Method for predicting growth on basis of growth age and providing solution by using artificial intelligence model

Abstract

A method for predicting growth on the basis of growth age and providing a solution by using an artificial intelligence model according to exemplary embodiments of the present invention may comprise the steps of: receiving biometric data of a measurement target; extracting data regarding the predicted age of peak height velocity (APHV), at which the growth velocity is expected to reach the maximum value, by using the biometric data of the measurement target; classifying the growth step of the measurement target into one of multiple growth steps on the basis of the extracted data regarding the predicted APHV; predicting the final height by inputting the extracted data regarding the predicted APHV into a trained neural network; and providing a growth management solution on the basis of the classified growth step and the predicted final height.

Description

Method for providing growth prediction and solutions based on growth age using an artificial intelligence model

[0001] The present invention relates to a method for classifying growth stages and predicting final height based on APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches its maximum value, extracted from the biological data of children and adolescents in their growth period, and then providing a growth management solution based on the classified growth stages and the predicted final height.

[0002] With the recent development of artificial intelligence technology, it is being applied to various fields, and methods for extracting features inherent in data through neural network models and generating additional information are being developed and used as replacements for existing data processing methods.

[0003] Neural network models used for artificial intelligence can detect and recognize features within input data more quickly and accurately through learning than conventional data processing. Recently, AI technology has been applied beyond simple object tracking and detection to learning past history, predicting the future, and deriving current features that reflect time-series changes.

[0004] Among these, predictive analytics is a statistical and data mining technique that extracts information from data and uses it to predict trends and behavioral patterns. This predictive analytics can be applied to any area requiring decision-making based on information gleaned from data. The core of predictive analytics is understanding the relationships between variables and then predicting unknown variables.

[0005] For this purpose, various approaches are being used depending on the data characteristics and prediction target.

[0006] Among the various fields requiring predictive analysis, the area of ​​physical growth in adolescents is particularly noteworthy. Parents and adolescents alike are deeply interested in the timing and extent of height growth.

[0007] Conventional predictions of height growth suggest methods for predicting growth plates by X-ray or analyzing the relationship with genetic / environmental factors (Patent Publication No. 10-2075743, Patent Publication No. 10-1866208), or for converting the body data of sample subjects with different measurement periods or measurement counts into a form suitable for learning a growth prediction model (Patent Publication No. 10-2198302).

[0008] Although methods for predicting the physical growth of children and adolescents have been suggested, as mentioned above, research on methods for predicting and providing solutions for the risk of developing precocious puberty and obesity that may accompany the growth of children and adolescents is insufficient.

[0009] Additionally, since children and adolescents have different growth stages with different characteristics, taking this into account can increase the reliability of solutions provided through predicted data and analysis.

[0010] Meanwhile, in the case of conventional technologies disclosed to date, various methods for predicting growth by directly inputting physical or biometric data of children and adolescents into a predetermined growth prediction tool have been disclosed. However, according to these conventional technologies, growth is predicted only by general indicators such as past and present height, weight, and BMI without considering individual growth characteristics such as the growth rate of each child and adolescent or whether the growth stage was reached quickly compared to other sample subjects, so there is a disadvantage in that it is difficult to provide a personalized solution.

[0011] In addition, the SITAR (SuperImposition by Translation and Rotation) methodology has emerged in relation to height growth rate, but there is a problem in that only a posteriori estimation is possible because measurements from the entire growth period are required to estimate the growth rate curve using the SITAR methodology.

[0012] Meanwhile, the existing height growth prediction model is a model that predicts the height growth curve using the LGBM model as input variables such as height, weight, body composition, their quantiles, and the current quantile-based benchmark growth rate. However, there is a possibility that the estimation model may be upwardly biased in certain data sets with a high proportion of observations due to high height quantiles at later ages. In addition, the existing height growth prediction model has the disadvantage of low prediction accuracy because it utilizes only 8 variables centered on height, weight, and body composition mass despite having 52 body composition-related variables.

[0013] Consequently, in the existing height growth prediction model, although the growth rate tendency of the sample subject is important for height prediction, information on past measurements is not reflected in the estimation model, so it is necessary to measure the follow-up growth rate that reflects past information.

