Digital Biomarker-Based Hair Loss Management Method and System

Abstract

The present invention relates to a digital biomarker-based hair loss management method and system. More specifically, the present invention relates to a technology for calculating image reproducibility and image analysis reliability indices from a scalp image captured through a user terminal, calculating a three-dimensional exposure index that reflects the connection structure of an exposure patch, changes in the shape of the parting, erythema-sebum separation results, hair miniaturization patterns, and shading directionality around the hair roots in the scalp image, and generating a digital biomarker for hair loss management using the three-dimensional exposure index. In addition, the present invention relates to a technology that provides hair loss management data corresponding to changes in scalp condition for each user by evaluating whether scalp images from multiple time points were acquired under conditions where they can be compared with one another, and selectively performing guidance on re-shooting or subsequent analysis based on the evaluation result.

Description

Technology Field

[0001] The present invention relates to a digital biomarker-based hair loss management method and system.

[0002] More specifically, the present invention relates to a technology for calculating image reproducibility and image analysis reliability indices from a scalp image captured through a user terminal, calculating a three-dimensional exposure index that reflects the connection structure of an exposure patch, changes in the shape of the parting, erythema-sebum separation results, hair miniaturization patterns, and shading directionality around the hair roots in the scalp image, and generating a digital biomarker for hair loss management using the three-dimensional exposure index.

[0003] In addition, the present invention relates to a technology that provides hair loss management data corresponding to changes in scalp condition for each user by evaluating whether scalp images from multiple time points were acquired under conditions where they can be compared with one another, and selectively performing guidance on re-shooting or subsequent analysis based on the evaluation result.

[0004] The digital biomarker described above is not limited to a confirmed medical diagnostic value, but may be state data for generating reference information for hair loss management based on scalp images acquired through a user terminal, shooting condition information, and cumulative shooting history per user. Additionally, the digital biomarker may be composed of a data structure corresponding to at least one of a stereoscopic exposure index, pore contour deviation per user, hair thinning persistence index, hair follicle cluster preservation rate, individual seasonality explainability, and time-series hair loss progression risk. Background Technology

[0006] With the recent improvement in camera performance of user devices such as smartphones and tablets, there is an increasing demand for users to photograph their scalps without visiting a hospital or using professional imaging equipment, and to use the captured images to check their hair loss or scalp condition.

[0007] General scalp image-based analysis technology can distinguish between hair and scalp regions in captured scalp images and provide information related to the state of hair loss by calculating the number of hairs, hair density, hair thickness, or exposed scalp area. In addition, some technologies can guide users on changes in their scalp condition by comparing the captured scalp image with past images or by displaying the amount of change in specific areas as a time-series graph.

[0008] However, image quality when capturing the scalp using a user terminal can vary depending on the shooting location, lighting direction, shooting distance, shooting angle, hand shake, focus status, and hair arrangement. For example, if supersaturated reflection patches occur due to scalp sebum or external light sources, hair areas or pore contours may partially transform into areas that are difficult to identify. Furthermore, if the user terminal shakes at the moment of shooting or if a different part of the scalp is captured compared to the previous point in time, it is difficult to reliably calculate changes in the same area using only a method of simply comparing scalp images from multiple viewpoints.

[0009] Furthermore, if the condition is judged based solely on the exposed scalp area, the exposure around a normally formed part and the exposure formed irregularly due to changes in hair arrangement may be treated as having the same exposure area. In this case, even if the number of exposed scalp pixels is similar, factors such as whether the exposed patches are connected, whether the centerline of the part is curved, whether the width of the part is locally uneven, whether the red areas are due to sebum reflection or color changes, and whether the shading around the hair roots indicates the three-dimensional arrangement of the hair may not be sufficiently reflected.

[0010] Furthermore, when determining the state of hair loss based solely on the number of hairs or hair thickness at a single point in time, the user's usual hair density, pore contour, degree of follicular cluster maintenance, and seasonal change characteristics may not be sufficiently considered. Even if the same decrease in hair density is observed, there may be limitations in distinguishing whether the decrease is due to a temporary change in shooting conditions, falls within the range of temporary changes related to seasonal or humidity changes, or is a change that deviates from the same user's past baseline.

[0011] Therefore, in user terminal-based scalp image analysis, a technology is required that first evaluates the comparability of shooting conditions, selects images for analysis based on the evaluation results, calculates scalp condition values ​​by reflecting exposure structure, parting shape, and shading around hair roots together rather than just the simple exposure area, and provides hair loss management data by linking the condition values ​​with user-specific baselines. The problem to be solved

[0012] The present invention has been devised to solve the aforementioned problems, and the objective of the present invention is to provide a digital biomarker-based hair loss management method and system that evaluates whether scalp images at multiple viewpoints captured by a user terminal have shooting conditions that allow for comparison with one another, and prevents the shooting conditions from being reflected in subsequent analysis if they are not included in the analysis allowable range.

[0013] Another problem that the present invention aims to solve is to provide a digital biomarker-based hair loss management method and system that calculates imaging reproducibility and image analysis reliability indices using scalp micro-texture preservation rate, supersaturated reflection patch occupancy rate, reflection patch boundary steepness, posture shake index at the moment of shooting, same scalp area registration residual, and hair direction entropy, moving away from a method of judging image quality based only on overall brightness or average illumination in scalp images.

[0014] Another problem that the present invention aims to solve is to provide a digital biomarker-based hair loss management method and system that calculates a three-dimensional exposure index using the exposure patch connectivity correction area, the cumulative value of the part centerline curvature, the local variability of the part width, the erythema-sebum separation index, the hair miniaturization pattern index, and the consistency of the hair root tangential shadow, rather than using only the simple scalp exposure area or the number of hair pixels.

[0015] Another problem that the present invention aims to solve is to provide a digital biomarker-based hair loss management method and system that transmits a scalp image as primary data for subsequent analysis when the imaging reproducibility and image analysis reliability indices fall within the analysis acceptance range, and classifies the image as auxiliary analysis data or provides re-imaging guidance data to a user terminal when the indices are below the analysis acceptance range.

[0016] Another problem that the present invention aims to solve is to provide a digital biomarker-based hair loss management method and system that generates a digital biomarker reflecting a change in condition relative to a baseline for the same user by linking the past imaging history, imaging area, imaging time, and scalp condition values ​​for each user.

[0017] Another problem that the present invention aims to solve is to provide a digital biomarker-based hair loss management method and system that calculates a time-series hair loss progression risk using the rate of change of digital biomarkers generated at multiple time points, a hair loss progression acceleration index, a progression inflection index, and the correspondence of the direction of change between multiple scalp regions, and provides guidance on adjusting the management cycle, guidance on re-photography, or guidance on reviewing consultation with a specialized institution to a user terminal according to the time-series hair loss progression risk.

[0018] However, the problems that the present invention aims to solve are not limited to the above problems, and may include other problems that can be identified from the configuration of the present invention. means of solving the problem

[0019] In a digital biomarker-based hair loss management system according to an embodiment of the present invention for solving the above problem,

[0020] An image acquisition unit that acquires an image of a user's scalp through a camera of a user terminal and collects shooting time information and shooting area information corresponding to the scalp image;

[0021] An image preprocessing module that performs shooting condition correction and alignment of the same scalp area between multiple viewpoint images for the scalp image received from the image acquisition unit;

[0022] An image analysis module that calculates a plurality of image feature values ​​related to the hair loss state from a corrected image processed by the above image preprocessing module;

[0023] A biomarker generation module that generates digital biomarkers related to the hair loss status of each user using the above plurality of image feature values;

[0024] A state analysis module that calculates a state value related to hair loss progression by comparing the digital biomarkers generated at multiple points in time; and

[0025] A service providing module that provides hair loss management data corresponding to the output result of the above-mentioned state analysis module to the user terminal; comprising

[0026] The image preprocessing module can set the scalp fine texture preservation rate calculated in the same scalp area of ​​the reference image and the current image as a first reliability reference value, calculate a second reliability value by subtracting a first correction value corresponding to the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness from the first reliability reference value, and calculate the shooting reproducibility and image analysis reliability index by subtracting a second correction value corresponding to at least one of the shooting moment pose shake index, same scalp area matching residual, and hair direction entropy from the second reliability value.

[0027] The above image analysis module can calculate a stereoscopic exposure index by setting the exposure patch connectivity correction area calculated from the correction image as a base exposure value, adjusting the base exposure value upward according to the cumulative value of the parting centerline curvature and the local variability of the parting width, further weighting the upwardly adjusted value according to the erythema-sebum separation index and the hair miniaturization pattern index, and downwardly adjusting the further weighted value according to the consistency of shadows in the tangential direction of the hair root.

[0028] The above biomarker generation module generates the digital biomarker including the stereochemical exposure index, and

[0029] The above service providing module is characterized by providing at least one of a reshoot guide, area-specific management reference information, or a shooting cycle guide to the user terminal according to the range of the shooting reproducibility and image analysis reliability index or the stereoscopic exposure index.

[0030] The above image acquisition unit is,

[0031] Receiving at least one of shooting time information, shooting area information, image resolution, file format, color channel information, and user account identification value together with the scalp image from the user terminal, and

[0032] The above imaging area information includes at least one of the crown, frontal area, temporal area, and occipital area, and

[0033] The above image acquisition unit stores the shooting number and shooting area identification value corresponding to the received scalp image, and

[0034] The image acquisition unit is configured to transmit re-shooting request information to the user terminal without transmitting the scalp image as primary data for subsequent analysis if the resolution of the scalp image is below a preset allowable range, if the scalp area within the scalp image is included at a ratio below a preset area ratio, or if the information on the shooting area is missing.

[0035] The above re-shooting request information includes at least one of guidance on reassigning the shooting area, guidance on adjusting the shooting distance, guidance on including the scalp area, and guidance on checking the image resolution, and

[0036] The above image preprocessing module is,

[0037] High-frequency components corresponding to pore contours and scalp surface texture are extracted from the reference image and the current image, respectively, and

[0038] The scalp micro-texture preservation rate is calculated by normalizing the degree to which the high-frequency component of the current image is maintained relative to the high-frequency component of the reference image within the same scalp area to a range of 0 to 1, and

[0039] The above scalp micro-texture preservation rate is set as the primary reliability standard value, and

[0040] Among the entire scalp area of ​​the above current image, pixel clusters whose brightness values ​​fall within the upper reference range are classified as supersaturated reflection patches, and

[0041] The area ratio occupied by the above supersaturated reflective patch in the entire scalp area is calculated as the supersaturated reflective patch occupancy rate, and

[0042] The reflection patch boundary steepness is calculated by calculating the amount of brightness change between adjacent pixels based on the boundary line of the above-mentioned supersaturated reflection patch, and

[0043] When the above-mentioned supersaturated reflection patch occupancy rate and the above-mentioned reflection patch boundary steepness are each greater than or equal to a preset reference interval, and both the above-mentioned supersaturated reflection patch occupancy rate and the above-mentioned reflection patch boundary steepness are included in a high interval, a combined correction rule is applied to increase the subtraction strength of the first correction value, and

[0044] It is characterized by calculating a second reliability value by subtracting the first correction value from the first reliability standard value.

[0045] The above image preprocessing module is,

[0046] An angular velocity variance value or an acceleration variance value is obtained from at least one of the gyroscope sensor and the accelerometer sensor included in the user terminal for a time interval before and after the shooting input time, and the angular velocity variance value or the acceleration variance value is normalized to calculate an attitude shake index at the moment of shooting.

[0047] Feature point matching is performed between the reference image and the current image, and the average of the positional errors between the matched feature points is normalized to calculate the same scalp region matching residual.

[0048] The hair direction vectors detected in the current image are classified into multiple direction segments, and the hair direction entropy is calculated by normalizing the distribution non-uniformity or dispersion of the hair direction vectors.

[0049] Calculate a secondary correction value corresponding to the section to which the above shooting moment attitude shake index, the above same scalp area matching residual, and the above hair direction entropy belong, and

[0050] The final shooting reproducibility and image analysis reliability indices are calculated by subtracting the second correction value from the second reliability value.

[0051] If the above final shooting reproducibility and image analysis reliability indices are greater than or equal to a preset analysis allowance range, the above current image is transmitted as the main data of the image analysis module, and

[0052] If the above final shooting reproducibility and image analysis reliability indices are below the above analysis allowance range, the above current image is classified as auxiliary analysis data or re-shooting guidance data is provided to the user terminal through the above service provision module,

[0053] If the above final shooting reproducibility and image analysis reliability indices are calculated to be less than a preset lower limit, the above lower limit is set as the final value, and

[0054] In cases where the attitude shake index at the moment of shooting cannot be obtained due to permission restrictions on the user terminal, sensor deactivation, or sensor error, the weight corresponding to the same scalp area matching residual is adjusted upward by a preset correction ratio.

[0055] The above image analysis module is,

[0056] Receive the correction image, scalp region mask, same scalp region coordinates, and supersaturated reflection patch information from the image preprocessing module, and

[0057] Separating the hair region and the scalp region in the above correction image,

[0058] A continuous pixel cluster in the above scalp area where hair pixels are not detected is classified as an exposure patch, and

[0059] The size, shape, and adjacency of the exposure patch are calculated using at least one of connected component labeling, region segmentation, or pixel cluster analysis, and

[0060] The exposure patch connection correction area is calculated by reflecting the size, shape, and proximity of the above exposure patch, and the above exposure patch connection correction area is set as the basic exposure value for calculating the three-dimensional exposure index.

[0061] Among the above scalp regions, a band-shaped region that is continuous in the longitudinal direction is classified as a parting candidate region, and

[0062] Extracting the center point in the width direction between the left hair boundary and the right hair boundary in the above parting candidate area, and

[0063] A parting centerline is created by connecting the above width-direction center points, and

[0064] The accumulated curvature value of the parting centerline is calculated by accumulating the degree of curvature of the above parting centerline compared to a reference straight line or the parting centerline at a previous point in time, and

[0065] The distance between the left hair boundary and the right hair boundary is measured at preset intervals along the above-mentioned parting centerline, and the local variability of the parting width is calculated using the variance value or the difference value per interval of the parting width measured at multiple points.

[0066] When the accumulated value of the parting centerline curvature and the local variability of the parting width are included in a high range together, the degree of upward adjustment of the basic exposure value is increased.

[0067] The above image analysis module is,

[0068] The color channels of the above-mentioned corrected image are analyzed to extract a red region, wherein the region overlapping with the above-mentioned supersaturated reflection patch classified by the above-mentioned image preprocessing module is excluded from the red region, and

[0069] The erythema-sebum separation index is calculated using the occupancy, saturation, or color distribution values ​​of the red area excluding the area overlapping with the above-mentioned supersaturated reflective patch, and

[0070] Using at least one of thinning processing, linear structure detection processing, or hair width estimation processing, detect hair candidate pixel clusters that are less than a preset thickness criterion and less than a preset length criterion, and

[0071] The degree to which the above hair candidate pixel clusters are observed clustered in a specific area is calculated as a hair miniaturization pattern index, and

[0072] In the section where the above erythema-sebum separation index and the above hair miniaturization pattern index increase, the above upwardly adjusted base exposure value is additionally weighted, and

[0073] Calculates the degree of brightness change in the area surrounding the hair root point where the hair pixel begins,

[0074] The ratio in which the shading of the area surrounding the hair root point is continuously formed in the tangential direction of the hair growth direction or in an adjacent direction is calculated as the hair root tangential shadow consistency, and

[0075] If the above hair root tangential shadow consistency is greater than or equal to a preset standard, it is classified as a section where the three-dimensional arrangement state of the hair is maintained, and the above additionally weighted value is lowered by an offset rate corresponding to the hair root tangential shadow consistency section to calculate the final three-dimensional exposure index.

[0076] If the contrast value of the entire image is below a preset standard, the downward adjustment step based on the above-mentioned root tangential shadow consistency is omitted, or a basic offset value is assigned to the above-mentioned root tangential shadow consistency, and

[0077] It may be characterized by setting the upper limit value as the final value when the final stereoscopic exposure index exceeds the preset upper limit value, and setting the lower limit value as the final value when the final stereoscopic exposure index is less than the preset lower limit value. Effects of the invention

[0078] According to one embodiment of the present invention, by calculating the shooting reproducibility and image analysis reliability indices for a scalp image captured through a user terminal, images reflecting shooting shake, supersaturated reflection, out-of-focus, or failure to match the same scalp area can be distinguished and processed in subsequent analysis.

[0079] According to one embodiment of the present invention, an image preprocessing module sets the scalp micro-texture preservation rate as a first-order reliability reference value and sequentially reflects a first-order correction value based on the supersaturated reflection patch occupancy rate and reflection patch boundary steepness, and a second-order correction value based on the pose shake index at the moment of shooting, the same scalp area matching residual, and hair direction entropy, so that it can help in selecting images of degraded quality that are difficult to distinguish based on simple brightness criteria alone.

[0080] According to one embodiment of the present invention, when the shooting reproducibility and image analysis reliability indices are below the analysis allowable range, the scalp image can be classified as a re-shooting guide or auxiliary analysis data rather than being used as primary data, thereby contributing to reducing analysis deviations due to differences in shooting conditions during the process of comparing images from multiple viewpoints.

[0081] According to one embodiment of the present invention, since the image analysis module calculates a stereoscopic exposure index using the exposure patch connectivity correction area, the accumulated curvature value of the parting centerline, and the local variability of the parting width, even images having the same scalp exposure area can be distinguished into different state values ​​depending on the exposure structure around the parting and changes in hair arrangement.

[0082] According to one embodiment of the present invention, when an image analysis module calculates an erythema-sebum separation index, it can exclude red areas that overlap with supersaturated reflection patches, thereby reducing the extent to which areas where color distortion occurs due to sebum reflection or strong lighting are reflected as scalp color changes.

[0083] According to one embodiment of the present invention, since the image analysis module can utilize both the hair thinning pattern index and the hair root tangential shadow consistency, it is possible to distinguish between the area where short and thin hair candidate groups are observed and the area where the three-dimensional arrangement state around the hair root is maintained, and reflect this in the three-dimensional exposure index.

[0084] According to one embodiment of the present invention, the biomarker generation module can generate digital biomarkers by linking the stereoscopic exposure index with the past shooting history of each user, thereby providing management reference information that reflects changes in the baseline for each user, rather than a method based only on the number of hairs or hair thickness at a specific point in time.

[0085] According to one embodiment of the present invention, since the state analysis module can reflect the rate of change of digital biomarkers at multiple time points, the hair loss progression acceleration index, the progression inflection index, and the consistency of diffusion between sites, it can provide a detailed pattern of time-series change compared to a method that classifies the cautionary management interval based solely on the magnitude of increase or decrease between two time points.

[0086] According to one embodiment of the present invention, a state analysis module can determine whether to provide a result of calculating the risk of hair loss progression in a time series or classify an auxiliary analysis value using a shooting cycle deviation correction value and cumulative shooting reproducibility, thereby contributing to reducing deviations in time series analysis due to non-uniform shooting intervals or differences in shooting conditions.

[0087] According to one embodiment of the present invention, the condition analysis module can not only lower the risk value based solely on the management execution history, but also confirm the degree to which the rate of increase of digital biomarkers or the rate of increase of scalp exposure-related condition values ​​is mitigated within a preset observation period after the management execution, thereby allowing the trend of change in condition values ​​after management execution for each user to be reflected in the management reference information.

[0088] According to one embodiment of the present invention, a service provision module may provide guidance on re-shooting, reference information for management by body part, guidance on the shooting cycle, or guidance on reviewing consultation with a specialized institution, depending on the shooting reproducibility and image analysis reliability index, stereoscopic exposure index, and the range of digital biomarkers.

[0089] Accordingly, one embodiment of the present invention can be utilized to provide user-specific hair loss management data by stepwise reflecting shooting conditions, scalp exposure structure, changes in parting shape, and changes in user-specific baselines in user terminal-based scalp image analysis. Brief explanation of the drawing

[0090] Figure 1 illustrates a flowchart between all components according to the present invention. FIG. 2 illustrates a flowchart for deriving shooting reproducibility and image analysis reliability according to the present invention. Figure 3 illustrates a flowchart for deriving a stereoscopic exposure index according to the present invention. Figure 4 illustrates a flowchart for deriving digital biomarkers based on individual baseline deviation according to the present invention. Figure 5 illustrates a flowchart for deriving the risk of hair loss progression in a time series according to the present invention. Specific details for implementing the invention

[0091] Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention.

[0092] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another.

[0093] In this specification, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. When a component is described as being “connected” or “connected” to another component, it should be understood that it may be directly connected to or connected to that other component, or that there may be other components in between. On the other hand, when a component is described as being “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

[0094] Meanwhile, a "module" or "part" for a component as used in this specification performs at least one function or operation. Furthermore, a "module" or "part" may perform a function or operation by hardware, software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts," excluding a "module" or "part" that must be performed on specific hardware or on at least one processor, may be integrated into at least one module. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0095] In this specification, the data processing, storage, transmission and reception, display, and user input processing operations of each module may be performed by one or more processors included in a user terminal, a server device, or a combination thereof. Additionally, known information processing configurations, such as data transmission and reception over a communication network, database storage, user authentication, and screen output, may be described to the extent necessary to understand the processing flow of the present invention.

[0096] In addition, the operation embodiments and configurations described in a general manner without being explained in detail below follow the prior art and are omitted in order to focus on describing the purpose of the present invention and the resulting effects.

[0097] Furthermore, in describing the present invention, if it is determined that a detailed description of related known functions or configurations may unnecessarily obscure the essence of the invention, such detailed description is abbreviated or omitted.

[0098] The simulation figures below are normalized example values ​​intended to illustrate the operation of thresholds, interval classification criteria, and step-by-step correction rules used in each module. The figures are not limited to representing medical diagnostic performance or therapeutic effects for an entire specific user group and may vary depending on the specifications of the imaging device, imaging site, imaging patterns by user group, image resolution, and the degree of accumulation of system operation data.

[0099] Comparison figures for the same simulation dataset may be example values ​​comparing the simple standard method and the stepwise judgment method of the present invention under the same conditions. The comparison figures are intended to explain how the interval classification method of the present invention derives different branching results depending on the input value, and are not limited to actual medical judgment values ​​or clinical verification results.

[0100] A digital biomarker-based hair loss management system (100) according to the present invention may be configured to use a scalp image obtained through a user terminal (10) to calculate, in stages, a shooting reproducibility and image analysis reliability index, a stereoscopic exposure index, a digital biomarker based on individual baseline deviation, and a time-series hair loss progression risk, and to provide customized hair loss management data corresponding to the calculated results to the user terminal (10).

[0101] The above hair loss management system (100) may include an image acquisition unit (110), an image preprocessing module (120), an image analysis module (130), a biomarker generation module (140), a state analysis module (150), and a service provision module (160).

[0102] The image acquisition unit (110) can acquire multiple scalp images from a user terminal (10). The image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices by evaluating the shooting conditions of the scalp images and the possibility of comparing the same scalp area. The image analysis module (130) can calculate a three-dimensional exposure index from the corrected scalp images that reflects the exposure patch connection structure, parting shape, erythema-sebum separation results, hair thinning pattern, and shading directionality around the hair roots.

[0103] The biomarker generation module (140) can generate a digital biomarker based on individual baseline deviation using the stereoscopic exposure index and the same user's past imaging history. The condition analysis module (150) can calculate a time-series hair loss progression risk by comparing the digital biomarkers at multiple points in time and reflecting the rate of change, progression acceleration, progression inflection point, consistency of diffusion between sites, and correlation of response delay after management execution. The service provision module (160) can generate user-specific management reference information based on the time-series hair loss progression risk and imaging reliability.

