A cardiovascular disease risk prediction method and system

Abstract

The application relates to the technical field of cardiovascular internal medicine disease automation, in particular to a cardiovascular internal medicine disease onset risk prediction method and system. The application can effectively evaluate and predict the cardiovascular internal medicine disease, especially the coronary heart disease, by jointly using multiple groups of clinical indexes and multiple image group feature data, the score data is more intuitive to determine the onset degree of the thyroid papillary carcinoma, so that the coronary heart disease onset risk can be accurately judged. Compared with the blood vessel CT angiography technology in the prior art, the application embodiment combines the image group feature data in the ultrasonic image with the clinical indexes, so that the discrimination result is more accurate.

Description

Technical Field

[0001] This application relates to the field of automated technology for cardiovascular diseases, specifically a method and system for predicting the incidence risk of cardiovascular diseases. Background Technology

[0002] Coronary artery disease is the most common cardiovascular disease and the leading cause of death, disability, and human suffering worldwide. The development of coronary CT angiography has provided a non-invasive method for assessing coronary artery lesions, offering high-resolution images of the coronary arteries to help clinicians accurately assess their anatomy and degree of stenosis. While this technology has certain applications in clinical practice...

[0003] However, traditional angiography techniques mainly rely on anatomical information and cannot fully reflect the functional status of coronary arteries and the biological characteristics of plaques. In terms of assessing the degree of coronary artery stenosis, traditional angiography techniques cannot accurately assess the functional impact of stenosis on myocardial blood flow, which limits their accuracy in predicting the risk of coronary heart disease. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this application provides an auxiliary diagnostic method and system for cardiovascular diseases, especially coronary heart disease, that combines computer medical images with multiple clinical indicators. By acquiring ultrasound images of the arterial region and extracting radiographic features from the ultrasound images, the extracted features are combined with clinical data to identify the risk of coronary heart disease based on a predictive model.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, a method for predicting the incidence risk of cardiovascular diseases is provided. The method includes: acquiring clinical data of a patient to be diagnosed and imaging feature data of a target region of the patient; the target region is the location of the right clavicular artery; the imaging feature data includes ultrasound image feature data; the clinical data includes basic medical data and biophysical and chemical data; the basic medical data is the rate of change of blood pressure per unit time; the biophysical and chemical data includes body mass index, triglyceride content, triglyceride-glucose index, and lipoprotein a content; inputting the clinical data and the imaging feature data into a risk prediction model to calculate a risk value; the risk prediction model is a nomogram model.

[0007] Furthermore, the image group feature data includes multiple first-order features, multiple gray-level co-occurrence matrix features, neighborhood gray-level difference matrix features, gray-level size region matrix features, multiple gray-level correlation matrix features, 2D shape features, and multiple gray-level run length matrix features.

[0008] Furthermore, the plurality of first-order features include first-order feature skewness and first-order feature minimum.

[0009] Furthermore, the multiple features of the gray-level co-occurrence matrix include the information metric correlation 1 of the gray-level co-occurrence matrix, the maximum correlation coefficient of the gray-level co-occurrence matrix, and the correlation of the gray-level co-occurrence matrix.

[0010] Furthermore, the neighborhood gray-level difference matrix feature includes the busyness of the neighborhood gray-level difference matrix, the gray-level size region matrix feature includes the non-uniformity of the region size of the gray-level size region matrix, and the 2D shape feature is the perimeter.

[0011] Furthermore, the multiple grayscale correlation matrix features include the busyness of the grayscale correlation matrix, low grayscale emphasis for low dependence of the grayscale correlation matrix, and high grayscale emphasis for high dependence of the grayscale correlation matrix.

[0012] Furthermore, the multiple features of the grayscale run length matrix include the run variance of the grayscale run length matrix, the normalized run length non-uniformity of the grayscale run length matrix, and the run entropy of the grayscale run length matrix.

[0013] Furthermore, the step of inputting the clinical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining, based on the nomogram, the first score, second score, third score, fourth score, fifth score, and sixth score corresponding to the blood pressure change rate per unit time, the body mass index, the triglyceride content, the triglyceride-glucose index, the lipoprotein a content, and the imaging feature data, as well as the corresponding total score.

