Low-density lipoprotein risk screening method and device, storage medium and electronic device

Abstract

The invention discloses a low-density lipoprotein risk screening method and device, a storage medium and an electronic device. The low-density lipoprotein risk screening method comprises the following steps: acquiring a face video; determining a risk index of low-density lipoprotein based on the face video by adopting a preset risk index algorithm; judging whether the risk index is within a preset index threshold range or not; if yes, determining that the population belongs to the low-density lipoprotein normal population; if not, determining that the crowd belongs to the low-density lipoprotein risk crowd; therefore, non-contact online automatic screening of the low-density lipoprotein risk is realized, the intelligent degree can be effectively improved, and discomfort of a human body is avoided. The technical problems that the automation degree is low, and the human body is likely to feel uncomfortable are solved.

Description

Technical Field

[0001] This application relates to the field of disease screening. Specifically, it relates to a method, device, storage medium, and electronic device for screening the risk of low-density lipoprotein. Background Art

[0002] Low-density lipoprotein is a lipoprotein particle that transports cholesterol into peripheral tissue cells and can be oxidized into oxidized low-density lipoprotein. The main function of low-density lipoprotein is to transport cholesterol to extrahepatic tissues and is an important lipoprotein that causes atherosclerosis. Low-density lipoprotein is an indicator during blood lipid prevention and control examinations and can be used to judge the risk of coronary heart disease. Generally speaking, when low-density lipoprotein, especially oxidized modified low-density lipoprotein (OX-LDL), is excessive (too high), the cholesterol it carries accumulates on the arterial wall and is likely to cause arteriosclerosis over time. Therefore, low-density lipoprotein is called "bad cholesterol". In addition, a low level of low-density lipoprotein is usually caused by reasons such as malnutrition, abnormal liver metabolism, excessive exercise, or drug effects.

[0003] In the prior art, it is necessary to first draw blood to obtain human serum or plasma, and then use corresponding equipment to detect the low-density lipoprotein (LDL) index, and judge the risk population based on the index. The operation is cumbersome, the degree of automation is low, and it is easy to cause discomfort to the human body.

[0004] Regarding the problem of low degree of automation and easy discomfort to the human body in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, storage medium, and electronic device for screening the risk of low-density lipoprotein, so as to solve the problems of low degree of automation and easy discomfort to the human body.

[0006] To achieve the above purpose, according to one aspect of this application, a method for screening the risk of low-density lipoprotein is provided.

[0007] The method for screening the risk of low-density lipoprotein according to this application includes: obtaining a face video; using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the face video; judging whether the risk index is within a preset index threshold range; if so, it is determined to belong to the normal low-density lipoprotein population; if not, it is determined to belong to the low-density lipoprotein risk population.

[0008] Further, using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the face video includes: preprocessing the face video to obtain preprocessing data; determining the risk index of low-density lipoprotein based on the preprocessing data.

[0009] Further, preprocess the facial video to obtain preprocessed data, including: collecting the facial video through a near-infrared dynamic spectroscopy device to obtain the spectral information of the blood components under the facial skin; screening the wavenumber segment combinations that have obvious correlations with the components of the compound to be measured in the spectral information through interval partial least squares to obtain wavenumber segment information; and extracting the wavenumber segment information into wavenumber point information by combining the Monte Carlo algorithm.

[0010] Further, determining the risk index of low-density lipoprotein based on the preprocessed data includes: extracting a number of original features based on the preprocessed data; calculating the correlation coefficients of the number of original features corresponding to the low-density lipoprotein risk population; and extracting the original features whose correlation coefficients exceed the preset standard range value as the risk index of low-density lipoprotein.

[0011] Further, when obtaining the facial video, it also includes: obtaining the user mobile phone number corresponding to the facial video, and obtaining the unique identification code configured by the administrator for each screening area.

[0012] Further, if not, after determining that it belongs to the low-density lipoprotein risk population, it further includes: adopting a look-up table method to determine the corresponding treatment plan based on the low-density lipoprotein risk population; and sending the treatment plan to the corresponding user terminal according to the user mobile phone number.

[0013] Further, if not, after determining that it belongs to the low-density lipoprotein risk population, it further includes: obtaining the location of the community low-density lipoprotein risk screening area corresponding to the low-density lipoprotein risk population through the unique identification code.

