Construction method and device of tissue inflammation evaluation model and electronic equipment

Abstract

The invention relates to a tissue inflammation assessment model construction method and device and electronic equipment, and the method comprises the steps: obtaining a training data set which comprises at least one of ultrasonic quantitative parameters, biochemical test parameters, physiological quantitative parameters and morphological detection quantitative parameters of to-be-tested tissues of multiple subjects, the inflammation detection results of the to-be-detected tissues of the subjects are obtained; training the machine learning model according to the training data set to obtain a tissue inflammation evaluation model; input data of the machine learning model at least comprises at least one of ultrasonic quantitative parameters, biochemical test parameters, physiological quantitative parameters and morphological detection quantitative parameters of to-be-detected tissues of subjects in the training data set, and output data is inflammation detection results of the to-be-detected tissues of the corresponding subjects in the training data set; the ultrasonic quantitative parameters are determined on the basis of echo signals corresponding to ultrasonic signals emitted to the to-be-detected tissue and are used for evaluating parameters of the state of the to-be-detected tissue.

Description

Technical Field

[0001] The present disclosure relates to artificial intelligence technology, and more specifically, to a method, device and electronic device for constructing a tissue inflammation assessment model. Background Art

[0002] Increasingly prominent liver diseases have a profound negative impact on human well-being. In the development of many liver diseases, liver fibrosis, as a common and critical pathological process, is closely linked to the dynamic changes in liver elasticity. In recent years, researchers have conducted extensive and in-depth research based on the application of transient elastography (TE) in the field of liver fibrosis assessment. This technology provides doctors with a new means to quantify the degree of liver fibrosis by measuring liver stiffness (LSM).

[0003] However, the single indicator of liver stiffness value cannot fully and timely reflect the complexity and dynamic evolution of liver lesions. In clinical practice, high liver fibrosis does not necessarily mean that active drug treatment is needed immediately, because the disease process of some patients may be in a stable or static state. Closely related to the progression of liver disease is liver inflammation. If livers with seemingly mild fibrosis are neglected for regular monitoring, they may rapidly deteriorate due to high inflammation and miss the opportunity for early intervention. Therefore, in current clinical practice, doctors still rely heavily on liver biopsy, the gold standard for clinical pathological testing, to obtain more detailed lesion information. But liver biopsy is not perfect, and its limitations cannot be ignored. First, as an invasive examination, biopsy itself is accompanied by a certain risk of complications, which limits its application in long-term follow-up monitoring. Secondly, the biopsy results are limited by sampling errors, that is, the tissue samples taken may not fully represent the lesion state of the entire liver, thus affecting the accuracy of diagnosis.

[0004] In view of this, although transient elastography has opened up a new approach for the assessment of liver fibrosis, it is still very important to explore a more comprehensive and comprehensive non-invasive liver lesion assessment system.

[0005] Public Content

[0006] One objective of the present disclosure is to provide a new technical solution for the construction method of a tissue inflammation assessment model.

[0007] According to a first aspect of the present disclosure, a method for constructing a tissue inflammation assessment model is provided, comprising:

[0008] Acquire a training data set, wherein the training data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and inflammation detection results of the tissues to be tested of each subject;

[0009] Training the machine learning model according to the training data set to obtain a tissue inflammation assessment model;

[0010] Among them, the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

[0011] Optionally, the ultrasonic quantitative parameter is at least one of the volume, nonlinear parameters, scattering characteristic parameters, attenuation characteristic parameters, tissue elasticity parameters, and motion parameters of the tissue to be measured.

[0012] Optionally, the nonlinear parameter is a parameter corresponding to a nonlinear effect generated by the propagation of an ultrasonic signal in the tissue to be measured;

[0013] The nonlinear parameter is determined based on the corresponding relationship between the second harmonic and the nonlinear parameter when the ultrasonic signal propagates in the tissue to be measured, and the attenuation coefficient when the ultrasonic signal propagates in the tissue to be measured.

[0014] Optionally, the nonlinear parameter is determined according to the following calculation formula:

[0015]

[0016] Among them, β represents the nonlinear parameter, ρ 0 Represents the density of the tissue to be tested, c 0 represents the speed of ultrasound propagation in the tissue to be tested, f represents the emission frequency of ultrasound, and p 2 (x) represents the amplitude of the second harmonic, x represents the distance the ultrasonic wave propagates, p 0 Represents the amplitude of the ultrasonic wave at the transmitting source, α 1 Represents the attenuation coefficient of the fundamental wave, α 2 Represents the harmonic attenuation coefficient, p 2 (x) / xp 0 2 The value of is when x approaches 0.

[0017] Optionally, the scattering characteristic parameter is at least one of an average intensity parameter, a scattering peak value, a scatterer density, and a scatterer distribution characteristic.