[0104] The above hair loss management system (100) may operate in a sequential processing structure that uses a value calculated in a previous step as an input value for the next step. Specifically, the shooting reproducibility and image analysis reliability indices are used as reference values ​​to determine whether the scalp image can be used as main data for the image analysis module (130), the stereoscopic exposure index is used as a basic state value for the biomarker generation module (140), and the digital biomarker can be used as source data for time-series analysis for the state analysis module (150). Accordingly, each component may not merely generate a value independently but may have a linked structure that transmits the value of the previous step as a judgment criterion for the subsequent step.

[0105] The user terminal (10) may refer to an electronic device used by the user to take a picture of the scalp and to check the management reference information generated by the hair loss management system (100).

[0106] The above user terminal (10) may be implemented as at least one of a smartphone, tablet, laptop, personal computer, a wearable device equipped with a camera, or a terminal connected to an external shooting device.

[0107] The user terminal (10) may include at least one of a camera, a display unit, an input unit, a communication unit, a storage unit, a gyroscope sensor, and an accelerometer sensor. The user terminal (10) may be connected to the hair loss management system (100) directly or through a communication network.

[0108] The user terminal (10) can generate a scalp image according to the user's shooting input. The user terminal (10) can provide a guidance screen to shoot at least one shooting area among the crown of the head, the frontal area, the temporal area, and the occipital area.

[0109] The above user terminal (10) can generate at least one of the following as shooting condition information: shooting time, shooting area, shooting distance, shooting direction, lighting condition, number of shots, change in the posture of the shooting device, and sensor values ​​immediately before and after shooting.

[0110] The user terminal (10) can display customized hair loss management data provided by the service provision module (160) on the screen. The customized hair loss management data may include at least one of the following: guidance on image reproducibility, guidance on re-image, reference information for management by area, information on changes in digital biomarkers, time-series hair loss progression risk ranges, guidance on the image cycle, and guidance on reviewing consultation with a specialized institution.

[0111] The user terminal (10) can acquire a scalp image from a built-in camera or an external shooting device. The user terminal (10) can display a baseline, shooting distance guide, shooting angle guide, shooting area guide, or a guide for re-shooting the same area on the shooting screen.

[0112] The above user terminal (10) can generate at least one of image resolution, focus degree, brightness distribution, color distribution, shooting time, and shooting location tag as additional data from the captured image.

[0113] The above shooting distance can be estimated using at least one of the size of a reference marker within the shooting screen, the area of ​​the scalp region within the image, the distance sensor value included in the user terminal (10), or the degree of correspondence between the reference frame displayed on the shooting guide screen and the actual scalp region. The above shooting direction can be calculated using at least one of the posture sensor value of the user terminal (10), the slope value of the scalp reference line within the shooting screen, or the arrangement direction of the face and scalp reference regions.

[0114] The above-mentioned shooting location tag is not limited to precise location coordinates and may consist of a non-identifiable area code for querying regional information at the city, county, or district level obtained with user consent or regional weather information. The above-mentioned non-identifiable area code may be used by the biomarker generation module (140) to query regional humidity, temperature, and seasonal information to calculate the individual seasonality explainability.

[0115] Additionally, the user terminal (10) can obtain an angular velocity variance value or an acceleration variance value from a gyroscope sensor and an accelerometer sensor during a time interval before and after the shooting input time. The angular velocity variance value or the acceleration variance value can be used as data to calculate the attitude shake index at the moment of shooting in the image preprocessing module (120).

[0116] In one embodiment, a user can capture an image of the scalp of the crown area using a camera of a user terminal (10). The user terminal (10) can transmit the captured image of the crown, information on the time of capture, information on the area captured, and sensor values ​​at the moment of capture to an image acquisition unit (110).

[0117] In another embodiment, the user terminal (10) provides a guidance screen to photograph the frontal area, the crown of the head, and the temporal area, respectively, and can distinguish the images for each photographed area and transmit them to the hair loss management system (100).

[0118] In another embodiment, the user terminal (10) may receive and display on the screen retake guidance data from the service providing module (160) that says, “The shooting reproducibility and image analysis reliability index is below the analysis allowable range. Please shoot the same part again in a fixed position.”

[0119] The hair loss management system (100) may refer to a system that processes a scalp image obtained from a user terminal (10) to generate a digital biomarker, and analyzes changes in the digital biomarker at multiple points in time to provide hair loss management data for each user.

[0120] The above hair loss management system (100) may be implemented in the form of an application installed on a user terminal (10), or in the form of being installed on a server device and communicating with the user terminal (10). Additionally, the above hair loss management system (100) may be implemented in a structure in which functions are distributed between the user terminal (10) and the server device.

[0121] The hair loss management system (100) can sequentially perform the acquisition of scalp images, evaluation of image reproducibility, calculation of a three-dimensional exposure index, generation of digital biomarkers, calculation of a time-series hair loss progression risk, and provision of services.

[0122] The above hair loss management system (100) can acquire scalp images at multiple viewpoints through an image acquisition unit (110). The above hair loss management system (100) can calculate shooting reproducibility and image analysis reliability indices using the scalp micro-texture preservation rate, reflection patch characteristics, pose shake index at the moment of shooting, same scalp area matching residual, and hair direction entropy through an image preprocessing module (120).

[0123] The above hair loss management system (100) can calculate a three-dimensional exposure index using the exposure patch connectivity correction area, the cumulative value of the parting centerline curvature, the local variability of the parting width, the erythema-sebum separation index, the hair miniaturization pattern index, and the consistency of the hair root tangential shadow through the image analysis module (130).

[0124] The above hair loss management system (100) can generate digital biomarkers that reflect a three-dimensional exposure index, pore contour deviation by user, hair thinning persistence index, hair follicle cluster preservation rate, and individual seasonality explainability through a biomarker generation module (140).

[0125] The above hair loss management system (100) can calculate the risk of time-series hair loss progression using the rate of change of digital biomarkers at multiple points in time, the acceleration index of hair loss progression, the inflection point of progression, the consistency of diffusion between areas, the correction value for deviation from the shooting cycle, the cumulative shooting reproducibility, and the correlation of response delay after management execution through the state analysis module (150).

[0126] The hair loss management system (100) can receive scalp images, shooting time information, shooting area information, shooting condition information, shooting moment sensor value and user identification information from a user terminal (10).

[0127] The above user identification information may consist of a user account identifier, an encrypted identifier, or a temporary identifier instead of direct identifiers such as a name.

[0128] In addition, the hair loss management system (100) can retrieve past captured images, past stereoscopic exposure indices, past digital biomarkers, past time-series hair loss progression risk, user-specific shooting history, and user-specific management execution history from the storage unit.

[0129] In one embodiment, the hair loss management system (100) analyzes a crown scalp image taken in January 2026 and a crown scalp image taken in March 2026, respectively, and can determine whether the reproducibility of the image and the reliability of the image analysis in the same scalp area are included in the analysis allowable range.

[0130] The above hair loss management system (100) can generate a digital biomarker by calculating a three-dimensional exposure index from the scalp images and comparing it with a user-specific baseline when the scalp images are included in the analysis allowance range.

[0131] The above hair loss management system (100) can calculate the time-series hair loss progression risk when the rate of change of digital biomarkers at multiple points in time increases in the direction of the risk increase, and can provide guidance on adjusting the management cycle or guidance on reviewing consultation with a specialized institution through the service provision module (160).

[0132] The image acquisition unit (110) is a hardware or software configuration that acquires a scalp image captured through the camera of the user terminal (10) or receives a scalp image captured from the user terminal (10).

[0133] The above image acquisition unit (110) can acquire a scalp image at a single viewpoint and can also collect multiple scalp images acquired at different shooting times.

[0134] The image acquisition unit (110) receives a scalp image generated from a user terminal (10) and can correspond shooting time, shooting area, and shooting condition information to the scalp image.

[0135] The image acquisition unit (110) can check whether the captured scalp image satisfies basic conditions for use in analysis. For example, the image acquisition unit (110) can check at least one of image resolution, whether a scalp area is included, whether information on the captured area exists, whether information on the time of capture exists, and whether the file is damaged.

[0136] In one embodiment, the image acquisition unit (110) may classify the scalp image as candidate data for subsequent analysis if the image resolution is greater than or equal to a preset minimum resolution, the scalp area is included in a preset ratio of the total image area, and shooting area identification value and shooting time information exist. On the other hand, if the image resolution is less than the preset minimum resolution, the scalp area is included in a ratio less than a preset ratio of the total image area, or the shooting area identification value or shooting time information is missing, the image acquisition unit (110) may not classify the scalp image as candidate data for subsequent analysis and may provide re-shooting request information to the user terminal (10).

[0137] The above minimum resolution and the preset ratio of the scalp area may be changed according to the specifications of the shooting device, the screen guidance method of the user terminal (10), the shooting area, and the degree of accumulation of system operation data. For example, the preset ratio of the scalp area may be set as a range value for determining whether the scalp area is included as an analyzable area within the entire image.

[0138] The above image acquisition unit (110) can transmit re-shooting request information to the user terminal (10) if the verification result deviates from the preset shooting allowance range.

[0139] The image acquisition unit (110) can receive image data generated directly through the camera of the user terminal (10). In addition, the image acquisition unit (110) can acquire existing scalp images stored in the user terminal (10) or scalp images transmitted from an external shooting device.

[0140] The above image acquisition unit (110) can acquire shooting time information, shooting area information, shooting condition information, image attribute information and user reference information along with the scalp image.

[0141] The above shooting time information may include the shooting date and time or the shooting session. The above shooting area information may include at least one of the crown of the head, the frontal area, the temporal area, and the occipital area. The above shooting condition information may include at least one of brightness, focus, shake, distance, angle estimate, and sensor value at the moment of shooting. The above image attribute information may include resolution, file format, and color channel information. The above user reference information may include a user account identification value or a shooting history identification value.

[0142] In one embodiment, the image acquisition unit (110) receives a current scalp image of the crown area and a scalp image from three months ago from a user terminal (10), and can assign shooting time information and shooting area information to each scalp image.

[0143] In another embodiment, the image acquisition unit (110) may provide re-shooting guidance data to the user terminal (10) if the resolution of the captured image is lower than a preset allowable range or if the scalp area is not sufficiently included.

[0144] The image preprocessing module (120) is a software configuration that converts a scalp image acquired from an image acquisition unit (110) into a form suitable for analysis and evaluates whether multiple scalp images at different viewpoints have physical conditions that allow for comparison with each other.

[0145] The above image preprocessing module (120) can perform at least one of shooting environment correction, image quality correction, scalp region separation, same scalp region matching, and shooting reproducibility evaluation.

[0146] The image preprocessing module (120) can calculate the reproducibility of shooting and the reliability of image analysis for scalp images at multiple viewpoints.

[0147] The image preprocessing module (120) can extract high-frequency components corresponding to pore contours and scalp surface textures from the reference image and the current image, respectively, and calculate the scalp fine texture preservation rate by normalizing the degree to which the high-frequency components of the current image are maintained relative to the reference image within the same scalp area. The image preprocessing module (120) can set the scalp fine texture preservation rate as a first reliability reference value.

[0148] The image preprocessing module (120) can classify pixel clusters in which the brightness value of the entire scalp area of ​​the current image is included in the upper reference range as supersaturated reflection patches, and calculate the ratio of the area occupied by the supersaturated reflection patches in the entire scalp area to obtain the supersaturated reflection patch occupancy rate.

[0149] The image preprocessing module (120) can calculate the amount of brightness change between adjacent pixels based on the boundary line of the supersaturated reflection patch and normalize the amount of brightness change to calculate the reflection patch boundary steepness.

[0150] The image preprocessing module (120) can apply a combined correction rule that increases the first correction value when the oversaturated reflection patch occupancy rate and the reflection patch boundary steepness are both high, and calculate the second reliability value by subtracting the first correction value from the first reliability reference value.

[0151] Subsequently, the image preprocessing module (120) can calculate the pose shake index at the moment of shooting, the same scalp area matching residual, and the hair direction entropy. The image preprocessing module (120) can calculate a secondary correction value corresponding to the section to which each of the indicators belongs, and subtract the secondary correction value from the secondary reliability value to calculate the final shooting reproducibility and image analysis reliability index.

[0152] The image preprocessing module (120) can transmit the image as main data to the image analysis module (130) if the final shooting reproducibility and image analysis reliability indices fall within the analysis allowance range, and if they fall below the analysis allowance range, it can classify the image as auxiliary analysis data or generate reshoot guidance data.

[0153] The image preprocessing module (120) can receive a scalp image, shooting time information, shooting area information, shooting condition information, and a sensor value at the moment of shooting from the image acquisition unit (110).

[0154] The image preprocessing module (120) can apply an edge detection algorithm or a high-frequency component extraction algorithm to a reference image and a current image, and normalize the degree of retention of high-frequency components within the same scalp area to a range of 0 to 1.

[0155] The image preprocessing module (120) can classify a group of pixels in an upper reference range of the brightness distribution of the entire scalp area as an oversaturated reflection patch. For example, the upper reference range can be set to a group of pixels having brightness values ​​within the top 5% of the brightness distribution of the entire scalp area.

[0156] The above image preprocessing module (120) can obtain an angular velocity variance value or an acceleration variance value during a time interval before and after the shooting input time from a gyroscope sensor and an accelerometer sensor embedded in the user terminal (10), and normalize the variance value to calculate an attitude shake index at the moment of shooting.

[0157] The image preprocessing module (120) can calculate the same scalp region matching residual by normalizing the average of the positional errors that occur after feature point matching between the reference image and the current image. In addition, the image preprocessing module (120) can obtain hair direction entropy by calculating the non-uniformity of the distribution of the hair direction vector histogram.

[0158] In one embodiment, the image preprocessing module (120) may set the scalp micro-texture preservation rate of the current image relative to the reference image as 0.80 when calculated as 0.80 as the first reliability reference value. The image preprocessing module (120) may calculate the first correction value as 0.25 and the second reliability value as 0.55 according to the combined correction rule when the supersaturated reflection patch occupancy rate is 0.30 and the reflection patch boundary steepness is 0.50.

[0159] The image preprocessing module (120) can calculate a secondary correction value of 0.30 and derive a final shooting reproducibility and image analysis reliability index of 0.25 when the pose shake index at the moment of shooting is 0.40, the same scalp area matching residual is 0.20, and the hair direction entropy is 0.30.

[0160] If the final shooting reproducibility and image analysis reliability index of 0.25 is less than 0.6, which is the lower limit of the analysis allowance range, the image preprocessing module (120) classifies the image as a target for reshooting and can provide reshooting guidance data to the user terminal (10) through the service provision module (160).

[0161] The image analysis module (130) is a software configuration that analyzes the corrected image processed by the image preprocessing module (120) and generates a three-dimensional exposure index used to calculate a state value related to hair loss progression.

[0162] The above image analysis module (130) can detect at least one of a hair area, a scalp area, an exposed patch, a parting candidate area, an erythema candidate area, a hair miniaturization pattern, and a shadow around the hair root.

[0163] The image analysis module (130) can classify a continuous pixel cluster in which no hair pixels are detected in the scalp area as an exposure patch. The image analysis module (130) can extract the exposure patch using at least one of connection component labeling, region segmentation, or pixel cluster analysis, and can calculate an exposure patch connectivity correction area by reflecting the size, shape, and proximity of the exposure patch.

[0164] The image analysis module (130) can classify a band-shaped area that is continuous in the longitudinal direction among the exposed scalp areas as a parting candidate area. The image analysis module (130) can extract a center point in the width direction between the left hair boundary and the right hair boundary in the parting candidate area and create a parting center line by connecting the center points in the width direction.

[0165] The above image analysis module (130) can calculate the accumulated curvature value of the parting centerline by accumulating the degree of curvature of the parting centerline. In addition, the above image analysis module (130) can measure the parting width at preset intervals along the parting centerline and calculate the local variability of the parting width using the variance value or the difference value between intervals of the parting width measured at multiple points.

[0166] The above image analysis module (130) sets the exposure patch connection correction area as the basic exposure value, and can increase the degree of upward adjustment of the basic exposure value when the accumulated value of the parting centerline curvature and the local variability of the parting width are included in a high range together.

[0167] The image analysis module (130) analyzes the color channels of the scalp image to extract a red region, but may exclude the region overlapping with the supersaturated reflection patch classified by the image preprocessing module (120) from the red region. The image analysis module (130) may calculate an erythema-sebum separation index using the occupancy rate, saturation, or color distribution value of the red region from which the region overlapping with the supersaturated reflection patch has been excluded.

[0168] Additionally, the image analysis module (130) can detect hair candidate pixel clusters that are less than a preset thickness standard and less than a preset length standard by using at least one of thinning processing, linear structure detection processing, or hair width estimation processing. The image analysis module (130) can calculate a hair thinning pattern index by using the degree to which the hair candidate pixel clusters are observed clustered in a specific area.

[0169] The thickness standard set above may be set according to the resolution of the shooting device, the shooting distance, and the pixel-to-actual length conversion ratio within the scalp image. For example, the image analysis module (130) may convert the hair width in pixels into a physical thickness candidate value using a reference marker within the shooting screen, an estimated shooting distance value of the user terminal (10), or a reference distribution of the average hair width.

[0170] The above-mentioned pre-set length standard may be set according to at least one of the skeletal line length of a hair candidate pixel cluster, the number of connected pixels, or the relative length relative to the standard length within the scalp area. For example, if a linear hair candidate group of less than a certain length is repeatedly detected in the same local area, the image analysis module (130) may reflect the hair candidate group in the calculation of the hair thinning pattern index. However, the thickness standard and the length standard are reference values ​​for distinguishing candidate patterns in the image and are not limited to standards for directly determining medical causes.

[0171] The above image analysis module (130) can further weight the upwardly adjusted value in the section where the erythema-sebum separation index and the hair softening pattern index increase.

[0172] The image analysis module (130) calculates a brightness variation in the area around the hair root point where the hair pixel begins, and can calculate the ratio in which the directionality of the shading around the hair root has a constant relationship with the hair growth direction as the hair root tangential shadow consistency. The image analysis module (130) can calculate the final stereoscopic exposure index by lowering the additionally weighted value in the section where the hair root tangential shadow consistency is high.

[0173] The image analysis module (130) can receive a corrected image, a scalp region mask, identical scalp region coordinates, supersaturated reflection patch information, and shooting reproducibility and image analysis reliability indices from the image preprocessing module (120).

[0174] The above image analysis module (130) can separate the hair region and the scalp region in the corrected image and extract a cluster of continuous pixels classified as not covered by hair among the scalp regions as an exposure patch.

[0175] The above image analysis module (130) can calculate the left and right hair boundaries of the parting candidate area, the parting centerline, the parting width distribution, the color channel values ​​of the red area, the overlapping area with the supersaturated reflection patch, the short and thin hair candidate group, and the shading directionality around the hair roots.

[0176] In one embodiment, the image analysis module (130) can calculate the exposure patch connectivity correction area in the crown scalp image as 0.45 and set this as the basic exposure value. The image analysis module (130) can increase the basic exposure value to 0.55 if the accumulated value of the parting centerline curvature and the local variability of the parting width are included in a section with relatively high values.

[0177] The image analysis module (130) can further increase the first adjustment value to 0.65 when the erythema-sebum separation index is calculated to be 0.20. Subsequently, when the image analysis module (130) calculates the hair root tangential shadow consistency to be 0.60, it classifies that the three-dimensional arrangement state of the hair is maintained at a certain level and can lower the final three-dimensional exposure index to 0.52.

[0178] The above image analysis module (130) can transmit the final stereoscopic exposure index to the biomarker generation module (140).

[0179] The biomarker generation module (140) is a software configuration that generates a digital biomarker based on individual baseline deviation using the stereoscopic exposure index calculated by the image analysis module (130) and the cumulative shooting history of the same user.

[0180] The digital biomarker described above may be composed of a single numerical value representing only the state value at a specific point in time, and may also be composed of a data structure including at least one of a user identification value, shooting time information, shooting area information, stereoscopic exposure index, pore contour deviation by user, hair miniaturization persistence index, hair follicle cluster preservation rate, individual seasonality explainability, shooting reproducibility and image analysis reliability index, and a final state value.

[0181] The biomarker generation module (140) receives a stereoscopic exposure index from the image analysis module (130) and can set the stereoscopic exposure index as a base state value.

[0182] The above biomarker generation module (140) can calculate the pore contour deviation for each user by comparing the past and current images of the same user. The pore contour deviation for each user may indicate the degree of change in the contour around the pore, the brightness distribution around the pore opening, the high-frequency components around the pore, or the boundary sharpness of the pore candidate region in the current image compared to the reference image.

[0183] The above biomarker generation module (140) can calculate a hair thinning persistence index by using the degree to which a hair thinning pattern is repeatedly observed in the same local area in the shooting history of multiple times for the same user.

[0184] The above biomarker generation module (140) can increase the base state value in the range where the pore contour deviation and the hair thinning persistence index increase according to the user.

[0185] The above biomarker generation module (140) can calculate the ratio of a hair follicle group containing multiple hairs that is maintained at the current time from a past baseline set for the same user as the hair follicle cluster preservation rate. The above biomarker generation module (140) can correct the upwardly adjusted state value downward in the range where the hair follicle cluster preservation rate is high.

[0186] The above biomarker generation module (140) can calculate individual seasonality explainability only when the imaging reproducibility and image analysis reliability indices fall within the analysis allowable range. The individual seasonality explainability may indicate the degree to which the change in state value observed at the current time of imaging corresponds to the range of temporary change observed in the same user's past during the same season or under similar humidity conditions.

[0187] The above biomarker generation module (140) may adjust the state value downward by a correction ratio corresponding to the individual seasonality explanation range when the individual seasonality explanation range is included in a range with high individual seasonality explanation range. However, if the user's pore contour deviation or hair thinning persistence index is included in a pre-set upper caution range, the above biomarker generation module (140) may limit the downward correction ratio or not apply the downward correction.

[0188] The biomarker generation module (140) can receive a stereoscopic exposure index, shooting area information, and analysis area identification value from the image analysis module (130).

[0189] The above biomarker generation module (140) can retrieve past images of the same user, past stereoscopic exposure indices, past digital biomarkers, past pore contour distributions, and past hair follicle cluster information from the storage unit.

[0190] The biomarker generation module (140) can receive regional humidity, temperature, and seasonal information for the current shooting date from a user terminal (10) or an external data provider server. The biomarker generation module (140) can calculate the individual seasonality explainability by comparing the climate information with the range of state value changes under the same user's past same season or similar humidity conditions.

[0191] In one embodiment, the biomarker generation module (140) may receive a stereoscopic exposure index of 0.50 from the image analysis module (130) and set it as a baseline state value.

[0192] The above biomarker generation module (140) can calculate a pore contour deviation of 0.10 and a hair thinning persistence index of 0.30 by comparing the past shooting history and current shooting results of the same user. The above biomarker generation module (140) can adjust the base state value upward to 0.58 by applying a weighting ratio corresponding to the hair thinning persistence index range.

[0193] Subsequently, if the above biomarker generation module (140) calculates the follicle cluster preservation rate to be 0.80, it classifies that the cluster state of the hair candidate group is maintained at a certain level compared to the past baseline and can lower the state value to 0.48.

[0194] When the above shooting reproducibility and image analysis reliability indices are calculated as 0.85 and fall within the analysis allowable range, and the individual seasonality explainability is calculated as 0.70, the biomarker generation module (140) can calculate the final digital biomarker as 0.38 by applying a correction ratio corresponding to the individual seasonality explainability range.

