceiver circuit to transmit ultrasonic waves and control the motor drive circuit to drive the motor to vibrate when determining that the pressure value meets the preset requirements, so as to generate low-frequency shear waves through the ultrasonic transducer, including:

[0026] The control device is used to control the ultrasonic transceiver circuit to transmit and receive ultrasonic signals when it is determined that the pressure value is greater than a first preset pressure threshold;

[0027] The processor is further configured to determine whether the probe is aligned with the tissue to be measured based on the measurement data;

[0028] The control device is further configured to control the motor driving circuit to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer, only when it is determined that the pressure value is greater than a second preset pressure threshold and when it is determined that the probe is aligned with the tissue to be measured;

[0029] The first preset pressure threshold is lower than the second preset pressure threshold.

[0030] Optionally, the device further comprises a display device, the display device being connected to the control device, the display device comprising a pressure indication area, a signal quality indication area and a test result quality indication area, wherein:

[0031] The control device is used to determine the pressure level information corresponding to the pressure value according to the obtained pressure value, and send the pressure level information to the display device, and the display device is used to display the pressure level information in the pressure indication area;

[0032] The control device is used to determine signal quality level information based on the measurement data, and send the signal quality level information to the display device, and the display device is used to display the signal quality level information in the signal quality indication area;

[0033] The control device is further configured to determine confidence information of the state of the tissue to be tested, and send the confidence information of the state of the tissue to be tested to the display device. The display device is further configured to display the confidence information in the detection result quality indication area.

[0034] Optionally, the device establishes a connection with the cloud, wherein the device is further configured to send the measurement data to the cloud so that the cloud executes the tissue status assessment method.

[0035] Optionally, the device further includes a communication device, the communication device includes a USB communication device and a WIFI communication device, and the control device is connected to the USB communication device and the WIFI communication device respectively.

[0036] The control device is used to detect whether the device has established a communication connection with the host computer through the USB communication device. When the device has established a communication connection with the host computer through the USB communication device, the control device detects the connection status of the USB communication device and the host computer. When the connection status is disconnected, the control device controls the WIFI communication device to establish a connection with the host computer.

[0037] Optionally, the control device is further configured to, when the connection state is disconnected, control the WIFI communication device to initiate a hotspot scan to obtain a scan result, and when the scan result is that a preset hotspot is obtained by scanning, establish a communication connection with the preset hotspot, wherein the preset hotspot is turned on by the host computer, and,

[0038] When the scanning result shows that no preset hotspot is scanned, the WIFI communication device is controlled to turn on the hotspot, so that after the host computer detects the hotspot turned on by the device, it initiates a communication connection establishment request, so that the host computer establishes a communication connection with the device.

[0039] Optionally, the device is provided with a probe interface, and the probe is detachably electrically connected to the device via the probe interface.

[0040] The control device is further configured to adjust preset requirements corresponding to electrical characteristic parameters, signal parameters, and pressure values based on the determined category information of the probe to adapt to the corresponding probe.

[0041] According to a second aspect of the present disclosure, a tissue elasticity detection device is provided, comprising a memory and a processor, wherein the memory stores a computer program for controlling the processor to operate so as to execute the method according to any one of the first aspects of the present disclosure.

[0042] The method for determining the state of the tissue to be tested provided in an embodiment of the present invention involves two models, one is a trained inflammation degree assessment model, and the other is a trained tissue state assessment model. The measurement data of the tissue to be tested is first input into the trained inflammation degree assessment model to obtain the inflammation degree of the tissue to be tested, and then the inflammation degree of the tissue to be tested and the measurement data of the tissue to be tested are simultaneously used as input data of the trained tissue state assessment model to obtain the state information of the tissue to be tested. Since the inflammation degree of the tissue to be tested has a high correlation with the state of the tissue to be tested, this can improve the accuracy of the assessment of the state information of the tissue to be tested.

[0043] Features and advantages of the embodiments of the present specification will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.

[0045] Figure 1 is a flowchart of a method for evaluating tissue status according to one embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of the structure of the tissue elasticity detection device according to the present invention. Figure 1 .

[0047] Figure 3 This is a schematic diagram of the structure of the tissue elasticity detection device according to the present invention. Figure 2 .

[0048] Figure 4 The flowchart of a method for controlling an ultrasonic transceiver circuit and a motor drive circuit by a control device according to an embodiment of the present invention is shown.

[0049] Figure 5 This is a schematic diagram of the structure of the tissue elasticity detection device according to the present invention. Figure 3 .

[0050] Figure 6 This is a schematic diagram of the structure of the tissue elasticity detection device according to the present invention. Figure 4 .

[0051] Figure 7 The present invention is a flowchart of a method for establishing a communication connection between a tissue elasticity detection device and a host computer according to an embodiment of the present invention.

[0052] Figure 8 The present invention is a flowchart of a method for establishing a communication connection between a tissue elasticity detection device and a host computer via a WIFI module according to an embodiment of the present invention.

[0053] Figure 9 Schematic diagram of the communication method between a tissue elasticity detection device and a host computer according to an embodiment of the present invention.

[0054] Figure 10 This is a schematic diagram of the structure of the tissue elasticity detection device according to the present invention. Figure 5 . DETAILED DESCRIPTION

[0055] Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

[0056] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments of this specification, its application, or uses.

[0057] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0058] <First embodiment>

[0059] This embodiment provides a method for evaluating tissue status. Figure 1 As shown, the tissue status assessment method includes the following steps S101 to S104.

[0060] Step S101 , obtaining measurement data of the tissue to be measured, wherein the measurement parameters of the tissue to be measured are obtained by measuring a tissue elasticity detection device, including elasticity detection parameters, blood flow detection parameters and / or ultrasonic detection parameters.

[0061] The tissue to be tested can be any organ tissue, for example, liver, kidney or spleen.

[0062] Elasticity detection parameters include shear wave group velocity, phase velocity, attenuation coefficient, dispersion characteristics, and / or anisotropy. Blood flow detection parameters include vascular density, blood flow velocity, blood flow velocity gradient, vascular tortuosity, and / or blood flow resistance index. Ultrasound detection parameters include scattering, attenuation, scatterer distribution characteristics, and / or nonlinear acoustic parameters.

[0063] The blood flow detection parameters and the ultrasound detection parameters can be obtained in one ultrasound detection process, or can be obtained separately in different ultrasound detection processes.

[0064] Step S102: determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured.

[0065] In some embodiments, the comprehensive evaluation parameter is determined based on a linear algorithm, specifically according to the following steps: when the measurement data of the tissue under test includes multiple parameters, obtaining a weight coefficient for each parameter in the measurement data of the tissue under test; and performing a weighted summation based on the weight coefficients of each parameter in the measurement data of the tissue under test and the corresponding parameter to obtain the comprehensive evaluation parameter. The weight coefficients of each parameter are pre-stored values and can be directly obtained.

[0066] In some embodiments, the comprehensive assessment parameter is determined based on a nonlinear algorithm, specifically by the following steps: inputting measurement data of the tissue to be tested into a trained neural network model to obtain the comprehensive assessment parameter. The trained neural network model can determine the range of the comprehensive assessment parameter corresponding to different levels of inflammation.

[0067] The input layer of the trained neural network model receives the measurement data of the tissue to be tested as input data. The hidden layer performs nonlinear transformation and feature extraction on the input data through connections between multiple layers of neurons. The output layer outputs comprehensive scoring parameters.