[0014] To achieve the above object, according to another aspect of the present application, a low-density lipoprotein risk screening device is provided.

[0015] The low-density lipoprotein risk screening device according to the present application includes: a video acquisition module for acquiring a facial video; an index determination module for using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the facial video; and an index judgment module for judging whether the risk index is within a preset index threshold range; if so, it is determined to belong to the low-density lipoprotein normal population; if not, it is determined to belong to the low-density lipoprotein risk population.

[0016] To achieve the above object, according to another aspect of the present application, a computer-readable storage medium is provided.

[0017] In the computer-readable storage medium according to the present application, a computer program is stored, wherein the computer program is set to execute the low-density lipoprotein risk screening method when running.

[0018] To achieve the above object, according to another aspect of the present application, an electronic device is provided.

[0019] The electronic device according to the present application includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the low-density lipoprotein risk screening method described above.

[0020] In the embodiment of the present application, a method of screening for low-density lipoprotein risk is adopted. By obtaining a face video; using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the face video; judging whether the risk index is within a preset index threshold range; if so, it is determined to belong to the normal low-density lipoprotein population; if not, it is determined to belong to the low-density lipoprotein risk population; the purpose of non-contact online automatic screening for low-density lipoprotein risk is achieved, thereby realizing the technical effect of effectively improving the degree of intelligence and avoiding causing discomfort to the human body, and further solving the technical problems of low automation and easy discomfort to the human body. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings that form a part of this application are used to provide a further understanding of this application, making other features, objects, and advantages of this application more apparent. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0022] Figure 1 is a schematic flowchart of the low-density lipoprotein risk screening method according to the embodiment of the present application;

[0023] Figure 2 is a schematic structural diagram of the low-density lipoprotein risk screening device according to the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0025] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so as to implement the embodiments of the present application described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] In this application, the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present invention and its embodiments, and are not used to limit that the indicated devices, elements or components must have a specific orientation, or be constructed and operated in a specific orientation.

[0027] Moreover, in addition to being used to represent the orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in the present invention can be understood according to specific circumstances.

[0028] In addition, the terms "install", "set", "provided with", "connect", "connected", "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or there is an internal connection between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will detail this application with reference to the drawings and in combination with the embodiments.

[0030] According to an embodiment of the present invention, a method for screening low-density lipoprotein risk is provided, as Figure 1 shown, the method includes the following steps S101 to step S104:

[0031] Step S101, obtain a face video;

[0032] The person to be tested can collect their own facial video through devices such as cameras and mobile phones. It should be noted that when collecting, the camera, mobile phone, etc. need to be aimed at the face for shooting, and it is best to be able to shoot a frontal face video.

[0033] Step S102: Using a preset risk index algorithm, determine the risk index of low-density lipoprotein based on the facial video.

[0034] In this embodiment, preferably, using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the facial video includes:

[0035] Preprocess the facial video to obtain preprocessing data.

[0036] Determine the risk index of low-density lipoprotein based on the preprocessing data.

[0037] Specifically, preprocessing the facial video to obtain preprocessing data includes: collecting the facial video through a near-infrared dynamic spectroscopy device to obtain the spectral information of the blood components under the facial skin; screening the wave number segment combinations that have obvious correlations with the components of the compound to be measured in the spectral information through interval partial least squares to obtain wave number segment information; combining the Monte Carlo algorithm to extract the wave number segment information into wave number point information.

[0038] Specifically, determining the risk index of low-density lipoprotein based on the preprocessing data includes: extracting a number of original features based on the preprocessing data; calculating the correlation coefficients of the number of original features corresponding to the low-density lipoprotein risk population; extracting the original features whose correlation coefficients exceed the preset standard range value as the risk index of low-density lipoprotein.

[0039] Through the combination of interval partial least squares and the Monte Carlo algorithm, the extraction of wave number point information can be realized, and then through feature extraction and calculation, the extraction of the risk index can be realized; thus, non-contact detection and automatic extraction of the risk index are achieved, improving the degree of intelligence and avoiding discomfort to the testers.

[0040] Step S103: Judge whether the risk index is within the preset index threshold range.