[0195] The state analysis module (150) is a software configuration that calculates the risk of time-series hair loss progression using digital biomarkers at multiple points in time generated by the biomarker generation module (140).

[0196] The above time-series hair loss progression risk may be a risk range for management reference calculated by not evaluating only the number of hairs, hair density, or scalp exposure at a specific point in time, but by progressively reflecting the rate of change in digital biomarkers at different time intervals, the pattern of increase in the rate of change, the correspondence of the direction of change between multiple scalp areas, the degree of deviation from the imaging cycle, and the degree of mitigation of changes in status values ​​after management execution.

[0197] The state analysis module (150) can arrange digital biomarkers generated at multiple points in time for the same user in chronological order.

[0198] The above state analysis module (150) can set a first time interval and a second time interval according to adjacent shooting times or a preset period unit. The above state analysis module (150) can calculate the rate of change per unit period using the amount of change of digital biomarkers in each time interval and the elapsed time between shooting times.

[0199] The above state analysis module (150) can calculate a hair loss progression acceleration index by comparing the rate of change in the first time interval and the rate of change in the second time interval. The above state analysis module (150) can calculate a progression inflection index using the change pattern of the hair loss progression acceleration index calculated in multiple time intervals.

[0200] The above-mentioned state analysis module (150) can set a reference risk value using the digital biomarker change rate, hair loss progression acceleration index and progression inflection index.

[0201] The above-described state analysis module (150) compares digital biomarkers generated in at least two scalp regions among the crown, frontal region, temporal region, and occipital region, and can calculate the degree of correspondence between the directions of change of digital biomarkers in multiple scalp regions as the diffusion consistency between regions. If the diffusion consistency between regions is calculated to be greater than or equal to a preset reference range, the above-described state analysis module (150) can further increase the reference risk value.

[0202] The above-mentioned state analysis module (150) can derive a shooting cycle deviation correction value by calculating the time difference between the recommended shooting cycle and the actual shooting cycle. The above-mentioned state analysis module (150) can normally calculate the change rate and hair loss progression acceleration index when the shooting cycle deviation correction value is included in the allowable range, adjust the confidence level or weighting level of the calculated risk value when it is included in the caution range, limit the calculation of the progression acceleration index when it is included in the excess range, and calculate an auxiliary risk value using the simple change rate at the time of the last two shootings.

[0203] The above state analysis module (150) can calculate cumulative shooting reproducibility using the shooting reproducibility and image analysis reliability index of multiple viewpoints and the same scalp area matching success rate. If the cumulative shooting reproducibility falls within the analysis allowable range, the state analysis module (150) can calculate the response delay correlation after management execution.

[0204] The correlation of response delay after the above management execution may refer to the degree to which the rate of increase of digital biomarkers or the rate of increase of scalp exposure-related status values ​​is mitigated within a preset observation period after the time of management execution based on the management data provided to the user corresponds temporally to the history of the above management execution.

[0205] The above state analysis module (150) can lower the weighted reference risk value by a ratio corresponding to the response delay correlation range after the management execution when the response delay correlation after the management execution is calculated to be greater than or equal to a preset reference range.

[0206] The state analysis module (150) receives the current digital biomarker from the biomarker generation module (140) and can look up past digital biomarkers from the storage unit.

[0207] The above-mentioned state analysis module (150) can obtain digital biomarkers at multiple points in time, shooting time information, shooting area information, shooting reproducibility and image analysis reliability index, same scalp area matching success rate, recommended shooting cycle, actual shooting cycle, and user-specific management execution history.

[0208] The above state analysis module (150) can receive the type of management data provided to the user, the time of provision, and the recommended execution cycle from the service provision module (160). In addition, the above state analysis module (150) can receive at least one of a management execution record, execution date, execution frequency, or user input confirmation value from the user terminal (10).

[0209] In one embodiment, the condition analysis module (150) can analyze digital biomarkers of the crown and frontal area generated over January, March and May.

[0210] The above state analysis module (150) can set a first time interval from January to March and a second time interval from March to May. The above state analysis module (150) can calculate the rate of change of the digital biomarker as 0.15 in the first time interval and the rate of change of the digital biomarker as 0.20 in the second time interval.

[0211] The state analysis module (150) can calculate a hair loss progression acceleration index of 0.30 by normalizing the speed difference when the rate of change of the second time interval is higher than the rate of change of the first time interval. Additionally, the state analysis module (150) can calculate a progression inflection index of 0.25 by using the degree to which the hair loss progression acceleration index increases compared to the previous comparison interval.

[0212] The above state analysis module (150) can set the reference risk value to 0.65 when the change rate, hair loss progression acceleration index and progression inflection index are classified as increasing in the upward direction of risk.

[0213] The above-mentioned state analysis module (150) can increase the above-mentioned standard risk value to 0.75 when the digital biomarker changes in an upward direction of risk not only in the crown but also in the frontal area, and the diffusion consistency between areas is calculated to be 0.80.

[0214] The above-mentioned state analysis module (150) can determine the final time-series hair loss progression risk as 0.55 by applying a downward adjustment ratio corresponding to the response delay correlation range after management execution when the cumulative shooting reproducibility is calculated as 0.75 and included in the analysis allowable range and the response delay correlation after management execution is calculated as 0.60.

[0215] The service provision module (160) is a software configuration that generates hair loss management data for each user based on the analysis results of the state analysis module (150) and provides the hair loss management data to the user terminal (10).

[0216] The above service providing module (160) can generate different management data according to at least one of the shooting reproducibility and image analysis reliability index, stereoscopic exposure index, digital biomarker, time-series hair loss progression risk, shooting cycle deviation correction value and response delay correlation after management execution.

[0217] The service providing module (160) may provide reshoot guidance data to the user terminal (10) when the shooting reproducibility and image analysis reliability indices are below the analysis allowable range. The reshoot guidance data may include at least one of lighting reduction guidance, shooting distance adjustment guidance, shooting posture fixation guidance, reshooting of the same area guidance, and hair styling guidance.

[0218] The above service providing module (160) can provide area-specific management reference information based on exposure patch connection diagrams, parting shape changes, erythema-sebum separation results, or hair miniaturization patterns when the three-dimensional exposure index is included in the management caution section.

[0219] The service providing module (160) may provide management reference information explaining the difference between the past and current states of the same user when the digital biomarker falls within the individual baseline deviation range. When the individual seasonality explanation is high, the service providing module (160) may also provide information that the change in the current state value corresponds to the range of temporary changes observed in the past under the same season or similar humidity conditions.

[0220] The above service provision module (160) may provide guidance on regular imaging and maintenance of care when the risk of time-series hair loss progression is low and the care caution period is in place. When the risk of time-series hair loss progression is medium and the care caution period is in place, the above service provision module (160) may provide guidance on adjusting the imaging cycle, guidance on lifestyle management, or guidance on checking the scalp condition. When the risk of time-series hair loss progression is high and the care caution period is in place, the above service provision module (160) may provide guidance on reviewing consultation with a specialized institution or guidance on additional imaging.

[0221] In one embodiment, the service providing module (160) may classify the time series hair loss progression risk as a low management caution section when the risk is less than a first threshold value, classify the time series hair loss progression risk as a medium management caution section when the risk is greater than or equal to the first threshold value and less than a second threshold value, and classify the time series hair loss progression risk as a high management caution section when the risk is greater than or equal to the second threshold value.

[0222] The above first reference value and the above second reference value may be adjusted according to cumulative data by user group, shooting area, shooting cycle, shooting reproducibility and image analysis reliability index distribution or initial operation test data. Additionally, if the cumulative shooting reproducibility is less than the analysis allowable range, the service providing module (160) may not definitively display the time-series hair loss progression risk range but may display it as an auxiliary analysis range or a reshoot required range.

[0223] In addition, the service providing module (160) may not definitively provide the time-series hair loss progression risk result when the cumulative shooting reproducibility is below the analysis allowable range, and may configure the user screen to focus on auxiliary analysis results or reshoot guidance.

[0224] The service provision module (160) can receive the time-series hair loss progression risk, reference risk value, diffusion consistency between areas, shooting cycle deviation correction value, cumulative shooting reproducibility, and response delay correlation after management execution from the state analysis module (150).

[0225] The above service providing module (160) can receive digital biomarkers, individual baseline deviation information, follicle cluster preservation rate, and individual seasonality explainability from the biomarker generation module (140).

[0226] The above service providing module (160) can receive the shooting reproducibility and image analysis reliability indices and the reason for reshooting guidance from the image preprocessing module (120).

[0227] The above service provision module (160) can retrieve the shooting history, past management guidance history, set management cycle, and user's management execution history from the storage unit.

[0228] In one embodiment, if the shooting reproducibility and image analysis reliability index is less than 0.6, the service providing module (160) may provide reshoot guidance data to the user terminal (10) saying, “Avoid areas with strong lighting and reshoot the same scalp area while keeping the user terminal fixed.”

[0229] In another embodiment, the service providing module (160) may provide area-specific management reference information indicating that when the three-dimensional exposure index of the crown area is included in the management caution section, the parting shape change and exposure patch connection degree of the crown area have increased.

[0230] In another embodiment, the service providing module (160) may provide information that, when included in a range with high individual seasonality explainability, the current state value change may correspond to a range of temporary changes observed in the past in the same season or under similar humidity conditions.

[0231] In another embodiment, the service providing module (160) may provide guidance on shortening the shooting cycle and guidance on reviewing consultation with a specialized institution to the user terminal (10) when the user is included in a management caution section with a high risk of time-series hair loss progression and has high consistency of diffusion between areas.

[0232] The hair loss management system (100) according to the present invention can operate in the following order.

[0233] First, the user terminal (10) can capture a scalp image of at least one of the crown, frontal, temporal, and occipital regions according to the user's shooting input. The user terminal (10) can generate shooting time information, shooting area information, shooting condition information, and a sensor value at the moment of shooting together with the scalp image.

[0234] Next, the image acquisition unit (110) receives the scalp image and the shooting condition information from the user terminal (10), and can correspond the shooting time and shooting area information to the scalp image.

[0235] Afterwards, the image preprocessing module (120) can calculate the scalp micro-texture preservation rate by comparing the reference image and the current image, and calculate the shooting reproducibility and image analysis reliability indices using the supersaturated reflection patch occupancy rate, reflection patch boundary steepness, shooting moment pose shake index, same scalp area matching residual and hair direction entropy.

[0236] When the above shooting reproducibility and image analysis reliability indices are included in the analysis allowable range, the image analysis module (130) calculates the exposure patch connectivity correction area, the cumulative value of the parting centerline curvature, the local variability of the parting width, the erythema-sebum separation index, the hair miniaturization pattern index, and the hair root tangential shadow consistency in the corrected image, and can generate a stereoscopic exposure index using the above values.

[0237] Subsequently, the biomarker generation module (140) can set the above-mentioned stereoscopic exposure index as a baseline value and generate a digital biomarker based on individual baseline deviation by reflecting the pore contour deviation, hair thinning persistence index, hair follicle cluster preservation rate, and individual seasonality explainability.

[0238] Next, the state analysis module (150) can calculate the change rate, hair loss progression acceleration index, progression inflection index, diffusion consistency between areas, shooting cycle deviation correction value, cumulative shooting reproducibility, and response delay correlation after management execution by comparing digital biomarkers at multiple time points, and can determine the time-series hair loss progression risk using the calculated values.

[0239] Finally, the service provision module (160) can generate customized hair loss management data corresponding to the time-series hair loss progression risk, image reproducibility and image analysis reliability index, digital biomarker and individual seasonality explainability, and provide the customized hair loss management data to the user terminal (10).

[0240] The above image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices to evaluate whether scalp images taken at multiple points in time have physical conditions that allow for mutual comparison.

[0241] The above shooting reproducibility and image analysis reliability indices can be used as reference values ​​to limit the reflection of images with degraded quality, caused by differences in illumination, light reflection distortion, posture shaking at the moment of shooting, alignment errors in the same scalp area, and irregular hair arrangement, into subsequent state analysis.

[0242] The image preprocessing module (120) can evaluate the visual characteristics of the scalp image and the physical sensor characteristics of the user terminal (10) in stages to calculate the shooting reproducibility and image analysis reliability indices. Specifically, the image preprocessing module (120) can set the scalp micro-texture preservation rate as a first reliability reference value, calculate a first correction value using the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness, and calculate a second correction value using the pose shake index at the moment of shooting, the same scalp area matching residual, and the hair direction entropy.

[0243] The image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices by sequentially subtracting the first correction value and the second correction value from the first reliability reference value. Unlike a method that judges image quality based solely on the overall brightness or average illumination of the image, the stepwise subtraction method may be a processing procedure that reflects the shape characteristics of the reflection area, physical shaking at the moment of shooting, and the possibility of comparing the same area between multiple viewpoint images.

[0244] The image preprocessing module (120) can extract high-frequency components corresponding to pore contours and scalp surface textures from the reference image and the current image, respectively. The high-frequency components can be obtained through at least one of an edge detection algorithm, a spatial frequency analysis algorithm, or a texture feature extraction algorithm.

[0245] The image preprocessing module (120) can calculate the degree to which the high-frequency components of the current image are maintained relative to the reference image within the same scalp area, and normalize the calculated value to a range of 0 to 1 to derive the scalp fine texture preservation rate.

[0246] For example, if the pore contours and scalp surface texture are extracted relatively clearly in the reference image and the high-frequency components of the same scalp area are similarly maintained in the current image, the scalp fine texture preservation rate can be calculated as a high value such as 0.85. On the other hand, if the current image is blurry or the pore contours are weakly detected due to excessive reflection, the scalp fine texture preservation rate can be calculated as a low value.

[0247] The image preprocessing module (120) can set the scalp micro-texture preservation rate as a first reliability reference value. The first reliability reference value can be used as a value that serves as a subtraction reference for subsequent correction values.

[0248] The image preprocessing module (120) can classify a cluster of pixels whose brightness value falls within the upper reference range of the entire scalp area of ​​the current image as an oversaturated reflection patch. For example, the upper reference range can be set as a group of pixels having a brightness value within the top 5% of the brightness distribution of the entire scalp area.

[0249] The image preprocessing module (120) can obtain the supersaturated reflection patch occupancy rate by calculating the area ratio of the supersaturated reflection patch to the entire scalp area. For example, if the area ratio of a pixel cluster classified as a supersaturated reflection patch to the entire scalp area is 15%, the supersaturated reflection patch occupancy rate can be normalized to 0.15.

[0250] Additionally, the image preprocessing module (120) can calculate the amount of brightness change between adjacent pixels based on the boundary line of the supersaturated reflection patch. The image preprocessing module (120) determines that the reflection patch boundary is formed more abruptly in sections where the amount of brightness change is large, and can calculate the steepness of the reflection patch boundary by normalizing the amount of brightness change to a range of 0 to 1.

[0251] The image preprocessing module (120) may apply a combined correction rule that increases the first correction value when both values ​​are included in a high range after normalizing the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness, respectively. The combined correction rule is intended to reflect the fact that the larger the area of ​​the reflection patch and the steeper the boundary, the higher the likelihood that the hair area or scalp surface texture will be converted into a difficult-to-identify area.

[0252] The image preprocessing module (120) can calculate a second reliability value by subtracting the first correction value from the first reliability reference value.

[0253] The above image preprocessing module (120) can obtain an angular velocity variance value or an acceleration variance value during a preset time interval before and after the shooting input time from a gyroscope sensor and an accelerometer sensor embedded in the user terminal (10). For example, the preset time interval may be 0.5 seconds before and after the shooting button input time.

[0254] The image preprocessing module (120) can calculate the attitude shake index at the moment of shooting by normalizing the angular velocity variance value or the acceleration variance value to a range of 0 to 1. The attitude shake index at the moment of shooting may be a value that reflects the possibility of image blurring that may occur due to grip instability of the user terminal (10) or shooting while moving.

[0255] Additionally, the image preprocessing module (120) can perform feature point matching between a reference image and a current image and calculate the average position error between the matched feature points. The image preprocessing module (120) can normalize the average position error to calculate the same scalp region matching residual. The same scalp region matching residual may be a value indicating whether scalp images from multiple viewpoints can be compared based on the same scalp area.

[0256] Additionally, the image preprocessing module (120) can classify the hair direction vectors detected in the current image into multiple direction segments and calculate the degree of non-uniformity or dispersion of the hair direction vectors. The image preprocessing module (120) can calculate the hair direction entropy by normalizing the degree of dispersion or non-uniformity of distribution. The hair direction entropy may be a value indicating whether the hair is arranged in a certain direction or is arranged irregularly.

[0257] The image preprocessing module (120) can normalize the pose shake index at the moment of shooting, the same scalp region matching residual, and the hair direction entropy, respectively, and calculate a subtraction correction value corresponding to the section to which each index belongs. The image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices by sequentially subtracting the subtraction correction value from the second reliability value.

[0258] The above first correction value may be set as a low subtraction section when both the oversaturated reflection patch occupancy rate and the reflection patch boundary steepness fall within a low section, set as a medium subtraction section when either the oversaturated reflection patch occupancy rate or the reflection patch boundary steepness falls within a medium section, and set as a high subtraction section when both the oversaturated reflection patch occupancy rate and the reflection patch boundary steepness fall within a high section. The low subtraction section, the medium subtraction section, and the high subtraction section may be set based on the section where the distribution of the normal analysis image group and the re-captured image group is distinguished in the initial test data of system operation.

[0259] The above secondary correction value may be set as a first subtraction section when one of the pose shake index at the moment of shooting, the same scalp area matching residual, and the hair direction entropy is included in a high section, and may be set as a second subtraction section when two or more of the indicators are included in a high section. In addition, when the pose shake index at the moment of shooting and the same scalp area matching residual are simultaneously included in a high section, the image preprocessing module (120) may classify the image as having a low probability of same scalp area comparison and increase the subtraction strength of the secondary correction value.

[0260] The above low range, middle range, and high range may be set for values ​​normalized to a range of 0 to 1. For example, a range of 0 or more and less than a first reference value may be classified as a low range, a range of 1 or more and less than a second reference value as a middle range, and a range of 2 or more as a high range. The above first reference value and the above second reference value may be adjusted according to the specifications of the imaging device, the imaging area, the image resolution, and the distribution of accumulated imaging data.

[0261] The image preprocessing module (120) can determine whether the shooting reproducibility and image analysis reliability indices are included in a preset analysis allowance range.

[0262] If the above shooting reproducibility and image analysis reliability indices fall within the above analysis allowable range, the image preprocessing module (120) can transmit the image to the image analysis module (130) to be used as primary data for generating digital biomarkers.

[0263] On the other hand, if the above shooting reproducibility and image analysis reliability indices are below the above analysis allowance range, the image preprocessing module (120) may classify the image as auxiliary analysis data or provide reshoot guidance data to the user terminal (10) through the service provision module (160).

[0264] The above re-shooting guidance data may include at least one of lighting mitigation guidance, shooting distance adjustment guidance, shooting posture fixation guidance, re-shooting guidance for the same area, and hair styling guidance. For example, the user terminal (10) may output a guidance message such as “Avoid areas with strong lighting, and re-shoot the same scalp area while holding the user terminal fixed.”

[0265] The analysis allowance threshold mentioned in this specification may be set based on a test database during the initial operation of the system. Specifically, the analysis allowance threshold may be set based on the interval where the distribution of the image reproducibility and image analysis reliability indices of the two image groups are distinguished, after distinguishing between image groups readable as the same area and image groups requiring recapitulation.

[0266] For example, an analysis allowance range of 0.6 or higher may be set between the lower range value of the normal analysis image group and the upper range value of the re-shot image group. However, the above 0.6 corresponds to one embodiment and may be changed depending on the specifications of the shooting device, image resolution, shooting area, shooting pattern by user group, and the degree of accumulation of system operation data.

[0267] The image preprocessing module (120) may set the lower limit value as the shooting reproducibility and image analysis reliability index when the sum of the subtraction correction values ​​exceeds the first reliability reference value and the calculated value falls below a preset lower limit value. For example, the lower limit value may be set to 0.1. The lower limit value may be used as a minimum reference value to prevent negative notation errors during the subtraction operation and to distinguish between images with simple quality degradation and system processing errors.

[0268] Additionally, if the calculated value exceeds a preset upper limit, the image preprocessing module (120) may set the upper limit as an index of shooting reproducibility and image analysis reliability. For example, the upper limit may be set to 1.0.

[0269] If the pose shake index at the moment of shooting cannot be obtained due to permission restrictions of the user terminal (10), sensor deactivation, or sensor error, the image preprocessing module (120) may apply missing data supplementation rules.

[0270] The above missing data compensation rule can be performed by increasing the weight corresponding to the same scalp region matching residual by a preset correction ratio. For example, the correction ratio can be set within a range of up to 2 times the increase in the matching error of the inertial sensor missing image group in the initial verification data.

[0271] The above missing data supplementation rule is intended to indirectly reflect shooting condition instability by utilizing the degree of alignment failure between multiple viewpoint images, even when the attitude shake index at the moment of shooting is not directly acquired.

[0272] In one embodiment, a situation can be assumed in which a user takes a picture of the top of their head with a user terminal (10) while moving under strong lighting.

[0273] The image preprocessing module (120) calculates the scalp fine texture preservation rate of the current image relative to the reference image, and if the scalp fine texture preservation rate is 0.80, it can be set as the first reliability reference value.

[0274] The image preprocessing module (120) can calculate a first correction value of 0.25 by processing the two values ​​according to a combined correction rule when the supersaturated reflection patch occupancy rate and reflection patch boundary steepness are calculated as 0.30 and 0.50 in the current image due to strong lighting. The image preprocessing module (120) can calculate a second reliability value of 0.55 by subtracting the first correction value of 0.25 from the first reliability reference value of 0.80.

[0275] Next, the image preprocessing module (120) can calculate a secondary correction value of 0.30 corresponding to the interval to which the values ​​belong when the pose shake index at the moment of shooting is calculated as 0.40 from the inertial sensor of the user terminal (10), the same scalp area matching residual between the reference image and the current image is calculated as 0.20, and the hair direction entropy is calculated as 0.30.

[0276] The image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability index as 0.25 by subtracting the second correction value of 0.30 from the second reliability value of 0.55.

[0277] Since the above shooting reproducibility and image analysis reliability index of 0.25 does not satisfy the analysis allowable range of 0.6 or higher, the image preprocessing module (120) does not transmit the corresponding image as main data to the image analysis module (130), and can provide reshoot guidance data to the user terminal (10) through the service provision module (160).

[0278] The stepwise subtraction algorithm of the image preprocessing module (120) can be applied multiple times under various shooting environment conditions. Each of the above indicators can be applied by normalizing it to a range of 0 to 1, and the final shooting reproducibility and image analysis reliability indices can be calculated by subtracting the first correction value and the second correction value from the first reliability reference value.

[0279] In the first to fifth embodiments, the scalp micro-texture preservation rate is calculated to be in the range of 0.80 to 0.95, and the supersaturated reflection patch occupancy rate, reflection patch boundary steepness, pose shake index at the moment of shooting, same scalp area matching residual, and hair direction entropy may be calculated as being in a low range. In this case, the final shooting reproducibility and image analysis reliability indices are derived to be in the range of 0.61 to 0.88 and may be included in the normal analysis range.

[0280] In the 6th embodiment, the scalp micro-texture preservation rate is calculated as 0.75, but due to strong lighting, the supersaturated reflection patch occupancy rate may be calculated as 0.40 and the reflection patch boundary steepness as 0.50. In this case, the final shooting reproducibility and image analysis reliability index is calculated as 0.35, so it may be classified as a subject for re-shooting.