[0068] The trained neural network model can determine the range of comprehensive evaluation parameters corresponding to different degrees of inflammation. The operations involved in the training process include: establishing a subject operating characteristic curve, with sensitivity and specificity as the horizontal and vertical coordinates respectively; calculating the sensitivity, specificity, positive predictive value and negative predictive value corresponding to the cutoff value of the comprehensive evaluation parameter based on the subject operating characteristic curve and the degree of tissue inflammation; determining the Youden index based on the sensitivity and specificity; and selecting the one with the largest Youden index as the optimal cutoff value to determine the range of the comprehensive evaluation parameter corresponding to each degree of tissue inflammation.

[0069] Step S103: input the comprehensive evaluation parameters into the trained tissue inflammation degree evaluation model to obtain the inflammation degree of the tissue to be tested.

[0070] Step S104 , inputting the measurement data of the tissue to be measured and the inflammation level of the tissue to be measured into the trained tissue state assessment model to obtain state information of the tissue to be measured, wherein the state information is one of the occurrence of lesions and the absence of lesions.

[0071] In some embodiments, when the tissue to be tested is the liver, the status information of the tissue to be tested includes fibrosis, fatty change, inflammatory cell infiltration, and / or bleb-like changes obtained based on pathological interpretation.

[0072] Pathological interpretation is a trained tissue status assessment model that accurately identifies the degree of fibrosis, distribution of fatty degeneration, inflammatory cell infiltration and bubble-like changes in liver pathological images, thereby achieving multi-dimensional quantitative assessment of liver damage.

[0073] Furthermore, by combining liver imaging, gene expression and clinical biochemical data, we systematically analyze the mechanism of fibrosis progression, fatty degeneration metabolic associations and inflammatory activity, providing accurate classification and prognosis prediction for non-alcoholic fatty liver disease, viral hepatitis, etc.

[0074] Furthermore, the tissue status assessment model adopts weakly supervised learning, that is, using a small number of labeled pathological sections and massive unlabeled data to quickly build an automated analysis model for rare liver diseases (such as hereditary metabolic liver diseases) to expand the diagnostic coverage.

[0075] In this embodiment, when training the tissue status assessment model, reinforcement learning dynamically optimizes the liver pathology assessment process, automatically marks high-risk lesion areas and recommends further testing (such as elastography or genetic testing), assisting clinical immediate decision-making.

[0076] In this embodiment, through the visualization of heat maps and feature attribution analysis, the identification basis of fibrosis intervals, fat vacuoles and inflammatory foci is clearly displayed, thereby enhancing the doctor's trust in the evaluation results and supporting doctor-patient communication.

[0077] Edge computing enables localized real-time analysis of liver pathology sections in primary healthcare institutions, quickly outputting fibrosis staging and steatosis grading results, and improving primary liver disease diagnosis and treatment capabilities. Federated learning jointly trains liver pathology models across hospitals, integrating multi-center data while protecting patient privacy, and improving the model's generalization performance for liver damage of different causes. Digital pathology and whole-slice imaging technologies enable full-field digital storage and remote consultation of liver biopsy tissue, supporting multidisciplinary teams in collaborative diagnosis of difficult cases. These technologies collaboratively cover the entire process of liver pathology assessment, from microstructural quantification to molecular mechanism analysis, helping clinicians achieve early screening, precise staging, and dynamic efficacy monitoring of liver disease. They also alleviate the uneven distribution of liver disease expert resources and promote the intelligent, standardized, and homogenized development of liver disease diagnosis and treatment.

[0078] In some embodiments, the training process of the tissue state assessment model includes the following steps: obtaining a first training sample set, wherein each sample in the first training sample set includes measurement data of the tissue, the degree of inflammation of the tissue and corresponding tissue state information; training the tissue state assessment model according to the first training sample set to obtain a trained tissue state assessment model; wherein the measurement data of the tissue is measured by a handheld tissue elasticity detection device, including elasticity detection parameters, blood flow detection parameters, and / or ultrasonic detection parameters of the tissue; the degree of inflammation of the tissue is obtained based on inputting the comprehensive parameters of the tissue into the trained tissue inflammation degree assessment model; the comprehensive parameters of the tissue are determined based on the measurement data of the tissue; and the tissue state information is whether a lesion has occurred or not.

[0079] The samples in the first training set encompass patients of varying ages, genders, disease types, and lesion severity, ensuring diversity and representativeness. Furthermore, the measurement data included in each sample in the first training set is preprocessed data. Preprocessing includes cleaning outliers and missing values, and standardization or normalization.

[0080] The pre-trained tissue state assessment model is a machine learning model, such as a deep neural network (DNN) or convolutional neural network (CNN). Specifically, spatial features of ultrasound detection parameters are extracted through structures such as convolutional layers and pooling layers. Meanwhile, elasticity detection parameters, blood flow detection parameters, and tissue inflammation levels are fused and nonlinearly transformed through structures such as fully connected layers and activation functions.

[0081] During the training process of the tissue status assessment model, model parameters are continuously adjusted through optimization methods such as backpropagation or gradient descent. A loss function, such as mean squared error or cross-entropy loss, is used to measure the difference between the predicted and actual results, thereby determining the degree of training of the tissue status assessment model.

[0082] The trained tissue state assessment model is evaluated using a test dataset to generate evaluation results. Each sample in the test dataset includes tissue measurement data, tissue inflammation level, and corresponding tissue state information. If the evaluation metrics in the evaluation results meet the preset requirements, the trained machine learning model is used as a comprehensive disease assessment model. Evaluation metrics include precision, recall, and F1 score.

[0083] In some embodiments, the training process of the tissue inflammation assessment model includes the following steps: obtaining a second training sample set, wherein each sample in the second training sample set includes comprehensive parameters of the tissue and the corresponding degree of tissue inflammation; training the tissue inflammation degree assessment model based on the second training sample set to obtain a trained tissue inflammation degree assessment model.

[0084] The process of training the tissue inflammation degree assessment model using the second training sample set is actually to use the second training sample set to enable the tissue inflammation degree assessment model to learn the relationship between the comprehensive parameters of the tissue and the corresponding tissue inflammation degree.

[0085] In some embodiments, the measurement data of the tissue, the degree of inflammation of the tissue, and / or the corresponding tissue status information can be displayed on a display device of the tissue elasticity detection device.

[0086] For example, the numerical value corresponding to the measurement data of the tissue, the state description corresponding to the inflammation degree of the tissue, and / or the state description corresponding to the tissue state information are displayed on the display device in text form.

[0087] For example, different colored graphics may be used to display the relative magnitude of the numerical values corresponding to the tissue measurement data, the state description corresponding to the tissue inflammation level, and / or the state description corresponding to the tissue state information. Different graphics may be used to display the numerical values corresponding to the tissue measurement data, the state description corresponding to the tissue inflammation level, and / or the state description corresponding to the tissue state information.

[0088] For example, indicator lights of different colors are used to display the relative size of the numerical value corresponding to the tissue measurement data, the status description corresponding to the inflammation degree of the tissue, and / or the status description corresponding to the tissue status information.

[0089] In some embodiments, the measurement data of the tissue, the degree of inflammation of the tissue, and / or the corresponding tissue status information can be output in the form of voice broadcast.