[0014] Furthermore, obtaining the sixth score corresponding to the image group feature data includes: obtaining the first-order feature skewness, the first-order feature minimum value, the information metric correlation I of the gray-level co-occurrence matrix, the maximum correlation coefficient of the gray-level co-occurrence matrix, the correlation of the gray-level co-occurrence matrix, the busyness of the neighborhood gray-level difference matrix, the regional size non-uniformity of the gray-level size region matrix, the perimeter, the busyness of the gray-level correlation matrix, the low dependence and low gray-level emphasis of the gray-level correlation matrix, the high dependence and high gray-level emphasis of the gray-level correlation matrix, the run variance of the gray-level run length matrix, the normalized run length non-uniformity of the gray-level run length matrix, and the run entropy of the gray-level run length matrix, and updating the sub-scores according to the calculation weights corresponding to the above data to obtain the sixth score.

[0015] Secondly, a cardiovascular disease incidence risk prediction system is provided. The system includes: a data acquisition unit for acquiring clinical data of a patient to be diagnosed and imaging feature data of a target region of the patient; the target region is the location of the right clavicle artery; the imaging feature data includes ultrasound image feature data; the clinical data includes basic medical data and biophysical and chemical data; the basic medical data is the rate of change of blood pressure per unit time; the biophysical and chemical data includes body mass index, triglyceride content, triglyceride-glucose index, and lipoprotein a content; and a risk prediction unit for inputting the clinical data and the imaging feature data into a risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

[0016] The technical solution provided in this application combines multiple sets of clinical indicators and multiple sets of imaging feature data to effectively assess and predict the risk of cardiovascular diseases, especially coronary heart disease. This scoring data provides a more intuitive determination of the severity of papillary thyroid carcinoma, thereby achieving an accurate assessment of the risk of coronary heart disease. Compared to existing technologies that rely solely on vascular CT angiography, this application combines imaging feature data from ultrasound images with clinical indicators, resulting in a more accurate assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] The methods, systems, and / or procedures shown in the accompanying drawings will be further described with reference to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example figures represent similar mechanisms in the various views of the drawings.

[0019] Figure 1 This is a schematic diagram of the cardiovascular disease incidence risk prediction method provided in the embodiments of this application.

[0020] Figure 2 This is a schematic diagram of the line graph model in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the system structure provided in the embodiments of this application.

[0022] Figure 4 This is a schematic diagram of the terminal device structure provided in the embodiments of this application. Detailed Implementation

[0023] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0024] In the detailed description below, numerous specific details are illustrated with examples to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that this application can be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of this application.

[0025] This application uses flowcharts to illustrate the execution process performed by a system according to embodiments of this application. It should be clearly understood that the execution processes in the flowcharts may not be executed sequentially. Instead, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0026] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0027] (1) In response to, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which the operation is performed are met, one or more operations may be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0028] (2) Based on, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which it depends are met, one or more operations can be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order of execution of the multiple operations.

[0029] This application provides a method for predicting the risk of cardiovascular diseases, particularly for the risk identification and prediction of coronary heart disease (CHD). With the accelerating aging of the global population and continuous changes in lifestyle, the incidence of coronary atherosclerotic heart disease (CHD) caused by atherosclerotic plaques is increasing year by year. When plaques rupture and thrombus formation occurs, it can easily induce myocardial infarction, heart failure, or even sudden death, becoming one of the leading causes of death among middle-aged and elderly people. Therefore, early and accurate diagnosis of CHD is of great clinical significance for improving patient prognosis and reducing mortality.

[0030] Currently, coronary angiography is considered the "gold standard" for diagnosing coronary heart disease (CHD) in clinical practice. However, this method is invasive and involves radiation exposure, limiting its widespread application in certain populations. Besides coronary angiography, electrocardiography (ECG), cardiac magnetic resonance imaging (MRI), and computed tomography (CT) are also commonly used auxiliary diagnostic tools in clinical practice. However, these techniques all have certain limitations. While ECG is simple to perform and inexpensive, its diagnostic specificity is relatively poor, especially in identifying early lesions. Cardiac MRI and CT offer high spatial resolution and image quality, but they are expensive, complex, and require a high degree of patient cooperation, making them difficult to popularize in primary healthcare settings. Therefore, developing a simple, non-invasive, economical, and widely applicable method for early assessment of CHD has significant clinical value.