[0018] Optionally, the parameter corresponding to the scatterer distribution feature is determined according to the following calculation formula:

[0019]

[0020] Wherein, k is the parameter corresponding to the scatterer distribution characteristic, β is the ratio of coherent scattered energy to diffuse scattered energy, μ represents the number of effective scatterers in the ultrasonic resolution unit, and the parameters μ and β are calculated based on the mean value of the intensity I of the envelope amplitude. The value obtained by X statistics and the value obtained by U statistics are calculated, wherein the value obtained by X statistics and the value obtained by U statistics are obtained based on XU statistics, wherein the calculation formulas corresponding to U statistics and X statistics are as follows:

[0021] U=E[lnI]-lnE[I]

[0022] X=E[IlnI] / E[I]-E[lnI]

[0023] The calculation of the parameters μ and β can be transformed into the solution of the following nonlinear equations,

[0024] U HK =U,X HK =X

[0025] Among them, U HK and X HK The corresponding calculation formula is as follows:

[0026]

[0027] Among them, γ E is the Euler constant, Γ(·) represents the gamma function, is the double gamma function.

[0028] Optionally, the volume of the tissue to be measured is determined based on the transverse diameter, the anterior-posterior diameter and the superior-inferior diameter of the tissue to be measured determined by the echo signal; or,

[0029] The volume of the tissue to be measured is determined based on a three-dimensional image of the tissue to be measured.

[0030] Optionally, training a machine learning model according to the training data set to obtain a tissue inflammation assessment model includes:

[0031] Training the parameters of the machine learning model according to the training data set to obtain a trained machine learning model;

[0032] Acquire a test data set, wherein the test data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and the corresponding inflammation results of the tissues to be tested of each subject;

[0033] Using the test data set, evaluating the trained machine learning model to obtain an evaluation result;

[0034] When the evaluation indicators in the evaluation results meet the preset requirements, the trained machine learning model is used as the organization verification evaluation model.

[0035] Optionally, the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on one region of the tissue to be tested, or the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different regions of the tissue to be tested.

[0036] According to a second aspect of the present disclosure, a device for constructing a tissue inflammation assessment model is provided, comprising:

[0037] an acquisition module, configured to acquire a training data set, wherein the training data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and inflammation detection results of the tissues to be tested of each subject;

[0038] A training module is used to train the machine learning model according to the training data set to obtain a tissue inflammation assessment model; wherein the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

[0039] According to a third aspect of the present disclosure, an electronic device is provided, comprising 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 execute the method according to any one of the first aspects of the present disclosure.

[0040] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described in any one of the first aspects is implemented.

[0041] According to a fifth aspect of the present disclosure, there is provided a tissue inflammation assessment device, comprising:

[0042] An acquisition module, used to acquire at least one of an ultrasound quantitative parameter, a biochemical test parameter, a physiological quantitative parameter, and a morphological detection quantitative parameter of a tissue to be tested of a subject;

[0043] An evaluation module is used to input at least one of the obtained ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested into a tissue inflammation evaluation model constructed by the method described in any one of the first aspects to obtain an inflammation evaluation result of the tissue to be tested of the subject.

[0044] The method for constructing a tissue inflammation assessment model provided by the present invention constructs a tissue inflammation assessment model through the correspondence between at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject and the inflammation results of the tissue to be tested of each subject. On the one hand, since the ultrasound quantitative parameters and morphological detection quantitative parameters of the tissue to be tested are parameters of the state of the tissue to be tested, they have a high correlation with the inflammation results of the tissue to be tested. Although the biochemical test parameters and physiological quantitative parameters represent the overall health status of the organism, they are also correlated with the inflammation results of the tissue to be tested to a certain extent. Therefore, the constructed tissue inflammation assessment model has high accuracy and can provide strong support for the inflammation assessment of the tissue to be tested. On the other hand, constructing the tissue inflammation assessment model can realize the automated assessment of the inflammation of the tissue to be tested, thereby improving the assessment efficiency.

[0045] 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

[0046] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.

[0047] Figure 1 4 is a process flow chart of a method for constructing a tissue inflammation assessment model according to an embodiment of the present invention.

[0048] Figure 2 It is a principle block diagram of a device for constructing a tissue inflammation assessment model according to an embodiment of the present invention.

[0049] Figure 3 is a hardware structure block diagram of an electronic device according to an embodiment of the present invention.

[0050] Figure 4 4 is a structural block diagram of a device for assessing tissue inflammation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

[0052] 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 the present specification and its application or uses.

[0053] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0054] In one embodiment of the present disclosure, a method for constructing a tissue inflammation assessment model is provided. Figure 1 As shown, a method for constructing a tissue inflammation assessment model provided in this embodiment may include the following steps S110 to S120.

[0055] Step S110, obtaining a training data set, the training data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and the inflammation detection results of the tissues to be tested of each subject.

[0056] In some embodiments, the tissue to be tested can be any one of liver, kidney, spleen, muscle, brain tissue, pancreas, and adipose tissue.

[0057] Ultrasonic quantitative parameters are parameters determined based on the echo signals corresponding to the ultrasonic signals transmitted to the tissue to be tested, and are used to evaluate the state of the tissue to be tested. Before transmitting the ultrasonic signal to the tissue to be tested, the position of the tissue to be tested is first determined to ensure the accuracy of the ultrasonic signal transmission. In some embodiments, an ultrasonic image is acquired, and then the area corresponding to the tissue to be tested is determined from the ultrasonic image.