[0090] The method for determining the state of the tissue to be tested provided in an embodiment of the present invention involves two models, one is a trained inflammation degree assessment model, and the other is a trained tissue state assessment model. The measurement data of the tissue to be tested is first input into the trained inflammation degree assessment model to obtain the inflammation degree of the tissue to be tested, and then the inflammation degree of the tissue to be tested and the measurement data of the tissue to be tested are simultaneously used as input data of the trained tissue state assessment model to obtain the state information of the tissue to be tested. Since the inflammation degree of the tissue to be tested has a high correlation with the state of the tissue to be tested, this can improve the accuracy of the assessment of the state information of the tissue to be tested.

[0091] <Second embodiment>

[0092] Figure 2 A tissue elasticity detection device is shown. Figure 2 As shown, the tissue elasticity detection device 100 includes a processor 101 , a control device 102 , an ultrasonic transceiver circuit 103 , a motor drive circuit 104 , a motor 105 , an ultrasonic transducer 106 , and a pressure sensor 107 .

[0093] according to Figure 1 As shown, the control device 102 is connected to the processor 101, the ultrasonic transceiver circuit 103, the motor drive circuit 104 and the pressure sensor 107 respectively. The motor drive circuit 104 is connected to the motor 105. The ultrasonic transducer 106 is connected to the ultrasonic transceiver circuit 103 and the motor 105 respectively. The ultrasonic transducer 106 and the pressure sensor 107 are arranged on the probe ( Figure 2 not shown).

[0094] The pressure sensor 107 is used to collect the pressure applied by the probe to the tissue to be measured, and send the collected pressure value to the control device 102.

[0095] The control device 102 is used to control the ultrasonic transceiver circuit 103 to transmit and receive ultrasonic waves and control the motor drive circuit 104 to drive the motor 105 to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer 106 when it is determined that the pressure value meets the preset requirements.

[0096] The preset requirement is a pre-set pressure range. If the pressure applied by the probe to the user's tissue under test is within the pressure range, the current pressure value is determined to be moderate, which will neither affect the accuracy of the test nor cause discomfort to the user.

[0097] The control device 102 is further configured to obtain measurement data collected by the ultrasonic transceiver circuit 103 .

[0098] The tissue status assessment method provided in the above embodiment can be executed by the processor 101.

[0099] The tissue elasticity detection device provided in this embodiment has a close connection relationship between the components, forming a highly integrated device. In addition, by setting up a pressure sensor, subsequent detection operations are performed only when the pressure value collected by the sensor meets the preset requirements. This can ensure the accuracy of the detection without causing discomfort to the user, thereby improving the user experience.

[0100] according to Figure 3 As shown, the ultrasonic transceiver circuit 103 includes an ultrasonic transmitter 103a and an ultrasonic receiver 103b.

[0101] according to Figure 3 As shown, the control device 102 includes a motor control unit 102a and an ultrasonic transceiver control unit 102b. The motor control unit 102a is used to control the motor drive circuit 104 to drive the motor 105 to vibrate. The ultrasonic transceiver control unit 102b is used to control the ultrasonic transmitter 103a to transmit ultrasonic waves, obtain ultrasonic signals received by the ultrasonic receiver 103b, and obtain measurement data based on the received ultrasonic signals.

[0102] In some embodiments, the device 100 further includes a power supply. The power supply is connected to the processor 101, the control device 102, the ultrasonic transceiver circuit 103, the motor drive circuit 104, the motor 105, the ultrasonic transducer 106, and the pressure sensor 107. The power supply is used to power the processor 101, the control device 102, the ultrasonic transceiver circuit 103, the motor drive circuit 104, the motor 105, the ultrasonic transducer 106, and the pressure sensor 107.

[0103] The power supply includes a battery and a power manager. The battery is connected to the power manager. The power manager is also connected to a control device. The control device is configured to output a control signal to the power manager, causing it to adjust the battery's output voltage.

[0104] In the prior art, the power manager is installed on the control device, which complicates the control device's circuit design and occupies a large space. In this embodiment, the battery is integrated with the power manager. This eliminates the need for the power manager on the control device, reducing the complexity of the control device's circuit design and facilitating heat dissipation.

[0105] In some embodiments, the processor 101 , the control device 102 , the ultrasonic transceiver circuit 103 , the motor drive circuit 104 , the motor 105 , the battery, and the power manager are integrated into a housing, and the probe is disposed at one end of the housing.

[0106] In some embodiments, the control device generates a square wave excitation signal to control the motor drive circuit to drive the motor to vibrate. The high energy requirements of the motor drive circuit complicate the circuit design of the control device. However, the square wave excitation signal can be directly generated using a simple logic circuit or timer. This reduces the complexity of the circuit design and the number and variety of components required, thereby lowering the circuit's manufacturing cost and maintenance. Furthermore, the use of square wave excitation effectively reduces the weight of the probe. Existing sinusoidal wave excitation circuits often require heavy digital-to-analog conversion circuits, operational amplifier circuits, and bulky high-voltage, high-current power amplifier circuits to achieve a stable sinusoidal output. Square wave excitation circuits, on the other hand, eliminate these bulky components, significantly reducing the weight of the probe and making it easier to operate while holding. Furthermore, the use of square wave excitation reduces power supply requirements. A single power supply is sufficient for the square wave excitation circuit, whereas a sinusoidal wave excitation circuit may require more complex power management circuitry or a higher voltage level. The use of a single power supply not only simplifies the power supply design but also improves the device's portability and battery life. The control device may send a square wave excitation signal to control the ultrasonic transceiver circuit, or may send other excitation signals, for example, a sine wave excitation signal to control the ultrasonic transceiver circuit.

[0107] <Third embodiment>

[0108] Vibration excitation cannot adaptively adjust the low-frequency shear wave frequency and amplitude based on the subject's size, age, and test site. This limits the excitation effect of the low-frequency shear wave and affects the accuracy of tissue elasticity testing. Ultrasonic excitation, however, is often not adjustable, and the frequency and amplitude of the ultrasound waves emitted by the ultrasonic transceiver circuit cannot be optimized to meet the penetration depth and resolution requirements of tissue elasticity testing, affecting the quality of ultrasound images and the effectiveness of elasticity testing.

[0109] In this regard, this embodiment proposes a method of adaptively adjusting control parameters of vibration excitation and ultrasonic excitation to improve the accuracy of tissue elasticity detection.

[0110] Based on this, in some embodiments, the control device is further used to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information.

[0111] In this embodiment, the detection object can be the object on which the tissue elasticity detection device performs tissue elasticity detection, and can be an adult or an infant.

[0112] The detection object information may be the detection object's body type (eg, adult fat body type, adult thin body type, infant, etc.), and may also include the detection object's height, weight, age and other information, which is not limited here.

[0113] In this embodiment, the ultrasonic excitation can be adjusted by an ultrasonic control parameter, and the vibration excitation can be adjusted by a vibration control parameter. The ultrasonic control parameter includes at least one of ultrasonic frequency and ultrasonic amplitude, and the vibration control parameter includes at least one of vibration frequency and vibration amplitude.

[0114] In one embodiment, the subject information includes one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness. The ultrasound control parameter includes the ultrasound frequency, and the vibration control parameter includes the vibration frequency. The control device can be configured to determine the ultrasound frequency and the vibration frequency based on the tissue type of the tissue being tested and one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness.

[0115] In this embodiment, one or more parameters of the test subject's age, height, weight, BMI parameter, subcutaneous fat thickness and the tissue type of the tissue to be tested are input into the adaptive adjustment algorithm to output the ultrasonic frequency and vibration frequency of this tissue elasticity test.

