Method and device for early diagnosis and management of varicose veins

Abstract

An embodiment may be a method for early diagnosis and management of varicose veins by a server, which may include the steps of acquiring varicose vein-related information from a user device, quantifying the degree of capillary exposure based on an analysis of the varicose vein-related information, evaluating the risk of varicose veins based on the quantified degree of capillary exposure, and transmitting evaluation result information for varicose veins to the user device based on the evaluated risk of varicose veins.

Description

[Technical Field]

[0001] Embodiments relate to methods and devices for early diagnosis and management of varicose veins, and more particularly to methods and devices for early diagnosis and management of varicose veins by analyzing images acquired from a user device. [Background technology]

[0002] Varicose veins are a condition in which superficial veins in the lower limbs expand abnormally due to malfunction of the one-way valves in the veins. Varicose veins may not cause any particular symptoms other than discomfort in the early stages, but if left untreated, they may gradually progress and cause various complications. In general, the incidence of varicose veins increases with aging, and they are a disease that is more common in women than men.

[0003] The number of patients with varicose veins is increasing year by year, and the associated social costs are also increasing. Therefore, in order to reduce varicose vein disease and the associated social costs, it is necessary to establish and operate an evaluation system for the early diagnosis and management of varicose veins. Based on the above points, the following describes a method for the early diagnosis and management of varicose veins using a non-face-to-face service platform. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Korean Patent Publication No. 10-2022-0019180 Summary of the Invention [Problem to be solved by the invention]

[0005] The present specification can provide a method for early diagnosis and management of varicose veins of the lower extremities.

[0006] The present specification can provide a method related to a platform that acquires image information from a user device for early diagnosis of varicose veins in the lower limbs and thereby assesses the risk.

[0007] The present specification can provide a method for early diagnosis and management of varicose veins via a non-face-to-face service platform.

[0008] The present specification can provide a method for extracting images for the assessment of varicose veins.

[0009] The present specification can provide a method for analyzing images related to varicose veins of the lower extremities to determine the risk of varicose veins of the lower extremities.

[0010] The problem to be solved by this specification is not limited to the above-mentioned content, but can be expanded to various matters that can be derived from the embodiments of the invention described below. [Means for solving the problem]

[0011] According to one embodiment of the present specification, a method for early diagnosis and management of varicose veins by a server may include the steps of acquiring varicose vein-related information from a user device, quantifying the degree of capillary exposure based on an analysis of the varicose vein-related information, evaluating the risk of varicose veins based on the quantified degree of capillary exposure, and transmitting evaluation result information of varicose veins to the user device based on the evaluated risk of varicose veins.

[0012] Furthermore, according to one embodiment of the present specification, a server for early diagnosis and management of varicose veins of the lower limbs includes a memory for storing information related to varicose veins of the lower limbs, a transceiver unit for communicating with a user device, and a processor for controlling the memory and the transceiver unit, and the processor acquires the information related to varicose veins of the lower limbs from the user device, quantifies the degree of exposure of capillaries based on an analysis of the information related to varicose veins of the lower limbs, evaluates the risk of varicose veins of the lower limbs based on the quantified degree of exposure of capillaries, and transmits evaluation result information of varicose veins of the lower limbs to the user device based on the evaluated risk of varicose veins of the lower limbs.

[0013] Furthermore, according to one embodiment of the present specification, there is provided a method for early diagnosis and management of varicose veins by a user device, the method including the steps of transmitting varicose vein-related information to a server and obtaining evaluation result information of varicose veins from the server based on the risk of varicose veins, wherein the server can quantify the degree of capillary exposure based on an analysis of the varicose vein-related information, evaluate the risk of varicose veins based on the quantified degree of capillary exposure, and generate evaluation result information of varicose veins.

[0014] In addition, the following points may be commonly applied:

[0015] According to an embodiment of the present specification, the varicose vein-related information may include at least one of a varicose vein-related image, a varicose vein-related video, and an additional image.

[0016] Furthermore, according to one embodiment of the present specification, the varicose vein-related image is an image of the user's lower body part, and when quantifying the degree of capillary exposure based on the analysis of varicose vein-related information, the server derives pixel-by-pixel parameters for the user's lower body part from the varicose vein-related image, derives a standard deviation based on multiple pixel-by-pixel parameters to identify capillaries, and quantifies the degree of capillary exposure based on the identified capillaries that are equal to or greater than a predetermined value.

[0017] Furthermore, according to one embodiment of the present specification, when the risk of varicose veins is evaluated based on the quantified degree of capillary exposure, multiple capillary lines are checked, and the risk of varicose veins is evaluated by weighting the degree of distribution of capillaries in the multiple capillary lines that is equal to or greater than a predetermined value, and if any one of the multiple capillary lines has a large number of capillaries that is equal to or greater than the predetermined value, the risk of varicose veins may increase.

[0018] Furthermore, according to one embodiment of the present specification, the server acquires a plurality of images related to varicose veins of the lower limbs, the plurality of images related to varicose veins of the lower limbs being images relating to the same part of the lower body of the user, and in each of the plurality of images related to varicose veins of the lower limbs, the degree of exposure of capillaries is quantified and compared, and the comparison result information is weighted to evaluate the risk of varicose veins of the lower limbs.