[0281] In the 7th embodiment, due to hand shake, the posture shake index at the moment of shooting may be calculated as 0.60 and the same scalp area registration residual as 0.45. In this case, the final shooting reproducibility and image analysis reliability indices are derived as 0.31, and the image may be classified as a subject for re-shooting.

[0282] In the eighth embodiment, the same scalp region registration residual may be calculated as 0.60 and the hair direction entropy as 0.70 due to shooting of other parts or differences in shooting angles. In this case, the final shooting reproducibility and image analysis reliability index may be calculated as 0.15 and classified as a subject for re-shooting.

[0283] In the ninth embodiment, strong lighting, hand shake, and alignment failure may occur together, causing the sum of the subtraction correction values ​​to exceed the first reliability reference value. In this case, the image preprocessing module (120) may set the final shooting reproducibility and image analysis reliability index to a preset lower limit of 0.10 and classify the image as a target for re-shooting.

[0284] In the 10th embodiment, the posture shake at the moment of shooting is calculated to be in a low range, but the same scalp area registration residual may be calculated to be high at 0.80. In this case, the final shooting reproducibility and image analysis reliability index is derived as 0.48 and may be classified as a target for re-shooting.

[0285] The image preprocessing module (120) can first determine whether the scalp fine texture preservation rate is successfully obtained. If the scalp fine texture preservation rate is calculated, the image preprocessing module (120) can set the scalp fine texture preservation rate as a first reliability reference value. On the other hand, if the scalp fine texture preservation rate is not calculated, the image preprocessing module (120) can withhold analysis of the image or generate re-shooting guidance data.

[0286] Subsequently, the image preprocessing module (120) can determine whether the oversaturated reflection patch occupancy rate exceeds a preset threshold or whether the reflection patch boundary steepness exceeds a preset threshold. If the oversaturated reflection patch occupancy rate or the reflection patch boundary steepness exceeds the threshold, the image preprocessing module (120) can normalize the two variables and apply a combined correction rule to calculate a first correction value.

[0287] Additionally, the image preprocessing module (120) can determine whether at least one of the pose shake index at the moment of shooting, the same scalp area matching residual, and the hair direction entropy exceeds a preset threshold. As a result of the determination, if at least one of the indicators exceeds the threshold, the image preprocessing module (120) can normalize each indicator and apply a correction coefficient for each interval to calculate a secondary correction value.

[0288] The image preprocessing module (120) can sequentially subtract the first correction value and the second correction value from the first reliability reference value and determine whether the result of the subtraction is less than a preset lower limit value or exceeds an upper limit value. If the result of the subtraction is less than the lower limit value, the image preprocessing module (120) can set the lower limit value as the final shooting reproducibility and image analysis reliability index. If the result of the subtraction exceeds the upper limit value, the image preprocessing module (120) can set the upper limit value as the final shooting reproducibility and image analysis reliability index.

[0289] The image preprocessing module (120) determines whether the final calculated shooting reproducibility and image analysis reliability index is greater than or equal to a preset analysis allowance range, and if it is greater than or equal to the analysis allowance range, it can transmit the original image or the corrected image to the image analysis module (130). On the other hand, if the shooting reproducibility and image analysis reliability index is less than the analysis allowance range, the image preprocessing module (120) can classify the image as auxiliary analysis data or perform a reshoot guidance process.

[0290] Some of the prior art can perform brightness adjustment, binarization, hair contour extraction, hair thickness calculation, hair density calculation, or correction of hair areas lost due to light reflection in scalp images.

[0291] However, the above image preprocessing module (120) is not limited to simple brightness adjustment or light reflection correction, but can evaluate the shape-related effects of the reflection area using the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness, and evaluate the comparability of multiple viewpoint images using the inertial sensor-based shooting moment pose shake index of the user terminal (10), same scalp area matching residual, and hair direction entropy.

[0292] The image preprocessing module (120) can limit the inclusion of degraded images into subsequent time-series analysis by evaluating the visual and physical variables in stages. Accordingly, it can contribute to reducing analysis deviations caused by differences in shooting conditions when calculating hair density, hair thickness, scalp exposure, or digital biomarkers.

[0293] In addition, the image preprocessing module (120) can exclude the image from the main data or provide re-shooting guidance data when the shooting reproducibility and image analysis reliability indices are below the analysis allowable range, thereby increasing the consistency of comparison of the same area during the process of comparing scalp images from multiple viewpoints.

[0294] The analysis allowance threshold used in the image preprocessing module (120) can be set based on the distribution of a pre-collected scalp image dataset. For example, in a scalp image dataset of 500 or more images, a pair of images that a reader visually identifies as the same scalp region can be compared with a pair of images that an algorithm classifies as the same region through feature point matching.

[0295] The above image preprocessing module (120) can classify a group of images in which the reader reading result and the feature point matching result correspond to a certain level or higher as a normal analysis image group, and classify a group of images that require re-shooting due to shooting shake, supersaturated reflection, out-of-focus, or failure to match the same area as a re-shoot image group. Afterwards, the section where the two image groups are distinguished between the lower section value of the normal analysis image group and the upper section value of the re-shoot image group can be set as an analysis allowance section.

[0296] In one embodiment, the lower limit of the analysis allowable range may be set to 0.6 or higher. The 0.6 is an example value and may be adjusted according to the performance of the imaging device, the imaging area, the image resolution, and the degree of accumulation of system operation data.

[0297] In one embodiment, the image preprocessing module (120) calculates the scalp fine texture preservation rate of the current image relative to the reference image, and if the scalp fine texture preservation rate is calculated to be 0.85, this can be set as the basic reliability.

[0298] Subsequently, when the oversaturated reflection patch occupancy rate is 0.20 and the reflection patch boundary steepness is 0.40, the image preprocessing module (120) can normalize the two values ​​and apply a combined correction rule to calculate a first subtraction value of 0.15. The image preprocessing module (120) can calculate an intermediate reliability of 0.70 by subtracting the first subtraction value of 0.15 from the basic reliability of 0.85.

[0299] Next, the image preprocessing module (120) can calculate a secondary subtraction value of 0.15 corresponding to the interval of the values ​​when the pose shake index at the moment of shooting is 0.10, the same scalp area matching residual is 0.10, and the hair direction entropy is 0.10. The image preprocessing module (120) can further subtract the secondary subtraction value of 0.15 from the intermediate reliability of 0.70 to calculate the final shooting reproducibility and image analysis reliability index as 0.55.

[0300] Since the above final reliability index of 0.55 is less than 0.6, which is the lower limit of the analysis allowance range, the image preprocessing module (120) does not transmit the image as main data to the image analysis module (130), but classifies it as auxiliary analysis data or generates re-shooting guidance data.

[0301] In the first embodiment, for a scalp image under indoor standard light conditions, the scalp micro-texture preservation rate is calculated as 0.95, and the reflection characteristic subtraction value and physical factor subtraction value can each be calculated as 0.05. In this case, the final reliability index is calculated as 0.85, which can be included in the analysis allowable range, and can also correspond to a normal analysis image with the reader standard.

[0302] In the second embodiment, the low-light image may have a scalp micro-texture preservation rate lowered to 0.70, a reflection characteristic subtraction value of 0.10, and a physical factor subtraction value of 0.15. In this case, the final reliability index is calculated as 0.45, and it may be classified as a subject for re-촬영.

[0303] In the third embodiment, even if the scalp microtexture preservation rate under strong direct light conditions is maintained at 0.85, the reflection characteristic subtraction value may be calculated as high at 0.45 and the physical factor subtraction value may be calculated as 0.10. In this case, the final reliability index is calculated as 0.30 and may be classified as a subject for re-imaging.

[0304] In the fourth embodiment, for an image with fine shaking, the scalp fine texture preservation rate can be calculated as 0.90, the reflection characteristic subtraction value as 0.05, and the physical factor subtraction value as 0.20. In this case, the final reliability index is calculated as 0.65 and can be included in the analysis allowable range.

[0305] In the fifth embodiment, even if the scalp micro-texture preservation rate of an image with severe shaking is calculated as 0.85, the physical factor subtraction value may be calculated as high as 0.55 and the reflection characteristic subtraction value as 0.10. In this case, the final reliability index is calculated as 0.20 and the image may be classified as a subject for re-imaging.

[0306] In the 6th embodiment, an image close to the recommended shooting environment may have a scalp micro-texture preservation rate of 0.98, a reflection characteristic subtraction value of 0.02, and a physical factor subtraction value of 0.03. In this case, the final reliability index may be calculated as 0.93 and included in the analysis allowable range.

[0307] In the 7th embodiment, for an image where out-of-focus occurs, the scalp micro-texture preservation rate is calculated to be low at 0.50, the reflection characteristic subtraction value is calculated to be 0.15, and the physical factor subtraction value is calculated to be 0.20. In this case, the final reliability index is calculated to be 0.15, and the image may be classified as a subject for re-imaging.

[0308] In the 8th embodiment, for an image with poor alignment of the same scalp region, even if the scalp micro-texture preservation rate is calculated as 0.88, the physical factor subtraction value may be calculated as high at 0.45 and the reflection characteristic subtraction value may be calculated as 0.10. In this case, the final reliability index is calculated as 0.33 and the image may be classified as a subject for re-imaging.

[0309] In the ninth embodiment, for an image in which partial reflection occurs, the scalp micro-texture preservation rate may be calculated as 0.80, the reflection characteristic subtraction value as 0.25, and the physical factor subtraction value as 0.15. In this case, the final reliability index may be calculated as 0.40 and classified as a subject for re-imaging.

[0310] In the 10th embodiment, the image capable of standard correction may have a scalp micro-texture preservation rate of 0.82, a reflection characteristic subtraction value of 0.08, and a physical factor subtraction value of 0.12. In this case, the final reliability index is calculated as 0.62 and may be included in the analysis allowable range.

[0311] According to the above 10 simulations, the image preprocessing module (120) can classify images with a final reliability index of 0.6 or higher as primary analysis targets, and images with a final reliability index of less than 0.6 as re-shot or auxiliary analysis targets.

[0312] An illuminance-centered filtering method and a stepwise subtraction method can be compared for the same simulation dataset. In the illuminance-centered filtering method, if the overall average brightness falls within a reference range, some images with poor alignment or images affected by reflection may be classified as normal analysis images. In one embodiment, the illuminance-centered filtering method may yield a misclassification rate of 28% in which images with degraded quality pass through as main analysis data.

[0313] On the other hand, the stepwise subtraction method of the image preprocessing module (120) reflects the scalp micro-texture preservation rate, reflection patch characteristics, pose shake index at the moment of shooting, same scalp area matching residual, and hair direction entropy together, so it is possible to have an example where the misclassification rate of a degraded image in the same simulation dataset is calculated at the level of 4%.

[0314] In addition, in one embodiment, when the reading criterion matching rate of the illuminance-centered filtering method is calculated as 72.5%, the reading criterion matching rate of the stepwise subtraction method can be calculated as 94.8%. The above figures are example values ​​to explain that the stepwise subtraction method of the present invention can increase the selection consistency of data input into image analysis.

[0315] The above image analysis module (130) can calculate a three-dimensional exposure index to calculate a state value related to hair loss progression by supplementing a simple two-dimensional scalp exposure area.

[0316] The above stereoscopic exposure index may be a value calculated not only by using the area of ​​the exposure region where hair is not detected in the scalp image, but also by using the connectivity structure of the exposure region, changes in the shape of the part centerline, local variability of the part width, the results of distinguishing between erythema and sebum reflection, the hair miniaturization pattern, and the directionality of shading around the hair roots.

[0317] The image analysis module (130) can set the exposure patch connectivity correction area as the basic exposure value to calculate the three-dimensional exposure index, and can primarily increase the basic exposure value using the accumulated curvature value of the parting centerline and the local variability of the parting width. Subsequently, the image analysis module (130) can further increase the value primarily increased using the erythema-sebum separation index and the hair miniaturization pattern index. On the other hand, the image analysis module (130) classifies that the three-dimensional arrangement state of the hair is maintained in sections with high consistency of shadows in the direction of the hair root tangential direction, and can lower the value additionally increased.

[0318] Unlike a method that simply sums the number of pixels where the scalp is exposed, the above processing procedure is intended to calculate a different three-dimensional exposure index depending on whether the exposed area is widely connected, whether the parting shape changes unevenly, whether red areas are observed as color changes rather than sebum reflections, whether short and fine hair candidate groups are repeatedly observed, and whether the shading around the hair roots has a constant relationship with the hair growth direction, even if the scalp exposure area is the same.

[0319] The image analysis module (130) can separate the hair region and the scalp region in the scalp image corrected through the image preprocessing module (120). The image analysis module (130) can classify a cluster of continuous pixels in which no hair pixels are detected within the scalp region as an exposure patch.

[0320] The above exposure patch may refer to a clustered area within the binarized scalp image where hair is absent or not covered by hair, and where skin pixel values ​​are continuously detected.

[0321] The image analysis module (130) can extract the exposure patch using at least one of connection component labeling, region segmentation, or pixel clustering. The image analysis module (130) can calculate the exposure patch connectivity correction area by reflecting the size, shape, and proximity of the exposure patch.

[0322] For example, even if they have the same exposure area, an image in which small exposure patches are distributed at multiple locations and an image in which a single wide exposure patch is continuously formed around the parting can be classified as having different exposure states. The image analysis module (130) can calculate the exposure patch connectivity correction area as a higher range as the degree of connectivity of the exposure patches increases.

[0323] The above image analysis module (130) can set the exposure patch connection correction area as a basic exposure value for calculating the stereoscopic exposure index.

[0324] The image analysis module (130) can classify a band-shaped area that is continuous in the longitudinal direction among the exposed scalp areas as a parting candidate area. The image analysis module (130) can extract a center point in the width direction between the left hair boundary and the right hair boundary in the parting candidate area and create a parting center line by connecting the center points in the width direction.

[0325] The above image analysis module (130) can calculate the degree of curvature of the direction of the parting centerline compared to a reference straight line or the parting centerline at a previous point in time, and accumulate the degree of curvature to obtain an accumulated value of the parting centerline curvature.

[0326] Additionally, the image analysis module (130) can measure the width of the parting at preset intervals along the centerline of the parting. The width of the parting can be calculated as the distance between the left hair boundary and the right hair boundary. The image analysis module (130) can calculate the local variability of the width of the parting using the variance value or the difference value between intervals of the width of the parting measured at multiple points.

[0327] The above image analysis module (130) can segment the accumulated value of the parting centerline curvature and the local variability of the parting width, respectively, and increase the degree of upward adjustment of the basic exposure value when both values ​​are included in a high segment together.

[0328] The above-mentioned cumulative value of the curvature of the parting centerline and the above-mentioned local variability of the parting width are not merely values ​​indicating whether the parting has simply widened, but can be used as auxiliary judgment values ​​indicating a state in which the arrangement of hair around the parting is not maintained uniformly.

[0329] The image analysis module (130) can analyze the color channels of the scalp image to extract a red area. At this time, the image analysis module (130) can exclude the area overlapping with the supersaturated reflection patch classified by the image preprocessing module (120) from the red area.

[0330] The image analysis module (130) can calculate an erythema-sebum separation index using the occupancy, saturation, or color distribution values ​​of a red area that excludes areas overlapping with a supersaturated reflection patch. The erythema-sebum separation index may be a value for distinguishing and reflecting areas that are less likely to have color distortion due to sebum reflection among areas observed as red in the scalp image.

[0331] Additionally, the image analysis module (130) can detect hair candidate pixel clusters that are less than a preset thickness standard and less than a preset length standard by using at least one of thinning processing, linear structure detection processing, or hair width estimation processing. The image analysis module (130) can calculate a hair thinning pattern index by using the degree to which the hair candidate pixel clusters are observed clustered in a specific area.

[0332] In this specification, the hair miniaturization pattern may refer to an imaging pattern in which candidate hairs that are relatively short and thin compared to normal hair are observed clustered in a specific area of ​​a scalp image. The hair miniaturization pattern is a candidate pattern observed through image analysis and is not limited to the meaning of directly determining the growth cycle or medical cause.

[0333] The image analysis module (130) may additionally weight the primary upwardly adjusted value in the section where the erythema-sebum separation index and the hair miniaturization pattern index increase. The additional weighting may be performed according to a weighting ratio corresponding to the section to which the erythema-sebum separation index and the hair miniaturization pattern index belong.

[0334] The image analysis module (130) can calculate a brightness variation in the area around the hair root point where the hair pixel begins. Based on the brightness variation, the image analysis module (130) can calculate the degree to which shading around the hair root is continuously formed in the tangential direction of the hair growth direction or in an adjacent direction.

[0335] The above image analysis module (130) can calculate the ratio in which the directionality of the shadow around the hair root has a constant relationship with the hair growth direction as the hair root tangential shadow consistency.

[0336] The above-mentioned hair root tangential shadow consistency can be used as an auxiliary indicator to determine whether the hair is in a state of flat adhesion to the scalp surface or has a consistent three-dimensional arrangement around the hair root. In the region where the above-mentioned hair root tangential shadow consistency is high, it can be classified as maintaining a three-dimensional arrangement state in which the hair covers the scalp surface to a certain extent.

[0337] The image analysis module (130) can adjust the additionally weighted value downward by an offset rate corresponding to the hair root tangential shadow consistency range when the hair root tangential shadow consistency is calculated to be greater than or equal to a preset standard.

[0338] In this specification, the offset rate may refer to a correction value that lowers the three-dimensional exposure index, which is elevated by the erythema-sebum separation index, the miniaturization pattern index, or the parting shape variable, by a certain ratio according to the three-dimensional arrangement state around the hair root. The offset rate may be set as a segment-specific value of the hair root tangential shadow consistency.

[0339] The above image analysis module (130) can calculate the final stereoscopic exposure index after performing the above step-by-step upward adjustment, additional weighting, and downward offset.

[0340] The image analysis module (130) may set the upper limit value as the final stereoscopic exposure index when the final stereoscopic exposure index exceeds a preset upper limit value. Additionally, the image analysis module (130) may set the lower limit value as the final stereoscopic exposure index when the final stereoscopic exposure index falls below a preset lower limit value.

[0341] The above upper limit and the above lower limit may be used as reference values ​​to limit the stereoscopic exposure index from being excessively amplified or calculated as a negative or abnormal range during the data processing process. For example, the stereoscopic exposure index may be normalized to a range of 0 to 1, and the upper limit may be set to 1.0 and the lower limit to 0. However, the above values ​​correspond to one embodiment and may be changed depending on the degree of accumulation of system operation data, the shooting area, image resolution, and shooting patterns by user group.

[0342] In environments where light is evenly irradiated from all directions or where shadows are weakly formed, low shading around the hair roots may be detected even if the hair has a consistent three-dimensional arrangement. In this case, if the above-mentioned tangential shadow consistency is applied as is, the stereoscopic exposure index may be calculated to be higher than that under actual shooting conditions.

[0343] To supplement this, the image analysis module (130) can check the brightness contrast value of the entire image calculated by the image preprocessing module (120). If the brightness contrast value of the entire image is below a preset standard, the image analysis module (130) may omit the downward offset step based on the hair root tangential shadow consistency or assign a basic offset value to the hair root tangential shadow consistency.

[0344] Before applying the root tangential shadow consistency, the image analysis module (130) can check at least one of the bias in the direction of the brightness gradient within the image, the degree of bias of the supersaturated reflection patch, and the brightness contrast value of the entire image. If the brightness gradient direction is not constant, the supersaturated reflection patch is excessively biased in a specific direction, or the brightness contrast value of the entire image is below a preset standard, the image analysis module (130) can reduce the reflection ratio of the root tangential shadow consistency.

[0345] The above processing is intended to limit the excessive reflection of the hair root tangential shadow consistency in the stereoscopic exposure index even when the shadow around the hair root is not sufficiently detected due to the lighting direction or shadowless environment. Accordingly, the image analysis module (130) can distinguish and process images in which the shadow around the hair root is weakened by the shooting environment and images in which the stereoscopic arrangement state of the hair is actually classified as low.

[0346] Additionally, if the correction area of ​​the exposure patch connection is calculated to be less than a preset lower limit, the image analysis module (130) may omit the upward adjustment step based on the accumulated curvature of the parting centerline and local variability of the parting width, and output the basic exposure value as is. This processing is intended to limit unnecessary parting analysis for images where the exposure area is small or the parting candidate area is not sufficiently formed.

[0347] Additionally, if the center line of the parting is not stably extracted, the image analysis module (130) may classify the stereoscopic exposure index for the image as an auxiliary analysis value, or generate a guide for re-shooting the same area through the image preprocessing module (120) or the service provision module (160).

[0348] In one embodiment, it can be assumed that the user has photographed the crown of the head, and as the parting widens, some areas of the scalp are observed to be red, and the existing hairs have a certain three-dimensional arrangement.

[0349] The image analysis module (130) can separate the hair region and the scalp region in the image and classify a cluster of continuous pixels where no hair pixels are detected as an exposure patch. The image analysis module (130) can analyze the connectivity of the exposure patch to calculate an exposure patch connectivity correction area and set this as a basic exposure value. For example, the basic exposure value can be set to 0.45.

[0350] Subsequently, the image analysis module (130) can extract a candidate parting area of ​​the crown of the head and generate a centerline of the parting by connecting the center points in the width direction of the candidate parting area. If the image analysis module (130) determines that the degree of curvature of the centerline of the parting and the local variability of the width calculated along the centerline of the parting correspond to a section with relatively high values, the basic exposure value can be adjusted upward. For example, the first adjustment value can be calculated as 0.55.

[0351] The image analysis module (130) can detect red areas through color channel analysis of the scalp image and calculate an erythema-sebum separation index by excluding areas that overlap with supersaturated reflection patches classified by the image preprocessing module (120). For example, if the erythema-sebum separation index is calculated as 0.20, the image analysis module (130) can calculate a second adjustment value of 0.65 by further weighting the first adjustment value.

[0352] Subsequently, the image analysis module (130) can analyze the brightness change of the area around the hair root point to calculate the hair root tangential shadow consistency. For example, if the hair root tangential shadow consistency is calculated as 0.60, the image analysis module (130) classifies that the three-dimensional arrangement state of the hair is maintained at a certain level, and can apply a compensation rate corresponding to the hair root tangential shadow consistency range to lower the final three-dimensional exposure index to 0.52.

[0353] The image analysis module (130) can transmit the final stereoscopic exposure index to the biomarker generation module (140). The biomarker generation module (140) can use the final stereoscopic exposure index as basic data for generating digital biomarkers based on individual baseline deviation.

[0354] The image analysis module (130) can first determine whether the extraction of the exposure patch area is successful. If the exposure patch area is extracted, the image analysis module (130) can calculate the exposure patch connection correction area and set the exposure patch connection correction area as the basic exposure value. On the other hand, if the exposure patch area is not extracted or the separation of the scalp area fails, the image analysis module (130) can withhold the calculation of the stereoscopic exposure index for the image or request guidance for re-shooting.

[0355] Subsequently, the image analysis module (130) can determine whether a parting candidate region is extracted. If the parting candidate region is extracted, the image analysis module (130) can calculate the cumulative value of the parting centerline curvature and the local variability of the parting width. If the cumulative value of the parting centerline curvature and the local variability of the parting width are both included in a high range, the image analysis module (130) can adjust the basic exposure value upward according to the weighting ratio for each range.