[0116] Frequency (ultrasound frequency or vibration frequency), penetration depth, and resolution are mutually restrictive. The higher the frequency and the shorter the wavelength, the greater the ability to resolve fine tissue structures (i.e., higher resolution). However, high-frequency waves attenuate more rapidly in tissue, resulting in shallower penetration depth. Conversely, low-frequency waves have greater penetration depth, but their longer wavelengths reduce the ability to resolve fine structures (i.e., lower resolution).

[0117] Based on this, for superficial tissue, since deeper penetration depth is not required, higher vibration and ultrasound frequencies can be used. For smaller tissue, since fine tissue detection and higher resolution are required, higher vibration and ultrasound frequencies are also used. For deep tissue, since penetration depth is prioritized, lower vibration and ultrasound frequencies are used.

[0118] For example, the liver is a deep tissue that requires deep energy transfer, so it corresponds to a lower vibration frequency and a lower ultrasonic frequency (e.g., no more than 7.5 MHz). The spleen is a superficial tissue that requires a higher vibration frequency (e.g., no less than 50 Hz) and a higher ultrasonic frequency (e.g., no less than 1.5 MHz). For muscles, due to the small size of muscle tissue, a high-frequency vibration frequency (e.g., no less than 50 Hz) and a high-frequency ultrasonic frequency (e.g., no less than 3.5 MHz) are used.

[0119] One or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness can be used to reflect the subject's body shape. That is, the control device can determine the subject's body shape based on one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness.

[0120] The body types of the test subjects can be divided into various types such as adult fat body type, adult thin body type, infant and young children, etc., which are not limited here.

[0121] For example, the body types of the test subjects can be divided into three types: adult fat body type, adult thin body type, and infants and young children. The corresponding vibration frequency range and ultrasonic frequency range are different for test subjects of different body types. The vibration frequency range corresponding to the adult thin body type is 25Hz-500Hz, and the ultrasonic frequency range is 1MHz-10MHz. The vibration frequency range corresponding to the adult fat body type is 10Hz-200Hz, and the ultrasonic frequency range is 500Khz-8MHz. For infants and young children, because their tissues and organs are small, higher resolution and higher frequency are required. The corresponding vibration frequency range for infants and young children is 40Hz-1500Hz, and the ultrasonic frequency range is 1.5MHz-20MHz.

[0122] The control device may first determine a first ultrasonic frequency range and a first vibration frequency range based on the body type of the test subject. Then, based on the type of tissue being tested, the control device may determine a vibration mode, a second vibration frequency range, and a second ultrasonic frequency range. Finally, the control device may determine a final vibration frequency based on the first vibration frequency range and the second vibration frequency range, and a final ultrasonic frequency based on the first ultrasonic frequency range and the second ultrasonic frequency range.

[0123] In one example, if the tissue elasticity test targets muscle tissue from an obese adult, then based on the above example, the corresponding vibration frequency range for a lean adult is 25Hz-500Hz, and the ultrasonic frequency range is 1MHz-10MHz. The corresponding vibration frequency range for muscle tissue is no less than 50Hz, and the corresponding ultrasonic frequency range is no less than 3.5MHz. Based on these two vibration frequency ranges, the vibration frequency can be determined to be 100Hz and the ultrasonic frequency to be 5MHz.

[0124] In another embodiment, the subject information includes one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness. The ultrasound control parameter includes ultrasound amplitude, and the vibration control parameter includes vibration amplitude. The control device can be configured to determine the ultrasound amplitude and vibration amplitude based on the tissue type of the tissue being tested and one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness.

[0125] In this embodiment, the vibration amplitude and the ultrasound amplitude affect the energy transfer depth, that is, the vibration amplitude and the ultrasound amplitude are related to the penetration depth.

[0126] The body type of the subject can be determined based on one or more of the subject's age, height, weight, BMI, and subcutaneous fat thickness. The ultrasound amplitude and vibration amplitude are different for subjects of different body types.

[0127] For overweight adults, a deeper penetration depth is required, so a larger vibration amplitude and a larger ultrasound amplitude are used. For thin adults, a deeper penetration depth is required, so a smaller vibration amplitude and a smaller ultrasound amplitude are used. For infants and young children, a smaller vibration amplitude and a smaller ultrasound amplitude are used to avoid tissue damage.

[0128] Different types of tissue require different ultrasound and vibration amplitudes. For superficial tissue, such as the spleen, a smaller ultrasound and vibration amplitude can be used. For deeper tissue, such as the liver, a larger ultrasound and vibration amplitude can be used.

[0129] In another embodiment, the ultrasonic control parameters include ultrasonic frequency and ultrasonic amplitude, and the vibration control parameters include vibration mode, vibration frequency and vibration amplitude.

[0130] In this embodiment, since the above embodiments have already described the determination of the ultrasonic frequency, vibration mode and vibration frequency, they will not be elaborated here.

[0131] In one embodiment, after obtaining the ultrasonic frequency and ultrasonic amplitude, the vibration frequency and vibration amplitude, the coding excitation technology can be combined to realize two-dimensional coding of frequency and amplitude, that is, the vibration frequency and vibration amplitude are two-dimensionally encoded, and the ultrasonic frequency and ultrasonic amplitude are two-dimensionally encoded to further optimize the ultrasonic imaging effect.

[0132] For example, a Chirp signal is used for frequency encoding, which extends the bandwidth to a larger extent and achieves a 1mm resolution at a depth of 8cm.

[0133] For the excitation mode of the square wave excitation signal, the control device often uses a fixed single-excitation vibration mode, that is, the control device sends a single square wave excitation signal to control the motor drive circuit to drive the motor vibration. It is unable to adaptively select the corresponding excitation mode according to the differences in the body shape, age, detection part, etc. of the detection object, resulting in poor excitation effect of the square wave excitation signal, affecting the detection results of tissue elasticity.

[0134] In this regard, the inventors have proposed a method for adaptively adjusting the excitation mode of a square wave excitation signal.

[0135] Based on this, in some embodiments, the control device controls the motor drive circuit to drive the motor vibration by issuing a square wave excitation signal. The control device is also used to determine the excitation method for driving the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information.

[0136] In this embodiment, multiple excitation modes of square wave excitation signals are preset, for example, continuous excitation, multi-pulse excitation, and complex pulse excitation modes. The control device can determine one of them as the excitation mode for driving the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information.

[0137] The excitation method is related to the requirements for deep energy delivery and viscoelastic observation, which in turn are related to the tissue type and the subject being examined. Different tissue types and subjects have different corresponding deep energy delivery and viscoelastic observation requirements. Therefore, the excitation method for driving the motor driver circuit can be determined based on the tissue type and subject information.

[0138] Different tissue types require different excitation methods. For tissues requiring deep energy delivery, such as the liver, the motor driver circuit can be driven by continuous excitation or multi-pulse excitation. For tissues requiring viscoelastic properties (such as muscle), the motor driver circuit can be driven by complex pulse excitation.

[0139] Different test subject body types require different excitation methods. For example, for an adult obese test subject, if the tissue to be tested is located in the superficial layer, but the subcutaneous fat is thick, the excitation method may be continuous excitation or multi-pulse excitation instead of complex pulse vibration.

[0140] In one embodiment, after determining the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit, the control device can also be used to adjust the vibration control parameters and the ultrasonic control parameters according to the ultrasonic signal data received by the ultrasonic transceiver circuit.