[0014] In addition, the importance of a method for estimating biological growth age, that is, age that indicates how old the body is biologically based on growth status, rather than chronological age, that is, actual age calculated uniformly from the time of birth, by using this tracking growth rate is emerging.

[0015] One of the various tasks of the present invention is to provide a method for classifying growth stages and predicting final height based on APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity of children and adolescents reaches its maximum, and then providing a growth management solution based on the classified growth stages and the predicted final height.

[0016] One of the various tasks of the present invention is to provide a method, device and computer program for predicting growth, including height, obesity and disease, based on the growth stage of children and adolescents using a growth management model based on growth age generated through artificial intelligence learning and providing a customized solution for each growth stage.

[0017] A method for predicting growth based on growth age and providing a solution using an artificial intelligence model according to exemplary embodiments of the present invention may include a step of receiving biometric data of a subject of measurement, a step of extracting predicted APHV (Age of Peak Height Velocity) data, which is an age at which growth velocity is expected to reach a maximum value, using the biometric data of the subject of measurement, a step of classifying the growth stage of the subject of measurement into one of a plurality of growth stages based on the extracted predicted APHV data, a step of inputting the extracted predicted APHV data into a trained neural network to predict final height, and a step of providing a growth management solution based on the classified growth stage and the predicted final height.

[0018] The above multiple growth stages may include a normal growth phase, a rapid growth phase, a decelerated growth phase, a slow growth phase, and a no-growth phase.

[0019] If the subject of the measurement is in the general growth period, a solution for increasing the growth prediction value of the subject of the measurement can be provided.

[0020] If the subject of the measurement is in a rapid growth period, a solution for increasing the period of the rapid growth period can be provided.

[0021] If the subject of the measurement is in a decelerating growth period, a solution for controlling the period of the decelerating growth period can be provided.

[0022] If the subject of the measurement is in a slow growth period, a solution can be provided to slow down the rate at which the subject's growth plate closes.

[0023] A device for predicting growth based on growth age and providing a solution using an artificial intelligence model according to exemplary embodiments of the present invention may include an input unit for receiving biometric data of a subject to be measured, a predicted APHV (Age of Peak Height Velocity) extraction unit for extracting predicted APHV data, which is an age at which a growth rate is expected to reach a maximum value, based on the inputted biometric data of the subject to be measured, a growth stage determination unit for classifying the growth stage of the subject to be measured into any one of a plurality of growth stages based on the extracted predicted APHV data, a growth prediction unit for inputting the extracted predicted APHV data into a trained neural network to predict growth, a solution generation unit for generating a growth management solution based on the classified growth stage and the predicted final height, and a display unit for displaying the generated growth management solution.

[0024] The above growth prediction unit may include first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity extracted based on time-series biometric data of a plurality of sample subjects reaches its maximum value, and an artificial intelligence model learned using the final height of the sample subjects as target data.

[0025] The method for providing a growth prediction and solution based on biometric data using artificial intelligence according to exemplary embodiments of the present invention estimates a growth velocity curve by applying a statistical methodology to a traced growth velocity extracted from past information, and extracts APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches the maximum value, for the growth velocity curve, and then utilizes the APHV data for growth stage classification, so that the reliability of various prediction models, such as an integrated height growth prediction model, a precocious puberty prediction model, and an obesity prediction model, constructed using the classified growth stages can be maximized.

[0026] In addition, according to various embodiments of the present invention, it is possible to accurately predict height by considering the growth stage of children and adolescents through a growth stage classification method constructed through artificial intelligence learning, and to provide a solution necessary for height growth by considering each growth stage.

[0027] FIG. 1 is a schematic diagram illustrating the configuration of a device for providing growth prediction and solutions based on growth age using an artificial intelligence model according to exemplary embodiments of the present invention.

[0028] Figure 2 is a diagram showing predicted height and target height for each growth stage according to exemplary embodiments of the present invention.

[0029] FIG. 3 is a flowchart illustrating a method for providing growth prediction and solutions based on growth age using an artificial intelligence model according to exemplary embodiments of the present invention.

[0030] Figure 4 is a diagram showing multiple first growth rate curves that represent the growth rate of height compared to actual age using time-series biometric data of sample subjects.