[0041] Step S104: If so, it is determined to belong to the normal low-density lipoprotein population; if not, it is determined to belong to the low-density lipoprotein risk population.

[0042] Specifically, the normal value of low-density lipoprotein is 3.13 - 3.59 mmol / L; the low-density lipoprotein index less than 3.13 mmol / L is considered low; the low-density lipoprotein index higher than 3.59 mmol / L is considered high.

[0043] Thus, the normal range of the low-density lipoprotein index is between 3.13 - 3.59 mmol / L, and it is determined as the normal population with low-density lipoprotein risk; if the low-density lipoprotein index is less than 3.13 mmol / L, it is considered low, and it is determined as the low-density lipoprotein risk population; if the low-density lipoprotein index is higher than 3.59 mmol / L, it is considered high, and it is determined as the low-density lipoprotein risk population.

[0044] By setting the threshold range, the automatic judgment of whether it is a low-density lipoprotein risk is realized, further improving the degree of intelligence.

[0045] From the above description, it can be seen that the present invention achieves the following technical effects:

[0046] In the embodiment of the present application, a method for screening low-density lipoprotein risk is adopted. By obtaining the facial video of a person, and using a preset risk index algorithm to determine the low-density lipoprotein risk index based on the facial video; then judging whether the risk index is within the preset index threshold range; if so, it is determined as belonging to the normal population of low-density lipoprotein; if not, it is determined as belonging to the low-density lipoprotein risk population. The purpose of non-contact online automatic screening of low-density lipoprotein risk is achieved, thus realizing the technical effect of effectively improving the degree of intelligence and avoiding discomfort to the human body, and further solving the technical problems of low automation and easy discomfort to the human body.

[0047] According to the embodiment of the present invention, preferably, when obtaining the facial video of a person, it further includes:

[0048] Obtaining the user's mobile phone number corresponding to the facial video of a person, and obtaining the unique identification code configured by the administrator for each screening area.

[0049] When obtaining the facial video of a person, the user also needs to upload their own mobile phone number and establish a binding relationship with the facial video, and the administrator needs to configure a unique identification code for determining the screening area for each facial video according to the current screening area, so as to provide support for subsequent feedback and location determination management.

[0050] According to the embodiment of the present invention, preferably, after determining that it belongs to the low-density lipoprotein risk population if not, it further includes:

[0051] Adopting a look-up table method to determine the corresponding treatment plan based on the low-density lipoprotein risk population;

[0052] Sending the treatment plan to the corresponding user terminal according to the user's mobile phone number.

[0053] A relationship table between low-density lipoprotein index levels and treatment plans has been established in advance. Specifically, when the low-density lipoprotein index is between 3.13 - 3.59 mmol / L, it is considered normal, and the population is determined to be at normal risk of low-density lipoprotein, corresponding to no treatment plan; when the low-density lipoprotein index is less than 3.13 mmol / L, it is considered low, and the population is determined to be at risk of low-density lipoprotein, corresponding to a low-treatment plan; when the low-density lipoprotein index is higher than 3.59 mmol / L, it is considered high, and the population is determined to be at risk of low-density lipoprotein, corresponding to a high-treatment plan. After determining the corresponding treatment plan, the treatment plan can be sent to the corresponding user terminal according to the user's mobile phone number for the user to view conveniently.

[0054] According to an embodiment of the present invention, preferably, if not, after determining that it belongs to the population at risk of low-density lipoprotein, it further includes:

[0055] Obtaining the location of the community low-density lipoprotein risk screening area corresponding to the population at risk of low-density lipoprotein through the unique identification code.

[0056] Through the unique identification code, the location of the community low-density lipoprotein risk screening area corresponding to the population at risk of low-density lipoprotein can be quickly determined, facilitating targeted home treatment by medical staff (administrators).

[0057] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0058] According to an embodiment of the present invention, there is also provided a device for implementing the above low-density lipoprotein risk screening method, as Figure 2 shown, the device includes:

[0059] A video acquisition module 10 for acquiring a face video;

[0060] The person to be detected can collect their own face video through devices such as cameras and mobile phones. It should be noted that when collecting, the camera, mobile phone, etc. need to be aligned with the face for shooting, preferably a frontal face video can be obtained.