[0058] Specifically, before determining the area corresponding to the tissue to be tested from the ultrasound image, the ultrasound image is denoised, enhanced, and smoothed to improve the quality of the ultrasound image so as to more accurately determine the area corresponding to the tissue to be tested. The area corresponding to the tissue to be tested is determined from the ultrasound image using an image processing algorithm. Specifically, key features are extracted from the ultrasound image using an image processing algorithm. The key features include shape features, contour features, and texture features. The image processing algorithm is at least one of an edge detection algorithm, a corner detection algorithm, and a texture analysis algorithm. Based on the extracted key features, each area in the ultrasound image is classified using a classification algorithm to determine the area corresponding to the tissue to be tested. The classification algorithm is any one of an algorithm based on a support vector machine, an algorithm based on a decision tree, and a neural network model algorithm. A sampling frame is placed in the area corresponding to the determined tissue to be tested to ensure that the sampling frame covers the entire area and has clear boundaries.

[0059] In some embodiments, after determining the area corresponding to the tissue to be measured, an ultrasonic signal is transmitted to the area corresponding to the tissue to be measured, and the transmitted ultrasonic signal should avoid the blood vessels and connective tissues in the tissue to be measured, and then the ultrasonic echo signal is received. Radio frequency data is collected from the ultrasonic echo signal, and the ultrasonic quantitative parameters of the tissue to be measured are determined based on the radio frequency data. Before collecting radio frequency data from the ultrasonic echo signal, the ultrasonic echo signal is reconstructed to obtain a reconstructed ultrasonic echo signal to improve the quality of the ultrasonic echo signal and provide a basis for subsequent analysis of the ultrasonic echo signal. The reconstruction of the ultrasonic echo signal includes signal denoising and signal enhancement. The reconstructed ultrasonic echo signal is subjected to multi-dimensional analysis to collect radio frequency data, and then the ultrasonic quantitative parameters of the tissue to be measured are determined based on the radio frequency data. The multi-dimensional analysis includes time domain analysis (e.g., waveform analysis, statistical analysis), frequency domain analysis (e.g., spectrum analysis, power spectrum analysis) and time-frequency analysis (e.g., short-time Fourier transform, wavelet transform).

[0060] The inflammation detection result of the tissue to be tested of each subject may be the presence of inflammation in the tissue to be tested or the absence of inflammation in the tissue to be tested.

[0061] Taking the liver as an example, the inflammation result of the tissue to be tested of each subject is determined according to any of the following scoring systems: the NASH scoring system proposed by the American Association for the Study of Liver Diseases, and the SAF scoring system proposed by the European Association for the Study of the Liver. Any of the above scoring systems is applicable to non-alcoholic steatohepatitis (NASH). The inflammation result of the tissue to be tested of each subject may be a score determined based on any of the above scoring systems, or may be a result of whether the subject suffers from NASH, determined based on any of the above scoring systems. When the inflammation result of the tissue to be tested of each subject is a score determined based on any of the above scoring systems, the score includes a score corresponding to liver steatosis, a score corresponding to lobular inflammation, a score corresponding to ballooning degeneration, and a total NASH score.

[0062] The more commonly used NASH scoring system is the semi-quantitative scoring system. In this scoring system, the liver cell fatty degeneration score is divided into 0-3 points, the liver lobular inflammation score is divided into 0-3 points, the ballooning degeneration score is divided into 0-2 points, and the full score is 8 points. The specific scoring rules are shown in Table 1.

[0063] Table 1 NASH scoring system

[0064]

[0065] The SAF scoring system consists of three parts: fatty degeneration (scoring range: 0-3 points); activity (scoring range: 0-4 points), which includes the sum of lobular inflammation (0-2 points) and ballooning degeneration (0-2 points); fibrosis (scoring range: 0-4 points), with a total score of 0 to 11. According to the SAF scoring system, if fatty degeneration, lobular inflammation and ballooning degeneration are present in the histological manifestations at the same time, non-alcoholic steatohepatitis can be diagnosed.

[0066] Biochemical test parameters refer to the content of specific chemical substances in blood, urine or other biological fluids. Specific chemical substances include enzymes, proteins, lipids, and electrolytes. Biochemical test parameters can reflect the body's metabolic status, organ function, and overall health status. Changes in biochemical test parameters can indirectly reflect changes in physiological functions and are an important reference for assessing health status, diagnosing diseases, and treating them. For example, blood sugar levels can reflect the state of sugar metabolism and are closely related to the diagnosis and treatment of diabetes; transaminase levels can reflect the health of the liver and are of great significance for the diagnosis of diseases such as hepatitis and cirrhosis. Biochemical test parameters include blood sugar, blood lipids, liver function indicators (such as transaminase), renal function indicators (such as urea nitrogen, creatinine), and electrolytes (such as sodium, potassium, chloride, calcium, and magnesium). Biochemical test parameters can be measured by laboratory equipment such as biochemical analyzers, and blood, urine, and other samples need to be collected for analysis.