[0031] Therefore, in order to solve the above-mentioned technical problems, this application provides a method for predicting the incidence risk of cardiovascular diseases. This method combines clinical indicators with image group feature data from CT images to achieve the identification of cardiovascular diseases, especially cardiovascular diseases. For more information on this method, please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

[0032] Step S11. Obtain the clinical data of the patient to be diagnosed and the image group feature data of the target region of the patient to be diagnosed.

[0033] In this embodiment, the method uses a joint prediction approach to assess the risk of coronary heart disease. The logic of the joint prediction approach is to obtain the score corresponding to each indicator based on the nomogram through the prediction model, and then obtain the total score. The total score is used to determine the risk level of cardiovascular disease, especially the risk level of coronary heart disease.

[0034] In this embodiment, the target area is the location of the right clavicular artery. The imaging feature data includes ultrasound image feature data, and the clinical data includes basic medical data and biophysical and chemical data. The basic medical data is the rate of change of blood pressure per unit time, and the biophysical and chemical data includes body mass index, triglyceride content, triglyceride-glucose index, and lipoprotein a content.

[0035] To acquire radiomics features, ultrasound images of the patient's right clavicular artery were first obtained. Then, regions of interest were manually delineated along plaque boundaries, and radiomics features were extracted. Based on the acquired radiomics features, statistical analysis was performed using SPSS 26.0 software. Count data were expressed as n (%), and the chi-square test was used to assess differences between groups. Normally distributed continuous data were expressed as mean ± standard deviation, and the independent samples t-test was used to assess differences between groups. Non-normally distributed continuous data were expressed as median, and the Mann-Whitney U test (rank-sum test) was used to assess differences between groups. Variables statistically significant in the univariate logistic regression analysis were further analyzed using multivariate logistic regression for feature selection. A binary logistic regression model was established. Receiver operating characteristic (ROC) curves for different models were plotted using MedCalc (Version 20.2) statistical software, and the area under the curve, sensitivity, specificity, and accuracy were calculated to evaluate the diagnostic efficacy of the models. The DeLong test was used to verify whether the differences in the AUC of the ROC curves of different models were statistically significant. Decision curve analysis was performed using R software, and charts were optimized using Adobe Photoshop 2022. A p-value < 0.05 indicates statistical significance. A total of 1125 radiomics features were extracted from the ultrasound images of the plaques. Using the aforementioned screening method, the top 10% of features most important for classification were selected, resulting in 113 features. Then, using the "Select by Model - Logistic Regression" component with L2 regularization and 5-fold cross-validation, redundant features with accuracy below the threshold were removed, ultimately retaining 14 features. The selected features include multiple first-order features, multiple gray-level co-occurrence matrix features, neighborhood gray-level difference matrix features, gray-level size region matrix features, multiple gray-level correlation matrix features, 2D shape features, and multiple gray-level run-length matrix features.

[0036] The first-order features include first-order feature skewness and first-order feature minimum; the multiple gray-level co-occurrence matrix features include the information metric correlation 1 of the gray-level co-occurrence matrix, the maximum correlation coefficient of the gray-level co-occurrence matrix, and the correlation of the gray-level co-occurrence matrix; the neighborhood gray-level difference matrix features include the busyness of the neighborhood gray-level difference matrix; the gray-level size region matrix features include the region size non-uniformity of the gray-level size region matrix; the 2D shape feature is the perimeter; the gray-level correlation matrix features include the busyness of the gray-level correlation matrix, the low gray-level emphasis of the small dependency of the gray-level correlation matrix, and the high gray-level emphasis of the large dependency of the gray-level correlation matrix; the gray-level run length matrix features include the run variance of the gray-level run length matrix, the normalized run length non-uniformity of the gray-level run length matrix, and the run entropy of the gray-level run length matrix.

[0037] Step S12. Input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

[0038] In this embodiment, the risk prediction model is a nomogram model. The nomogram model obtains the total score by mapping the scores corresponding to the multiple biophysical and chemical data and image group feature data obtained in step S11, and based on the score corresponding to each indicator and image group feature data. This total score is the risk value in this embodiment, which is used to characterize the risk level corresponding to coronary heart disease.