[0061] An index determination module 20 for determining the risk index of low-density lipoprotein based on the face video using a preset risk index algorithm;

[0062] In this embodiment, preferably, determining the risk index of low-density lipoprotein based on the face video using a preset risk index algorithm includes:

[0063] Preprocessing the face video to obtain preprocessed data;

[0064] Determine the risk index of low-density lipoprotein based on the preprocessed data.

[0065] Specifically, preprocess the face video to obtain preprocessed data, including: collecting the face video through a near-infrared dynamic spectroscopy acquisition device to obtain the spectral information of the blood components under the facial skin; screening the wavenumber segment combinations that have obvious correlations with the components of the compound to be measured in the spectral information through interval partial least squares to obtain wavenumber segment information; combining the Monte Carlo algorithm to extract the wavenumber segment information into wavenumber point information.

[0066] Specifically, determining the risk index of low-density lipoprotein based on the preprocessed data includes: extracting a number of original features based on the preprocessed data; calculating the correlation coefficients of the number of original features corresponding to the low-density lipoprotein risk population; extracting the original features whose correlation coefficients exceed the preset standard range value as the risk index of low-density lipoprotein.

[0067] The extraction of wavenumber point information can be realized through interval partial least squares combined with the Monte Carlo algorithm, and then through feature extraction and calculation, the extraction of risk indicators can be realized; thus, non-contact detection and automatic extraction of risk indicators are realized, the degree of intelligence is improved, and discomfort to the detection personnel is avoided.

[0068] The index judgment module 30 is used to judge whether the risk index is within the preset index threshold range; if so, it is determined to belong to the normal low-density lipoprotein population; if not, it is determined to belong to the low-density lipoprotein risk population.

[0069] Specifically, the normal value of low-density lipoprotein is 3.13 - 3.59 mmol / L; the low-density lipoprotein index less than 3.13 mmol / L is low; the low-density lipoprotein index higher than 3.59 mmol / L is high.

[0070] In this way, the low-density lipoprotein index is normal between 3.13 - 3.59 mmol / L, and it is determined to be the normal low-density lipoprotein risk population; the low-density lipoprotein index less than 3.13 mmol / L is low, and it is determined to be the low-density lipoprotein risk population; the low-density lipoprotein index higher than 3.59 mmol / L is high, and it is determined to be the low-density lipoprotein risk population.

[0071] The automatic judgment of whether it is a low-density lipoprotein risk is realized by setting the threshold range, further improving the degree of intelligence.

[0072] From the above description, it can be seen that the present invention achieves the following technical effects:

[0073] In the embodiments of the present application, a method for screening low-density lipoprotein risk is adopted. By obtaining a face video; using a preset risk index algorithm to determine the risk index of low-density lipoprotein based on the face video; determining whether the risk index is within the preset index threshold range; if so, it is determined to belong to the normal population of low-density lipoprotein; if not, it is determined to belong to the low-density lipoprotein risk population. The purpose of non-contact online automatic screening of low-density lipoprotein risk is achieved, thereby realizing the technical effect of effectively improving the degree of intelligence and avoiding discomfort to the human body, and further solving the technical problems of low automation and easy discomfort to the human body.

[0074] According to an embodiment of the present invention, preferably, when obtaining the face video, it further includes:

[0075] Obtaining the user mobile phone number corresponding to the face video, and obtaining the unique identification code configured by the administrator for each screening area.

[0076] When obtaining the face video, the user is also required to upload their own user mobile phone number and establish a binding relationship with the face video, and the administrator needs to configure a unique identification code for determining the screening area for each face video according to the currently screened area, so as to provide support for subsequent feedback and location determination management.

[0077] According to an embodiment of the present invention, preferably, if not, after determining that it belongs to the low-density lipoprotein risk population, it further includes:

[0078] Adopting a look-up table method to determine the corresponding treatment plan based on the low-density lipoprotein risk population;

[0079] Sending the treatment plan to the corresponding user terminal according to the user mobile phone number.