[0356] The image analysis module (130) can calculate an erythema-sebum separation index through color channel analysis of a scalp image and calculate a hair thinning pattern index through thinning processing, linear structure detection processing, or hair width estimation processing. If the image analysis module (130) is included in a section where the erythema-sebum separation index and the hair thinning pattern index increase, the image analysis module (130) can further weight the value that was initially adjusted upward.

[0357] The image analysis module (130) can calculate the hair root tangential shadow consistency by analyzing the brightness change of the area around the hair root point. If the hair root tangential shadow consistency is calculated to be greater than or equal to a preset standard, the image analysis module (130) can lower the additionally weighted value by an offset rate corresponding to the hair root tangential shadow consistency range.

[0358] The above image analysis module (130) determines whether the stereoscopic exposure index calculated after the step-by-step weighting and offset processing exceeds a preset upper limit or is less than a lower limit, and if necessary, can correct the final stereoscopic exposure index to the upper limit or the lower limit.

[0359] Some of the prior art can determine the degree of scalp exposure by binarizing scalp images to separate hair regions and scalp regions, and by calculating the number or area of ​​pixels classified as scalp. Additionally, some of the prior art can provide information related to the state of hair loss by using the number of hairs, hair density, hair thickness, or light reflection correction results.

[0360] However, the above image analysis module (130) can calculate a three-dimensional exposure index by not only calculating the number of simple scalp exposure pixels, but also by gradually reflecting the connection structure of the exposure patch, the curvature of the parting centerline, the local variability of the parting width, the separation result of erythema and sebum reflection, the hair thinning pattern, and the consistency of the shadow in the direction of the hair root tangential direction.

[0361] According to the above processing procedure, even for images with the same scalp exposure area, different stereoscopic exposure indices can be calculated depending on whether the exposure is around a normally formed parting or whether the exposure involves uneven changes in the shape of the parting and a group of short, fine hair candidates being observed together.

[0362] In addition, the image analysis module (130) can distinguish between areas that appear red due to light reflection and areas where color changes are observed using an erythema-sebum separation index, and can reflect the three-dimensional arrangement state of the hair using the hair root tangential shadow consistency. Accordingly, the image analysis module (130) can subdivide exposure patterns that are difficult to distinguish in a simple pixel counting method and provide them to the biomarker generation module (140).

[0363] In this specification, an exposure patch may refer to a clustered area within a binarized scalp image where hair is absent or not covered by hair, and where pixel values ​​of the skin are continuously detected.

[0364] In this specification, the parting centerline may refer to a line connecting the center points in the width direction between the left hair boundary and the right hair boundary in a band-shaped area that is continuous in the longitudinal direction among the exposed scalp areas.

[0365] In this specification, the accumulated curvature value of the parting centerline may refer to a value calculated by accumulating the degree of curvature of the parting centerline compared to a reference straight line or the parting centerline at a previous point in time.

[0366] In this specification, local variability in parting width may refer to the variance value or the difference value between intervals of the parting width measured at multiple points along the parting centerline.

[0367] In this specification, the erythema-sebum separation index may refer to a color change index calculated after excluding the area overlapping with the supersaturated reflective patch among the red areas of the scalp image.

[0368] In this specification, the hair miniaturization pattern may refer to an imaging pattern in which candidate hairs that are relatively short and thin compared to normal hair are observed clustered in a specific area in a scalp image.

[0369] In this specification, the consistency of tangential shadows at the hair root may refer to the ratio in which shading around the hair root point is continuously formed in the tangential direction of the hair growth direction or in an adjacent direction.

[0370] In this specification, the offset rate may refer to a segmental correction value that lowers the three-dimensional exposure index, which is increased by the parting shape variable, the erythema-sebum separation index, or the miniaturization pattern index, by a certain ratio according to the three-dimensional arrangement state around the hair root.

[0371] The upward weighting section and downward offset section of the stereoscopic exposure index used in the above image analysis module (130) can be set by comparing the parting shape, exposure patch connection structure, and shading characteristics around the hair roots extracted from the hair loss management target image group and the reference range image group.

[0372] In one embodiment, the image analysis module (130) can extract the cumulative value of the centerline curvature of the parting, the local variability of the parting width, the erythema-sebum separation index, the hair thinning pattern index, and the consistency of the shadow in the direction of the hair root tangential direction from 300 scalp image groups, and set a threshold value for each interval by cluster analysis of the distribution of the indicators.

[0373] For example, if a high proportion of images in which the shading directionality around the hair roots is observed to be consistent is in an image group in which the tangential shadow consistency of the hair roots is 0.5 or higher, the above-mentioned section may be set as a downward offset application section. The above-mentioned downward offset application section may be used as a criterion for distinguishing image groups in which the three-dimensional arrangement state of the hair is maintained at a certain level.

[0374] In one embodiment, the image analysis module (130) can set the exposure patch connection correction area to 0.45 as the basic exposure value.

[0375] Subsequently, when the cumulative value of the part centerline curvature is 0.20 and the local variability of the part width is calculated as 0.20, the image analysis module (130) can calculate a first upward weighting of 0.12 corresponding to the combined section of the two values. The image analysis module (130) can calculate a first adjustment value of 0.57 by reflecting the first upward weighting of 0.12 to the basic exposure value of 0.45.

[0376] Next, when the image analysis module (130) calculates an erythema-sebum separation index of 0.25, it can calculate a secondary adjustment value of 0.67 by additionally reflecting a secondary upward weighting of 0.10 corresponding to the interval.

[0377] Finally, if the image analysis module (130) calculates the root tangential shadow consistency to be 0.60 and is included in the downward offset application range, it can apply an offset rate of 15% corresponding to the second adjustment value of 0.67. The subtraction value according to the offset rate can be calculated as approximately 0.10, and the image analysis module (130) can determine the final stereoscopic exposure index to be 0.57.

[0378] In the first embodiment, a normally formed parting image may have a base exposure value of 0.25, a parting variation weight of 0.05, an erythema or hair miniaturization weight of 0.02, and a shadow cancellation value of 0.08. In this case, the final stereoscopic exposure index is calculated as 0.24 and can be classified as a reference range interval.

[0379] In the second embodiment, a crown image requiring initial care attention may be calculated with a base exposure value of 0.40, a parting variation weighting of 0.15, an erythema or hair miniaturization weighting of 0.08, and a shadow cancellation value of 0.05. In this case, the final stereoscopic exposure index is calculated as 0.58 and may be classified as a care attention section.

[0380] In the third embodiment, a scalp image accompanied by red color change may have a base exposure value of 0.45, a parting change weighting of 0.12, an erythema or miniaturization weighting of 0.35, and a shadow cancellation value of 0.02. In this case, the final stereoscopic exposure index is calculated as 0.90, which may be classified as a high care caution range.

[0381] In the fourth embodiment, an image in which a candidate for reduced hair thickness is observed may have a base exposure value of 0.35, a parting variation weighting of 0.10, an erythema or miniaturization weighting of 0.20, and a shadow offset value of 0.03. In this case, the final stereoscopic exposure index is calculated as 0.62 and may be classified as a care-caution section.

[0382] In the fifth embodiment, an image with widely diffused scalp exposure may have a base exposure value of 0.60, a parting variation weighting of 0.20, an erythema or miniaturization weighting of 0.15, and a shadow cancellation value of 0.02. In this case, the final stereoscopic exposure index is calculated as 0.93, which may be classified as a high care caution range.

[0383] In the 6th embodiment, an image in which the three-dimensional arrangement of the hair is relatively well maintained may have a base exposure value of 0.30, a parting variation weighting of 0.03, an erythema or miniaturization weighting of 0.02, and a shadow cancellation value of 0.15. In this case, the final three-dimensional exposure index is calculated as 0.20 and may be classified as a reference range interval.

[0384] In the seventh embodiment, for an image in which parting width expansion and shape change are observed, the base exposure value may be calculated as 0.50, the parting variation weight as 0.25, the erythema or hair miniaturization weight as 0.05, and the shadow offset value as 0.05. In this case, the final stereoscopic exposure index may be calculated as 0.75 and classified as a high care caution range.

[0385] In the eighth embodiment, an image in which a localized hair reduction pattern is observed may have a base exposure value of 0.38, a parting variation weighting of 0.08, an erythema or hair reduction weighting of 0.25, and a shadow reduction value of 0.04. In this case, the final stereoscopic exposure index is calculated as 0.67 and may be classified as a high care caution range.

[0386] In the ninth embodiment, a scalp image close to the reference range may be calculated with a base exposure value of 0.20, a parting variation weighting of 0.02, an erythema or miniaturization weighting of 0.01, and a shadow cancellation value of 0.10. In this case, the final stereoscopic exposure index is calculated as 0.13 and may be classified as a reference range segment.

[0387] In the 10th embodiment, a composite change image in which the exposure patch connectivity, parting variation, and erythema or hair miniaturization weighting are all calculated to be high together may have a base exposure value of 0.55, a parting variation weighting of 0.18, an erythema or hair miniaturization weighting of 0.22, and a shadow offset value of 0.01. In this case, the final stereoscopic exposure index is calculated to be 0.94 and may be classified as a high care caution range.

[0388] The planar pixel analysis method and the 3D exposure index method can be compared for the same simulation dataset. Since the planar pixel analysis method classifies condition ranges based solely on the number of pixels or scalp exposure area rather than hair, it may not reflect the curvature of the part centerline, local variability of the part width, erythema-sebum separation results, or shadow consistency around the hair roots.

[0389] In one embodiment, the planar pixel analysis method may yield an initial care attention interval classification rate of 64%. On the other hand, since the three-dimensional exposure index method reflects both parting variation and hair thinning patterns, it is possible to have an example where the initial care attention interval classification rate is calculated as 91% in the same simulation dataset.

[0390] In addition, in one embodiment, if the classification error rate related to lighting distortion of the planar pixel analysis method is calculated as 19%, the classification error rate related to lighting distortion of the stereoscopic exposure index method may be calculated as 3%. The above figures are example values ​​to explain that the image analysis module (130) can subdivide the state range by reflecting not just the simple exposure area, but also the connection structure of the exposure patch and the stereoscopic arrangement state around the hair roots.

[0391] The above biomarker generation module (140) can generate a personal baseline deviation-based digital biomarker to quantify baseline deviation changes related to hair loss status based on the cumulative shooting history of a specific user, rather than the overall average data of all users.

[0392] The above-mentioned individual baseline deviation-based digital biomarker may be state data generated by using the stereoscopic exposure index calculated by the image analysis module (130) as basic data, and by progressively reflecting the degree of repeated observation of pore contour changes, hair thinning patterns, degree of hair follicle cluster maintenance, and the explanatory potential for seasonal changes between the same user's past shooting history and current shooting results.

[0393] The biomarker generation module (140) first sets the stereoscopic exposure index as a base state value, and can adjust the base state value upward in sections where the pore contour deviation and hair thinning persistence indices increase for each user. Subsequently, the biomarker generation module (140) can correct the upwardly adjusted state value downward in sections where the hair follicle cluster preservation rate is high. In addition, the biomarker generation module (140) can generate a final digital biomarker by applying individual seasonality explainability only when the imaging reproducibility and image analysis reliability indices fall within a pre-set analysis allowance section.

[0394] Unlike methods that evaluate only the number of hairs or hair thickness at a specific point in time, the above processing procedure is intended to reflect whether a change deviating from the same user's usual state is repeated over multiple points in time.

[0395] The above biomarker generation module (140) can receive a stereoscopic exposure index from the image analysis module (130). The stereoscopic exposure index may be a value calculated using at least one of the exposure patch connectivity correction area, the cumulative value of the parting centerline curvature, the local variability of the parting width, the erythema-sebum separation index, the hair miniaturization pattern index, and the consistency of the shadow in the tangential direction of the hair root.

[0396] The above biomarker generation module (140) can set the stereoscopic exposure index as a basic state value for generating a digital biomarker based on individual baseline deviation.

[0397] The above baseline state value can be used as a value representing the scalp exposure pattern and hair arrangement state observed in the currently captured image, and can subsequently be utilized as an input value for personalized processing compared with the same user baseline.

[0398] The above biomarker generation module (140) can calculate the pore contour deviation for each user by comparing the past and current images of the same user.

[0399] The above-mentioned pore contour deviation per user may indicate the degree of change in the contour around the pore, the brightness distribution around the pore opening, the high-frequency components around the pore, or the boundary sharpness of the pore candidate region in the current image compared to the reference image.

[0400] The above biomarker generation module (140) can set the pore contour distribution extracted from past images as a user baseline and normalize the degree to which the pore contour distribution extracted from current images deviates from the user baseline to a range of 0 to 1.

[0401] For example, in one embodiment, the biomarker generation module (140) can extract the pixel intensity distribution around the pore opening from the same user's image taken 6 months ago and the current image taken, respectively, and normalize the difference in the pixel intensity distribution to calculate a user-specific pore contour deviation such as 0.25.

[0402] The section where the deviation of the pore contour by user increases may refer to a state in which the contour or brightness distribution around the pores changes in the current image compared to the past image of the same user, and the biomarker generation module (140) may adjust the basic state value upward according to the section.

[0403] The above biomarker generation module (140) can calculate a hair thinning persistence index by using the degree to which a hair thinning pattern is repeatedly observed in the same local area in the shooting history of multiple times for the same user.

[0404] In this specification, the hair miniaturization pattern may refer to an imaging pattern in which candidate hairs that are relatively short and thin compared to normal hair are observed clustered in a specific area of ​​a scalp image. The hair miniaturization pattern is a candidate pattern observed through image analysis and is not limited to the meaning of directly determining the growth cycle or medical cause.

[0405] The biomarker generation module (140) can calculate the frequency at which hair candidate groups with a thickness criterion or less and a length criterion or less are repeatedly detected in the same local area during multiple recent shooting sessions. The biomarker generation module (140) can calculate a hair thinning persistence index by normalizing the frequency to a range of 0 to 1.

[0406] For example, in one embodiment, the biomarker generation module (140) can calculate a hair thinning persistence index such as 0.40 by calculating the frequency at which hair candidate groups with a thickness of 30 micrometers or less are repeatedly detected in the same local area in three or more recent imaging sessions. However, the number of imaging sessions and the thickness criteria correspond to one embodiment and may be changed depending on the resolution of the imaging device, the imaging area, and the system operation data.

[0407] The above biomarker generation module (140) can increase the base state value in the section where the hair thinning persistence index increases. This processing is intended to distinguish between cases where a group of short hair candidates is observed temporarily at a single point in time and cases where a group of short and thin hair candidates is repeatedly observed at the same location.

[0408] The above biomarker generation module (140) can segment the pore contour deviation and hair thinning persistence index for each user.

[0409] The above biomarker generation module (140) can adjust the state value based on the three-dimensional exposure index upward when the pore contour deviation for each user or the hair thinning persistence index is calculated to be greater than or equal to a preset reference range.

[0410] The above upward adjustment can be performed according to a weighting ratio corresponding to the range to which the user-specific pore contour deviation and the hair thinning persistence index belong. For example, if the user-specific pore contour deviation and the hair thinning persistence index are both included in a high range, the biomarker generation module (140) can increase the degree of upward adjustment of the state value.

[0411] The above processing is intended to reflect cases where the contour or brightness distribution around pores changes compared to the same user's past images, or where candidate groups of short, fine hairs are repeatedly observed in the same local area, as candidates for image changes related to hair loss progression.

[0412] The above biomarker generation module (140) can calculate the ratio of hair follicle clusters that have multiple hairs at a past baseline set for the same user and are maintained at the present time as the hair follicle cluster preservation rate.

[0413] The above follicle cluster preservation rate is not a value obtained by simply counting the number of hairs per pore at a single point in time, but may be a value normalized to the extent that follicle clusters containing multiple hairs identified at the same user's past baseline are maintained at the present point in time.

[0414] The biomarker generation module (140) can identify pore candidate regions within a unit area and calculate the number of hair candidate pixel clusters for each pore candidate region. Subsequently, the biomarker generation module (140) can determine whether pore candidate regions classified as having multiple hairs in past shooting history maintain the same clustering state in the currently captured image.

[0415] For example, in one embodiment, the biomarker generation module (140) can calculate the ratio of multiple hair candidate groups that are maintained in the current image among the pore candidate regions in which two or more hair candidate groups per pore were observed in the past image, and normalize the ratio to a range of 0 to 1 to derive a hair follicle cluster preservation rate such as 0.70.

[0416] The above biomarker generation module (140) can correct the upwardly adjusted state value downward in the range where the hair follicle cluster preservation rate is high. This processing is intended to limit the excessive reflection of the upwardly adjusted state value by the stereoscopic exposure index or the hair thinning persistence index when the cluster state of the hair candidate group is maintained at a certain level compared to the same user's past baseline.

[0417] The above biomarker generation module (140) can calculate the individual seasonality explainability only when the imaging reproducibility and image analysis reliability indices fall within a preset analysis allowance range.

[0418] The above individual seasonality explainability may indicate the extent to which the change in state value observed at the current time of shooting corresponds to the range of temporary change observed by the same user in the past under the same season or similar humidity conditions.

[0419] The biomarker generation module (140) can collect regional humidity, temperature, seasonal information, and shooting time information for the current shooting date. The biomarker generation module (140) can compare the collected information with the same user's past digital biomarker change range under the same season or similar humidity conditions.

[0420] If there is a lack of past data for the same user in the same season or shooting history under similar humidity conditions, the biomarker generation module (140) may not calculate individual seasonality explainability, or may apply a temporary seasonality criterion based on cumulative data of the same shooting area and similar user groups as an auxiliary value. In this case, the biomarker generation module (140) may store a status value indicating that the temporary seasonality criterion has been applied together with the digital biomarker.

[0421] The above temporary seasonality criteria are not used alone for the downcorrection of the final digital biomarker, but may be reviewed together with at least one of the imaging reproducibility and image analysis reliability indices, pore contour deviation by user, hair miniaturization persistence index, and hair follicle cluster preservation rate. When the biomarker generation module (140) secures cumulative imaging data of the same user exceeding a preset number of imaging attempts, the above temporary seasonality criteria may be updated to individual seasonality criteria based on the same user's past imaging history.

[0422] For example, in one embodiment, the biomarker generation module (140) can compare the regional humidity and temperature data of the current shooting date with the user's digital biomarker change range of the same quarter of the previous year, and normalize the degree to which the current change falls within the range of temporary change of the same season in the past to a range of 0 to 1 to calculate an individual seasonality explainability such as 0.60.

[0423] The above individual seasonality explainability can be used as a conditional correction value to limit the excessive reflection of temporary changes due to seasonal or climatic conditions as deviations from the individual baseline.

[0424] If the above biomarker generation module (140) is included in a range with high individual seasonality explanatory power, the state value can be adjusted downward by a correction ratio corresponding to the individual seasonality explanatory power range.

[0425] However, the biomarker generation module (140) may check whether the imaging reproducibility and image analysis reliability indices fall within the analysis allowable range before applying the individual seasonality explainability. If the imaging reproducibility and image analysis reliability indices are below the analysis allowable range, the biomarker generation module (140) may not apply downward correction based on the seasonality explainability.

[0426] Additionally, the biomarker generation module (140) may limit the downward correction ratio based on the individual seasonality explainability or not apply the downward correction when the user-specific pore contour deviation or hair thinning persistence index falls within a pre-set upper caution range.

[0427] The above processing is intended to prevent treating changes as merely simple seasonal variations when the repeated observation of pore contour changes or hair miniaturization patterns by the same user exceeds a certain threshold, even in cases where the individual seasonality explanation is high.

[0428] The above biomarker generation module (140) can generate a final digital biomarker by progressively reflecting the stereoscopic exposure index, user-specific pore contour deviation, hair miniaturization persistence index, hair follicle cluster preservation rate, and individual seasonality explainability.

[0429] The above digital biomarker may be stored in a data structure including at least one of a user identification value, shooting time information, shooting area information, stereoscopic exposure index, pore contour deviation per user, hair miniaturization persistence index, hair follicle cluster preservation rate, individual seasonality explainability, shooting reproducibility and image analysis reliability index, and a final state value.

[0430] The above biomarker generation module (140) can transmit the digital biomarker to the state analysis module (150). The state analysis module (150) can compare the digital biomarker at multiple points in time to calculate the speed of hair loss progression, the acceleration of progression, or the risk of time-series hair loss progression.

[0431] In addition, the service providing module (160) can provide the user with management reference information including at least one of baseline deviation, possibility of seasonal change, imaging reliability, and management caution period using the digital biomarker.

[0432] The above biomarker generation module (140) may have difficulty immediately setting a baseline for each user in cases where there is insufficient cumulative imaging history, such as for new users.

[0433] In this case, the biomarker generation module (140) can set a temporary baseline using at least one of the following: a shooting area, user group information, image resolution, initial shooting result, and service usage setting information.

[0434] The above user group information may be applied based on information provided according to the user's selected input or service usage settings. For example, the above user group information may include at least one of age group, gender, shooting area, or hair type.

[0435] The above biomarker generation module (140) can update the above temporary baseline to a user-specific baseline based on the same user's cumulative shooting history when the cumulative number of shots exceeds a preset number.

[0436] The above biomarker generation module (140) can assign a temporary baseline application status value to the digital biomarker calculated during the temporary baseline application period, and the status value can be displayed together when the service provision module (160) provides management reference information to the user terminal (10).

[0437] In one embodiment, it can be assumed that a user, whose hair density and thickness are usually maintained in a relatively high range, took a picture of the crown area around October.

[0438] The above biomarker generation module (140) can receive a stereoscopic exposure index of 0.50 from the image analysis module (130) and set it as a base state value.

[0439] The above biomarker generation module (140) can calculate the pore contour deviation per user by comparing the past shooting history and current shooting results of the same user. For example, if the pore contour deviation per user is calculated to be 0.10, the pore contour change can be classified as a low range.

[0440] Additionally, the biomarker generation module (140) can obtain a hair thinning persistence index by calculating the degree to which short and fine hair candidate groups are repeatedly observed in the same local area during recent multiple shooting sessions. For example, if the hair thinning persistence index is calculated to be 0.30, the biomarker generation module (140) can adjust the base state value upward to 0.58 by applying a weighting ratio corresponding to the hair thinning persistence index range.

[0441] Subsequently, the biomarker generation module (140) can calculate the ratio of follicle groups containing multiple hairs that are maintained at the current time from the past baseline of the same user. For example, if the follicle group preservation rate is calculated as 0.80, the biomarker generation module (140) classifies that the cluster state of the hair candidate group is maintained at a certain level compared to the past baseline and can correct the state value downward to 0.48.

[0442] When the shooting reproducibility and image analysis reliability index calculated by the image preprocessing module (120) is calculated as 0.85 and falls within the analysis allowance range, the biomarker generation module (140) can compare the seasonal information and regional humidity information at the current shooting time with the range of state value changes under the same user's past same season or similar humidity conditions.

[0443] For example, if the individual seasonality explainability is calculated to be 0.70 as a result of comparing the current October climate conditions with the shooting history of the same quarter of the previous year, the biomarker generation module (140) can apply a correction ratio corresponding to the individual seasonality explainability range to lower the final digital biomarker to 0.38.

[0444] However, if the above-mentioned pore contour deviation or hair thinning persistence index for each user falls within a pre-set upper caution range, the biomarker generation module (140) may limit the downward correction ratio based on the individual seasonality explainability or not apply downward correction.

[0445] The biomarker generation module (140) can first determine whether there is a past shooting history of the same user. If there is a past shooting history, the biomarker generation module (140) can set a user-specific baseline using the past shooting history. On the other hand, if there is no past shooting history or if the number is less than a preset number, the biomarker generation module (140) can set a temporary baseline.