[0019] Furthermore, according to one embodiment of the present specification, among a plurality of varicose vein-related images, a first image is an image acquired in a first time period, and a second image is an image acquired in a second time period, and the degree of capillary exposure in the first image is compared with the degree of capillary exposure in the second image to obtain comparison result information, and based on the comparison result information, the greater the difference between the degree of capillary exposure in the first image and the degree of capillary exposure in the second image, the greater the risk of varicose veins.

[0020] In addition, according to one embodiment of the present specification, the server may collect user information regarding the user's activities during the first time period and the second time period via at least one of the user device and the wearable device, and reflect the collected user information in assessing the risk of varicose veins.

[0021] Furthermore, according to one embodiment of this specification, the varicose vein-related video is a video of the user's lower body taken in a vertical direction, and the server can derive multiple varicose vein-related images from the varicose vein-related video.

[0022] In addition, according to one embodiment of the present specification, the server provides guide information to the user device for shooting videos related to varicose veins, and when the videos related to varicose veins shot by the user device based on the guide information are transmitted to the server, the videos related to varicose veins may be encrypted and transmitted.

[0023] Furthermore, according to an embodiment of the present specification, the server may acquire user interview information from the user device and evaluate the risk of varicose veins of the lower extremities based on the user interview information. [Effects of the Invention]

[0024] The present specification has the effect of providing a method for early diagnosis and management of varicose veins of the lower extremities.

[0025] The present specification has the effect of providing a method related to a platform that acquires image information from a user device for early diagnosis of varicose veins in the lower limbs and thereby assesses the risk.

[0026] The present specification has an effect of providing a method for early diagnosis and management of varicose veins of the lower extremities via a non-face-to-face service platform.

[0027] The present specification has the effect of providing a method for extracting images for the assessment of varicose veins of the lower extremities.

[0028] The present specification has an effect of providing a method for determining the risk of varicose veins by analyzing images related to varicose veins.

[0029] The effects of this specification are not limited to the above-mentioned contents, but can be extended to various matters that can be derived from the embodiments of the invention described below. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 illustrates an example operating environment for a system according to an embodiment of the present disclosure. [Figure 2]2 is a block diagram illustrating the internal configuration of a computing device 200 according to an embodiment of the present specification. [Figure 3a] 1 illustrates a service platform and server operations for early diagnosis and management of varicose veins in one embodiment of the present disclosure. [Figure 3b] 1 illustrates a service platform and server operations for early diagnosis and management of varicose veins in one embodiment of the present disclosure. [Figure 3c] FIG. 10 is a diagram illustrating a method for providing risk information of varicose veins to a user via a service platform for early diagnosis and management of varicose veins in one embodiment of the present specification. [Figure 4] 1 illustrates the operation of a user device and a server in accordance with an embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates a method for deriving images from a video to diagnose varicose veins in accordance with an embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram showing an image related to the risk of varicose veins in the lower extremities in one embodiment of the present specification. [Figure 7] FIG. 10 is a diagram showing an image related to the risk of varicose veins in the lower extremities in one embodiment of the present specification. [Figure 8a] FIG. 1 illustrates a method for utilizing multiple images for the diagnosis of varicose veins in accordance with an embodiment of the present disclosure. [Figure 8b] FIG. 1 illustrates a method for utilizing multiple images for the diagnosis of varicose veins in accordance with an embodiment of the present disclosure. [Figure 9] 1 illustrates a method for analyzing varicose vein-related images to assess risk according to one embodiment of the present disclosure. [Figure 10] 1 illustrates a method for analyzing varicose vein-related images to assess risk according to one embodiment of the present disclosure. [Figure 11] 1 illustrates a method for analyzing varicose vein-related images to assess risk according to one embodiment of the present disclosure. [Figure 12]1 is a flow chart illustrating a method for early diagnosis and management of varicose veins according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0031] In describing the embodiments of the present specification, if it is determined that a detailed description of a known configuration or function would obscure the gist of the embodiments of the present specification, the detailed description thereof will be omitted. In addition, in the drawings, parts that are not related to the description of the embodiments of the present specification will be omitted, and similar parts will be denoted by similar reference numerals.

[0032] In the embodiments of this specification, when a component is described as being "coupled," "coupled," or "connected" to another component, this means that it can include not only a direct connection, but also an indirect connection where other components are interposed between them. Furthermore, when a component is described as "including" or "having" other components, this does not exclude other components and means that it can further include other components, unless otherwise specified.

[0033] In the embodiments of the present specification, terms such as "first" and "second" are used only to distinguish one component from another component, and do not limit the order or importance of the components unless otherwise specified. Therefore, within the scope of the embodiments of the present specification, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly, a second component in one embodiment may be referred to as a first component in another embodiment.

[0034] In the embodiments of this specification, components that are distinguished from one another are used to clearly describe the respective features and do not necessarily mean that the components are separate. That is, multiple components may be integrated into a single hardware or software unit, or a single component may be distributed into multiple hardware or software units. Therefore, unless otherwise specified, such integrated or distributed embodiments are also included within the scope of the embodiments of this specification.

[0035] In this specification, the term "network" may refer to both wired and wireless networks. In this specification, the term "network" refers to a communication network through which data can be exchanged between devices and systems, and between devices, and is not limited to a specific network.

