Method and device for realizing blood vessel quantification based on near-infrared dynamic spectrum

Abstract

The invention discloses a method and device for realizing blood vessel aging quantification based on near-infrared dynamic spectrums, and the method comprises the steps: obtaining the age information of a to-be-predicted user, and the near-infrared spectrum target face information of the to-be-predicted user; and inputting the age information and the near infrared spectrum information of the face of the to-be-predicted user into the blood vessel age prediction model to predict the blood vessel age of the to-be-predicted user. A near-infrared dynamic spectrum technology and a model are utilized to obtain specified blood components, and evaluation is carried out in combination with data such as the age of a user, so that the problem of rapid quantitative calculation of vascular aging is solved.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a method and apparatus for quantifying blood vessels based on near-infrared dynamic spectroscopy. Background Technology

[0002] Currently, the quantitative calculation of vascular aging typically involves multiple indicators, with common methods including pulse wave velocity (PWV), arterial stiffness index (ASI), arterial calcification score (CAC), and vascular age assessment. These indicators are usually combined with clinical examinations, imaging examinations (such as CT scans and ultrasound), and biomarkers (such as blood glucose, cholesterol, and CRP) to comprehensively assess vascular health and the degree of aging. While these assessment methods help doctors understand the health status of blood vessels and better assess the risk of cardiovascular disease, they are relatively crude and simplistic. Traditional methods for obtaining biomarkers require drawing blood to obtain serum or plasma, followed by testing. This process is cumbersome, has low automation, can cause discomfort, and is time-consuming and labor-intensive. Summary of the Invention

[0003] The main objective of this invention is to provide a method for quantifying blood vessels based on near-infrared dynamic spectroscopy, in order to address the shortcomings of related technologies.

[0004] To achieve the above objectives, according to a first aspect of the present invention, a method for quantifying blood vessels based on near-infrared dynamic spectroscopy is provided, comprising acquiring the age information of a user to be predicted and the near-infrared spectral target facial information of the user to be predicted; inputting the age information and the near-infrared spectral information of the user's face into the blood vessel age prediction model to predict the blood vessel age of the user to be predicted.

[0005] Optionally, when constructing the vascular age prediction model, the method includes: acquiring near-infrared spectral samples of a face, and processing the near-infrared spectral samples of the face to obtain blood component information; determining the arteriosclerosis index (ASI) corresponding to the blood vessels based on the blood component information, and determining the vascular function level based on the ASI; adjusting the arteriosclerosis index (ASI) to determine the grading information; and constructing the vascular age prediction model based on the function level, grading information, age information of the sample corresponding to the face spectral information, and age factors.

[0006] Optionally, acquiring near-infrared spectral samples of a face and processing the near-infrared spectral samples of the face to obtain blood component information includes: determining the spectral information of different blood components in the near-infrared spectral samples of the face; filtering the wavenumber band combinations related to specified components in the spectral information through interval partial least squares to obtain wavenumber band information; extracting the wavenumber band information into wavenumber point information using a Monte Carlo algorithm; and determining the features corresponding to different specified components based on the wavenumber point information, and using the features as blood component information.

[0007] Optionally, determining the Arteriosclerosis Index (ASI) corresponding to the blood vessel based on the blood component information, and determining the vascular function level based on the ASI, includes: determining the ASI based on total cholesterol characteristics, high-density lipoprotein (HDL), and high-density lipoprotein (HDL); determining the initial vascular function level based on the ASI; and correcting the initial vascular function level based on the remaining blood component information to obtain the vascular function level determined by the ASI.

[0008] Optionally, the conversion of the Arteriosclerosis Index (ASI) to determine grading information includes: based on level = e 0.173 *x Determine the hierarchical information, among which, "Abnormal" refers to the remaining component information, including lipoproteins, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein.

[0009] Optionally, constructing a vascular age prediction model based on the functional level, grading information, facial spectral information, and corresponding sample age information and age factors includes: establishing a mapping relationship between different age groups, different grades, different vascular function levels, and dynamically adjustable age factors corresponding to different samples, thereby obtaining the vascular age prediction model.

[0010] According to a second aspect of the present invention, a device for quantifying vascular aging based on near-infrared dynamic spectroscopy is provided, comprising a data acquisition unit for acquiring the age information of a user to be predicted and the near-infrared spectral target facial information of the user to be predicted; and a vascular age prediction unit for inputting the age information and the near-infrared spectral information of the user's face into the vascular age prediction model to predict the vascular age of the user to be predicted.

[0011] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the method described in any one of the first aspects.

[0012] According to a fourth aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method described in any implementation of the first aspect.