[0446] The above biomarker generation module (140) can set the stereoscopic exposure index calculated by the image analysis module (130) as a baseline value and calculate the user-specific pore contour deviation and hair thinning persistence index based on the user-specific baseline or the temporary baseline.

[0447] The biomarker generation module (140) can determine whether the pore contour deviation per user or the hair thinning persistence index exceeds a preset threshold. If the pore contour deviation per user or the hair thinning persistence index exceeds the threshold, the biomarker generation module (140) can adjust the base state value upward according to a weighting ratio corresponding to the range to which the indices belong.

[0448] The above biomarker generation module (140) determines whether a hair follicle group containing multiple hairs is maintained at the current time from a past baseline set for the same user, and can calculate the ratio of said maintenance as the hair follicle cluster preservation rate. If the hair follicle cluster preservation rate is calculated to be greater than or equal to a preset reference range, the above biomarker generation module (140) can downwardly correct the upwardly adjusted state value by a ratio corresponding to the hair follicle cluster preservation rate range.

[0449] The biomarker generation module (140) can determine whether the imaging reproducibility and image analysis reliability indices fall within a preset analysis allowance range. If the imaging reproducibility and image analysis reliability indices fall within the analysis allowance range, the biomarker generation module (140) can calculate the individual seasonality explainability by comparing the seasonal information and regional climate information at the current time of imaging with the range of state value changes under the same user's past same season or similar humidity conditions.

[0450] The biomarker generation module (140) may lower the state value by a correction ratio corresponding to the individual seasonality explanation range when the individual seasonality explanation range is calculated to be greater than or equal to a preset reference range. However, if the user-specific pore contour deviation or the hair thinning persistence index is included in a preset upper caution range, the biomarker generation module (140) may limit the downward correction ratio according to the individual seasonality explanation range or not apply the downward correction.

[0451] The above biomarker generation module (140) can generate a state value after the step adjustment is completed as a final digital biomarker and store the final digital biomarker in a user-specific shooting history database.

[0452] Some of the prior art can calculate the number of hairs, hair density, hair thickness, or hair root type from scalp images and use the calculated results to provide information related to the state of hair loss. Additionally, some of the prior art can provide a hair loss progression rate or a time series graph by comparing past data with current data.

[0453] However, the above biomarker generation module (140) can generate digital biomarkers by reflecting, in stages, the pore contour deviation per user, the hair thinning persistence index repeatedly observed at multiple points in time, the hair follicle cluster preservation rate compared to a past baseline, and the individual seasonality explainability based on the cumulative shooting history of the same user, rather than simply comparing the number of hairs or hair thickness at a single point in time.

[0454] According to the above processing procedure, even if the amount of hair loss appears to have increased at the current point in time, if the change in pore contour is within the reference range, pore groups containing multiple hairs are maintained, and the current change corresponds to the range of temporary changes observed in the same user's past under the same season or similar humidity conditions, the final digital biomarker can be adjusted so as not to be excessively upscaled.

[0455] In addition, the above processing procedure can limit downward correction based on seasonality explainability when changes deviating from the user's cumulative baseline are repeatedly observed, thereby reducing the underestimation of baseline deviation changes due to simple seasonality correction alone.

[0456] Accordingly, the biomarker generation module (140) can subdivide candidate state changes related to hair loss progression based on a user-specific baseline and increase the consistency of time-series source data provided to the state analysis module (150).

[0457] In this specification, a personal baseline may refer to a user-specific comparison standard established using at least one of past images, past stereoscopic exposure indices, past digital biomarkers, and past shooting conditions accumulated by the same user.

[0458] In this specification, user-specific pore contour deviation may refer to the degree of change in the contour around the pore, the brightness distribution around the pore opening, or the boundary sharpness of the pore candidate region in the current image compared to the reference image.

[0459] In this specification, the hair thinning persistence index may refer to the degree to which a group of hair candidates below a preset thickness standard and below a preset length standard is repeatedly observed over multiple time points in the same local area.

[0460] In this specification, the follicle cluster preservation rate may refer to the ratio of pore candidate regions containing multiple hairs from the same user's past baseline that are maintained at the present time.

[0461] In this specification, the individual seasonality explainability may refer to the degree to which a change in state value observed at the current time of shooting corresponds to a range of temporary changes observed by the same user in the past under the same season or similar humidity conditions.

[0462] In this specification, the range of temporary change may refer to a range of fluctuations related to hair condition that can be statistically observed due to seasonal or climate changes, and may be set in conjunction with the user's regional weather data or the same user's past shooting history.

[0463] In this specification, a digital biomarker may refer to a data structure generated by including at least one of a stereoscopic exposure index, user-specific pore contour deviation, hair miniaturization persistence index, hair follicle cluster preservation rate, individual seasonality explainability, and a final state value.

[0464] The individual seasonality explainability threshold used in the biomarker generation module (140) can be set by analyzing the degree of correspondence between the one-year periodic time series data of the same user and external climate data. The external climate data may include at least one of humidity, temperature, seasonal information and shooting location information.

[0465] In one embodiment, the biomarker generation module (140) can calculate a correlation coefficient between the direction of past digital biomarker changes of the same user and the direction of humidity or temperature changes. If the correlation coefficient is 0.7 or higher, the biomarker generation module (140) can classify the corresponding section as a seasonal corresponding section.

[0466] However, the above correlation coefficient of 0.7 is used as a reference value to indicate the degree of correspondence between climate variables and the direction of change of digital biomarkers, and is not limited to the meaning that climate variables explain 70% of the change in state values. The above seasonal correspondence interval may be used as an auxiliary criterion to determine whether the current change in state values ​​corresponds to the range of temporary changes observed in the same user's past in the same season or under similar climate conditions.

[0467] In one embodiment, the biomarker generation module (140) may receive a stereoscopic exposure index of 0.60 from the image analysis module (130) and set it as a base state value.

[0468] Subsequently, when the pore contour deviation per user is calculated to be 0.20 and the hair thinning persistence index is calculated to be 0.20, the biomarker generation module (140) can calculate an average upward range corresponding to the interval of the two values ​​as 0.12. The biomarker generation module (140) can calculate an intermediate state value of 0.72 by reflecting the average upward range of 0.12 to the basic state value of 0.60.

[0469] Next, when the hair follicle cluster preservation rate is calculated to be 0.80, the biomarker generation module (140) can calculate a downward correction value corresponding to the hair follicle cluster preservation rate range as 0.15. The biomarker generation module (140) can calculate a corrected state value of 0.57 by subtracting the downward correction value of 0.15 from the intermediate state value of 0.72.

[0470] Finally, the biomarker generation module (140) can apply a correction ratio corresponding to the seasonality explanation range to the correction state value of 0.57 when the individual seasonality explanation range at the current time is calculated to be 0.80 and the value corresponds to a seasonality response range threshold of 0.7 or higher. For example, if the correction ratio is set to 20%, the biomarker generation module (140) can subtract approximately 0.11 to calculate the final digital biomarker as 0.46.

[0471] In the first embodiment, for an image in which a temporary change in autumn is observed, the stereoscopic exposure value can be calculated as 0.55, the weighting value based on pore contour deviation and hair miniaturization persistence as 0.05, the follicle cluster subtraction value as 0.15, and the seasonality explanatory ability as 0.85. In this case, the final digital biomarker is calculated as 0.35 and can be classified as a seasonality corresponding range.

[0472] In the second embodiment, for an image with persistent baseline deviation, the stereoscopic exposure value can be calculated as 0.55, the weighting value based on pore contour deviation and persistence of hair miniaturization as 0.35, the follicular cluster subtraction value as 0.05, and the seasonality explanatory power as 0.20. In this case, the final digital biomarker is calculated as 0.85, which can be classified as a high management caution range.

[0473] In the third embodiment, the new user image is calculated to have a stereoscopic exposure value of 0.40, but due to insufficient cumulative shooting history, the weighting value for pore contour deviation and hair miniaturization persistence may be processed as 0.00. Additionally, when the follicle cluster subtraction value is calculated as 0.10 and the seasonality explanatory capability is calculated as 0.00, the final digital biomarker may be calculated as 0.30 and may be classified as having a provisional baseline applied.

[0474] In the fourth embodiment, for an image in which a change in pore contour is observed, the stereoscopic exposure value can be calculated as 0.45, the weighting value based on pore contour deviation and persistence of hair miniaturization as 0.30, the follicular cluster subtraction value as 0.05, and the seasonality explanatory capability as 0.10. In this case, the final digital biomarker is calculated as 0.70 and can be classified as a baseline deviation confirmation section.

[0475] In the fifth embodiment, the image in which follicular clusters are maintained may be calculated with a stereoscopic exposure value of 0.60, a weighting value based on pore contour deviation and hair miniaturization persistence of 0.10, a follicular cluster subtraction value of 0.30, and a seasonality explanatory capability of 0.40. In this case, the final digital biomarker is calculated as 0.40 and can be classified as a follicular cluster maintenance section.

[0476] In the 6th embodiment, for an image in which changes related to winter dry conditions are observed, the stereoscopic exposure value can be calculated as 0.50, the weighting value for pore contour deviation and hair miniaturization persistence as 0.08, the follicle cluster subtraction value as 0.10, and the seasonality explanatory power as 0.75. In this case, the final digital biomarker is calculated as 0.38 and can be classified as an environmental factor reflection range.

[0477] In the 7th embodiment, an image in which the hair miniaturization pattern is concentrated in a specific local area may have a stereoscopic exposure value of 0.42, a weighting value based on pore contour deviation and hair miniaturization persistence of 0.45, a follicle cluster subtraction value of 0.03, and a seasonality explanatory ability of 0.15. In this case, the final digital biomarker is calculated as 0.84, which may be classified as a high management caution range.

[0478] In the eighth embodiment, an image in which a state value relaxation trend is observed may have a stereoscopic exposure value of 0.48, a weighting value based on pore contour deviation and hair miniaturization persistence of 0.10, a follicle cluster subtraction value of 0.25, and a seasonality explanatory ability of 0.30. In this case, the final digital biomarker is calculated as 0.33 and can be classified as a state value relaxation trend section.

[0479] In the ninth embodiment, an image in which a composite baseline deviation is observed may have a stereoscopic exposure value of 0.65, a weighting value based on pore contour deviation and hair miniaturization persistence of 0.25, a follicular cluster subtraction value of 0.05, and a seasonality explanatory capability of 0.10. In this case, the final digital biomarker is calculated as 0.85 and may be classified as a high management caution range.

[0480] In the 10th embodiment, for an image in which transient changes related to changes in climate conditions are observed, the stereoscopic exposure value can be calculated as 0.52, the weighting value based on pore contour deviation and hair miniaturization persistence as 0.05, the follicle cluster subtraction value as 0.10, and the seasonality explanatory ability as 0.90. In this case, the final digital biomarker is calculated as 0.37 and can be classified as a seasonality correction application range.

[0481] A general image analysis method and a personal baseline-based method can be compared on the same simulation dataset. Since the general image analysis method can classify conditions based on a single indicator, such as hair density or hair thickness, in an image at a specific point in time, there is a possibility that seasonal changes or temporary changes in shooting conditions may be classified as baseline deviation changes.

[0482] In one embodiment, the general image analysis method may yield a misclassification rate related to seasonal change of 32%. On the other hand, the personal baseline-based method of the biomarker generation module (140) reflects the range of state value changes in the same user's past same season or similar humidity conditions, so the misclassification rate related to seasonal change in the same dataset may yield 5%.

[0483] In addition, if the user baseline classification match rate is calculated as 68% in the general image analysis method, the user baseline classification match rate can be calculated as 96% in the above-mentioned personal baseline-based method. The above results are examples to explain that the biomarker generation module (140) can subdivide user baseline deviation changes by reflecting the same user's past history, pore contour deviation, hair thinning persistence, follicle cluster preservation rate, and seasonality explainability together.

[0484] The above-mentioned state analysis module (150) can calculate the risk of time-series hair loss progression using digital biomarkers generated at multiple points in time.

[0485] The above time-series hair loss progression risk may be a risk range for management reference calculated by not evaluating only the number of hairs, hair density, or scalp exposure at a specific point in time, but by progressively reflecting the rate of change in digital biomarkers at different time intervals, the pattern of increase in the rate of change, the correspondence of the direction of change between multiple scalp areas, the degree of deviation from the imaging cycle, and the degree of mitigation of changes in status values ​​after management execution.

[0486] The above state analysis module (150) can first set multiple different time intervals and compare the direction of change and the rate of change of digital biomarkers calculated in each time interval. The above state analysis module (150) can calculate a hair loss progression acceleration index by comparing the rate of change in the first time interval with the rate of change in the second time interval, and can calculate a progression inflection index using the temporal change pattern of the hair loss progression acceleration index.

[0487] The above-described state analysis module (150) can set a reference risk value upward in response to the increase section when the change rate, the hair loss progression acceleration index, and the progression inflection index correspond to at least one of the increasing direction of the digital biomarker, the decreasing direction of the hair density-related state value, or the increasing direction of the scalp exposure-related state value.

[0488] In addition, the state analysis module (150) can calculate the diffusion consistency between regions when the direction of change of digital biomarkers corresponds to each other in at least two scalp regions among the crown, frontal region, and temporal region, and can further increase the reference risk value according to the diffusion consistency interval between regions.

[0489] The above state analysis module (150) can adjust the calculation confidence level of the change rate and progress acceleration index using the shooting cycle deviation correction value. Additionally, the above state analysis module (150) can calculate the response delay correlation after management execution only when the cumulative shooting reproducibility is included in the analysis allowable range, and can lower the weighted reference risk value according to the response delay correlation after management execution.

[0490] The above-described state analysis module (150) can arrange digital biomarkers generated at multiple points in time for the same user in chronological order. The digital biomarkers can be stored in correspondence with at least one of shooting time information, shooting site information, final state value, shooting reproducibility, and image analysis reliability index.

[0491] The above state analysis module (150) can set a first time interval and a second time interval according to adjacent shooting times or pre-set period units. For example, the first time interval can be set as a interval from the shooting time in January to the shooting time in March, and the second time interval can be set as a interval from the shooting time in March to the shooting time in May.

[0492] The above-mentioned state analysis module (150) can calculate the rate of change per unit period using the amount of change of the digital biomarker in each time interval and the elapsed time between the time of shooting. The rate of change can be classified into one of the directions in which the digital biomarker increases, decreases, or is maintained.

[0493] If the above-mentioned state analysis module (150) is configured such that the higher the digital biomarker, the higher the state value related to hair loss progression, the direction in which the digital biomarker increases can be classified as the direction of increasing risk. Additionally, even if the state value related to hair density decreases or the state value related to scalp exposure increases, the above-mentioned state analysis module (150) can classify the change as the direction of increasing risk.

[0494] The above state analysis module (150) can calculate a hair loss progression acceleration index by comparing a first change rate calculated in a first time interval and a second change rate calculated in a second time interval.

[0495] The above hair loss progression acceleration index may be a value indicating the degree to which the rate of change increases in a subsequent time interval by comparing the amount of change per unit period in different time intervals.

[0496] For example, if the rate of change of the digital biomarker in the first time interval is calculated as 0.15 and the rate of change of the digital biomarker in the second time interval is calculated as 0.20, the state analysis module (150) can calculate the hair loss progression acceleration index by normalizing the increase between the two rates of change.

[0497] The above state analysis module (150) can classify the above state value increase rate as a section where the rate of increase in the state value increases over time, rather than a simple increase in the state value, when the hair loss progression acceleration index is calculated to be greater than a preset reference section.

[0498] The above hair loss progression acceleration index can be used as a variable to increase the reference risk value when the change in the digital biomarker progresses in an upward direction of risk. On the other hand, when the change in the digital biomarker progresses in a mitigating direction, the above state analysis module (150) can exclude the change from the risk-up factor or classify it as a separate mitigation section.

[0499] The above state analysis module (150) can calculate a progression inflection index using the change pattern of the hair loss progression acceleration index calculated in multiple time intervals.

[0500] The above progression inflection index may be a value indicating whether the hair loss progression acceleration index remains constant, shifts in an increasing direction, or shifts in a decreasing direction.

[0501] For example, if the hair loss progression acceleration index is calculated as a low range in the first comparison range, but moves to a high range in the second comparison range, the state analysis module (150) can calculate the progression inflection index as an upward range.

[0502] The above state analysis module (150) can additionally set a reference risk value upward in correspondence with the progression inflection index section when the progression inflection index is calculated in an upward direction of risk.

[0503] The above-mentioned progression inflection index can be used as an auxiliary judgment value to reflect a change in the direction of the rate of change that is difficult to confirm based solely on the rate of change of a single section.

[0504] The above-mentioned state analysis module (150) can set a reference risk value using the digital biomarker change rate, hair loss progression acceleration index and progression inflection index.

[0505] The above state analysis module (150) can set the reference risk value to a low range when the rate of change, the hair loss progression acceleration index, and the progression inflection index are all included in a low range.

[0506] The above state analysis module (150) can set the reference risk value to an intermediate range when the rate of change is low but the hair loss progression acceleration index or progression inflection index increases.

[0507] The above state analysis module (150) can set the reference risk value to a high range when the rate of change, the hair loss progression acceleration index, and the progression inflection index all increase in the upward direction of risk.

[0508] The above standard risk value may be further adjusted based on the consistency of diffusion between sites, correction values ​​for deviation from the imaging cycle, and correlations for response delay after management execution.

[0509] The above-mentioned condition analysis module (150) can compare digital biomarkers generated in at least two scalp regions among the crown, frontal, temporal, and occipital regions.

[0510] The above-mentioned state analysis module (150) can calculate the degree to which the direction of change of digital biomarkers in multiple scalp regions corresponds to each other as the diffusion consistency between regions. For example, if digital biomarkers increase in the direction of increasing risk in both the crown and the frontal region, the above-mentioned state analysis module (150) can calculate the diffusion consistency between regions as a high range.

[0511] The diffusion consistency between the above regions may be a value used to distinguish between changes observed temporarily in only a specific region and changes observed in similar directions in multiple regions.

[0512] The above state analysis module (150) can additionally increase the standard risk value by a ratio corresponding to the standard diffusion consistency interval between the parts when the diffusion consistency between the parts is calculated to be greater than or equal to a preset standard interval.

[0513] For example, if digital biomarkers of the crown and frontal regions change simultaneously in an upward direction of risk and the diffusion consistency between the regions is calculated as 0.80, the state analysis module (150) can adjust the reference risk value upward by applying a weighting ratio corresponding to the diffusion consistency between the regions.

[0514] The above state analysis module (150) can calculate the time difference between the recommended shooting cycle and the actual shooting cycle to derive a shooting cycle deviation correction value.

[0515] The above shooting cycle deviation correction value may be a value for adjusting the calculation confidence level of the rate of change and progression acceleration index when the shooting interval is not constant.

[0516] The above state analysis module (150) can normally calculate the rate of change of the multiple time intervals and the hair loss progression acceleration index when the above shooting cycle deviation correction value is included in a preset allowable interval.

[0517] The above state analysis module (150) can adjust the confidence level or weighting level of the calculated risk value when the shooting cycle deviation correction value falls within the caution period. For example, if the actual shooting interval is longer than the recommended shooting cycle and some intermediate changes are not confirmed, the above state analysis module (150) can lower the reflection ratio of the progress acceleration index or assign an auxiliary analysis state value to the risk value.

[0518] The above state analysis module (150) may limit the calculation of the progress acceleration index when the above shooting cycle deviation correction value is included in the excess interval and calculate an auxiliary risk value using the simple change rate of the last two shooting times.

[0519] For example, if the time difference between the recommended shooting cycle and the actual shooting cycle is calculated to be 15 days and the said 15 days fall within a pre-set warning period, the state analysis module (150) may adjust the confidence level of the calculated risk value or request additional shooting guidance.

[0520] The above state analysis module (150) can calculate cumulative shooting reproducibility using the shooting reproducibility and image analysis reliability index of multiple viewpoints and the same scalp area matching success rate.

[0521] The above cumulative imaging reproducibility can be used as a reliability criterion to determine whether the results of calculating the time-series hair loss progression risk can be provided to the user or classified as an auxiliary analysis result.

[0522] The above-mentioned state analysis module (150) can continue to perform the time-series hair loss progression risk calculation procedure when the cumulative shooting reproducibility is included in a preset analysis allowance interval.

[0523] On the other hand, if the cumulative shooting reproducibility is below the analysis allowable range, the state analysis module (150) may not use the cumulative shooting reproducibility as a risk reduction factor, and may withhold the provision of the risk calculation result or classify it as an auxiliary analysis value. In this case, the state analysis module (150) may request the service provision module (160) to provide a reshoot guidance or a shooting condition reset guidance to the user terminal (10).

[0524] The above-mentioned reshooting guidance or shooting condition reset guidance may include at least one of guidance on matching the shooting area, guidance on adjusting the shooting distance, guidance on adjusting the shooting lighting, guidance on complying with the shooting cycle, and guidance on reshooting the same area.

[0525] The above state analysis module (150) can calculate the response delay correlation after management execution only when the cumulative shooting reproducibility is included in the analysis allowable range.

[0526] The correlation of response delay after the above management execution may refer to the degree to which the rate of increase of digital biomarkers or the rate of increase of scalp exposure-related status values ​​is mitigated within a preset observation period after the time of management execution based on the management data provided to the user corresponds temporally to the history of the above management execution.

[0527] The above state analysis module (150) can receive the type of management data provided to the user, the time of provision, and the recommended execution cycle from the service provision module (160). In addition, the above state analysis module (150) can receive at least one of a management execution record, execution date, execution frequency, or user input confirmation value from the user terminal (10).

[0528] The above-mentioned state analysis module (150) can set a preset observation period after the management execution time and determine whether the rate of increase of digital biomarkers during the observation period is mitigated compared to the previous period.

[0529] For example, if a user is provided with scalp care routine or lifestyle care data starting from April, and after the user inputs the care execution record, the rate of increase of digital biomarkers from the latter half of May becomes lower than the previous period, the state analysis module (150) can calculate the degree of temporal correspondence between the care execution history and the mitigation of changes in the state value.

[0530] The above state analysis module (150) can classify the response delay correlation after management execution into a high range when the degree of temporal correspondence is calculated to be greater than or equal to a preset standard range.

[0531] The above state analysis module (150) can lower the weighted reference risk value by a ratio corresponding to the response delay correlation range after management execution when the response delay correlation after management execution is calculated to be greater than or equal to a preset reference range.

[0532] The above downward adjustment is intended to reflect the tendency for the rate of change in the state value to be mitigated after the management is executed. However, even if the above downward adjustment is applied, the state analysis module (150) may limit the downward adjustment ratio or not apply the above downward adjustment if the rate of change in digital biomarkers, the hair loss progression acceleration index, or the progression inflection index falls within a pre-set upper cautionary range.

[0533] The above processing is intended to prevent the risk value from being lowered solely on the basis of the existence of a management execution history, and to apply a downward adjustment only when a mitigation in the rate of increase of the status value is confirmed within a certain observation period following the management execution.

[0534] In addition, the state analysis module (150) can classify the management execution record as auxiliary data when the management execution record relies solely on user input, and determine whether to lower the rate of change of digital biomarkers and the imaging reproducibility index together.

[0535] When the management execution record is a user input value, the above-mentioned status analysis module (150) can check at least one of the management guidance screen viewing history, compliance with the shooting cycle, number of subsequent shots, compliance with the reshooting guidance, and changes in shooting reproducibility in addition to the user input value. The above-mentioned status analysis module (150) can reflect the response delay correlation after management execution in the risk downward adjustment only when the management execution record and the auxiliary verification value correspond to each other.