[0036] The embodiments described herein may be entirely comprised of hardware, partly comprised of hardware and partly comprised of software, or entirely comprised of software. As used herein, terms such as "unit," "device," or "system" refer to computer-related entities such as hardware, a combination of hardware and software, or software. For example, as used herein, a unit, module, device, or system may refer to, but is not limited to, a running process, a processor, an object, an executable, a thread of execution, a program, and / or a computer. For example, both an application running on a computer and the computer may be referred to as a unit, module, device, or system.

[0037] Furthermore, the device in this specification is not limited to mobile devices such as smartphones, tablet PCs, wearable devices, and HMDs (Head Mounted Displays), but may also be fixed devices such as PCs and home appliances with display functions. As an example, the device may be a cluster in a vehicle or an IoT (Internet of Things) device. That is, the device in this specification refers to a device capable of running an application, and is not limited to a specific type. Hereinafter, for convenience of explanation, a device on which an application runs will be referred to as a "device."

[0038] In this specification, the communication method of the network is not limited, and the connections between each component are not necessarily made using the same network method. The network may include communication methods that use communication networks (e.g., mobile communication networks, wired Internet, wireless Internet, broadcasting networks, satellite networks, etc.) as well as short-range wireless communication between devices. For example, the network may include all communication methods that allow objects to network with each other, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, the wired and / or wireless network may be a Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Networks (GSM), Enhanced Data GSM Environment (EDGE), High Speed ​​Downlink Packet Access (HSDPA), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Zigbee, Wi-Fi, Voice over Internet Protocol (VoIP), LTE Advanced, IEEE 802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for This refers to a communication network that uses one or more communication methods selected from the group consisting of, but not limited to, wireless (Wireless Network), wireless (Wireless Microwave Access), and communication using ultrasound.

[0039] The components described in the various embodiments are not necessarily required components, and some may be optional components. Therefore, embodiments consisting of a subset of the components described in the embodiments are also included in the scope of the embodiments of the present specification. Furthermore, embodiments including other components in addition to the components described in the various embodiments are also included in the scope of the embodiments of the present specification.

[0040] Hereinafter, embodiments of the present specification will be described in detail with reference to the drawings.

[0041] Fig. 1 is a diagram showing an example of an operating environment of a system according to an embodiment of the present specification. Referring to Fig. 1, one or more user devices 110-1, 110-2 and one or more servers 120, 130, 140 are connected via a network 1. Fig. 1 is an example for explaining the invention, and the number of user devices and the number of servers are not limited to those shown in Fig. 1.

[0042] The one or more user devices 110-1 and 110-2 may be fixed or mobile terminals implemented as computer systems. Examples of the one or more user devices 110-1 and 110-2 include smartphones, mobile phones, navigation systems, computers, laptops, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), tablet PCs, game consoles, wearable devices, internet of things (IoT) devices, virtual reality (VR) devices, and augmented reality (AR) devices. For example, the user device 110 in the embodiment may represent any of a variety of physical computer systems capable of communicating with other servers 120 to 140 via the network 1 using substantially wireless or wired communication methods.

[0043] Each server may be implemented as a computer device or multiple computers that communicate with one or more user devices 110-1, 110-2 via network 1 and provide commands, code, files, content, services, etc. For example, the server may be a system that provides respective services to one or more user devices 110-1, 110-2 connected via network 1. As a more specific example, the server may provide one or more user devices 110-1, 110-2 with a service (e.g., information provision) targeted by an application as a computer program installed and running on the one or more user devices 110-1, 110-2. As another example, the server may distribute files for installing and running the above-mentioned application to one or more user devices 110-1, 110-2, receive input information from users, and provide the corresponding service.

[0044] 2 is a block diagram illustrating the internal configuration of a computing device 200 according to one embodiment of the present specification. Such computing device 200 may be applied to one or more of the user devices 110-1 and 110-2 or servers 120 to 140 described above with reference to FIG. 1, and each device and server may have the same or similar internal configuration by adding or excluding some components.

[0045] Referring to FIG. 2 , the computing device 200 may include a memory 210, a processor 220, a communication module 230, and a transceiver 240. The memory 210 may include a non-transitory computer-readable recording medium such as a random access memory (RAM), a read-only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, or a permanent mass storage device. Here, a permanent mass storage device such as a ROM, an SSD, a flash memory, or a disk drive may be included in the above-mentioned device or server as a separate permanent storage device distinct from the memory 210. The memory 210 may also store an operating system and at least one program code (e.g., code for a browser installed and operating on the user device 110, or an application installed on the user device 110 to provide a particular service). These software components may be loaded from a computer-readable recording medium separate from the memory 210. Such a separate computer-readable recording medium may include a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, or the like.

[0046] In other embodiments, software components may be loaded into memory 210 via communication module 230 rather than from a computer-readable recording medium. For example, at least one program may be loaded into memory 210 based on a computer program (e.g., the above-mentioned application) being installed by a file provided over network 1 by a developer or a file distribution system (e.g., the above-mentioned server) that distributes application installation files.

[0047] Processor 220 may be configured to process computer program commands by performing basic arithmetic, logical, and input / output operations. The commands may be provided to processor 220 by memory 210 or by communication module 230. For example, processor 220 may be configured to execute received commands according to program code stored in a storage device such as memory 210.

[0048] The communication module 230 can provide functionality for the user device 110 and the servers 120-140 to communicate with each other via the network 1, and can provide functionality for each of the user device 110 and / or the servers 120-140 to communicate with other electronic devices.