[0013] This embodiment presents a method and apparatus for quantifying vascular aging based on near-infrared dynamic spectroscopy. The method includes acquiring the age information of the user to be predicted, as well as the near-infrared spectral target facial information of the user; inputting the age information and the near-infrared spectral information of the user's face into the vascular age prediction model to predict the user's vascular age. By utilizing near-infrared dynamic spectroscopy technology, the model acquires specified blood components and combines this with data such as the user's age for evaluation, thus solving the problem of rapid quantitative calculation of vascular aging. Attached Figure Description

[0014] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a method for quantifying blood vessels based on near-infrared dynamic spectroscopy according to an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Since spectral vascular function testing is based on various biomarkers in the blood, it is entirely possible to predict a user's current vascular function level and vascular age by using dynamic spectroscopy technology, employing the Aortic Stiffness Index (ASI) for grading, and combining this with data such as the user's age. Based on this, this invention provides a method for quantitatively calculating vascular aging using dynamic spectroscopy technology. Utilizing near-infrared dynamic spectroscopy, it takes the influence of various biomarker levels in the test subject's blood on atherosclerosis as a starting point, and combines this with data such as the user's age to predict the user's vascular function level and vascular age.

[0021] According to embodiments of the present invention, a method for quantifying blood vessels based on near-infrared dynamic spectroscopy is provided, such as... Figure 1 As shown, steps 101 to 103 are included below:

[0022] Step 101: Obtain the age information of the user to be predicted, as well as the near-infrared spectral target face information of the user to be predicted.

[0023] Step 102: Input the age information and the near-infrared spectral information of the user's face into the vascular age prediction model to predict the vascular age of the user.

[0024] As an optional implementation of this embodiment, near-infrared spectral samples of a face are acquired, and the near-infrared spectral samples of the face are processed to obtain blood component information; the arteriosclerosis index (ASI) corresponding to the blood vessels is determined based on the blood component information, and the vascular function level is determined based on the ASI; the arteriosclerosis index (ASI) is adjusted to determine the grading information; and a vascular age prediction model is constructed based on the function level, grading information, age information of the sample corresponding to the face spectral information, and age factors.

[0025] As an optional implementation of this embodiment, obtaining a near-infrared spectral sample of a face and processing the near-infrared spectral sample of the face to obtain blood component information includes: determining the spectral information of different blood components in the near-infrared spectral sample of the face; filtering the wavenumber segment combinations related to the specified components in the spectral information by interval partial least squares to obtain wavenumber segment information; extracting the wavenumber segment information into wavenumber point information using a Monte Carlo algorithm; and determining the features corresponding to different specified components based on the wavenumber point information, and using the features as blood component information.

[0026] In one optional implementation of this embodiment, determining the Arteriosclerosis Index (ASI) corresponding to the blood vessel based on the blood component information, and determining the vascular function level based on the ASI, includes: determining the ASI based on total cholesterol characteristics, high-density lipoprotein (HDL), and high-density lipoprotein (HDL); determining the initial vascular function level based on the ASI; and correcting the initial vascular function level based on the remaining blood component information to obtain the vascular function level determined by the ASI.

[0027] As an optional implementation of this embodiment, the conversion of the Arteriosclerosis Index (ASI) to determine the grading information includes: based on level = e 0.173*x Determine the hierarchical information, among which, "Abnormal" refers to the remaining component information, including lipoproteins, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein.

[0028] This embodiment utilizes near-infrared dynamic spectroscopy technology to scan the face of the subject to collect facial spectral samples and obtain the levels of various biomarkers in the subject's blood, including total cholesterol, high-density lipoprotein, lipoprotein, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, C-reactive protein, etc.

[0029] The specific steps are as follows:

[0030] By acquiring facial video using a near-infrared dynamic spectral acquisition device, spectral information of the blood components under the facial skin can be obtained;

[0031] Wavenumber band information is obtained by screening the spectral information for wavenumber band combinations that are significantly correlated with the components of the analyte compound using partial least squares intervals.

[0032] The wavenumber segment information is extracted into wavenumber point information using the Monte Carlo algorithm.

[0033] Furthermore, the acquisition of biomarker data corresponding to the preprocessed data through dynamic spectroscopy, including total cholesterol, high-density lipoprotein, lipoprotein, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein, includes:

[0034] Several original features are extracted based on the preprocessed data;

[0035] Calculate the correlation coefficients between several of the original features and risk populations for biomarkers such as total cholesterol, high-density lipoprotein, lipoprotein, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein;

[0036] The original features whose correlation coefficients exceed the preset standard range are extracted as indicator information data.