[0031] Figures 5 and 6 are diagrams showing the APHV estimation error according to the age of the girl and the boy, respectively.

[0032] Figures 7 and 8 are diagrams showing the importance of predictive variables for APHV in women and men, respectively.

[0033] Figure 9 is a diagram showing variable importance in a conventional growth prediction model for women and men, and Figure 10 is a diagram showing variable importance in a growth prediction model of the present invention.

[0034] Hereinafter, specific embodiments of the present invention will be described. The following detailed description is provided to facilitate a comprehensive understanding of the methods, devices, and / or systems described herein. However, these are merely examples and the present invention is not limited thereto.

[0035] In describing embodiments of the present invention, if a detailed description of a known technology related to the present invention is judged to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of their functions in the present invention, and this may vary depending on the intention or custom of the user or operator. Therefore, the definitions should be made based on the contents throughout this specification. The terminology used in the detailed description is only for the purpose of describing embodiments of the present invention and should not be limited in any way. Unless clearly used otherwise, the singular form includes the plural form. In this description, expressions such as "comprises" or "having" are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, and should not be construed to exclude the presence or possibility of one or more other features, numbers, steps, operations, elements, parts or combinations thereof other than those described.

[0036] Additionally, in describing components of embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms.

[0037] In exemplary embodiments of the present invention, "children and adolescents" can be understood as a concept encompassing the growth period of the human body. More specifically, "children and adolescents," referring to the sample subjects in exemplary embodiments of the present invention, are defined below through the meanings of infants, children, adolescents, toddlers, and children.

[0038] Infants are a continuation of the neonatal period, growing up biting their mother's nipple for up to two years after birth. The experiences of nutrition, caressing, and excretion during this period influence general tendencies thereafter. Children are generally people between the ages of 6 and 13, and in a broader sense, they can include infants (ages 1 to 5).

[0039] Adolescents are the transitional stage between childhood and adolescence, generally referring to individuals aged 13 to 19. Infants can refer to individuals from birth to 5 years of age. Children generally refer to children up to 15 years of age.

[0040] Therefore, the term “children and adolescents” referring to the above sample subjects may mean, in a narrow sense, the period from 5 years of age to 19 years of age, including children and adolescents, and in a broad sense, it may mean the period including all general periods of physical growth, from infancy to adolescence.

[0041] FIG. 1 is a schematic diagram illustrating the configuration of a device for providing growth prediction and solutions based on growth age using an artificial intelligence model according to exemplary embodiments of the present invention.

[0042] Referring to FIG. 1, a device for providing growth prediction and solutions based on growth age using artificial intelligence according to exemplary embodiments of the present invention may include a biometric data input unit (10), a preprocessing unit (20), a growth velocity curve estimation unit (30), a first APHV data extraction unit (40), a first APHV data learning unit (50), a predicted APHV data extraction unit (60), a second APHV data extraction unit (70), a growth age calculation unit (80), a growth stage classification unit (90), a growth prediction unit (100), a solution generation unit (110), and a display unit (120).

[0043] The growth stage classification model based on artificial intelligence using biometric data according to exemplary embodiments of the present invention can receive time-series physical information of a sample subject through a biometric data input unit (10). The physical information of the sample subject may include not only basic information such as grade (or age), gender, and height, but also additional information such as weight, protein, mineral content, body fat, body water, soft lean mass, fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference. Such physical information is merely an example to help understanding of the present invention, and the present embodiment is not limited thereto, and the types of information constituting the physical information may be varied depending on the embodiment.

[0044] The time-series physical information of the above sample subject may be continuous information or discontinuous information, but may be information included in at least one period corresponding to the growth stage of FIG. 2.

[0045] More specifically, the time-series physical information of the sample subjects may be collected at various times and at various collection frequencies. For example, the first sample subject may have physical information measured from the age of 8 to 12, which is part of the childhood and adolescence period, while the second sample subject may have physical information measured irregularly, such as at the ages of 8, 10 to 12, and 15. In addition, the third sample subject may have physical information measured multiple times within a certain period (within the period of one of the multiple growth stages in Figure 2), whereas the fourth sample subject may have physical information measured only once within a certain period (within the period of one of the multiple growth stages in Figure 2).