[0049] The transceiver 240 may be a means for interfacing with an external input / output device (not shown). For example, the external input device may include a keyboard, a mouse, a microphone, a camera, etc., and the external output device may include a display, a speaker, a haptic feedback device, etc. As another example, the transceiver 240 may be a means for interfacing with a device that has an integrated input and output function, such as a touch screen.

[0050] In other embodiments, computing device 200 may include more components than those shown in FIG. 2 depending on the nature of the device to which it is applied. For example, if computing device 200 is applied to user device 110, it may be configured to include at least some of the input / output devices described above, or may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, a database, etc. As a more specific example, if the user device is a smartphone, various components typically found in smartphones may be further implemented, such as an acceleration sensor, a gyro sensor, a camera module, various physical buttons, a touch panel button, an input / output port, and a vibrator for vibration.

[0051] The following description will be given based on a computing device 200 that operates based on Figures 1 and 2. As an example, a service platform for early diagnosis of varicose veins may be provided based on at least one of an application, software, and program that operates on the computing device 200.

[0052] Here, the service platform for early diagnosis of varicose veins may be installed and operated on a user device as a computing device 200, and the platform may be controlled by a server. The server may provide services by exchanging data with the user device as the computing device 200 via a network. For example, the server may be configured with a program or software for acquiring images or videos via the service platform for early diagnosis of varicose veins and analyzing the images or videos to determine the risk of varicose veins. The server may also build an artificial intelligence model or use an external artificial intelligence model to improve the accuracy of determining the risk of varicose veins, as will be described later.

[0053] As another example, the service platform for early diagnosis of varicose veins may be a platform managed based on multiple nodes and is not limited to a specific form. That is, the service platform for early diagnosis of varicose veins may be managed via a server or other node, and may provide services to users via applications or other programs operated by the computing device 200, and is not limited to a specific form. As an example, the service platform for early diagnosis of varicose veins may be provided to users in various forms, but for convenience of explanation, the following description will be based on a service platform for early diagnosis of varicose veins as a platform operated by the user device 300. However, this is merely an example for convenience of explanation and is not limited thereto.

[0054] 3A to 3C are diagrams illustrating the operation of a service platform and a server for the early diagnosis and management of varicose veins in one embodiment of the present specification. Referring to FIG. 3A, a service platform (or application) for the early diagnosis of varicose veins (hereinafter, referred to as the service platform 310) operates on a user device 300. For example, the user device 300 transmits a varicose vein-related image 311 to a server 400 via the service platform 310. The server 400 may provide varicose vein evaluation result information, which evaluates the risk of varicose veins based on the acquired varicose vein-related image 311, to the service platform 310 operating on the user device 300. That is, a user may receive a service for assessing the risk of varicose veins via the service platform 310 installed on the user device 300. Here, for example, the risk of varicose veins may be assessed using the varicose vein-related image 311. More specifically, varicose veins are a disease in which superficial veins in the lower limbs are abnormally dilated due to malfunction of one-way valves in the veins. Many of the symptoms of this disease are concentrated in the calves and thighs. Here, in severe cases, visible dilation of capillaries in the calves and thighs can be confirmed. In other words, the degree of capillary exposure in the calves and thighs can be used as a criterion for diagnosing or assessing the risk of varicose veins. For example, the degree of capillary exposure can be quantified to assess the risk of varicose veins.

[0055] In addition to the degree of capillary exposure, the degree of edema can also be used as a criterion for determining varicose veins. The degree of edema refers to the volume or volume change of a specific area. The degree of edema can be determined by the volume or volume change of the calf or the instep, but the present invention is not limited thereto.

[0056] Considering the above, the risk of varicose veins can be determined based on the quantified capillary exposure level in the varicose vein-related image 311. That is, the extent to which capillaries are exposed in the thigh or calf image submitted by the user is determined, the quantified capillary exposure level is derived, and the risk of varicose veins is determined based on this, thereby enabling early diagnosis of varicose veins. The server 400 obtains the varicose vein image 311 related to the user from the service platform 310 running on the user device 300, determines the risk of varicose veins based on this, and then provides the corresponding information via the service platform 310.

[0057] For example, to improve the accuracy of risk assessment of varicose veins, the analysis accuracy of the varicose vein image 311 may be improved or a large number of varicose vein images 311 may be required. As a specific example, a user's condition may change in real time. Furthermore, symptoms may appear severe in a specific area of ​​the user, but not in another specific area. That is, the varicose vein image 311 acquired by the server 400 may have limitations in accurately representing the user's condition, and a large number of images may be required to accurately assess the user's condition. Furthermore, risk assessment of varicose veins may have limitations because it is not performed through face-to-face medical treatment but through non-face-to-face images. Therefore, to accurately assess the risk of varicose veins, a method for improving the accuracy of image analysis may be required.

[0058] 3b, for example, the server 400 may obtain not only the varicose vein image 311 but also a varicose vein video 312 and an additional image 313 related to varicose veins via the service platform 310. That is, the server 400 may perform a risk analysis of varicose veins based on the video 312 and the related additional image 313, which will be described later.