[0067] Physiological quantitative parameters reflect the basic life activities of the body, such as the function of the cardiovascular system, temperature regulation, respiratory exchange, etc. Physiological quantitative parameters reflect the physiological function state of the body and are of great significance for the prevention, diagnosis and treatment of diseases. For example, heart rate monitoring can timely detect cardiovascular diseases such as arrhythmia; blood pressure measurement is crucial for the diagnosis and control of diseases such as hypertension. Physiological quantitative parameters include heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation. Physiological quantitative parameters can be measured by portable monitoring equipment or clinical examinations, such as electrocardiographs, sphygmomanometers, thermometers, etc.

[0068] It should be noted that there is a close correlation between biochemical test parameters and physiological quantitative parameters. Changes in physiological quantitative parameters may be caused by changes in biochemical test parameters, and changes in biochemical test parameters may be caused by changes in physiological quantitative parameters. For example, an increase in blood sugar levels may lead to changes in physiological quantitative parameters such as increased heart rate and blood pressure, and a decrease in heart function may also affect the metabolism of biochemical test parameters such as blood sugar. In short, biochemical test parameters and physiological quantitative parameters reflect the health status of organisms from the perspectives of chemical composition and physiological function respectively. In practical applications, the two complement each other and provide important basis for the prevention, diagnosis and treatment of diseases.

[0069] Morphological quantitative parameters include the size, shape, signal intensity of tissues or organs, and morphological characteristics of lesions. Morphological quantitative parameters can be detected by A-ultrasound, B-ultrasound, M-ultrasound, CT, MRI (Magnetic Resonance Imaging) or PET (Positron Emission Computed Tomography).

[0070] Step S120, training the machine learning model according to the training data set to obtain a tissue inflammation assessment model; wherein the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

[0071] Based on the training data set, the ultrasound quantitative parameters, biochemical detection parameters, physiological quantitative parameters, morphological detection quantitative parameters of the tested tissues of each subject and the inflammation detection results of the tested tissues of each subject have a one-to-one correspondence, and this one-to-one correspondence is also input into the machine learning model.

[0072] The method for constructing a tissue inflammation assessment model provided in an embodiment of the present invention constructs a tissue inflammation assessment model through the correspondence between at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject and the inflammation results of the tissue to be tested of each subject. On the one hand, since the ultrasound quantitative parameters and morphological detection quantitative parameters of the tissue to be tested are parameters of the state of the tissue to be tested, they have a high correlation with the inflammation results of the tissue to be tested. Although the biochemical test parameters and physiological quantitative parameters represent the overall health status of the organism, they are also correlated with the inflammation results of the tissue to be tested to a certain extent. Therefore, the constructed tissue inflammation assessment model has high accuracy and can provide strong support for the inflammation assessment of the tissue to be tested. On the other hand, constructing the tissue inflammation assessment model can realize the automated assessment of the inflammation of the tissue to be tested, thereby improving the assessment efficiency.

[0073] In some embodiments, the ultrasound quantitative parameter is at least one of the volume, nonlinear parameters, scattering characteristic parameters, attenuation characteristic parameters, and tissue elasticity parameters of the tissue to be measured.

[0074] When the ultrasonic quantitative parameters are multiple ones of the volume, nonlinear parameters, scattering characteristic parameters, attenuation characteristic parameters and tissue elasticity parameters of the tissue to be tested, it is possible to characterize the state of the tissue to be tested from a multi-dimensional perspective, thereby providing more comprehensive data for constructing an inflammation assessment model for the tissue to be tested, thereby making the constructed inflammation assessment model for the tissue to be tested have higher assessment accuracy.

[0075] In some embodiments, the volume of the tissue to be measured is determined based on the transverse diameter, anteroposterior diameter and superior-inferior diameter of the tissue to be measured determined by the echo signal. Specifically, the transverse diameter, anteroposterior diameter and superior-inferior diameter of the tissue to be measured are substituted into the ellipsoid volume calculation formula to obtain the volume of the tissue to be measured.

[0076] In some embodiments, the volume of the tissue to be measured is determined based on a three-dimensional image of the tissue to be measured. A three-dimensional image of the tissue to be measured is obtained by three-dimensional imaging technology, and the volume of the tissue to be measured is calculated based on the three-dimensional image. This method for determining the volume of the tissue to be measured can reduce human measurement errors and improve measurement accuracy.

[0077] Taking the liver as an example, when determining the volume of the liver, it is necessary to select appropriate standard sections, such as the maximum oblique section of the right liver lobe, the anteroposterior radial section of the right liver lobe, the sagittal longitudinal section of the left liver lobe, etc., to ensure the accuracy of the measurement results.

[0078] Taking the liver as an example of the tissue to be tested, the relationship between the liver volume factor and liver inflammation is explained.