[0080] A relationship table between low-density lipoprotein index levels and treatment plans has been established in advance. Specifically, when the low-density lipoprotein index is between 3.13 - 3.59 mmol / L, it is normal, and it is determined to be a normal population of low-density lipoprotein risk, corresponding to no treatment plan; when the low-density lipoprotein index is less than 3.13 mmol / L, it is considered low, and it is determined to be a low-density lipoprotein risk population, corresponding to a low treatment plan; when the low-density lipoprotein index is higher than 3.59 mmol / L, it is considered high, and it is determined to be a low-density lipoprotein risk population, corresponding to a high treatment plan. After determining the corresponding treatment plan, the treatment plan can be sent to the corresponding user terminal according to the user mobile phone number for the user to view.

[0081] According to an embodiment of the present invention, preferably, if not, after determining that it belongs to the low-density lipoprotein risk population, it further includes:

[0082] Obtain the location of the community low-density lipoprotein risk screening area corresponding to the low-density lipoprotein risk population through the unique identification code.

[0083] Through the unique identification code, the location of the community low-density lipoprotein risk screening area corresponding to the low-density lipoprotein risk population can be quickly determined, facilitating targeted home treatment by medical staff (administrators).

[0084] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0085] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for screening low-density lipoprotein risk, characterized in that: include: Get face video; Using a preset risk index algorithm to determine a risk index for low-density lipoprotein based on the facial video of the person; Determine whether the risk indicator is within the preset indicator threshold range; If yes, they are judged as belonging to the low-density lipoprotein normal population; if no, they are judged as belonging to the low-density lipoprotein risk population.

2. The method for screening low-density lipoprotein risk according to claim 1, characterized in that: Using a preset risk index algorithm, the risk index of low-density lipoprotein determined based on the face video includes: Preprocessing the face video to obtain preprocessed data; A risk index for low-density lipoprotein is determined based on the preprocessed data.

3. The low-density lipoprotein risk screening method according to claim 2, characterized in that: Preprocess the facial video to obtain preprocessed data including: The facial video of the human face is collected by a near-infrared dynamic spectrum acquisition device to obtain the spectrum information of the blood components under the skin of the human face; The wave number band information is obtained by screening the wave number band combination with obvious correlation with the components of the compound to be tested in the spectral information through interval partial least squares; The wave number segment information is extracted into wave number point information in combination with the Monte Carlo algorithm.

4. The method for screening low-density lipoprotein risk according to claim 2, characterized in that: Determining the risk index of low-density lipoprotein based on the pre-processed data includes: Extracting a plurality of original features based on the preprocessed data; Calculate the correlation coefficients of the original features corresponding to the low-density lipoprotein risk population; The original features with correlation coefficients exceeding the preset standard range were extracted as risk indicators of low-density lipoprotein.

5. The method for screening low-density lipoprotein risk according to claim 1, characterized in that: When obtaining a face video, it also includes: Get the user's mobile phone number corresponding to the face video, and get the unique identification code configured by the administrator for each screening area.

6. The method for screening low-density lipoprotein risk according to claim 5, characterized in that: If not, then those who are considered to be at risk for low-density lipoprotein cholesterol also include: Using a table lookup method, a corresponding treatment plan is determined based on the low-density lipoprotein risk group; The treatment plan is sent to the corresponding user terminal according to the user's mobile phone number.

7. The method for screening low-density lipoprotein risk according to claim 5, characterized in that: If not, then those who are considered to be at risk for low-density lipoprotein cholesterol also include: The location of the community low-density lipoprotein risk screening area corresponding to the low-density lipoprotein risk population is obtained through the unique identification code.

8. A low-density lipoprotein risk screening device, characterized in that: include: A video acquisition module is used to acquire facial videos of human faces; An indicator determination module, used to determine the risk indicator of low-density lipoprotein based on the face video of the human face by using a preset risk indicator algorithm; The indicator judgment module is used to judge whether the risk indicator is within the preset indicator threshold range; If yes, they are judged as belonging to the low-density lipoprotein normal population; if no, they are judged as belonging to the low-density lipoprotein risk population.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the low-density lipoprotein risk screening method according to any one of claims 1 to 7 when run.

10. An electronic device comprising: A memory and a processor, characterized in that a computer program is stored in the memory, wherein the processor is configured to run the computer program to execute the low-density lipoprotein risk screening method according to any one of claims 1 to 7.

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