[0059] 3c, for example, the server 400 may further acquire user interview information 314 from the service platform 310 and determine the risk of varicose veins based on the acquired information. Specifically, when a user wishes to receive a varicose vein risk assessment service via the service platform 310 operating on the user device 300, the user may input the user interview information 314 and provide it to the server 400, and then acquire and provide a video or image to the server 400. The server 400 may determine the risk of varicose veins based on the user interview information 314 and video or image information acquired via the service platform 310 of the user device 300, and provide the user with evaluation result information on the risk of varicose veins via the service platform 310. Here, the risk information for varicose veins may be expressed as a risk score or a risk level as numerical information, but is not limited to a specific form.

[0060] Specifically, FIG. 4 illustrates the operation of a user device and a server according to an embodiment of the present specification. Referring to FIG. 4, the user device 300 may transmit a request for varicose vein analysis to the server 400. The request for varicose vein analysis may be transmitted to the server 400 along with user medical information 314. That is, the user may transmit the request for varicose vein analysis along with the user medical information to the server 400 via the service platform 310 running on the user device 300. The server 400 may then request the user device 300 to capture at least one of video and images to assess the risk of varicose veins. For example, the video and images may be requested multiple times at regular intervals, as will be described later. The server 400 may acquire at least one of video and images from the user device 300, assess the risk of varicose veins based on the acquired video and images, and transmit assessment result information for varicose veins to the user device 300.

[0061] For example, FIG. 5 illustrates a method for deriving an image from a video to diagnose varicose veins in one embodiment of the present disclosure. Referring to FIG. 5, the server 400 may request and obtain a video 312 from the user device 300 to improve the accuracy of the risk assessment of varicose veins. Here, for example, the video 312 may be a video of a lower body part of a human body, i.e., a video of the lower body captured vertically. As a specific example, the video 312 may be an image of the entire lower body, i.e., a video capturing the left, right, front, and back. Another example is a video capturing the inside of the lower body, but is not limited to a specific form. Here, imaging guide information may be provided via the service platform 310, and the user may capture a video by imaging the lower body part based on the imaging guide of the service platform 310. As another example, because the lower body part is personal physical information, the video may be encrypted via the service platform 310 and transmitted to the server 400. That is, the server 400 can encrypt the video so that it cannot be played back, and provide it only as input to a program, software, or artificial intelligence for assessing the risk of varicose veins. The server 400 extracts varicose vein-related images 510, 520, and 530 from the acquired video 312. That is, the server 400 can check the degree of capillary dilation in the video 312 and extract at least one image 510, 520, and 530 that is related to varicose veins. Here, as an example, the server 400 can be connected to an artificial intelligence 500, and the video 312 can be provided as input to the artificial intelligence 500, and the above-mentioned images 510, 520, and 530 can be derived as output. For example, the artificial intelligence 500 can be equipped with a learning model related to varicose veins and can learn based on multiple images related to varicose veins, numerical information on capillaries, and risk information for varicose veins. That is, the artificial intelligence 500 can construct a learning model related to the diagnosis of varicose veins and thereby extract varicose vein-related images. The server 400 can then analyze the image to assess the risk of varicose veins.Furthermore, the artificial intelligence 500 can delete information unnecessary for determining the risk of varicose veins from the varicose vein-related image. For example, the artificial intelligence 500 can delete leg hair and other information unrelated to determining the risk of varicose veins from the varicose vein-related image, and can also recognize the relevant information through learning. In other words, the artificial intelligence 500 can not only derive an image for determining the risk of varicose veins, but also delete unnecessary parts from the image, thereby improving the accuracy of risk determination based on the varicose vein-related image from which unnecessary information has been deleted.

[0062] Here, as an example, FIGS. 6 and 7 are diagrams illustrating images related to the risk of varicose veins in one embodiment of the present specification. Referring to FIG. 6, the risk of varicose veins is determined based on varicose vein images 610, 620, and 630. For example, the first image 610 shows only some exposed capillaries in the corresponding area, but the capillaries are not highly exposed and are not dilated. That is, the first image 610 has a low value for the degree of capillary exposure. On the other hand, the second image 620 has a higher degree of capillary exposure than the first image, and the third image 630 has the highest value for the degree of capillary exposure. That is, the risk of varicose veins can be determined based on the value for the degree of capillary exposure, and it is necessary to derive a value for the degree of capillary exposure indicating the degree to which the capillaries are exposed in each of the images 610, 620, and 630.

[0063] Referring to FIG. 7, the risk of varicose veins is evaluated by taking into account the distribution of exposed capillary sites. Here, the first image 710 shows a case in which capillaries are present in multiple capillary lines, while the second image 720 shows a case in which multiple capillaries are exposed in a single capillary line. Considering the symptoms of varicose veins, if multiple capillaries are exposed in a single capillary line, it can be determined that the varicose veins are more advanced. Therefore, the risk of varicose veins may be higher in the second image 720 than in the first image 710. For example, when analyzing images related to varicose veins, quantification may be performed based on the exposed capillary sites in the images. Considering the location of each exposed capillary site, whether the exposed capillary sites are located in the same capillary line can be reflected in the risk assessment. That is, the above information may be weighted, and the risk of varicose veins may be set higher in that case. As an example, in FIG. 7, the second image 720 can be set to have a higher risk level than the first image 710.