[0079] Liver volume is an important indicator to measure liver reserve function, which can directly reflect liver capacity and the number of liver cells. Changes in liver volume are often closely related to the occurrence and development of liver lesions. Various liver lesions may lead to an increase in liver volume, including fatty liver, hepatitis, primary liver cancer, etc. Fatty liver patients have impaired fat metabolism in the body, which increases the fat content in liver cells, leading to an increase in liver volume. Viral hepatitis patients have impaired liver function and edema in liver cells, which can also cause an increase in liver volume. Primary liver cancer is a malignant space-occupying lesion of the liver. Due to abnormal proliferation of liver cells, the liver will swell. A reduction in liver volume may mean that the liver has a serious lesion, such as late-stage cirrhosis. Cirrhosis can cause scarring and necrosis of liver tissue, which gradually reduces the liver volume and seriously impairs its function. The liver volume of normal adults has a certain reference range, and the specific values ​​may vary depending on factors such as gender, age, and body shape. Generally speaking, the liver volume of men is slightly larger than that of women, and the liver volume may gradually decrease with age. If the measurement results show that the liver volume has increased or decreased beyond the normal range, it may indicate the presence of liver disease, such as hepatitis, cirrhosis, fatty liver, liver cancer, etc.

[0080] The nonlinear parameter is a parameter corresponding to the nonlinear effect generated by the ultrasonic signal propagating in the tissue to be tested. In some embodiments, the nonlinear parameter is determined based on the corresponding relationship between the second harmonic and the nonlinear parameter when the ultrasonic signal propagates in the tissue to be tested, and the attenuation coefficient of the ultrasonic signal when propagating in the tissue to be tested.

[0081] The mathematical relationship between the second harmonic and the nonlinear parameters is as follows:

[0082]

[0083] Among them, p 2 (x) represents the amplitude of the second harmonic, β represents the nonlinear parameter, and ρ 0 Represents the density of the tissue to be tested, represents the speed of ultrasound propagation in the tissue to be tested, x represents the distance of ultrasound propagation, f represents the emission frequency of ultrasound, and p 0 Represents the amplitude of the ultrasound at the source. When ultrasound propagates in the tissue to be tested, it is necessary to consider the attenuation of ultrasound in the tissue to be tested, and make corresponding compensation based on the attenuation to improve the accuracy of nonlinear parameter determination. Combining the above mathematical relationship and the attenuation coefficient of the ultrasound signal when propagating in the tissue to be tested, the nonlinear parameter is obtained based on the following calculation formula:

[0084]

[0085] Among them, α 1 Represents the attenuation coefficient of the fundamental wave, α 2represents the attenuation coefficient of harmonics. When x approaches 0, p 2 (x) / xp 0 2 Substitute the value of into the above calculation formula. When x approaches 0, p 2 (x) / xp 0 2 The value of is determined by determining the second harmonic amplitude at different propagation distances and plotting p 2 (x) / xp 0 2 The curve of x, and through linear deduction, we can get that when x approaches 0, p 2 (x) / xp 0 2 It should be noted that when the nonlinear parameter is obtained by using the above calculation formula, the value of x is determined according to the requirements.

[0086] In some embodiments, the scattering characteristic parameter is at least one of an average intensity parameter, a scattering peak value, a scatterer density, and a scatterer distribution feature.

[0087] The scattering characteristic parameter is a parameter used to characterize the energy distribution in all directions of the ultrasonic signal after scattering when propagating in the tissue to be tested. The scattering characteristic parameter can be at least one of a homodyne K distribution parameter and a nakagami distribution parameter.

[0088] The present invention discloses a probability statistical model of ultrasonic backscattered signal of homodyne K distribution, which is represented by the following probability density function:

[0089]

[0090] Where A represents the amplitude of the ultrasonic backscatter signal envelope, f(A) represents the probability density function of the envelope amplitude, x represents the integral variable, and J 0 (·) is the zero-order first-kind Bessel function, s 2 represents the energy of the coherent scattered signal, σ 2 represents the energy of the diffuse scattering signal, μ represents the number of effective scatterers in the ultrasonic resolution unit. Define parameter k = s / σ, which represents the ratio of the coherent scattering signal to the diffuse scattering signal.

[0091] Based on the calculation formula corresponding to the above probability density function, the corresponding discrete form can be obtained as follows:

[0092]

[0093] Where Γ(·) represents the gamma function, K p represents the p-order modified Bessel function of the second kind.

[0094] Furthermore, based on the above definition of parameter k, the applicant has noticed the influence of the difference between coherent scattering energy and diffuse scattering energy. In order to further improve the estimation accuracy of parameter k, the present invention also provides an improved evaluation algorithm, which is as follows:

[0095] Define parameter β as the coherent scattering energy s 2 and diffuse scattering energy 2σ 2 The ratio of β = s 2 / (2σ 2 ). Specifically, according to the mean value of the intensity I of the envelope amplitude The values ​​obtained by X statistics and U statistics are used to calculate the parameters μ and β. The values ​​obtained by X statistics and U statistics are statistically analyzed based on the XU statistics method. The corresponding calculation formulas for U statistics and X statistics are as follows:

[0096] U=E[lnI]-lnE[I]

[0097] X=E[IlnI] / E[I]-E[lnI]

[0098] The estimation of parameters μ and β can be transformed into the solution of the following nonlinear equations,

[0099] U HK =U,X HK =X

[0100] Among them, U HK and X HK The corresponding calculation formula is as follows:

[0101]

[0102] Among them, γ E is the Euler constant, which is generally taken as 0.577. is the double gamma function.

[0103] Solve the parameters μ and β according to the formula, and then estimate k based on the parameters μ and β:

[0104]

[0105] The improved homodyne K distribution model of the present invention can more accurately evaluate the scatterer distribution characteristic parameter k of the homodyne K distribution.