[0046] As described above, depending on the collection period and number of collections, the physical information of the sample subject may be included in two or more of the growth stages of Fig. 2 (the first sample subject, the second sample subject), but there may also be cases where this is not the case (the third sample subject, the fourth sample subject).

[0047] The preprocessing unit (20) can be controlled to perform preprocessing on the time-series biodata of the sample subjects.

[0048] In exemplary embodiments, the preprocessing unit (20) may be performed by determining all biometric data of a sample subject for which there is no biometric data collected during a specific growth period as noise and removing the same. This is to improve the prediction accuracy of the biometric data-based APHV estimation model according to the present invention, and may not be applied when data is sufficiently accumulated. In this case, preprocessing may be performed by supplementing or additionally generating some biometric data through various methods such as an artificial intelligence model and big data analysis, without determining all biometric data of a sample subject for which there is no biometric data collected during a specific growth period as noise and removing them.

[0049] Meanwhile, the biometric data input unit (10) can be controlled to further input the physical information of the subject for whom the APHV is to be estimated, in addition to receiving the time-series physical information of the sample subject. At this time, the physical information of the subject for measurement may include not only basic information such as grade (or age), gender, and height, but also additional information such as weight, protein, mineral content, body fat, body water, soft lean mass, fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference.

[0050] The growth rate curve estimation unit (30) can be controlled to estimate a plurality of first growth rate curves representing the growth rate of height compared to actual age of each of the plurality of sample subjects using the time series biometric data of the sample subjects.

[0051] In exemplary embodiments, the estimation of the first growth rate curve can be performed by extracting age data and height data from the time-series biometric data of the sample subjects and then applying the extracted age data and height data to a curve transformation model that uses the age data and height data as input variables.

[0052] At this time, the type of the curve transformation model is not particularly limited. That is, the curve transformation model may be a model constructed using a polynomial function, and for example, the curve transformation model may be a model constructed using mathematical expression 1.

[0053] [Mathematical Formula 1]

[0054]

[0055] Here, y it is α i , β i and γ i are random correction values ​​for adjusting the vertical shift, horizontal shift and slope of the curve, respectively, h is a specific function related to the height data, and t may be age data.

[0056] However, the concept of the present invention is not necessarily limited to this, and may be a module constructed using a polynomial function other than the above mathematical expression 1.

[0057] The first APHV data extraction unit (40) can be controlled to extract first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches the maximum value, for each of the plurality of first growth velocity curves.

[0058] The above first APHV data can be used to build a growth stage diagnosis and estimation model and to derive growth age, and the above growth age and follow-up growth velocity can ultimately be used to build various prediction models such as an integrated height growth prediction model, a precocious puberty prediction model, and an obesity prediction model.

[0059] Meanwhile, the existing height growth prediction model is a model that predicts the height growth curve using the LGBM model as input variables such as height, weight, body composition, their quantiles, and the current quantile-based benchmark growth rate. However, there is a possibility that the estimation model may be upwardly biased in certain data sets with a high proportion of observations due to high height quantiles at later ages. In addition, the existing height growth prediction model has the disadvantage of low prediction accuracy because it utilizes only 8 variables centered on height, weight, and body composition mass despite having 52 body composition-related variables.

[0060] That is, in the conventional height growth prediction model, although the growth velocity tendency of the sample subject is important for height prediction, information on past measurements is not reflected in the estimation model, so it is necessary to measure the trace growth velocity reflecting the past information. Therefore, the APHV estimation model based on artificial intelligence according to exemplary embodiments of the present invention estimates a growth velocity curve by applying a statistical methodology to the trace growth velocity extracted from past information, and extracts APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity is the maximum, for the growth velocity curve, and then utilizes the APHV data to build various prediction models such as an integrated height growth prediction model, a precocious puberty prediction model, and an obesity prediction model.

[0061] The first APHV data learning unit (50) can be controlled to input the first APHV data as target data into an artificial intelligence model and perform learning.

[0062] The predicted APHV data extraction unit (60) can be controlled to extract predicted APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity of the plurality of sample subjects reaches its maximum, by applying the biological data of the subject to an artificial intelligence model learned using the first APHV (Age of Peak Height Velocity) data as target data.