[0039] Specifically, based on the nomogram, the first score, second score, third score, fourth score, fifth score, and sixth score corresponding to the blood pressure change rate per unit time, the body mass index, the triglyceride content, the triglyceride-glucose index, the lipoprotein a content, and the image group feature data, as well as the corresponding total score, are obtained.

[0040] For the calculation results of this nomogram model, please refer to [link / reference]. Figure 2, where radsocre is the overall calculated score of the image group feature data, i.e., the sixth score value. The Radscore is calculated based on the following formula: Radscore = +0.094 × First-order feature skewness - 0.083 × Busyness of the neighborhood gray-level difference matrix + 0.081 × Small dependence and low gray-level emphasis of the gray-level correlation matrix - 0.081 × Minimum value of the first-order feature + 0.076 × Large dependence and high gray-level emphasis of the gray-level correlation matrix + 0.070 × Run variance of the gray-level run length matrix 0.070 × Perimeter + 0.064 × Information metric correlation of the gray-level co-occurrence matrix - 0.062 × Maximum correlation coefficient of the gray-level co-occurrence matrix - 0.060 × Normalized run length non-uniformity of the gray-level run length matrix + 0.053 × Region size non-uniformity of the gray-level size region matrix - 0.052 × Run entropy of the gray-level run length matrix + 0.050 × Correlation of the gray-level co-occurrence matrix + 0.044 × Dependence variance of the gray-level dependency matrix + 0.031.

[0041] For the specific calculation process of the nodal graph in this embodiment, please refer directly to [the relevant documentation / reference]. Figure 2 As shown, the calculation logic of the nodal chart can be used to achieve this, and will not be elaborated further in this embodiment.

[0042] In summary, by combining the above-mentioned multiple sets of clinical indicators and multiple sets of imaging feature data in this embodiment, the risk of cardiovascular diseases can be effectively assessed and predicted. This scoring data can more intuitively determine the severity of coronary heart disease, thereby achieving the identification of the risk of cardiovascular diseases.

[0043] See Figure 3 Regarding the method provided in steps S11-S12, this embodiment also provides a prediction system 30, the system comprising:

[0044] Data acquisition unit 31 is used to acquire clinical data of the patient to be diagnosed and image group feature data of the target area of ​​the patient to be diagnosed;

[0045] The risk prediction unit 32 is used to input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

[0046] In this embodiment, the risk prediction model is a nomogram model.

[0047] See Figure 4The above methods can also be integrated into the provided terminal device 40. Since the device may vary significantly due to differences in configuration or performance, it may include one or more processors 401 and memories 402. The memories 402 may store one or more application programs or data. The memories 402 can be temporary or persistent storage. The application programs stored in the memories 402 may include one or more modules (not shown in the figure), each module may include a series of computer-executable instructions from the terminal device. Furthermore, the processor 401 may be configured to communicate with the memories 402, and the terminal device may execute the series of computer-executable instructions stored in the memories 402. The terminal device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0048] In one specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for use in the terminal device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0049] Acquire clinical data of the patient to be diagnosed and radiographic feature data of the target region of the patient to be diagnosed;

[0050] The clinical data and the imaging feature data are input into the risk prediction model to calculate the risk value.

[0051] The following is a detailed introduction to each component of the processor:

[0052] In this embodiment, the processor is an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0053] Optionally, the processor can perform various functions, such as the above-mentioned functions, by running or executing software programs stored in memory and by calling data stored in memory. Figure 1 The method shown.

[0054] In a specific implementation, as one example, the processor may include one or more microprocessors.

[0055] The memory is used to store the software program that executes the solution of this application, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0056] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processing unit through the processor's interface circuitry; this application embodiment does not specifically limit this.

[0057] It should be noted that the processor structure shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0058] Furthermore, the technical effects of the processor can be referred to the technical effects of the methods described in the above-described method embodiments, and will not be repeated here.