[0064] As another example, FIGS. 8A and 8B illustrate a method for utilizing multiple images for diagnosing varicose veins in accordance with an embodiment of the present disclosure. Referring to FIG. 8A, the server 400 may acquire multiple images 311 and / or videos 312 related to varicose veins via the service platform 310. For convenience of explanation, the following description will be based on the varicose vein-related image 311, but the same applies to the video 312. That is, the server 400 may analyze multiple images 311 related to varicose veins to improve the accuracy of the analysis of varicose veins. For example, leg edema may be most pronounced in the evening after a user has performed daily activities, which may be related to symptoms of varicose veins. In consideration of the above, the server 400 may acquire multiple images 311 related to varicose veins and analyze the risk of varicose veins.

[0065] As a specific example, the server 400 acquires varicose vein-related images 311 from different time periods. For example, because the exposed portions of capillaries may differ between morning and evening, the server 400 may acquire varicose vein-related images 311 for each time period. The server 400 may compare multiple varicose vein-related images 311 to acquire information on the degree of capillary exposure and reflect this information in the risk level. For example, the degree of capillary exposure may increase due to a user's activities or other actions related to varicose vein symptoms. In other words, the more advanced the varicose vein symptoms, the more pronounced the symptoms may be due to the user's activities or actions. Considering the above, the greater the difference between the morning and evening images of the same area in the varicose vein-related images 311, the higher the risk of varicose vein development. The server 400 may analyze multiple varicose vein-related images 311 acquired from different time periods and weight the differences, so that the greater the difference, the higher the risk level. For example, referring to FIG. 8B, user information during the time period during which the varicose vein image 311 is acquired may be collected, and the risk level may be evaluated based on the collected information. For example, the service platform 310 may operate on the user device 300 and the user's wearable device 800. The service platform 310 may collect user information during the time period during which the varicose vein image 311 is acquired, and acquire information related to the user's activities and movements. For example, if the user's activity level is low during the time period during which the varicose vein image 311 is acquired, the difference in the degree of capillary exposure may be small. On the other hand, if the user's activity level is high during the time period during which the varicose vein image 311 is acquired, such as through mountain climbing or other activities, the difference in the degree of capillary exposure may be large. In other words, the user's activity level must be taken into consideration in order to determine the risk level of varicose veins. To this end, the server 400 acquires the collected user information and applies weighting to it. The server 400 can determine the risk of varicose veins based on the weighting of the user's numerical information, and can acquire user information via the user device 300 or the wearable device 800 for the above-mentioned operations.

[0066] FIG. 9 is a diagram illustrating a method for analyzing an image related to varicose veins to assess the risk level according to an embodiment of the present disclosure. Referring to FIG. 9, when server 400 assesses the risk level of varicose veins using image 311 related to varicose veins, server 400 derives parameters for each pixel in image 311 and acquires standard deviation information for each pixel parameter value. Based on each pixel parameter value and standard deviation, information about the distribution of capillaries can be obtained and the degree of capillary exposure can be quantified. For example, in FIG. 9, risk level information 930 and 940 for varicose veins can be derived based on information 910 regarding values ​​above a certain level, reflecting information 920 calculated from the standard deviation for each pixel of the imaged region. That is, pixels that are above a predetermined value based on the standard deviation between skin pixels are determined to have exposed capillaries, and the degree of capillary exposure can be quantified based on the number of corresponding pixels, and the risk level of varicose veins can be determined based on this information. Here, as an example, the risk assessment of varicose veins may reflect whether a large number of exposed capillaries exist in a capillary line or whether multiple exposed capillaries are reflected in a large number of capillary lines, based on the pixel information described above. Also, as an example, the information may be calculated for a plurality of images of varicose veins, and information regarding the quantified pixel change difference in the plurality of images may be reflected in the risk assessment, as described above.

[0067] As another example, when a user device 300 acquires a varicose vein-related image, it may be difficult to obtain an image suitable for analysis. For example, when capturing an image of the back of a calf, the user may bend their waist or legs, which may limit the ability to capture multiple images in the same position, and correction may be necessary. In consideration of the above, information related to the varicose vein-related image 311 and the user's position may be further considered. For example, the server 400 may acquire information on changes in exposed capillaries depending on the user's position in advance and correct the image based on this information. That is, the server 400 may further incorporate information on the user's shooting position into the acquired varicose vein-related image 311 to correct the image, thereby improving the accuracy of risk assessment. For example, since a user's posture may bend, the skin color may appear differently. Therefore, correcting the skin color based on previously captured video may improve the accuracy of risk assessment.

[0068] As another example, the above-described artificial intelligence 500 may be utilized to assess the risk of varicose veins. Here, the artificial intelligence 500 may be initially trained based on a doctor's clinical judgment information, and then trained based on reinforcement learning or other deep learning. That is, an artificial intelligence 500 model may be constructed to assess the risk of varicose veins, and the artificial intelligence 500 may derive an assessment value of the risk as an output, reflecting the weighting related to the risk and other learning information.

[0069] As another example, the server 400 can provide user linkage information based on the server's assessment of the risk of varicose veins. For example, the server 400 can provide a notification to a user whose risk of varicose veins is high via the service platform 310. The server 400 can also link with hospitals and other facilities. The server 400 can check for varicose veins by checking biomarkers using samples (e.g., saliva, urine, etc.) at the user's hospital or other facility, thereby enabling management of varicose veins for the user. For example, the server 400 can link with a reservation system for a hospital specializing in varicose veins, provide a varicose vein check service and other programs to the user via the service platform 310, and enable the user to manage varicose veins through the service platform 310.