[0141] For example, if the resolution of an ultrasonic image obtained based on the ultrasonic signal data received by the ultrasonic transceiver circuit is low, the vibration frequency and ultrasonic frequency may be increased. If the ultrasonic image obtained based on the ultrasonic signal data received by the ultrasonic transceiver circuit shows that the penetration depth does not reach the depth of the tissue to be measured, the vibration frequency and ultrasonic frequency may be reduced, or the vibration amplitude and ultrasonic amplitude may be increased.

[0142] The tissue elasticity detection device can automatically determine the vibration control parameters and ultrasonic control parameters based on the detection object information and the tissue type of the tissue to be tested. The tissue elasticity detection device can set different vibration control parameters and ultrasonic control parameters for different tissues of different detection objects, thereby improving the adaptability of the tissue elasticity detection device to different detection objects and different tissues to be tested, and improving the convenience of the tissue elasticity detection device.

[0143] <Fourth embodiment>

[0144] Since the probe is not aligned with the tissue to be tested and the pressure applied by the probe to the tissue to be tested is inappropriate (i.e., the pressure is too high or too low), it will seriously affect the accuracy of the state of the tissue to be tested detected by the tissue elasticity detection device. Therefore, in order to avoid the abnormal state of the tissue to be tested due to improper tissue elasticity detection operation of the user, and to improve the universality of the tissue elasticity detection device for users with different professional depths.

[0145] This embodiment proposes a pre-judgment solution for tissue elasticity detection to improve the accuracy of the state of the tissue to be tested obtained by users with different professional depths through the tissue elasticity detection device.

[0146] Based on this, Figure 4 As shown, the control device is further configured to execute the following steps S401 to S403.

[0147] When determining that the pressure value meets the preset requirement, the control device controls the ultrasonic transceiver circuit to transmit ultrasonic waves, and controls the motor drive circuit to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer, including the following steps S401 to S403.

[0148] Step S401 : When it is determined that the pressure value is greater than a first preset pressure threshold, the ultrasonic transceiver circuit is controlled to transmit and receive ultrasonic signals.

[0149] In this embodiment, after the tissue elasticity detection device is started, first, when a pressure greater than a first preset pressure threshold is applied to the user, the ultrasonic transceiver circuit is controlled to transmit and receive ultrasonic signals.

[0150] Step S402: acquiring measurement data collected by the ultrasonic transceiver circuit, and sending the measurement data to the processor, so that the processor determines whether the probe is aligned with the tissue to be measured based on the measurement data.

[0151] In this embodiment, the control device acquires ultrasonic signal data received by the ultrasonic transceiver circuit and analyzes the ultrasonic signal data to obtain measurement data. The measurement data includes ultrasonic image data. The ultrasonic image data can be used to characterize the tissue currently aligned with the probe. The processor determines whether the probe is aligned with the tissue to be measured based on the ultrasonic image data.

[0152] If the probe is not aligned with the tissue to be tested, the process returns to step S402.

[0153] Step S403 : When it is determined that the pressure value is greater than a second preset pressure threshold and the probe is aligned with the tissue to be measured, the motor driving circuit is controlled to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer.

[0154] In this embodiment, the second preset pressure threshold may be a pressure value range corresponding to the state of the tissue to be tested that is accurately determined based on historical tissue elasticity testing.

[0155] Since the first preset pressure threshold corresponds to the pressure value range when the detection probe is aligned with the tissue to be tested, it is smaller than the second preset pressure threshold during tissue elasticity testing.

[0156] If the probe is aligned with the tissue to be measured, a pressure value is reacquired. If the reacquired pressure value is determined to be greater than a second preset pressure threshold, the motor drive circuit is controlled to drive the motor to vibrate, thereby generating a low-frequency shear wave through the ultrasonic transducer. If the reacquired pressure value is less than or equal to the second preset pressure threshold, pressure values are continuously acquired until the reacquired pressure value exceeds the second preset pressure threshold.

[0157] To improve user comfort during tissue elasticity testing, the pressure value during tissue elasticity testing must not only be greater than the second preset pressure threshold but also be less than a third preset pressure threshold, where the third preset pressure threshold is the maximum pressure value that satisfies the user's comfort experience.

[0158] Because the pressure required to detect alignment with the tissue under test is lower than that required for tissue elasticity testing, the user is prompted to adjust the pressure applied to the skin. The Tissue Position Prompt Area displays the position detection results, providing a convenient reminder to the user that the probe is aligned with the tissue under test. Once the user is prompted that the probe is aligned with the tissue under test, they can increase the pressure applied to the skin surface, thereby facilitating the user's operation to meet the pressure requirements for tissue elasticity testing and improving user convenience.

[0159] In this embodiment, when the probe is aimed at the tissue to be tested and the pressure value is greater than the second pressure threshold, it means that the probe has met the requirements for tissue elasticity detection. At this time, the motor drive circuit is controlled to drive the motor to vibrate to generate low-frequency shear waves through the ultrasonic transducer, thereby regaining measurement data for tissue elasticity detection.

[0160] By setting different pressure thresholds for detecting probe alignment and tissue elasticity, user operation accuracy can be improved and user errors can be reduced. Furthermore, by setting the first pressure threshold lower than the second pressure threshold, energy consumption during the probe alignment test can be reduced.

[0161] In combination with the second embodiment, the control device is further configured to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information, including: step S501 and step S502.

[0162] In step S501 , the control device is configured to determine a first ultrasonic control parameter of the ultrasonic transceiver circuit according to the tissue type of the tissue to be tested and the test object information when it is determined that the pressure value is greater than a first preset pressure threshold.

[0163] In this embodiment, the first ultrasonic control parameter includes at least one of a first ultrasonic frequency and a first ultrasonic amplitude.

[0164] In step S502, the control device is used to determine the second ultrasonic control parameter of the ultrasonic transceiver circuit and the vibration control parameter of the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information when it is determined that the pressure value is greater than the second preset pressure threshold.

[0165] In this embodiment, the second ultrasonic control parameter includes at least one of a second ultrasonic frequency and a second ultrasonic amplitude.

[0166] The first preset pressure threshold is smaller than the second preset pressure threshold, the first ultrasonic frequency is larger than the second ultrasonic frequency, and the first ultrasonic amplitude is larger than the second ultrasonic amplitude.

[0167] Steps S501 and S5402 above indicate that when performing a tissue elasticity test on a subject's tissue, the probe is first aligned with the tissue, and then the tissue elasticity test is performed. In addition to different pressure thresholds, these two stages also utilize different ultrasound control parameters. Specifically, the probe alignment test uses the first ultrasound control parameter, while the tissue elasticity test uses the second ultrasound control parameter.

[0168] Since the morphology, boundaries and internal structure of the organ that the current probe is aimed at need to be clearly displayed when detecting whether the probe is aimed at the tissue to be tested, a higher penetration depth and resolution are required. Therefore, the first ultrasonic frequency and first ultrasonic amplitude corresponding to the detection of whether the probe is aimed at the tissue to be tested are higher.

[0169] During the tissue elasticity testing phase, however, no anatomical image formation is required. Therefore, the second ultrasonic frequency and amplitude can be lower—that is, the second ultrasonic frequency is lower than the first ultrasonic frequency, and the second ultrasonic amplitude is lower than the first ultrasonic amplitude. In this case, lower ultrasonic frequencies and amplitudes during tissue elasticity testing can reduce user discomfort and facilitate multiple tissue elasticity tests during this phase. Furthermore, lower ultrasonic frequencies and amplitudes during tissue elasticity testing can reduce device energy consumption, achieving energy savings.