[0059] It should be understood that the processor in the embodiments of this application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0060] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0061] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0062] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0063] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0066] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0067] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0069] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the incidence risk of cardiovascular diseases, characterized in that, The method includes: Acquire clinical data of the patient to be diagnosed and imaging feature data of the target region of the patient to be diagnosed; the target region is the location of the right clavicle artery, the imaging feature data includes ultrasound image feature data, the clinical data includes basic medical data and biophysical and chemical data, the basic medical data is the rate of change of blood pressure per unit time, and the biophysical and chemical data includes body mass index, triglyceride content, triglyceride-glucose index and lipoprotein a content; The clinical data and the imaging feature data are input into a risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

2. The method for predicting the incidence risk of cardiovascular diseases according to claim 1, characterized in that, The image group feature data includes multiple first-order features, multiple gray-level co-occurrence matrix features, neighborhood gray-level difference matrix features, gray-level size region matrix features, multiple gray-level correlation matrix features, 2D shape features, and multiple gray-level run length matrix features.

3. The method for predicting the incidence risk of cardiovascular diseases according to claim 2, characterized in that, The multiple first-order features include first-order feature skewness and first-order feature minimum.

4. The method for predicting the incidence risk of cardiovascular diseases according to claim 2, characterized in that, The multiple features of the gray-level co-occurrence matrix include the information metric correlation of the gray-level co-occurrence matrix, the maximum correlation coefficient of the gray-level co-occurrence matrix, and the correlation of the gray-level co-occurrence matrix.

5. The method for predicting the incidence risk of cardiovascular diseases according to claim 2, characterized in that, The neighborhood gray-level difference matrix feature includes the busyness of the neighborhood gray-level difference matrix, the gray-level size region matrix feature includes the non-uniformity of the region size of the gray-level size region matrix, and the 2D shape feature is the perimeter.

6. The method for predicting the incidence risk of cardiovascular diseases according to claim 2, characterized in that, The grayscale correlation matrix features include the busyness of the grayscale correlation matrix, low grayscale emphasis for low dependence of the grayscale correlation matrix, and high grayscale emphasis for high dependence of the grayscale correlation matrix.

7. The method for predicting the incidence risk of cardiovascular diseases according to claim 2, characterized in that, The features of the grayscale run length matrix include the run variance of the grayscale run length matrix, the normalized run length non-uniformity of the grayscale run length matrix, and the run entropy of the grayscale run length matrix.

8. The method for predicting the incidence risk of cardiovascular diseases according to any one of claims 1-7, characterized in that, The step of inputting the clinical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining the first score, second score, third score, fourth score, fifth score, and sixth score corresponding to the blood pressure change rate per unit time, the body mass index, the triglyceride content, the triglyceride-glucose index, the lipoprotein a content, and the imaging feature data, as well as the corresponding total score, based on the nomogram.

9. The method for predicting the risk of cardiovascular diseases according to claim 8, characterized in that, The process of obtaining the sixth score corresponding to the image group feature data includes: obtaining the first-order feature skewness, the first-order feature minimum value, the information metric correlation I of the gray-level co-occurrence matrix, the maximum correlation coefficient of the gray-level co-occurrence matrix, the correlation of the gray-level co-occurrence matrix, the busyness of the neighborhood gray-level difference matrix, the regional size non-uniformity of the gray-level size region matrix, the perimeter, the busyness of the gray-level correlation matrix, the low dependence and low gray-level emphasis of the gray-level correlation matrix, the high dependence and high gray-level emphasis of the gray-level correlation matrix, the run variance of the gray-level run length matrix, the normalized run length non-uniformity of the gray-level run length matrix, and the run entropy of the gray-level run length matrix. The sub-scores are then updated and weighted according to the calculation weights corresponding to the above data to obtain the sixth score.

10. A system for predicting the incidence risk of cardiovascular diseases, characterized in that, The system includes: The data acquisition unit acquires clinical data of the patient to be diagnosed and imaging feature data of the target area of ​​the patient to be diagnosed; the target area is the location of the right clavicle artery, the imaging feature data includes ultrasound image feature data, the clinical data includes basic medical data and biophysical and chemical data, the basic medical data is the rate of change of blood pressure per unit time, and the biophysical and chemical data includes body mass index, triglyceride content, triglyceride-glucose index and lipoprotein a content; The risk prediction unit inputs the clinical data and the imaging feature data into the risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

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