[0070] As another example, the server 400 can acquire additional information in cooperation with other devices (e.g., a body composition measuring device). For example, the server 400 can further acquire lower body blood flow analysis information measured by other devices and reflect it in the assessment of the risk of varicose veins, and is not limited to a specific embodiment.

[0071] 10 and 11 are diagrams illustrating a method for analyzing images related to varicose veins to assess risk according to an embodiment of the present disclosure. Referring to FIG. 10, the server 400 acquires images related to varicose veins 1011 and 1012 and performs the analysis described above to obtain analysis result images 1021 and 1022, based on which risk can be assessed. As described above, other information may be further utilized to assess risk. This provides an early diagnostic tool for patients with varicose veins, improving treatment efficacy and reducing social costs. For example, varicose veins are often left untreated, and by the time of diagnosis at a hospital, the condition may already be advanced, potentially reducing the effectiveness of treatment. Therefore, the risk of varicose veins can be determined preventatively, either at the time of the onset of early symptoms or before symptoms appear, via the service platform 310 and the server 400, thereby reducing treatment costs and duration. Furthermore, by using the above-described service platform 310 and server 400, varicose veins can be managed non-face-to-face, without the hassle of visiting a hospital. Furthermore, if a diagnosis is delayed due to the hassle of visiting a hospital when early symptoms appear, treatment can become difficult, reducing the effectiveness of treatment and increasing treatment costs. However, by using the above-described service platform 310 and server 400, tests can be easily performed without visiting a hospital, which can have a positive impact on the prevention and treatment of varicose veins in daily life. Furthermore, as an example, diagnostic biomarkers and factors can be discovered based on the data secured via the above-described service platform 310 and server 400, as shown in FIG. 11.

[0072] FIG. 12 is a flow chart illustrating a method for early diagnosis and management of varicose veins according to one embodiment of the present disclosure.

[0073] 12, the server 400 acquires varicose vein-related information from the user device 300 (S1210). The server 400 then quantifies the degree of capillary exposure based on an analysis of the varicose vein-related information (S1220) and evaluates the risk of varicose vein development based on the quantified degree of capillary exposure (S1230). The server 400 then transmits varicose vein evaluation result information to the user device 300 based on the evaluated risk of varicose vein development (S1240). The varicose vein-related information may include at least one of varicose vein-related images, varicose vein-related videos, and additional images. For example, the additional images may be images of other body parts of the user or other images, and are not limited to a specific form.

[0074] For example, the varicose vein-related image may be an image of a user's lower body. When quantifying the degree of capillary exposure based on the analysis of the varicose vein-related information, the server may derive pixel-by-pixel parameters for the user's lower body from the varicose vein-related image, identify capillaries by deriving a standard deviation based on the multiple pixel-by-pixel parameters, and quantify the degree of capillary exposure based on capillaries that are equal to or greater than a predetermined value among the identified capillaries, as described above.

[0075] As another example, when assessing the risk of varicose veins based on the quantified degree of capillary exposure, multiple capillary lines can be checked and weighted based on the distribution of capillaries in the multiple capillary lines that is equal to or greater than a predetermined value, thereby assessing the risk of varicose veins. Here, if any one of the multiple capillary lines has a large number of capillaries that is equal to or greater than a predetermined value, the risk of varicose veins may increase.

[0076] The server 400 can also acquire multiple varicose vein-related images. The multiple varicose vein-related images are images of the same lower body part of the user. The degree of capillary exposure in each of the multiple varicose vein-related images can be quantified and compared, and the comparison result information can be weighted to evaluate the risk of varicose veins. As a specific example, among the multiple varicose vein-related images, a first image can be acquired in a first time period, and a second image can be acquired in a second time period. The server 400 can acquire comparison result information by comparing the degree of capillary exposure in the first image with the degree of capillary exposure in the second image. Based on the comparison result information, the greater the difference between the degree of capillary exposure in the first image and the degree of capillary exposure in the second image, the greater the risk of varicose veins.

[0077] In addition, the server 400 may collect user information regarding the user's activities during the first time period and the second time period via at least one of the user device 300 and the wearable device 800, and may reflect the collected user information in assessing the risk of varicose veins.

[0078] As another example, the varicose vein-related video may be a video of the user's lower body captured in a vertical direction. The server 400 may derive multiple varicose vein-related images from the varicose vein-related video. The server 400 may also provide the user device with guide information for capturing the varicose vein-related video. Here, when the varicose vein-related video captured by the user device based on the guide information is transmitted to the server, the varicose vein-related video may be encrypted and transmitted. The server 400 may also acquire the user's medical interview information from the user device 300 and evaluate the risk of varicose vein development based on the user's medical interview information, as described above.

[0079] The above-described embodiments may be implemented at least in part as a computer program and recorded on a computer-readable recording medium. The computer-readable recording medium on which a program for implementing the embodiments is recorded includes any type of recording device on which data readable by the computer program is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, and optical data storage devices. The computer-readable recording media may also be distributed across computer systems connected to a network, so that computer-readable code is stored and operated in a distributed manner. Functional programs, codes, and code segments for implementing the present embodiments are readily understood by those skilled in the art to which the present embodiments pertain.