[0037] The levels of the above biomarkers were graded using the Aortic Stiffness Index (ASI). The Aortic Stiffness Index is an internationally recognized indicator used to measure the degree of arteriosclerosis. It is calculated as follows: Aortic Stiffness Index (ASI) = [Total Cholesterol (TC) - High-Density Lipoprotein (HDL)] ÷ High-Density Lipoprotein (HDL).

[0038] An arteriosclerosis index of <4 indicates that the degree of arteriosclerosis is not severe or is decreasing. The lower the value, the milder the degree of arteriosclerosis and the lower the risk of cardiovascular and cerebrovascular diseases.

[0039] An arteriosclerosis index of ≥4 indicates that arteriosclerosis has occurred. The higher the value, the more severe the arteriosclerosis and the higher the risk of cardiovascular and cerebrovascular diseases.

[0040] Here, in addition to total cholesterol and high-density lipoprotein, other markers (lipoprotein a, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein) can be used as auxiliary assessment items to adjust the vascular function assessment level based on the arteriosclerosis index (ASI).

[0041] The above four levels can be respectively identified as no abnormality, mild abnormality, moderate abnormality, and severe abnormality.

[0042] Using the Arteriosclerosis Index (ASI) calculation formula's output value of 4 as the critical point, an index conversion formula is constructed. Here, the specific formula for calculating the grading is:

[0043] level = e 0.173*x ,in The abnormal value can be an auxiliary marker, including lipoprotein a (LPA), fasting blood glucose (GLU_null), low-density lipoprotein (LDL), glycated hemoglobin (HbA1c), C-reactive protein (CRP), etc.

[0044] In one optional implementation of this embodiment, constructing a vascular age prediction model based on the age information and age factors of the samples corresponding to the functional level, grading information, and facial spectral information includes: establishing a mapping relationship between different age groups, different grades, different vascular function levels, and dynamically adjustable age factors corresponding to different samples, thereby obtaining the vascular age prediction model.

[0045] In this optional implementation, refer to the mapping relationship illustrated in Table 1.

[0046] Table 1

[0047]

[0048] Furthermore, after the model is built, the test subject's own attribute information (such as age) is entered. The level value is calculated according to the grading formula. Combined with the user's age, records meeting the level value are searched to determine the vascular function level and the adjusted age factor, thus obtaining the current user's vascular function level and vascular age prediction. For example: When testing a user, their own attribute information (such as age) is entered. The level value is calculated according to the grading formula. Combined with the user's age, records meeting the level value are searched to determine the vascular function level and the adjusted age factor. If the user is 55 years old and the level value is 0.7, then the current user's vascular function level is mildly abnormal, and the estimated vascular age is 55 + 2.5 = 57.5, or fifty-seven and a half years old.

[0049] This embodiment presents a method and system for rapid quantitative calculation of vascular aging based on dynamic spectroscopy technology. Specifically, it utilizes near-infrared dynamic spectroscopy technology to obtain the content of various biomarkers in the blood of the test subject, and performs an evaluation based on the blood biomarkers, combined with data such as the user's age, thus solving the problem of rapid quantitative calculation of vascular aging.

[0050] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0051] According to an embodiment of the present invention, a device for quantifying vascular aging based on near-infrared dynamic spectroscopy is also provided, including a data acquisition unit for acquiring the age information of the user to be predicted and the near-infrared spectral target facial information of the user to be predicted; and a vascular age prediction unit for inputting the age information and the near-infrared spectral information of the user's face into the vascular age prediction model to predict the vascular age of the user to be predicted.

[0052] As an optional implementation of this embodiment, when constructing the vascular age prediction model, the device acquires near-infrared spectral samples of a face and processes the near-infrared spectral samples of the face to obtain blood component information; determines the arteriosclerosis index (ASI) corresponding to the blood vessels based on the blood component information, and determines the vascular function level based on the ASI; adjusts the arteriosclerosis index (ASI) to determine the grading information; and constructs the vascular age prediction model based on the function level, grading information, age information of the sample corresponding to the face spectral information, and age factors.

[0053] As an optional implementation of this embodiment, obtaining a near-infrared spectral sample of a face and processing the near-infrared spectral sample of the face to obtain blood component information includes: determining the spectral information of different blood components in the near-infrared spectral sample of the face; filtering the wavenumber segment combinations related to the specified components in the spectral information by interval partial least squares to obtain wavenumber segment information; extracting the wavenumber segment information into wavenumber point information using a Monte Carlo algorithm; and determining the features corresponding to different specified components based on the wavenumber point information, and using the features as blood component information.