[0536] The above-mentioned status analysis module (150) may classify the response delay correlation after management execution as an auxiliary value when there is a user input value but there is a history of viewing the management guidance screen, compliance with the shooting cycle, or subsequent shooting data. In this case, the above-mentioned status analysis module (150) may not directly reflect the response delay correlation after management execution in the downward adjustment of the reference risk value, and may allow the service provision module (160) to provide additional shooting or management execution record supplementary guidance to the user terminal (10).

[0537] The above-mentioned state analysis module (150) can calculate the final time-series hair loss progression risk by stepwise reflecting the digital biomarker change rate, hair loss progression acceleration index, progression inflection index, diffusion consistency between areas, shooting cycle deviation correction value, cumulative shooting reproducibility, and response delay correlation after management execution.

[0538] The above-mentioned final time-series hair loss progression risk may be expressed as a normalized value in the range of 0 to 1 or as multiple risk intervals. For example, the above-mentioned risk intervals may be divided into a low management caution interval, a medium management caution interval, and a high management caution interval.

[0539] The above-mentioned state analysis module (150) can transmit the above-mentioned final time-series hair loss progression risk level to the service provision module (160). The above-mentioned service provision module (160) can generate customized hair loss management data including at least one of guidance on adjusting the management cycle, guidance on lifestyle management, guidance on the shooting cycle, guidance on re-shooting the same area, or guidance on reviewing consultation with a specialized institution, depending on the above-mentioned final time-series hair loss progression risk level.

[0540] In one embodiment, it can be assumed that the user took photos of the crown and frontal area over January, March, and May.

[0541] The above state analysis module (150) can set a first time interval from January to March and a second time interval from March to May. The above state analysis module (150) can calculate the rate of change of the digital biomarker as 0.15 in the first time interval and the rate of change of the digital biomarker as 0.20 in the second time interval.

[0542] The above state analysis module (150) can calculate a hair loss progression acceleration index by normalizing the speed difference when the rate of change of the second time interval is higher than the rate of change of the first time interval. For example, the hair loss progression acceleration index can be calculated as 0.30.

[0543] The above state analysis module (150) can calculate a progression inflection index using the degree to which the hair loss progression acceleration index increases compared to the previous comparison interval. For example, the progression inflection index can be calculated as 0.25.

[0544] The above state analysis module (150) can set the reference risk value to 0.65 when the change rate, the hair loss progression acceleration index, and the progression inflection index are classified as increasing in the upward direction of risk.

[0545] Subsequently, the state analysis module (150) can determine whether the digital biomarker changes in an upward direction of risk not only in the crown but also in the frontal area. If the direction of change of the digital biomarker in the crown and the frontal area corresponds to each other and the diffusion consistency between the areas is calculated as 0.80, the state analysis module (150) can increase the reference risk value to 0.75.

[0546] Meanwhile, the state analysis module (150) can calculate cumulative shooting reproducibility using the shooting reproducibility of multiple viewpoints, the image analysis reliability index, and the matching success rate. For example, if the cumulative shooting reproducibility is calculated to be 0.75 and falls within the analysis allowable range, the state analysis module (150) can perform the calculation of response delay correlation after management execution.

[0547] For example, if a user has entered a record of management execution based on scalp care data provided by the service provision module (160) since April, and the rate of increase of digital biomarkers is calculated to be moderated compared to the previous period from the latter half of May, the condition analysis module (150) can calculate the response delay correlation after management execution as 0.60.

[0548] The above state analysis module (150) can lower the weighted reference risk value of 0.75 to 0.55 by applying a downward adjustment ratio corresponding to the response delay correlation interval after the management execution. Accordingly, the above state analysis module (150) can determine the final time series hair loss progression risk to be 0.55.

[0549] The above final time series hair loss progression risk can be transmitted to a service provision module (160), and the service provision module (160) can provide management reference information corresponding to the above final time series hair loss progression risk to a user terminal (10).

[0550] In one embodiment, the recommended shooting cycle is set to a 30-day interval, but it can be assumed that the user has missed shooting for more than 90 days.

[0551] In this case, the state analysis module (150) determines that the actual shooting interval deviates significantly from the recommended shooting cycle and classifies the shooting cycle deviation correction value as an excess interval.

[0552] The above state analysis module (150) may limit the calculation of the progress acceleration index and the progress inflection index in the above excess section. This is because the reliability level of the calculated value may be lowered when the acceleration or inflection is calculated using a simple interpolation method when the direction of change at an intermediate point in time cannot be confirmed due to a long-term omission of filming.

[0553] In this case, the above-mentioned state analysis module (150) can calculate an auxiliary risk value using the rate of change of digital biomarkers at the time of the last two shots, and provide guidance on compliance with the regular shooting cycle or additional shooting guidance to the user terminal (10) through the service provision module (160).

[0554] The state analysis module (150) can first determine whether multiple digital biomarkers of the same user exist at multiple time points. If multiple digital biomarkers exist, the state analysis module (150) can set a first time interval and a second time interval. On the other hand, if multiple digital biomarkers are insufficient, the state analysis module (150) can request the service provision module (160) to withhold the calculation of the time-series hair loss progression risk or to generate single-time point management reference information.

[0555] The above state analysis module (150) can calculate the direction of change and the rate of change of the digital biomarker in the first time interval and the second time interval. The above state analysis module (150) can calculate a hair loss progression acceleration index when the rate of change increases in the direction of increasing risk.

[0556] The above state analysis module (150) can calculate a progression inflection index by analyzing the pattern of change in the hair loss progression acceleration index over multiple time intervals. If the hair loss progression acceleration index and the progression inflection index are calculated to be greater than or equal to a preset reference interval, the above state analysis module (150) can set the reference risk value upward.

[0557] The above state analysis module (150) can determine whether the direction of change of digital biomarkers in multiple scalp areas corresponds to each other. If the direction of change of the multiple scalp areas corresponds to each other, the state analysis module (150) can calculate the diffusion consistency between areas and increase the reference risk value by a ratio corresponding to the diffusion consistency interval between areas.

[0558] The above state analysis module (150) can derive a shooting cycle deviation correction value by calculating the difference between the recommended shooting cycle and the actual shooting cycle. If the above shooting cycle deviation correction value falls within the allowable range, the above state analysis module (150) can calculate the change rate and progress acceleration index normally. If the above shooting cycle deviation correction value falls within the caution range, the above state analysis module (150) can adjust the confidence level or weighting level of the calculated risk value. If the above shooting cycle deviation correction value falls within the excess range, the above state analysis module (150) can limit the calculation of the progress acceleration index and calculate an auxiliary risk value.

[0559] The above state analysis module (150) can calculate cumulative shooting reproducibility using the shooting reproducibility and image analysis reliability index of multiple viewpoints and the same scalp area matching success rate. If the cumulative shooting reproducibility is below the analysis allowable range, the above state analysis module (150) may withhold the provision of risk calculation results or classify them as auxiliary analysis values.

[0560] If the above cumulative shooting reproducibility is included in the analysis allowable range, the state analysis module (150) can calculate the response delay correlation after management execution. If the response delay correlation after management execution is calculated to be greater than or equal to a preset reference range, the state analysis module (150) can lower the weighted reference risk value by a ratio corresponding to the response delay correlation range after management execution.

[0561] The above state analysis module (150) can determine the value at which the above step-by-step judgment is completed as the final time-series hair loss progression risk and transmit the final time-series hair loss progression risk to the service provision module (160).

[0562] Some of the prior art can provide the degree of hair loss progression or time-series information by using the number of hairs, hair density, hair thickness, number of hair follicles, or variation by hair root type.

[0563] However, the above-mentioned state analysis module (150) can calculate the time-series hair loss progression risk interval by using not only simple increase / decrease range or time-series display, but also the difference in the rate of change of different time intervals, the hair loss progression acceleration index, the progression inflection index, whether the direction of change of multiple scalp areas corresponds, the degree of deviation from the shooting cycle, and the temporal correspondence with the mitigation of changes in the state value after management execution.

[0564] According to the above processing procedure, even if digital biomarkers temporarily increase at a specific point in time, the risk level can be calculated differently depending on whether the rate of change continues to increase, whether an inflection point occurs, whether changes in the same direction are observed in multiple sites, and whether the imaging cycle is within the analyzable range.

[0565] In addition, the above-mentioned condition analysis module (150) may apply downward adjustment only when the rate of increase of digital biomarkers or the rate of increase of scalp exposure-related condition values ​​is mitigated within a preset observation period after the execution of management, rather than lowering the risk value based solely on the management execution history.

[0566] Accordingly, the state analysis module (150) can distinguish between simple increases and decreases in hair loss volume and patterns of increasing state values ​​that are repeatedly confirmed over multiple time intervals, and can generate analysis data that allows the service provision module (160) to distinguish and provide shooting cycles, management cycles, or consultation review guidance.

[0567] In this specification, the time-series hair loss progression risk may refer to a risk range for management reference or a normalized state value calculated using at least one of the rate of change of digital biomarkers generated at multiple time points, hair loss progression acceleration index, progression inflection index, consistency of diffusion between sites, correction value for deviation from the imaging cycle, cumulative imaging reproducibility, and correlation of response delay after management execution.

[0568] In this specification, the rate of change may refer to the degree of change per unit period of digital biomarkers or scalp condition-related indicators during a specific time interval.

[0569] In this specification, the hair loss progression acceleration index may refer to a value indicating the degree to which the rate of change increases in a subsequent time interval by comparing the rates of change calculated in different time intervals.

[0570] In this specification, the progression inflection index may refer to a value indicating the degree to which the direction of temporal change of the hair loss progression acceleration index calculated over multiple time intervals is reversed or increased.

[0571] In this specification, diffusion consistency between regions may refer to the ratio of corresponding directions of change in digital biomarkers observed in at least two scalp regions among the vertex, frontal region, temporal region, and occipital region.

[0572] In this specification, the shooting cycle deviation correction value is calculated using the time difference between the recommended shooting cycle and the actual shooting cycle, and may refer to a value for adjusting the confidence level of the calculation of the rate of change and the acceleration of progress index.

[0573] In this specification, cumulative imaging reproducibility is calculated using the imaging reproducibility of multiple viewpoints, the image analysis reliability index, and the same scalp area matching success rate, and may refer to a criterion value for determining whether to provide the results of the time-series hair loss progression risk calculation or to classify auxiliary analysis values.

[0574] In this specification, the response delay correlation after management execution may refer to the degree to which the rate of increase of digital biomarkers or the rate of increase of scalp exposure-related status values ​​is mitigated within a preset observation period after the time of management execution according to the management data provided to the user corresponds temporally to the management execution history.

[0575] The weighting intervals and downward adjustment intervals of the time-series hair loss progression risk used in the above-mentioned state analysis module (150) can be set based on tracking observation data of a user group in which hair loss management history is confirmed and a user group in which management history is not confirmed.

[0576] In one embodiment, the condition analysis module (150) can extract an inflection point of increase in condition value, a consistency of diffusion between sites, and a mitigation point of the rate of increase in condition value after management execution using multiple digital biomarkers over a period of one year. If the condition analysis module (150) observes a tendency for the rate of increase in digital biomarkers or the rate of decrease in the follicle cluster preservation rate to be mitigated in a section where the response delay correlation after management execution exceeds 0.5, the section can be set as a risk downward adjustment review section.

[0577] The above risk downward adjustment review period is not intended to lower the risk value based solely on the management execution history, but may be applied only when a mitigation of the rate of increase in the status value is observed within a pre-set observation period after the management execution.

[0578] In one embodiment, the state analysis module (150) may set the rate of change of digital biomarkers relative to the previous section as 0.20 when calculated as such, as the basic risk value.

[0579] Subsequently, if the state analysis module (150) calculates the progress acceleration index as 0.15 and the progress inflection index as 0.10, it can calculate the intermediate risk value as 0.45 by reflecting the above values.

[0580] Next, if the state analysis module (150) calculates the diffusion consistency between parts as 0.80 and is included in the high range, it can calculate a weighted risk value of 0.54 by applying a weighting ratio of 1.2 times to the intermediate risk value. Additionally, if the shooting cycle deviation correction value is calculated as 0.10, the risk value can be adjusted to 0.64 by reflecting the correction value corresponding to the shooting cycle deviation range.

[0581] Finally, the state analysis module (150) may determine that the value is included in the downward adjustment review section when the degree of temporal correspondence between the mitigation of the rate of increase of the state value after the user's management execution and the management execution history is calculated to be 0.70. In this case, the state analysis module (150) may calculate the final time-series hair loss progression risk as 0.48 by subtracting 0.16, which corresponds to the correction ratio of 25% of the section, from the risk value of 0.64.

[0582] In the first embodiment, when a short-term increase section is observed, the rate of change can be calculated as 0.35, the acceleration and inflection weighting as 0.40, the diffusion or imaging deviation weighting as 0.15, and the management response subtraction as 0.00. In this case, the final time-series hair loss progression risk is calculated as 0.90 and can be classified as a high management caution section.

[0583] In the second embodiment, if a trend of relaxation of the status value is observed after the execution of management, the rate of change can be calculated as 0.30, the acceleration and inflection weighting as 0.15, the diffusion or imaging deviation weighting as 0.10, and the management response subtraction as 0.35. In this case, the final time-series hair loss progression risk is calculated as 0.20 and can be classified as a low management caution section.

[0584] In the third embodiment, when a gradual progression section is observed, the rate of change can be calculated as 0.15, the acceleration and inflection weighting as 0.05, the diffusion or imaging deviation weighting as 0.05, and the management response subtraction as 0.05. In this case, the final time-series hair loss progression risk is calculated as 0.20 and can be classified as a low management caution section.

[0585] In the fourth embodiment, when a diffusion candidate section corresponding to the direction of change of multiple parts is observed, the rate of change can be calculated as 0.20, the acceleration and inflection weighting value as 0.10, the diffusion or imaging deviation weighting value as 0.45, and the management response subtraction value as 0.05. In this case, the final time-series hair loss progression risk is calculated as 0.70 and can be classified as a management caution section.

[0586] In the fifth embodiment, if the mitigation of the change in the state value is observed to be delayed after the management is executed, the rate of change may be calculated as 0.25, the acceleration and inflection weighting as 0.20, the diffusion or imaging deviation weighting as 0.10, and the management response subtraction as 0.10. In this case, the final time-series hair loss progression risk may be calculated as 0.45 and classified as a monitoring section.

[0587] In the sixth embodiment, when a shooting cycle deviation is observed, the rate of change can be calculated as 0.10, the acceleration and inflection weighting as 0.05, the diffusion or shooting deviation weighting as 0.30, and the management response subtraction as 0.00. In this case, the final time series hair loss progression risk can be calculated as 0.45, and the state analysis module (150) can classify the result as a data deficiency or auxiliary risk range.

[0588] In the 7th embodiment, when an inflection point of increasing state value is observed, the rate of change can be calculated as 0.40, the acceleration and inflection point weighting value as 0.50, the diffusion or imaging deviation weighting value as 0.05, and the management response subtraction value as 0.05. In this case, the final time series hair loss progression risk is calculated as 0.90 and can be classified as a high management caution section.

[0589] In the eighth embodiment, when a section in which the state value is stably maintained is observed, the rate of change can be calculated as 0.05, the acceleration and inflection weighting as 0.02, the diffusion or imaging deviation weighting as 0.03, and the management response subtraction as 0.05. In this case, the final time series hair loss progression risk is calculated as 0.05 and can be classified as a low management caution section.

[0590] In the ninth embodiment, when a multi-part status value increase section is observed, the rate of change can be calculated as 0.30, the acceleration and inflection weighting as 0.35, the diffusion or imaging deviation weighting as 0.50, and the management response subtraction value as 0.20. In this case, the final time-series hair loss progression risk is calculated as 0.95 and can be classified as a high management caution section. Even if the management response subtraction value exists, the downward adjustment ratio may be limited if the rate of change and diffusion weighting are high.

[0591] In the 10th embodiment, if a significant mitigation of the rate of increase in the status value is observed after the management is executed, the rate of change may be calculated as 0.35, the acceleration and inflection weighting as 0.20, the diffusion or imaging deviation weighting as 0.05, and the management response subtraction as 0.50. In this case, the final time-series hair loss progression risk may be calculated as 0.10 and classified as a low management caution section.

[0592] For the same simulation dataset, a simple time-series increase / decrease comparison method can be compared with a method that reflects acceleration and management responsiveness. Since the aforementioned simple time-series increase / decrease comparison method classifies risk zones based solely on the difference in digital biomarkers between two adjacent imaging points, it may not adequately reflect the pattern of increasing rate of change, consistency of diffusion between sites, deviation from the imaging cycle, and whether the rate of increase in status values ​​has been mitigated after management execution.

[0593] In one embodiment, the simple time series increase / decrease comparison method may yield a subsequent management attention interval classification rate of 52%. On the other hand, since the acceleration and management responsiveness reflection method of the state analysis module (150) reflects the progression acceleration index, progression inflection index, and diffusion consistency between parts together, it is possible to have an example where the subsequent management attention interval classification rate is calculated as 88% in the same simulation dataset.

[0594] In addition, in one embodiment, when the degree of reflection of state value relaxation after the management execution of the simple time series increase / decrease comparison method is calculated as 21%, the response delay correlation-based method after the management execution of the state analysis module (150) also reflects whether the rate of increase of state value within the pre-set observation period after the management execution is relaxed, so it is possible to have an example where the degree of reflection of state value relaxation after the management execution is calculated as 94% in the same simulation dataset.

[0595] The above figures are example values ​​intended to explain the branching criteria of the time-series hair loss progression risk interval calculation method, and are not limited to guaranteeing whether a future change in condition will occur or the effectiveness of management actions.

[0596] In one embodiment, the user can use the user terminal (10) to photograph at least one scalp area among the crown, frontal area, and temporal area over multiple time points. For example, the user can photograph the crown and frontal area in January, March, and May, respectively.

[0597] The user terminal (10) can generate information on the time of shooting, information on the shooting area, image resolution, brightness distribution, color distribution, and gyroscope sensor values ​​and accelerometer sensor values ​​before and after the time of shooting input, along with the captured scalp image. The user terminal (10) can transmit the scalp image and the shooting condition information to the hair loss management system (100).

[0598] The above hair loss management system (100) can sequentially operate an image acquisition unit (110), an image preprocessing module (120), an image analysis module (130), a biomarker generation module (140), a state analysis module (150), and a service provision module (160) to process multiple scalp images received from the user terminal (10).

[0599] In the above embodiment, the image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices according to process 1. The image analysis module (130) can calculate the stereoscopic exposure index according to process 2. The biomarker generation module (140) can generate a digital biomarker based on individual baseline deviation according to process 3. The condition analysis module (150) can calculate the risk of time-series hair loss progression according to process 4. The service provision module (160) can provide customized hair loss management data to the user terminal (10) according to the above calculation results.

[0600] In one embodiment, the user terminal (10) may display a crown of the head shooting reference line and a frontal head shooting guide line on the shooting screen. The user may shoot a crown of the head image according to the shooting reference line and then shoot a frontal head image.

[0601] The user terminal (10) can record gyroscope sensor values ​​and accelerometer sensor values ​​for 0.5 seconds before and after the time of shooting button input. The sensor values ​​can then be used as basic data for the image preprocessing module (120) to calculate the attitude shake index at the moment of shooting.

[0602] The image acquisition unit (110) can receive a crown image, a frontal image, shooting time information, shooting area information, image resolution, color channel information, and a sensor value at the moment of shooting from the user terminal (10). The image acquisition unit (110) can correspond a user account identification value, a shooting time, and a shooting area identification value to the received image.

[0603] For example, the image acquisition unit (110) may assign identification information such as “User A, 1st shooting session, crown” to the January crown image and “User A, 2nd shooting session, crown” to the March crown image. The identification information can subsequently be used by the image preprocessing module (120) to perform same scalp region registration and by the state analysis module (150) to arrange digital biomarkers from multiple time points in chronological order.

[0604] The image preprocessing module (120) can calculate the shooting reproducibility and image analysis reliability indices by comparing the current image received from the image acquisition unit (110) with the past reference image stored in the storage unit.

[0605] First, the image preprocessing module (120) can extract high-frequency components corresponding to pore contours and scalp surface textures from the reference image and the current image, respectively. The image preprocessing module (120) can calculate the degree to which high-frequency components of the current image are maintained relative to the reference image within the same scalp region and normalize this to a scalp fine texture preservation rate.

[0606] For example, if the scalp micro-texture preservation rate in the March crown image is calculated to be 0.80, the image preprocessing module (120) can set this as the first reliability reference value.

[0607] Next, the image preprocessing module (120) can classify pixel clusters in which the brightness value of the entire scalp area of ​​the current image is included in the upper reference range as supersaturated reflection patches. The image preprocessing module (120) can calculate the area ratio of the supersaturated reflection patches as the supersaturated reflection patch occupancy rate, and calculate the brightness change amount between adjacent pixels based on the boundary line of the reflection patch to calculate the reflection patch boundary steepness.

[0608] For example, if the supersaturated reflection patch occupancy rate is calculated to be 0.30 and the reflection patch boundary steepness is calculated to be 0.50 in the March crown image, the image preprocessing module (120) classifies that the two values ​​are included in a high range together and can calculate a first correction value of 0.25 according to the combined correction rule. The image preprocessing module (120) can calculate a second reliability value of 0.55 by subtracting the first correction value of 0.25 from the first reliability reference value of 0.80.

[0609] Subsequently, the image preprocessing module (120) can calculate the attitude shake index at the moment of shooting using the gyroscope sensor value and the accelerometer sensor value of the user terminal (10). In addition, the image preprocessing module (120) can perform feature point matching between the past crown image and the current crown image, and calculate the same scalp region matching residual by normalizing the average of the positional errors between the matched feature points. Furthermore, the image preprocessing module (120) can calculate the hair direction entropy by analyzing the hair direction vector histogram of the current image.

[0610] For example, if the pose shake index at the moment of shooting is calculated to be 0.40, the same scalp area matching residual is 0.20, and the hair direction entropy is 0.30, the image preprocessing module (120) can calculate a secondary correction value corresponding to the interval of each indicator as 0.30. The image preprocessing module (120) can calculate the final shooting reproducibility and image analysis reliability index as 0.25 by subtracting the secondary correction value of 0.30 from the secondary reliability value of 0.55.

[0611] If the final shooting reproducibility and image analysis reliability index of 0.25 is less than 0.6, which is the lower limit of the analysis allowable range, the image preprocessing module (120) may not transmit the corresponding March crown image as main data to the image analysis module (130). In this case, the image preprocessing module (120) may transmit the reason for the reshoot guidance to the service providing module (160), and the service providing module (160) may provide reshoot guidance data to the user terminal (10) such as “Avoid areas with strong lighting and reshoot the same area while keeping the user terminal fixed.”

[0612] On the other hand, in the case where the scalp micro-texture preservation rate in the May crown image is calculated as 0.90, the reflection characteristic subtraction value as 0.08, and the physical factor subtraction value as 0.10, and the final shooting reproducibility and image analysis reliability index is calculated as 0.72, the image preprocessing module (120) can transmit the May crown image as the main data of the image analysis module (130).

[0613] The image analysis module (130) can calculate a stereoscopic exposure index for the correction image transmitted as main data from the image preprocessing module (120).