[0080] In the above description, the present specification has been described with reference to the embodiments shown in the drawings. However, this is merely an example, and a person skilled in the art will understand that various modifications and variations are possible from the embodiments. However, these modifications should be included within the technical scope of protection of the present specification. Therefore, the true scope of technical protection in the present specification should be determined based on the technical idea of ​​the appended claims, including other embodiments, other modified examples, and equivalents to the claims. [Industrial Applicability]

[0081] The present specification can provide a method related to a platform that acquires image information from a user device for early diagnosis of varicose veins of the lower limbs and thereby assesses the risk, and has industrial applicability.

Claims

1. A method for early diagnosis and management of varicose veins in lower limbs, comprising: acquiring varicose vein-related information from a user device; quantifying the degree of capillary exposure based on the analysis of the varicose vein-related information; assessing the risk of varicose veins based on the quantified capillary exposure; and transmitting varicose vein assessment result information to the user device based on the assessed risk of varicose vein.

2. The method according to claim 1 , wherein the varicose vein-related information includes at least one of a varicose vein-related image, a varicose vein-related video, and an additional image.

3. the varicose vein-related image is an image of a lower body part of the user, When quantifying the degree of capillary exposure based on the analysis of the varicose vein-related information, the server derives parameters for each pixel of the lower body part of the user from the varicose vein-related image; deriving a standard deviation based on a plurality of the per-pixel parameters to identify capillaries; The method according to claim 2 , wherein the degree of exposure of the capillaries is quantified based on the capillaries having a value equal to or greater than a preset value among the identified capillaries.

4. When evaluating the risk of varicose veins based on the quantified capillary exposure degree, a plurality of capillary lines are checked, and a weight is assigned to the distribution degree of capillaries in the plurality of capillary lines that is equal to or greater than a predetermined value to evaluate the risk of varicose veins; The method according to claim 3 , wherein the risk of varicose veins increases when a large number of capillaries equal to or greater than a predetermined value are distributed in any one of the plurality of capillary lines.

5. the server acquires a plurality of varicose vein-related images, the plurality of varicose vein-related images being images of the same lower body part of the user; The method according to claim 3 , further comprising the steps of: quantifying and comparing the capillary exposure levels in the plurality of varicose vein-related images; and weighting the comparison result information to evaluate the risk of varicose vein.

6. Among the plurality of varicose vein-related images, a first image is an image acquired in a first time period, and a second image is an image acquired in a second time period, comparing the degree of exposure of the capillaries in the first image with the degree of exposure of the capillaries in the second image to obtain the comparison result information; The method according to claim 5 , wherein the risk of varicose veins increases as the difference between the degree of exposure of capillaries in the first image and the degree of exposure of capillaries in the second image increases based on the comparison result information.

7. the server collects user information regarding the user's activities during the first time period and the second time period via at least one of the user device and the wearable device; The method according to claim 6 , wherein the collected user information is reflected in the assessment of the risk of varicose veins.

8. The method according to claim 2 , wherein the varicose vein-related video is a video of the user's lower body taken in a vertical direction, and the server derives a plurality of varicose vein-related images from the varicose vein-related video.

9. The method of claim 8, wherein the server provides the user device with guide information for shooting the varicose vein-related video, and when the varicose vein-related video shot by the user device based on the guide information is transmitted to the server, the varicose vein-related video is encrypted and transmitted.

10. The method according to claim 1 , wherein the server acquires user interview information from the user device and evaluates the risk of varicose veins of the lower extremities based on the user interview information.

11. A computer program stored on a computer readable medium for performing the method according to any one of claims 1 to 10 in combination with hardware.

12. A server for early diagnosis and management of varicose veins of the lower limbs, a memory for storing varicose vein-related information; a transceiver for communicating with a user device; a processor that controls the memory and the transceiver; The processor: acquiring varicose vein-related information from the user device; quantifying the degree of capillary exposure based on the analysis of the varicose vein-related information; assessing the risk of varicose veins based on the quantified capillary exposure; A server that transmits evaluation result information of varicose veins to the user device based on the evaluated risk of varicose veins.

13. A method for early diagnosis and management of varicose veins using a user device, comprising: transmitting varicose vein-related information to a server; acquiring evaluation result information of varicose veins from the server based on the risk of varicose veins, The server quantifies the degree of capillary exposure based on an analysis of the varicose vein-related information, evaluates the risk of varicose veins based on the quantified degree of capillary exposure, and generates evaluation result information for the varicose veins.

14. The method according to claim 13 , wherein the varicose vein-related information includes at least one of a varicose vein-related image, a varicose vein-related video, and an additional image.

15. the varicose vein-related image is an image relating to a lower body part of the user, When quantifying the degree of capillary exposure based on the analysis of the varicose vein-related information, the server derives parameters for each pixel of the lower body part of the user from the varicose vein-related image; deriving a standard deviation based on a plurality of the per-pixel parameters to identify capillaries; The method according to claim 14 , wherein the degree of exposure of the capillaries is quantified based on the capillaries having a value equal to or greater than a preset value among the identified capillaries.

16. When evaluating the risk of varicose veins based on the quantified capillary exposure degree, a plurality of capillary lines are checked, and a weight is assigned to the distribution degree of capillaries in the plurality of capillary lines that is equal to or greater than a predetermined value to evaluate the risk of varicose veins; The method according to claim 15, wherein the risk of varicose veins increases when a large number of capillaries equal to or greater than a predetermined value are distributed in any one of the plurality of capillary lines.

17. The user device acquires user inquiry information and transmits it to the server; The method according to claim 13 , wherein the risk of varicose veins is assessed based on medical interview information of the user.

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