[0106] The tissue elasticity parameter is a state parameter of the tissue to be tested. Taking the liver as an example, the tissue elasticity parameter can be obtained using a liver detection system. The liver detection system includes an elasticity detection probe and a control component that is communicatively connected to the elasticity detection probe. The elasticity detection probe includes a pressure measurement component. The elasticity detection probe is used to obtain the current pressure applied to the tissue to be tested based on the pressure measurement component, and allows the current pressure to be adjusted under the control of the control component so that the current pressure meets the preset requirements. The control component is used to obtain the current pressure applied by the elasticity detection probe to the tissue to be tested, identify the current pressure, and control the elasticity detection probe to adjust the current pressure according to the identification result. The elasticity detection probe is also used to trigger vibration excitation to generate shear waves in the tissue to be tested when the current pressure meets the preset requirements, and emit ultrasonic signals to track the propagation process of the shear waves in the tissue to be tested. The control component is also used to generate ultrasonic data based on the echo signal carrying the propagation process information in the tissue to be tested, and process the ultrasonic data to determine the elasticity parameters corresponding to the tissue to be tested as tissue elasticity parameters.

[0107] The attenuation characteristic parameter is the propagation characteristic parameter of the ultrasonic signal in the tissue to be measured.

[0108] The motion parameters are the displacement and strain of the tested area of ​​the tested tissue caused by the force, which is external pressure or active contraction and relaxation.

[0109] In some embodiments, the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on one region of the tissue to be tested, or the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different regions of the tissue to be tested.

[0110] The ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different areas of the tissue to be tested, which can ensure the accuracy of the determination of the ultrasonic quantitative parameters of the tissue to be tested. When the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different areas of the tissue to be tested, multiple transmitters and multiple receivers are used to realize parallel transmission of ultrasonic signals and parallel reception of ultrasonic echo signals. Multiple transmitters are used to transmit radio frequency signals at the same time to cover a wider area and realize multi-channel and multi-frequency signal acquisition. This parallel transmission method can greatly shorten the signal acquisition time and improve the acquisition efficiency. Equipped with multiple receivers, each receiver is responsible for receiving the signal of a specific transmitter. In this way, synchronous detection can be performed on different areas of the tissue to be tested at the same time, which improves the speed and efficiency of signal acquisition.

[0111] It should be noted that in order to ensure that each transmitter and receiver does not interfere with each other, an intelligent frequency allocation and management algorithm is used. This algorithm can dynamically adjust the transmission frequency of each transmitter and the receiving frequency of each receiver to ensure the accuracy and stability of signal acquisition. In order to ensure the synchronization of parallel transmission and parallel reception, high-precision clock synchronization technology and calibration algorithms are used. These technologies can ensure that the time error between each transmitter and receiver is within the microsecond level, thereby ensuring the accuracy and consistency of signal acquisition.

[0112] In some embodiments, step S120 specifically includes: training the machine learning model according to the training data set to obtain a tissue inflammation assessment model including: training the parameters of the machine learning model according to the training data set to obtain a trained machine learning model; obtaining a test data set, the test data set including at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and the corresponding inflammation results of the tissues to be tested of each subject; using the test data set to evaluate the trained machine learning model to obtain an evaluation result; when the evaluation indicators in the evaluation result meet the preset requirements, the trained machine learning model is used as a tissue verification evaluation model.

[0113] In some embodiments, before using a training data set to train a machine learning model, the data in the training data set is cleaned to remove outliers and missing values ​​to ensure the training accuracy of the subsequent machine learning model.

[0114] In some embodiments, before using the training data set to train the machine learning model, the ultrasonic quantitative parameters of the tissue to be tested are normalized to ensure that the ultrasonic quantitative parameters have the same scale to ensure the training accuracy of the subsequent machine learning model.

[0115] In some embodiments, when there are multiple ultrasound quantitative parameters of the tissue to be tested of the subject, a feature selection algorithm is used to screen out ultrasound quantitative parameters whose correlation with the inflammation result of the tissue to be tested exceeds a preset threshold value, so as to serve as data in the training data set. Since the selected ultrasound quantitative parameters have a high correlation with the inflammation result of the tissue to be tested, the prediction accuracy of the machine learning model is high.

[0116] The screened ultrasonic quantitative parameter is at least one of the volume, nonlinear parameter, scattering characteristic parameter, attenuation characteristic parameter and tissue elasticity parameter of the tissue to be measured.

[0117] The feature selection algorithm may be any of the following algorithms: recursive feature elimination algorithm, tree model-based feature selection algorithm, and dimension reduction algorithm. The dimension reduction algorithm is PCA (Principal Components Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding).

[0118] In some embodiments, the machine learning model can be any one of the following: a linear regression model, a logistic regression model, a support vector machine, a random forest model, a gradient boosting tree model.

[0119] In some embodiments, when training a machine learning model using data in a training data set, a grid search method or a random search method is used to optimize the parameters of the machine learning model to improve the accuracy and robustness of the machine learning model.