[0170] <Fifth embodiment>

[0171] In some embodiments, according to Figure 5 As shown, the tissue elasticity detection device 100 further includes a display device 110, which is connected to the control device 102. The display device is disposed on a housing of the tissue elasticity detection device.

[0172] The display device includes a pressure indication area, a signal quality indication area and a detection result quality indication area.

[0173] The display device may be an LED board connected to the control device, and the LED board includes a pressure indication area, a signal quality indication area, and a detection result quality indication area.

[0174] The control device is used to determine the pressure level information corresponding to the pressure value based on the obtained pressure value, and send the pressure level information to the display device, which is used to display the pressure level information in the pressure indication area.

[0175] In this embodiment, the control device pre-stores pressure value ranges corresponding to different pressure levels, for example, the pressure value range corresponding to the low pressure level, the pressure value range corresponding to the medium pressure level, and the pressure value corresponding to the high pressure level, so as to determine the corresponding pressure level information based on the obtained pressure value.

[0176] For example, the low pressure level is 0-10 kPa, the medium pressure level is 10-50 kPa, and the high pressure level is 50-100 kPa. If the current pressure value is 9 kPa, the pressure level information is determined to be a low pressure level.

[0177] The pressure indication area may display different pressure levels through first set colors of different color depths, or may display different pressure levels through different patterns, texts, etc., which is not limited here.

[0178] In one example, the first set color can be green. Green can be divided into light green, medium green, and dark green according to the color depth. Different depths of green indicate different pressure levels. Light green corresponds to low pressure levels, medium green corresponds to medium pressure levels, and dark green corresponds to high pressure levels. This allows users to quickly understand the current pressure status and adjust device operations accordingly.

[0179] The control device is used to determine signal quality level information based on the measurement data and send the signal quality level information to the display device, and the display device is used to display the signal quality level information in the signal quality indication area.

[0180] In this embodiment, the measurement data includes ultrasonic image data, and the control device determines signal quality level information based on the ultrasonic image data. The control device's quality assessment of the ultrasonic image data is primarily divided into two aspects: first, evaluating whether the ultrasonic image data is an ultrasonic image of the tissue to be measured, and second, evaluating whether the ultrasonic image data contains interference data caused by, for example, the subject's breathing / motion. If the ultrasonic image data is an ultrasonic image of the tissue to be measured and does not contain interference data caused by, for example, the subject's breathing / motion, the signal quality level is good. If the ultrasonic image data is not an ultrasonic image of the tissue to be measured and does contain interference data caused by, for example, the subject's breathing / motion, the signal quality level is poor.

[0181] The signal quality indicator area may display different signal quality levels through second set colors of different color depths, or may display different signal quality levels through different prompting methods such as patterns and texts, which are not limited here.

[0182] In one example, the second set color can be blue. Blue can be categorized by color depth as light blue, medium blue, and dark blue. Different shades of blue indicate different signal quality levels. Light blue corresponds to poor signal quality, medium blue corresponds to fair signal quality, and dark blue corresponds to good signal quality. This allows users to quickly understand the current signal quality status and adjust device operations accordingly.

[0183] The control device is further configured to determine confidence information of the state of the tissue to be tested, and send the confidence information of the state of the tissue to be tested to the display device, and the display device is further configured to display the confidence information in the detection result quality indication area.

[0184] In this embodiment, the confidence information of the state of the tissue to be tested can be divided into low confidence, average confidence, high confidence, etc.

[0185] The test result quality indicator area can display different confidence levels through the third set color of different color depths, or can display different confidence levels through different prompts such as different patterns and texts, which is not limited here.

[0186] In one example, the third set color is red. Red is divided into light red, medium red, and dark red according to its color depth. Different shades of red indicate different confidence levels. Light red corresponds to a fair confidence level, indicating that the status of the tissue under test is questionable or requires further examination. Medium red corresponds to a high confidence level, while dark red corresponds to a low confidence level, indicating that the status of the tissue under test is abnormal or there are serious problems. This allows users to quickly understand the confidence level of the tissue under test and take appropriate measures accordingly.

[0187] In some examples, to achieve more precise indications and provide customized setting options, a display device provided on the housing of the device can display the status of the tissue to be tested and user operation information. The display device is provided with a human-computer interaction interface, which is provided with multiple controls for user operation. The user can use the display device to adjust parameters such as the signal gain and focus of the B-ultrasound and control functions such as the start, stop, and mode switching of the elasticity test (E-ultrasound). At the same time, the display device can also display ultrasound image data, pressure indication, signal quality indication, and tissue status confidence indication in real time, so that the user can clearly and intuitively understand the process and results of tissue elasticity testing.

[0188] By providing a display device, the usability of the tissue elasticity testing device can be further improved, enhancing the user's operating experience. Users can intuitively and accurately obtain a variety of signal status information (pressure value, signal quality, and confidence level of the state of the tissue being tested), thereby performing elasticity testing operations more efficiently and accurately.

[0189] In conjunction with the fourth embodiment, the display device includes a detection tissue position prompt area. The control device is further configured to send position detection result information indicating whether the probe is aligned with the detection tissue to the display device, and the display device is configured to display the position detection result information in the detection tissue position prompt area.

[0190] In this embodiment, the position prompt area of the tissue to be tested can indicate the position where the probe is aligned with the tissue to be tested and the position where the probe is not aligned with the tissue to be tested by different colors, or can display the position detection result information by different texts, patterns, etc., which is not limited here.

[0191] In one example, the yellow color may indicate that the probe is aligned with the tissue to be measured, and the purple color may indicate that the probe is not aligned with the tissue to be measured.

[0192] <Sixth embodiment>

[0193] To reduce device power consumption, the state of the tissue under test can be determined through the cloud. A communication connection is established between the tissue elasticity detection device and the cloud. The device sends measurement data to the cloud, which determines the state of the tissue under test based on the measurement data and returns the status to the device.

[0194] In this embodiment, the tissue elasticity detection device includes a network communication module, and a communication connection is established between the tissue elasticity detection device and the cloud through the network communication module. The network communication module supports dual-mode data transmission, and the measurement data is encrypted and compressed and then uploaded to the cloud through a standard network protocol. In the case of poor communication quality between the device and the cloud, a direct connection channel can be enabled to prioritize the transmission of key data in the measurement data, such as ultrasound image data. After receiving the measurement data, the cloud server cluster automatically allocates computing resources, uses a distributed processing architecture to complete complex model calculations, and generates the status of the tissue to be tested. The cloud can automatically return the generated status of the tissue to be tested to the device, and the cloud can also return the status of the tissue to be tested based on the authority verification result when receiving a result query request. The cloud supports multi-dimensional query and historical record tracing.

[0195] In order to improve the security of communication between devices and the cloud, dynamic key management and transmission verification technology can be used to ensure data security.

[0196] By providing the device itself with the ability to determine the status of the organization to be tested based on measurement data and the cloud determining the status of the organization to be tested based on measurement data uploaded by the device, it is possible to ensure that the device has the ability to detect organizational elasticity while enhancing the depth of organizational elasticity analysis through the cloud to meet the application needs of the device in different scenarios.

[0197] <Seventh embodiment>

[0198] The tissue elasticity detection device provided in this embodiment further includes a communication device, which is connected to the control device.

[0199] according to Figure 6 As shown, the communication device includes a USB communication device 111 a and a WIFI communication device 111 b , and the control device 102 is connected to the USB communication device 111 a and the WIFI communication device 111 b respectively.