[0054] As an optional implementation of this embodiment, determining the Arteriosclerosis Index (ASI) corresponding to the blood vessel based on the blood component information, and determining the vascular function level based on the ASI includes: determining the ASI based on total cholesterol characteristics, high-density lipoprotein (HDL), and high-density lipoprotein (HDL); determining the initial vascular function level based on the ASI; and correcting the initial vascular function level based on the remaining blood component information to obtain the vascular function level determined by the ASI.

[0055] As an optional implementation of this embodiment, the arteriosclerosis index (ASI) is converted to determine the grading information, including based on level = e 0.173*x Determine the hierarchical information, among which, "Abnormal" refers to the remaining component information, including lipoproteins, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein.

[0056] As an optional implementation of this embodiment, constructing a vascular age prediction model based on the functional level, grading information, facial spectral information, corresponding sample age information, and age factors includes: establishing a mapping relationship between different age groups, different grades, different vascular function levels, and dynamically adjustable age factors corresponding to different samples, thereby obtaining the vascular age prediction model.

[0057] According to embodiments of the present invention, the present invention also provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the methods described in any of the above embodiments.

[0058] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to perform the methods described in any of the above embodiments when executed.

[0059] According to embodiments of the present invention, the present invention also provides a computer program product that, when executed by a processor, can implement the methods described in any of the above embodiments.

[0060] Figure 2 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0061] like Figure 2 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0062] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0063] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed.

[0064] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0065] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Claims

1. A method for quantifying blood vessels based on near-infrared dynamic spectroscopy, characterized in that, include: Obtain the age information of the user to be predicted, as well as the near-infrared spectral target facial information of the user to be predicted; Spectral analysis of the near-infrared spectral target facial information is performed to determine the specified blood components; The age information and the near-infrared spectral information of the user's face are input into the vascular age prediction model to predict the vascular age of the user.

2. The method for quantifying blood vessels based on near-infrared dynamic spectroscopy according to claim 1, characterized in that, The vascular age prediction model was constructed as follows: Obtain near-infrared spectral samples of a human face, and process the near-infrared spectral samples of the human face to obtain blood component information; Based on the blood component information, the Arterial Stiffness Index (ASI) corresponding to the blood vessel is determined, and the vascular function level is determined based on the ASI. The arteriosclerosis index (ASI) is adjusted to determine the grading information; and a vascular age prediction model is constructed based on the functional level, grading information, age information of the corresponding samples, and age factors of the facial spectral information.

3. The method for quantifying vascular aging based on near-infrared dynamic spectroscopy according to claim 2, characterized in that, Obtaining near-infrared spectral samples of the face and processing the near-infrared spectral samples to obtain blood component information includes: Determine the spectral information of different blood components in the near-infrared spectral sample of the human face; Wavenumber segment information is obtained by filtering the wavenumber segment combinations related to the specified components in the spectral information through interval partial least squares; the Monte Carlo algorithm extracts the wavenumber segment information into wavenumber point information. Based on the wavenumber point information, the features corresponding to different specified components are determined, and the features are used as blood component information.

4. The method for quantifying vascular aging based on near-infrared dynamic spectroscopy according to claim 3, characterized in that, Based on the blood component information, the Arterial Stiffness Index (ASI) corresponding to the blood vessel is determined, and based on the ASI, the vascular function level is determined, including: The Arteriosclerosis Index (ASI) was determined based on total cholesterol characteristics and high-density lipoprotein (HDL). The initial vascular function level is determined based on the Arteriosclerosis Index (ASI); and the initial vascular function level is corrected based on the information of other blood components to obtain the vascular function level determined by ASI.

5. The method for quantifying vascular aging based on near-infrared dynamic spectroscopy according to claim 4, characterized in that, The conversion of the Arteriosclerosis Index (ASI) to determine grading information includes: Based on level=e 0.173*x Determine the hierarchical information, among which, "Abnormal" refers to the remaining component information, including lipoproteins, fasting blood glucose, low-density lipoprotein, glycated hemoglobin, and C-reactive protein.

6. The method for quantifying vascular aging based on near-infrared dynamic spectroscopy according to claim 5, characterized in that, Based on the functional level, grading information, facial spectral information, and corresponding sample age information and age factors, a vascular age prediction model is constructed, including: A mapping relationship is established between different age groups, different grades, different vascular function levels, and dynamically adjustable age factors corresponding to different samples, to obtain the vascular age prediction model.

7. A device for quantifying vascular aging based on near-infrared dynamic spectroscopy, characterized in that, include: The data acquisition unit is used to acquire the age information of the user to be predicted, as well as the near-infrared spectral target facial information of the user to be predicted. The vascular age prediction unit is used to input the age information and the near-infrared spectral information of the user's face into the vascular age prediction model to predict the vascular age of the user.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-6.

9. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1-6.

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