[0614] The image analysis module (130) can first separate the hair region and the scalp region in the correction image. The image analysis module (130) can classify a cluster of continuous pixels in the scalp region where no hair pixels are detected as an exposure patch. The image analysis module (130) can calculate the exposure patch connectivity correction area by reflecting the size, shape, and proximity of the exposure patch.

[0615] For example, if the exposure patch connection correction area in the May crown image is calculated to be 0.45, the image analysis module (130) can set this as the default exposure value.

[0616] Subsequently, the image analysis module (130) can classify a band-shaped area that is continuous in the longitudinal direction among the exposed scalp areas as a parting candidate area. The image analysis module (130) can extract a center point in the width direction between the left hair boundary and the right hair boundary in the parting candidate area and create a parting center line by connecting the center points in the width direction.

[0617] The above image analysis module (130) can calculate the accumulated curvature value of the parting centerline by accumulating the degree of curvature of the parting centerline, and calculate the local variability of the parting width by measuring the parting width at multiple points along the parting centerline.

[0618] For example, if the cumulative value of the curvature of the centerline of the parting and the local variability of the width of the parting are calculated to be in the middle or higher range in the crown image of May, the image analysis module (130) can increase the basic exposure value of 0.45 to 0.55.

[0619] Next, the image analysis module (130) can analyze the color channels of the corrected image to extract a red area. At this time, the image analysis module (130) can calculate an erythema-sebum separation index using the occupancy or saturation of the remaining red area, excluding the area that overlaps with the supersaturated reflection patch classified by the image preprocessing module (120).

[0620] Additionally, the image analysis module (130) can detect hair candidate pixel clusters that are less than a preset thickness standard and less than a preset length standard by using at least one of thinning processing, linear structure detection processing, or hair width estimation processing. The image analysis module (130) can calculate the degree to which the hair candidate pixel clusters are observed clustered in a specific local area as a hair thinning pattern index.

[0621] For example, if the erythema-sebum separation index is calculated as 0.20 and the hair miniaturization pattern index is calculated as an intermediate range, the image analysis module (130) can further weight the first adjustment value of 0.55 to 0.65.

[0622] Next, the image analysis module (130) can calculate a brightness variation in the area around the hair root point where the hair pixel starts, and calculate the ratio of the shading around the hair root being continuously formed in the tangential direction of the hair growth direction or in an adjacent direction as the hair root tangential shadow consistency.

[0623] For example, if the hair root tangential shadow consistency is calculated as 0.60, the image analysis module (130) classifies that the three-dimensional arrangement state of the hair is maintained at a certain level and can apply a compensation rate corresponding to the hair root tangential shadow consistency range. Accordingly, the image analysis module (130) can calculate the final three-dimensional exposure index by lowering the adjustment value of 0.65 to 0.52.

[0624] The image analysis module (130) can transmit the final stereoscopic exposure index 0.52 to the biomarker generation module (140).

[0625] The biomarker generation module (140) can set the stereoscopic exposure index received from the image analysis module (130) as a baseline value and generate a digital biomarker based on individual baseline deviation by comparing the past shooting history and current shooting results of the same user.

[0626] For example, the biomarker generation module (140) can set a stereoscopic exposure index of 0.52 calculated from the May crown image as the base state value.

[0627] The above biomarker generation module (140) can retrieve the January crown image of the same user and the pore contour distribution of the previous shooting session from the storage unit. The above biomarker generation module (140) can calculate the pore contour deviation per user by comparing the pore surrounding contour, the brightness distribution around the pore opening, the high-frequency components around the pore, or the boundary clarity of the pore candidate region between the past image and the current image.

[0628] For example, if the pore contour deviation per user is calculated to be 0.10, the biomarker generation module (140) can classify it as being included in the range of low pore contour change.

[0629] Next, the biomarker generation module (140) can analyze the extent to which a hair thinning pattern is repeatedly observed in the same local area during the same user's recent multiple shooting sessions. For example, if short and thin hair candidate groups are detected in the same local area two or more times during the recent three shooting sessions, the biomarker generation module (140) can calculate a hair thinning persistence index of 0.30.

[0630] The above biomarker generation module (140) can increase the base state value of 0.52 to 0.60 by applying a weighting ratio corresponding to the range of the pore contour deviation and hair thinning persistence index for each user.

[0631] Subsequently, the biomarker generation module (140) can calculate the ratio of follicle clusters that maintain multiple hairs from the same user's past baseline at the present time as the follicle cluster preservation rate. For example, if 80% of the candidate follicle regions with multiple hairs in the past image are classified as maintaining a similar cluster state in the current image, the biomarker generation module (140) can calculate the follicle cluster preservation rate as 0.80.

[0632] The above biomarker generation module (140) can lower the upwardly adjusted state value of 0.60 to 0.50 when the above hair follicle cluster preservation rate is included in the range of high.

[0633] Next, the biomarker generation module (140) can check whether the imaging reproducibility and image analysis reliability indices fall within the analysis allowable range. For example, if the imaging reproducibility and image analysis reliability indices of the crown of the head in May are calculated as 0.72 and fall within the analysis allowable range, the biomarker generation module (140) can calculate the individual seasonality explainability.

[0634] The biomarker generation module (140) collects regional humidity, temperature, and seasonal information for the current shooting date and can compare the information with the range of digital biomarker changes under the same user's past same season or similar humidity conditions. For example, if the climate conditions at the time of current shooting partially correspond to the user's past same seasonal change range and the individual seasonality explainability is calculated as 0.60, the biomarker generation module (140) can apply a correction ratio corresponding to the seasonality explainability range to lower the state value to 0.45.

[0635] However, if the pore contour deviation or the hair miniaturization persistence index per user falls within the upper caution range, the biomarker generation module (140) may limit the downward correction ratio according to the seasonality explainability. In this embodiment, since the pore contour deviation per user is in the low range and the hair miniaturization persistence index is in the middle range, seasonality correction may be applied within a limited range.

[0636] The above biomarker generation module (140) can generate a digital biomarker including a final state value of 0.45. The digital biomarker can be stored in a data structure including a user identification value, shooting time information, shooting area information, stereoscopic exposure index, pore contour deviation by user, hair miniaturization persistence index, hair follicle cluster preservation rate, individual seasonality explainability, shooting reproducibility and image analysis reliability index, and a final state value.

[0637] The state analysis module (150) can arrange digital biomarkers at multiple points in time generated by the biomarker generation module (140) in chronological order and calculate the risk of time-series hair loss progression.

[0638] For example, the state analysis module (150) can query the crown digital biomarkers and frontal digital biomarkers generated in January, March, and May. The state analysis module (150) can set the period from January to March as the first time period and the period from March to May as the second time period.

[0639] The state analysis module (150) can calculate the rate of change of the crown digital biomarker in the first time interval and the rate of change of the crown digital biomarker in the second time interval. For example, if the rate of change in the first time interval is calculated to be 0.15 and the rate of change in the second time interval is calculated to be 0.20, the state analysis module (150) can classify that the rate of increase of the state value in the second time interval has increased.

[0640] The above state analysis module (150) can calculate a hair loss progression acceleration index by normalizing the difference between the rate of change of the first time interval and the rate of change of the second time interval. For example, the hair loss progression acceleration index can be calculated as 0.30. In addition, the above state analysis module (150) can calculate a progression inflection index as 0.25 by using the degree to which the hair loss progression acceleration index increases compared to the previous comparison interval.

[0641] The above state analysis module (150) can set the reference risk value to 0.65 when the rate of change, the hair loss progression acceleration index, and the progression inflection index are calculated in the direction of increasing risk.

[0642] Next, the state analysis module (150) can determine whether the direction of change in digital biomarkers appears in an upward direction of risk not only in the crown but also in the frontal area. For example, if the digital biomarkers in the crown and frontal areas are calculated to increase in the same period, the state analysis module (150) can calculate the diffusion consistency between areas as 0.80. The state analysis module (150) can adjust the reference risk value of 0.65 upward to 0.75 by applying a weighting ratio corresponding to the diffusion consistency interval between areas.

[0643] Next, the state analysis module (150) can calculate the time difference between the recommended shooting cycle and the actual shooting cycle. For example, if the recommended shooting cycle is 60 days and the actual shooting interval is 63 days, the state analysis module (150) can classify the shooting cycle deviation correction value as an acceptable range. In this case, the state analysis module (150) can use the change rate and the hair loss progression acceleration index as normal calculation results.

[0644] Next, the state analysis module (150) can calculate cumulative shooting reproducibility using the shooting reproducibility and image analysis reliability indices of multiple viewpoints and the same scalp area matching success rate. For example, if the average range of the shooting reproducibility, image analysis reliability indices, and matching success rates for January, March, and May is calculated to be 0.75, the state analysis module (150) can classify the cumulative shooting reproducibility as being included in the analysis allowable range.

[0645] If the above cumulative shooting reproducibility is included in the analysis allowable range, the state analysis module (150) can calculate the response delay correlation after management execution. For example, if the service provision module (160) provides the user with guidance on adjusting the shooting cycle and a scalp care routine in early April, and the user inputs a management execution record through the user terminal (10), the state analysis module (150) can determine whether the rate of increase of digital biomarkers is mitigated compared to the previous range within a preset observation period after the management execution time.

[0646] For example, if the rate of increase in the status value is calculated to be lower than the previous range in additional shooting in the latter half of May or in subsequent analysis of the May shooting data, the status analysis module (150) can calculate the response delay correlation after management execution as 0.60. The status analysis module (150) can lower the weighted reference risk value of 0.75 to 0.55 by applying a downward adjustment ratio corresponding to the response delay correlation range after management execution.

[0647] The above state analysis module (150) can determine the 0.55 as the final time series hair loss progression risk and transmit the final time series hair loss progression risk to the service provision module (160).

[0648] The service provision module (160) can provide customized hair loss management data to the user terminal (10) by combining data generated from the image preprocessing module (120), image analysis module (130), biomarker generation module (140), and condition analysis module (150).

[0649] For example, the service providing module (160) may first provide reshoot guidance data for shooting sessions in which the shooting reproducibility and image analysis reliability indices are below the analysis allowable range. The reshoot guidance data may include at least one of lighting reduction guidance, user terminal fixation guidance, reshoot guidance for the same area, and hair styling guidance.

[0650] The above service providing module (160) can provide area-specific management reference information, such as “It has been classified that the exposure patch connectivity and local variability of the parting width have increased in the crown area,” when the stereoscopic exposure index calculated by the image analysis module (130) falls within the management caution section.

[0651] The above service provision module (160) can provide personal baseline-based explanatory information, such as “the current state value has increased compared to the past baseline, but the follicle cluster preservation rate is maintained and the seasonality explanatory capability is partially reflected,” by using the personal baseline deviation-based digital biomarker calculated by the biomarker generation module (140).

[0652] Additionally, the service provision module (160) may provide guidance on adjusting the shooting cycle, guidance on lifestyle management, or guidance on tracking the same area when the time-series hair loss progression risk calculated by the state analysis module (150) corresponds to an intermediate management caution period. When the time-series hair loss progression risk falls within a high management caution period and there is high consistency in diffusion between areas, guidance on reviewing consultation with a specialized institution or guidance on additional shooting may be provided.

[0653] When the cumulative shooting reproducibility is below the analysis allowable range, the above service providing module (160) does not definitively display the time-series hair loss progression risk result, but can provide guidance data such as, “Since the difference in shooting conditions is large, the time-series comparison result is displayed as auxiliary information. Please shoot the same area again under the same lighting conditions.”

[0654] In one embodiment, data flow between components can be performed as follows.

[0655] The user terminal (10) generates a scalp image and shooting condition information. The image acquisition unit (110) receives the scalp image and the shooting condition information and assigns a shooting number and a shooting area identification value.

[0656] The image preprocessing module (120) calculates the shooting reproducibility and image analysis reliability indices by comparing the scalp image with a past reference image. If the index falls within the analysis allowable range, the image preprocessing module (120) transmits the same scalp region coordinates as the corrected image to the image analysis module (130).

[0657] The image analysis module (130) calculates a stereoscopic exposure index using the corrected image and the same scalp area coordinates. The stereoscopic exposure index is transmitted to the biomarker generation module (140).

[0658] The biomarker generation module (140) generates a digital biomarker based on individual baseline deviation by comparing the stereoscopic exposure index with the same user's past shooting history. The digital biomarker is transmitted to the state analysis module (150) and can simultaneously be stored in the storage unit as user-specific shooting history data.

[0659] The state analysis module (150) calculates the risk of time-series hair loss progression using digital biomarkers at multiple points in time. The time-series hair loss progression risk is transmitted to the service provision module (160).

[0660] The service provision module (160) converts the shooting reproducibility and image analysis reliability index, the stereoscopic exposure index, the digital biomarker, and the time-series hair loss progression risk into respective segment information and generates customized hair loss management data to be provided to the user terminal (10).

[0661] In one embodiment, if the image preprocessing module (120) calculates the shooting reproducibility and image analysis reliability index of a specific shooting session as 0.35, the image of the shooting session may not be transmitted as main data to the image analysis module (130). In this case, the biomarker generation module (140) may not generate a digital biomarker for the shooting session, or may generate a digital biomarker indicated as an auxiliary status value.

[0662] In another embodiment, if the image analysis module (130) fails to reliably extract the centerline of the parting, the image analysis module (130) may classify the stereoscopic exposure index as an auxiliary analysis value. The auxiliary analysis value may be transmitted to the biomarker generation module (140), but the biomarker generation module (140) may review the value together with the imaging reproducibility and image analysis reliability indices without reflecting the value directly in the final digital biomarker.

[0663] In another embodiment, in cases where there is insufficient cumulative shooting history, such as for a new user, the biomarker generation module (140) may apply a temporary baseline without immediately setting a personal baseline. The temporary baseline may be set using at least one of the shooting area, user group information, image resolution, and initial shooting results. Subsequently, when the cumulative number of shots exceeds a preset number, the biomarker generation module (140) may update the temporary baseline to a user-specific baseline.

[0664] In another embodiment, when the state analysis module (150) classifies the difference between the recommended shooting cycle and the actual shooting cycle as an excess interval, the state analysis module (150) may limit the calculation of the hair loss progression acceleration index and the progression inflection index and calculate an auxiliary risk value using the rate of change at the time of the last two shootings. In this case, the service provision module (160) may provide guidance on compliance with the regular shooting cycle and guidance on additional shooting to the user terminal (10).

[0665] According to the above embodiment, a scalp image acquired from a user terminal (10) is input to a hair loss management system (100) through an image acquisition unit (110), and an image preprocessing module (120) evaluates whether the scalp image has capture reproducibility that can be used for subsequent analysis.

[0666] If the above shooting reproducibility and image analysis reliability indices fall within the analysis allowable range, the image analysis module (130) calculates a stereoscopic exposure index from the scalp image. The biomarker generation module (140) generates a digital biomarker based on individual baseline deviation using the stereoscopic exposure index and the same user's past shooting history.

[0667] The state analysis module (150) calculates the time-series hair loss progression risk using the digital biomarkers at multiple points in time, and the service provision module (160) provides customized hair loss management data corresponding to the time-series hair loss progression risk and imaging reliability to the user terminal (10).

[0668] As described above, in this embodiment, rather than providing management data based solely on hair density or scalp exposure area at a single point in time, management reference information considering user-specific shooting conditions and cumulative change history can be provided by sequentially reflecting shooting reproducibility, stereoscopic exposure index, deviation from individual baseline, and time-series change patterns.

[0669] Although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Various modifications are possible by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0670] User terminal (10) Hair loss management system (100) Video acquisition unit (110) Image preprocessing module (120) Video analysis module (130) Biomarker generation module (140) State analysis module (150) Service provision module (160)

Claims

Claim 1 A digital biomarker-based hair loss management system comprises: an image acquisition unit that acquires a user’s scalp image through a camera of a user terminal and collects shooting time point information and shooting area information corresponding to the scalp image; an image preprocessing module that performs shooting condition correction and alignment of the same scalp area between multiple time point images on the scalp image received from the image acquisition unit; an image analysis module that calculates multiple image feature values ​​related to the hair loss state from the corrected image processed by the image preprocessing module; a biomarker generation module that generates digital biomarkers related to the hair loss state for each user using the multiple image feature values; and a state analysis module that calculates a state value related to the progression of hair loss by comparing the digital biomarkers generated at multiple time points. The digital biomarker-based hair loss method comprises: a service providing module that provides hair loss management data corresponding to the output result of the state analysis module to the user terminal; wherein the image preprocessing module calculates shooting reproducibility and image analysis reliability indices using at least two of the scalp micro-texture preservation rate, supersaturated reflection patch occupancy rate, reflection patch boundary steepness, pose shake index at the moment of shooting, same scalp area registration residual, and hair direction entropy of the scalp image; the image analysis module calculates a stereoscopic exposure index using at least two of the exposure patch connectivity correction area, part centerline curvature cumulative value, part width local variability, erythema-sebum separation index, hair miniaturization pattern index, and hair root tangential shadow consistency in the corrected image; the biomarker generation module generates the digital biomarker including the stereoscopic exposure index; and the service providing module provides at least one of a re-shooting guide, area-specific management reference information, or a shooting cycle guide to the user terminal according to the range of the shooting reproducibility and image analysis reliability index or the stereoscopic exposure index. Management system. Claim 2 A digital biomarker-based hair loss management system according to claim 1, wherein the image acquisition unit receives at least one of shooting time information, shooting area information, image resolution, file format, color channel information, and user account identification value together with the scalp image from the user terminal, and the shooting area information includes at least one of the crown, frontal area, temporal area, and occipital area, and the image acquisition unit stores the shooting number and shooting area identification value corresponding to the received scalp image, and the image acquisition unit is configured to transmit re-shooting request information to the user terminal without transmitting the scalp image as main data for subsequent analysis if the resolution of the scalp image is less than a preset allowable range, or if the scalp area within the scalp image is included at a ratio less than a preset area ratio, or if the shooting area information is missing, and the re-shooting request information includes at least one of a shooting area redesignation guide, a shooting distance adjustment guide, a scalp area inclusion guide, and an image resolution verification guide. Claim 3 In claim 1, the image preprocessing module extracts high-frequency components corresponding to pore contours and scalp surface textures from a reference image and a current image, respectively; calculates a scalp fine texture preservation rate by normalizing the degree to which the high-frequency components of the current image are maintained relative to the high-frequency components of the reference image within the same scalp area to a range of 0 to 1; sets the scalp fine texture preservation rate as a first reliability reference value; classifies pixel clusters within the entire scalp area of ​​the current image whose brightness values ​​fall within an upper reference range as supersaturated reflection patches; calculates the area ratio occupied by the supersaturated reflection patches within the entire scalp area as the supersaturated reflection patch occupancy rate; calculates the reflection patch boundary steepness by calculating the amount of brightness change between adjacent pixels based on the boundary line of the supersaturated reflection patch; and increases the subtraction strength of the first correction value when the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness are each greater than or equal to a preset reference range and both the supersaturated reflection patch occupancy rate and the reflection patch boundary steepness are included in a high range. A digital biomarker-based hair loss management system characterized by applying a combination correction rule and calculating a second reliability value by subtracting the first correction value from the first reliability reference value. Claim 4 In claim 3, the image preprocessing module obtains an angular velocity variance or an acceleration variance during a preset time interval before and after the shooting input time from at least one of a gyroscope sensor and an accelerometer sensor included in the user terminal, calculates a posture shake index at the moment of shooting by normalizing the angular velocity variance or the acceleration variance, performs feature point matching between the reference image and the current image, calculates a same scalp region matching residual by normalizing the average position error between the matched feature points, classifies a hair direction vector detected in the current image into a plurality of direction intervals, calculates a hair direction entropy by normalizing the distribution non-uniformity or dispersion of the hair direction vector, calculates a secondary correction value corresponding to the interval to which the posture shake index at the moment of shooting, the same scalp region matching residual, and the hair direction entropy belong, calculates a final shooting reproducibility and image analysis reliability index by subtracting the secondary correction value from the secondary reliability value, and the final shooting reproducibility and image analysis reliability index is greater than or equal to a preset analysis allowable interval. A digital biomarker-based hair loss management system characterized by: transmitting the current image as the main data of the image analysis module in the case where the final shooting reproducibility and image analysis reliability index is below the analysis allowable range, classifying the current image as auxiliary analysis data or providing reshoot guidance data to the user terminal through the service provision module, setting the lower limit value as the final value when the final shooting reproducibility and image analysis reliability index is calculated to be below a preset lower limit value, and when the posture shake index at the moment of shooting cannot be obtained due to permission restrictions, sensor deactivation, or sensor errors of the user terminal, adjusting the weight corresponding to the same scalp area matching residual upward by a preset correction ratio. Claim 5 In claim 1, the image analysis module receives the corrected image, scalp region mask, identical scalp region coordinates, and supersaturated reflection patch information from the image preprocessing module; separates the hair region and the scalp region in the corrected image; classifies a continuous pixel cluster in the scalp region where no hair pixels are detected as an exposure patch; calculates the size, shape, and adjacency of the exposure patch using at least one of connection component labeling, region segmentation, or pixel cluster analysis; calculates an exposure patch connectivity correction area by reflecting the size, shape, and adjacency of the exposure patch; sets the exposure patch connectivity correction area as a basic exposure value for calculating a stereoscopic exposure index; classifies a band-shaped region in the longitudinal direction within the scalp region as a parting candidate region; extracts a center point in the width direction between the left hair boundary and the right hair boundary in the parting candidate region; generates a parting center line by connecting the center points in the width direction; calculates an accumulated parting center line curvature value by accumulating the degree of curvature of the parting center line compared to a reference straight line or a parting center line at a previous point in time; and the parting A digital biomarker-based hair loss management system characterized by measuring the distance between the left hair boundary and the right hair boundary at predetermined intervals along a centerline, calculating the local variability of the part width using the variance value or the difference value per interval of the part width measured at multiple points, and increasing the degree of upward adjustment of the basic exposure value when the cumulative value of the part centerline curvature and the local variability of the part width are both included in a high interval. Claim 6 In claim 5, the image analysis module analyzes the color channels of the corrected image to extract a red region, excludes the region overlapping with the supersaturated reflection patch classified by the image preprocessing module from the red region, calculates an erythema-sebum separation index using the occupancy, saturation, or color distribution value of the red region from which the region overlapping with the supersaturated reflection patch has been excluded, detects a cluster of hair candidate pixels that is less than a preset thickness criterion and less than a preset length criterion by using at least one of thinning processing, linear structure detection processing, or hair width estimation processing, calculates the degree to which the hair candidate pixel cluster is observed clustered in a specific region as a hair miniaturization pattern index, additionally weights the upwardly adjusted base exposure value in the section where the erythema-sebum separation index and the hair miniaturization pattern index increase, calculates a brightness variation in the region surrounding the hair root point where the hair pixels start, calculates the ratio in which the shading in the region surrounding the hair root point is continuously formed in the tangential direction of the hair progression direction or in an adjacent direction as a hair root tangential shadow consistency, and the hair root tangential shadow A digital biomarker-based hair loss management system characterized by: when consistency is above a preset standard, classifying it into a section where the three-dimensional arrangement state of the hair is maintained, and calculating a final three-dimensional exposure index by lowering the additionally weighted value by an offset rate corresponding to the hair root tangential shadow consistency section; when the brightness contrast value of the entire image is below a preset standard, omitting the downward adjustment step based on the hair root tangential shadow consistency or assigning a basic offset value to the hair root tangential shadow consistency; when the final three-dimensional exposure index exceeds a preset upper limit, setting the upper limit as the final value; and when the final three-dimensional exposure index is below a preset lower limit, setting the lower limit as the final value.

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