[0200] The tissue elasticity detection device can establish a communication connection with a host computer via a USB or Wi-Fi communication device. The tissue elasticity detection device is configured to transmit measurement data to the host computer, which then determines the state of the tissue being tested based on the measurement data. The state of the tissue being tested can be displayed on the host computer, or the host computer can transmit the state of the tissue being tested to the tissue elasticity detection device for display on the device's display. The host computer can be a PC, tablet, or mobile phone.

[0201] When the tissue elasticity detection device first establishes a communication connection with a host computer via a Wi-Fi communication device, it must select a specific Wi-Fi connection and verify the connection using a key. Once verification is successful, the device can then establish a communication connection with the host computer via the Wi-Fi communication device. The Wi-Fi designation and key entry require user input. The tissue elasticity detection device records the specified Wi-Fi and corresponding key, allowing it to automatically establish a Wi-Fi communication connection with the host computer at a later time, eliminating the need for the user to manually connect each time. This greatly simplifies the operation process and improves the user experience.

[0202] In this embodiment, the priority of the tissue elasticity detection device in establishing a communication connection with the host computer via the USB communication device is higher than that of establishing a communication connection with the host computer via the WIFI communication device.

[0203] The control device is used to detect whether the device has established a communication connection with the host computer through the USB communication device. When the device has established a communication connection with the host computer through the USB communication device, the control device detects the connection status of the USB communication device and the host computer. When the connection status is disconnected, the control device controls the WIFI communication device to establish a communication connection with the host computer.

[0204] The control device is also used to detect the connection status of the device with the host computer through the USB communication device when the device has established a connection with the host computer through the WIFI communication device, and automatically disconnect the communication connection established with the host computer through the WIFI communication device when it is detected that the device has established a communication connection with the host computer through the USB communication device.

[0205] Figure 7 The figure shows a specific process diagram of establishing communication connection between tissue elasticity detection device and host computer. Figure 7 As shown, the specific flow chart includes steps S701 to S704.

[0206] In step S701, the control device detects whether the device has established a communication connection with the host computer via the USB communication device.

[0207] If the result of executing step S701 is yes, step S702 is executed, and the control device detects the connection status between the USB communication device and the host computer.

[0208] Step S703: When the connection state is disconnected, control the WIFI communication device to establish a communication connection with the host computer.

[0209] If the result of executing step S701 is no, step S703 is executed.

[0210] In step S704 , the control device detects the connection status between the USB communication device and the host computer, and automatically disconnects the communication connection established with the host computer via the WIFI communication device when the connection status is connected.

[0211] In some embodiments, the control device is also used to control the WIFI communication device to initiate a hotspot scan when the connection state is disconnected, obtain a scan result, and establish a communication connection with the preset hotspot when the scan result is that a preset hotspot is scanned and obtained, wherein the preset hotspot is turned on by the host computer, and, when the scan result is that the preset hotspot is not scanned and obtained, control the WIFI communication device to turn on the hotspot, so that the host computer initiates a communication connection establishment request after detecting the hotspot turned on by the device, so that the host computer and the device establish a communication connection.

[0212] When the control device detects that the device has not established a communication connection with the host computer through the USB communication device, Figure 8 The specific process diagram of establishing communication connection between tissue elasticity detection device and host computer through WIFI module is shown. Figure 8 As shown, the specific flow chart includes steps S801 to S804.

[0213] Step S801: The control device controls the WIFI communication device to initiate a hotspot scan to determine whether a preset hotspot is found. The preset hotspot is enabled by the host computer.

[0214] If the result of executing step S801 is yes, step S802 is executed to establish a communication connection with the preset hotspot.

[0215] If the result of step S801 is no, step S803 is executed to control the WIFI communication device to turn on the hotspot.

[0216] Step S804: Detect whether the host computer sends a request to establish a connection with the hotspot opened by the WIFI communication device.

[0217] If the result of executing step S804 is yes, step S805 is executed to establish a communication connection with the host computer through the hotspot opened by the WIFI communication device.

[0218] If the result of executing step S804 is no, continue executing step S804.

[0219] Figure 9 A schematic diagram showing a communication method between a tissue elasticity detection device and a host computer according to an embodiment of the present invention is shown.

[0220] according to Figure 9As shown, the tissue elasticity detection device can establish a communication connection with the host computer through a USB communication device, and can also establish a communication connection with the host computer through a WIFI communication device.

[0221] When the Wi-Fi communication device of the tissue elasticity detection device operates in STA (Station) mode, it initiates a hotspot scan to determine whether a preset hotspot has been scanned. The preset hotspot is activated by the host computer. If the preset hotspot has been scanned, a communication connection is established with the preset hotspot. When the Wi-Fi communication device of the tissue elasticity detection device operates in AP (Access Point) mode, it activates its own hotspot and waits for the host computer to establish a connection via the hotspot activated by the Wi-Fi communication device.

[0222] In an embodiment of the present invention, the tissue elasticity detection device supports both USB and WIFI communication modes, allowing users to flexibly choose wired or wireless connection methods based on actual usage scenarios and needs, greatly improving the device's flexibility and adaptability. Furthermore, the tissue elasticity detection device also takes into account the priority of the communication mode. USB mode connection takes priority over WIFI mode. During the USB mode connection process, the device continuously monitors the connection status. Once a connection interruption is detected, it automatically switches to WIFI connection mode, ensuring a stable connection and continuous data transmission under different conditions.

[0223] <Eighth Embodiment>

[0224] In some embodiments, the tissue elasticity detection device is provided with a probe interface. The probe is removably electrically connected to the tissue elasticity detection device via the probe interface. The probe interface is adaptable to probes of varying physical sizes and electrical characteristics. The probe can be magnetically attached to and removed from the probe interface, facilitating quick replacement of different probe types.

[0225] The control device is also used to adjust the preset requirements corresponding to the electrical characteristic parameters, signal parameters, and pressure values based on the probe type information to adapt to the corresponding probe. This can meet the needs of different probes and ensure system stability when different types of probes are working.

[0226] The category information of the probe can be input by the user based on an interactive interface displayed by the display device, or the identification information of the probe can be detected to determine the category of the probe.

[0227] Electrical characteristic parameters include voltage and / or current.

[0228] Signal parameters include ultrasonic excitation frequency, amplitude and sampling rate.

[0229] The preset requirement for the pressure value corresponds to a pre-set pressure range. If the pressure applied by the probe to the user's tissue under test is within the pressure range, the current pressure value is determined to be moderate, which neither affects the accuracy of the test nor causes discomfort to the user.

[0230] Ninth embodiment

[0231] The present invention also provides a tissue elasticity detection device for implementing any of the above method embodiments. Figure 10 FIG. 1 shows a hardware structure block diagram of a tissue elasticity detection device 1000 according to an embodiment of the present invention. Figure 10 As shown, the tissue elasticity detection device 1000 includes a processor 1010 and a memory 1020 for storing instructions executable by the processor 1010. The processor 1010 is configured to implement the method according to any embodiment of the present disclosure when executing the instructions stored in the memory 1020.

[0232] The processor 1010 is configured to execute computer instructions, which may be written using an instruction set of an architecture such as x86, Arm, RISC, MIPS, or SSE. The memory 1020 may include, for example, ROM (read-only memory), RAM (random access memory), or a non-volatile memory such as a hard disk, which is not limited herein.

[0233] An embodiment of the present invention further provides a storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the steps of the method provided in any of the above embodiments.