[0120] In some embodiments, when evaluating the trained machine learning model using a test data set, at least one evaluation indicator among the AUC value (Area Under the Curve, ROC curve area value), accuracy, and recall rate is used to evaluate the performance of the trained machine learning model.

[0121] The present invention also provides a device for constructing a tissue inflammation assessment model for implementing any of the above methods. Figure 2 FIG. 4 is a block diagram showing a structure of a device for constructing a tissue inflammation assessment model according to an embodiment of the present invention. Figure 2 As shown, the device for constructing a tissue inflammation assessment model may include an acquisition module 210 and a training module 220 .

[0122] The acquisition module 210 is used to acquire a training data set, which includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, as well as the inflammation detection results of the tissues to be tested of each subject.

[0123] The training module 220 is used to train the machine learning model according to the training data set to obtain a tissue inflammation assessment model; wherein the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

[0124] In some embodiments, the ultrasound quantitative parameter is at least one of the volume, nonlinear parameters, scattering characteristic parameters, attenuation characteristic parameters, and tissue elasticity parameters of the tissue to be measured.

[0125] In some embodiments, the nonlinear parameter is a parameter corresponding to the nonlinear effect generated by the ultrasonic signal propagating in the tissue to be measured, and the nonlinear parameter is determined based on the correspondence between the second harmonic and the nonlinear parameter when the ultrasonic signal propagates in the tissue to be measured, and the attenuation coefficient of the ultrasonic signal when propagating in the tissue to be measured.

[0126] In some embodiments, the scattering characteristic parameter is at least one of an average intensity parameter, a scattering peak value, a scatterer density, and a scatterer distribution feature.

[0127] In some embodiments, the volume of the tissue to be measured is determined based on the transverse diameter, the anterior-posterior diameter, and the superior-inferior diameter of the tissue to be measured determined by the echo signal; or, the volume of the tissue to be measured is determined based on a three-dimensional image of the tissue to be measured.

[0128] In some embodiments, the training module 220 is also used to: train the parameters of the machine learning model according to the training data set to obtain a trained machine learning model; obtain a test data set, the test data set includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and the corresponding inflammation results of the tissues to be tested of each subject; use the test data set to evaluate the trained machine learning model to obtain an evaluation result; when the evaluation indicators in the evaluation result meet the preset requirements, the trained machine learning model is used as a tissue verification evaluation model.

[0129] In some embodiments, the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on one region of the tissue to be tested, or the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different regions of the tissue to be tested.

[0130] The present invention also provides an electronic device for implementing any of the above method embodiments. Figure 3 FIG. 4 shows a hardware structure block diagram of an electronic device 300 according to an embodiment of the present invention. Figure 3 As shown, the electronic device 300 includes a processor 310 and a memory 320 for storing instructions executable by the processor 310. The processor 310 is configured to implement the method according to any embodiment of the present disclosure when executing the instructions stored in the memory 320.

[0131] The processor 310 is used to execute computer instructions, which can be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 320 includes, for example, ROM (read-only memory), RAM (random access memory), a non-volatile memory such as a hard disk, etc., which are not limited here.

[0132] An embodiment of the present invention further provides a storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of the method provided in any of the above embodiments are implemented.

[0133] An embodiment of the present invention further provides a tissue inflammation assessment device, which performs assessment based on a tissue inflammation assessment model constructed according to any one of the above embodiments. Figure 4 FIG. 4 shows a structural block diagram of a tissue inflammation assessment device according to an embodiment of the present invention. Figure 4 As shown, the tissue inflammation assessment device may include an acquisition module 410 and an assessment module 420 .

[0134] The acquisition module 410 is used to acquire at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of the subject.

[0135] The related introduction of the ultrasonic quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested can refer to any of the above embodiments, and will not be elaborated here.

[0136] The evaluation module 420 is used to input at least one of the obtained ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested into the tissue inflammation evaluation model constructed in any of the above embodiments to obtain the inflammation evaluation result of the tissue to be tested of the subject.

[0137] The training process of the tissue inflammation assessment model may refer to any of the above embodiments, and will not be described in detail here.

[0138] In some embodiments, a tissue inflammation assessment device is used to assess inflammation of the liver, and the tissue inflammation assessment device comprises an acquisition module and an assessment module.

[0139] The acquisition module is used to acquire at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the liver of the subject. The evaluation module is used to input at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the liver obtained into the tissue inflammation evaluation model constructed in any of the above embodiments to obtain the inflammation evaluation result of the liver of the subject.

[0140] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For the device embodiment, its related parts can be referred to the partial description of the method embodiment.

[0141] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] The embodiments of the present 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 the present specification.

[0143] A computer-readable storage medium may be a tangible device that can hold and store computer instructions used by a computer instruction execution device. A computer-readable storage medium may 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 of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) 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 disk 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 of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted 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 a wire.

[0144] 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 through a network layer, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network layer may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network layer adapter card or network layer interface in each computing / processing device receives the computer instructions from the network layer and forwards the computer instructions for storage in a computer-readable storage medium in each computing / processing device.