[0234] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. For the device embodiments, the relevant parts can be referred to the partial description of the method embodiments.

[0235] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0236] The embodiments of this specification may be systems, methods, and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer instructions for causing a processor to implement various aspects of the embodiments of this specification.

[0237] A computer-readable storage medium can be a tangible device that can hold and store computer instructions for use by a computer instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which computer instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0238] The computer instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network layer, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network layer can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network layer adapter card or network layer interface in each computing / processing device receives computer instructions from the network layer and forwards the computer instructions for storage in a computer-readable storage medium in each computing / processing device.

[0239] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to multiple embodiments of this specification. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of a computer instruction, and the module, program segment or part of a computer instruction contains one or more executable computer instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0240] The embodiments of the present specification have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating tissue status, characterized in that: The method comprises: Acquiring measurement data of the tissue to be measured, wherein the measurement parameters of the tissue to be measured are obtained by measuring tissue elasticity detection equipment, including elasticity detection parameters, blood flow detection parameters and / or ultrasonic detection parameters; Determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured; Inputting the comprehensive evaluation parameters into the trained tissue inflammation degree evaluation model to obtain the inflammation degree of the tissue to be tested; The measurement data of the tissue to be measured and the degree of inflammation of the tissue to be measured are input into the trained tissue state assessment model to obtain state information of the tissue to be measured, wherein the state information is one of the occurrence of lesions and the absence of lesions.

2. The method according to claim 1, characterized in that Determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured includes: In a case where the measurement data of the tissue to be measured includes multiple parameters, obtaining a weight coefficient of each parameter in the measurement data of the tissue to be measured; The comprehensive evaluation parameter is obtained by weighted summation based on the weight coefficient of each parameter in the measurement data of the tissue to be measured and the corresponding parameter.

3. The method according to claim 1, characterized in that Determining comprehensive evaluation parameters based on the measurement data of the tissue to be measured includes: The measurement data of the tissue to be measured is input into the trained neural network model to obtain comprehensive evaluation parameters.

4. The method according to claim 1, wherein When the tissue to be tested is the liver, the status information of the tissue to be tested includes fibrosis, fatty change, inflammatory cell infiltration, and / or bleb-like change obtained based on pathological interpretation.

5. The method according to claim 1, wherein The tissue elasticity detection device includes a processor, a control device, an ultrasonic transceiver circuit, a motor drive circuit, a motor, an ultrasonic transducer, and a pressure sensor, wherein the control device is respectively connected to the processor, the ultrasonic transceiver circuit, the motor drive circuit, and the pressure sensor, the motor drive circuit is connected to the motor, the ultrasonic transducer is respectively connected to the ultrasonic transceiver circuit and the motor, and the ultrasonic transducer and the pressure sensor are arranged on the probe of the device; wherein, The measurement parameters of the tissue to be tested are measured by the tissue elasticity detection device in the following manner: The pressure sensor is used to collect the pressure applied by the probe to the tissue to be measured, and send the collected pressure value to the control device; The control device is used to control the ultrasonic transceiver circuit to transmit and receive ultrasonic waves, and control the motor drive circuit to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer when it is determined that the pressure value meets the preset requirements; The control device is also used to obtain measurement data collected by the ultrasonic transceiver circuit.

6. The method according to claim 5, characterized in that The control device sends a square wave excitation signal to control the motor drive circuit to drive the motor to vibrate.

7. The method according to claim 5, characterized in that The control device is also used to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit based on the tissue type of the tissue to be tested and the detection object information; wherein the ultrasonic control parameters include at least one of the ultrasonic frequency and the ultrasonic amplitude, and the vibration control parameters include at least one of the vibration frequency and the vibration amplitude.

8. The method according to claim 7, characterized in that The detection object information includes one or more parameters of the detection object's age, height, weight, BMI parameter, and subcutaneous fat thickness. The control device is also used to determine the ultrasonic control parameters of the ultrasonic transceiver circuit and the vibration control parameters of the motor drive circuit based on the tissue type of the tissue to be tested and one or more parameters of the detection object's age, height, weight, BMI parameter, and subcutaneous fat thickness.

9. The method according to claim 7, characterized in that The control device is further configured to determine an excitation mode for driving the motor drive circuit according to the tissue type of the tissue to be tested and the detection object information, including continuous excitation, multi-pulse excitation, or complex pulse excitation mode.

10. The method according to claim 5, characterized in that The control device is used to control the ultrasonic transceiver circuit to transmit ultrasonic waves and control the motor drive circuit to drive the motor to vibrate when it is determined that the pressure value meets the preset requirements, so as to generate low-frequency shear waves through the ultrasonic transducer, including: The control device is used to control the ultrasonic transceiver circuit to transmit and receive ultrasonic signals when it is determined that the pressure value is greater than a first preset pressure threshold; The processor is further configured to determine whether the probe is aligned with the tissue to be measured based on the measurement data; The control device is further configured to control the motor driving circuit to drive the motor to vibrate, so as to generate low-frequency shear waves through the ultrasonic transducer, only when it is determined that the pressure value is greater than a second preset pressure threshold and when it is determined that the probe is aligned with the tissue to be measured; The first preset pressure threshold is lower than the second preset pressure threshold.

11. The method according to claim 5, characterized in that The device further comprises a display device connected to the control device, wherein the display device comprises a pressure indication area, a signal quality indication area and a test result quality indication area, wherein: The control device is used to determine the pressure level information corresponding to the pressure value according to the obtained pressure value, and send the pressure level information to the display device, and the display device is used to display the pressure level information in the pressure indication area; The control device is used to determine signal quality level information based on the measurement data, and send the signal quality level information to the display device, and the display device is used to display the signal quality level information in the signal quality indication area; The control device is further configured to determine confidence information of the state of the tissue to be tested, and send the confidence information of the state of the tissue to be tested to the display device. The display device is further configured to display the confidence information in the detection result quality indication area.

12. The method according to claim 5, characterized in that The device establishes a connection with the cloud, wherein the device is further configured to send the measurement data to the cloud so that the cloud executes the tissue status assessment method.

13. The method according to claim 5, characterized in that The device further includes a communication device, the communication device includes a USB communication device and a WIFI communication device, and the control device is connected to the USB communication device and the WIFI communication device respectively. The control device is used to detect whether the device has established a communication connection with the host computer through the USB communication device. When the device has established a communication connection with the host computer through the USB communication device, the control device detects the connection status of the USB communication device and the host computer. When the connection status is disconnected, the control device controls the WIFI communication device to establish a connection with the host computer.

14. The method according to claim 13, characterized in that The control device is further configured to control the WIFI communication device to initiate a hotspot scan when the connection state is disconnected, obtain a scan result, and establish a communication connection with the preset hotspot when the scan result indicates that a preset hotspot is obtained, wherein the preset hotspot is enabled by the host computer, and, When the scanning result shows that no preset hotspot is scanned, the WIFI communication device is controlled to turn on the hotspot, so that after the host computer detects the hotspot turned on by the device, it initiates a communication connection establishment request, so that the host computer establishes a communication connection with the device.

15. The method according to claim 5, characterized in that The device is provided with a probe interface, and the probe is detachably electrically connected to the device via the probe interface. The control device is further configured to adjust preset requirements corresponding to electrical characteristic parameters, signal parameters, and pressure values based on the determined category information of the probe to adapt to the corresponding probe.

16. A tissue elasticity detection device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is used to control the processor to operate so as to perform the method according to any one of claims 1 to 15.

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