[0145] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of this specification. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a computer instruction, and a part of a module, a program segment or a computer instruction contains one or more executable computer instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0146] The embodiments of the present specification have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for constructing a tissue inflammation assessment model, characterized in that: include: Acquire a training data set, wherein the training data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and inflammation detection results of the tissues to be tested of each subject; Training the machine learning model according to the training data set to obtain a tissue inflammation assessment model; Among them, the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

2. The method according to claim 1, characterized in that The ultrasonic quantitative parameter is at least one of the volume, nonlinear parameter, scattering characteristic parameter, attenuation characteristic parameter, tissue elasticity parameter, and motion parameter of the tissue to be measured.

3. The method according to claim 2, characterized in that: The nonlinear parameter is a parameter corresponding to the nonlinear effect generated by the propagation of the ultrasonic signal in the tissue to be measured; The nonlinear parameter is determined based on the corresponding relationship between the second harmonic and the nonlinear parameter when the ultrasonic signal propagates in the tissue to be measured, and the attenuation coefficient when the ultrasonic signal propagates in the tissue to be measured.

4. The method according to claim 3, characterized in that: The nonlinear parameters are determined according to the following calculation formula: Where β represents the nonlinear parameter, ρ0 represents the density of the tissue to be measured, c0 represents the speed of ultrasound propagation in the tissue to be measured, f represents the emission frequency of ultrasound, p2(x) represents the amplitude of the second harmonic, x represents the distance of ultrasound propagation, p0 represents the amplitude of ultrasound at the emission source, σ1 represents the attenuation coefficient of the fundamental wave, α2 represents the attenuation coefficient of the harmonic, and p2(x) / xp0 2 The value of is when x approaches 0.

5. The method according to claim 2, characterized in that: The scattering characteristic parameter is at least one of an average intensity parameter, a scattering peak value, a scatterer density, and a scatterer distribution feature.

6. The method according to claim 5, characterized in that The parameters corresponding to the scatterer distribution characteristics are determined according to the following calculation formula: Wherein, k is the parameter corresponding to the scatterer distribution characteristic, β is the ratio of coherent scattering energy to diffuse scattering energy, μ represents the number of effective scatterers in the ultrasonic resolution unit, and the parameters μ and β are calculated based on the mean value of the intensity I of the envelope amplitude. The value obtained by X statistics and the value obtained by U statistics are calculated, wherein the value obtained by X statistics and the value obtained by U statistics are obtained based on XU statistics, wherein the calculation formulas corresponding to U statistics and X statistics are as follows: U=E[lnI]-lnE[I] X=E[IlnI] / E[I]-E[lnI] The calculation of the parameters μ and β can be transformed into the solution of the following nonlinear equations, U HK =U,X HK =X Among them, U HK and X HK The corresponding calculation formula is as follows: Among them, γ E is the Euler constant, Γ(·) represents the gamma function, is the double gamma function.

7. The method according to claim 2, characterized in that The volume of the tissue to be measured is determined based on the transverse diameter, the anterior-posterior diameter and the superior-inferior diameter of the tissue to be measured determined by the echo signal; or, The volume of the tissue to be measured is determined based on a three-dimensional image of the tissue to be measured.

8. The method according to claim 1, characterized in that The machine learning model is trained according to the training data set to obtain a tissue inflammation assessment model including: Training the parameters of the machine learning model according to the training data set to obtain a trained machine learning model; Acquire a test data set, wherein the test data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and corresponding inflammation detection results of the tissues to be tested of each subject; Using the test data set, evaluating the trained machine learning model to obtain an evaluation result; When the evaluation indicators in the evaluation results meet the preset requirements, the trained machine learning model is used as the organization verification evaluation model.

9. The method according to claim 1, characterized in that: The ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on one region of the tissue to be tested, or the ultrasonic quantitative parameters of the tissue to be tested of each subject are parameters determined based on different regions of the tissue to be tested.

10. A device for constructing a tissue inflammation assessment model, characterized in that: include: an acquisition module, configured to acquire a training data set, wherein the training data set includes at least one of ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissues to be tested of multiple subjects, and inflammation detection results of the tissues to be tested of each subject; A training module is used to train the machine learning model according to the training data set to obtain a tissue inflammation assessment model; wherein the input data of the machine learning model includes at least one of the ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested of each subject in the training data set, and the output data of the machine learning model is the inflammation detection result of the tissue to be tested of each subject corresponding to the training data set; the ultrasound quantitative parameter is a parameter determined based on the echo signal corresponding to the ultrasonic signal emitted to the tissue to be tested, and is used to evaluate the state of the tissue to be tested.

11. An electronic 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 9.

12. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

13. A tissue inflammation assessment device, characterized in that: include: An acquisition module, used to acquire at least one of an ultrasound quantitative parameter, a biochemical test parameter, a physiological quantitative parameter, and a morphological detection quantitative parameter of a tissue to be tested of a subject; An evaluation module is used to input at least one of the obtained ultrasound quantitative parameters, biochemical test parameters, physiological quantitative parameters, and morphological detection quantitative parameters of the tissue to be tested into a tissue inflammation evaluation model constructed by the method according to any one of claims 1 to 9, so as to obtain an inflammation evaluation result of the tissue to be tested of the subject.

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