[0063] The second APHV data extraction unit (70) may be controlled to extract the second APHV data, which is an average value of the first APHV data. In exemplary embodiments, the second APHV data extraction unit (70) may be controlled to extract the second APHV data through an average value calculation algorithm that adds all observation values ​​and divides the sum by the number of observation values.

[0064] The growth age calculation unit (80) can be controlled to calculate the growth age of the subject of measurement based on the second APHV data, which is the average value of the first APHV data, and the predicted APHV.

[0065] At this time, the growth age calculation unit (80) can be controlled to calculate the growth age of the subject of measurement using the mathematical formula 2 below.

[0066] [Equation 2]

[0067] GA i '= (CA i - APHV i ') + APHV G

[0068] Here, GA i ' is the estimated growth age of the subject, and CA i is the chronological age / calendar age of the subject, and APHV i ' is the predicted APHV data including the estimated and predicted APHV values ​​of the subject of the measurement, and APHVG The second APHV data may be a representative value or average value of the first APHV data derived from the learning data group.

[0069] That is, in the case of conventional growth age estimation models, growth age is generally calculated using bone age, the degree of opening / closing of the growth plate, etc., but this requires X-ray measurement of the bone or growth plate, and growth age can be measured differently depending on the subject analyzing the X-ray image of the bone or growth plate, so objectivity is not guaranteed and accuracy is low.

[0070] In contrast, the growth age estimation model based on biometric data using artificial intelligence according to exemplary embodiments of the present invention estimates a growth velocity curve by applying a statistical methodology to a traced growth velocity extracted from past information, and extracts APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches the maximum value, for the growth velocity curve, and then utilizes the APHV data to build a growth age estimation model, so there is no need for additional processes such as X-ray measurement, and since the growth age is determined by applying an accurate numerical value to a preset mathematical formula, there is no possibility that the growth age will be measured differently depending on the subject, and accordingly, the objectivity of growth age estimation can be guaranteed and the accuracy is high.

[0071] The growth stage classification unit (90) can be controlled to classify the growth stage of the subject of measurement into one of a plurality of growth stages based on the extracted predicted APHV (Age of Peak Height Velocity) data.

[0072] The growth stages classified by the growth stage classification unit (90) will be described in detail later with reference to FIGS. 2 and 3.

[0073] The growth prediction unit (100) is a type of prediction model and can be implemented using artificial intelligence with a recursive neural network (RNN) structure to utilize not only current values ​​but also time-series values. For example, the prediction model can be implemented using architectures such as LSTM (Long Short Term Memory) or GRU (Gated Recurrent Units), which are recurrent neural networks. Of course, various conventional artificial intelligence architectures can also be applied to the prediction model of the present embodiment.

[0074] The solution generation unit (110) can generate a growth management solution based on the physical information of the subject corresponding to the classified growth stage, and the growth management solution will be described with reference to FIG. 2.

[0075] More specifically, when the subject of measurement is in the general growth stage (301), a solution for increasing the growth prediction value of the subject of measurement can be provided. The growth prediction value is a value corresponding to the y-axis in FIG. 2, and the solution for increasing the growth prediction value can be provided to the subject of measurement through various display units (120) for increasing the target value of the predicted y-axis.

[0076] Examples of solutions provided through the above display unit (120) may include current height, predicted height, obesity level, body fat mass, skeletal muscle mass, protein mass, mineral mass, sleep amount, exercise amount, nutritional information, lifestyle habits, posture, etc. Each indicator may be expressed as caution, average, good, etc. based on a preset range, or may be expressed as a level.

[0077] And for each indicator, the current status, customized solutions, and precautions can be displayed. The current status can be displayed in stages or levels based on the target value, and in the case of customized solutions, it can include information on adjusting protein, total minerals, body fat, body water, soft lean mass, fat-free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, etc. to reach the current target value based on the entered body information.

[0078] In the case of precautions, it may include information on adjusting the currently deficient protein, total minerals, body fat, body water, soft lean mass, fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, etc. based on the entered body information.

[0079] In addition, when the subject of measurement is in the rapid growth stage (303), a solution for increasing the period of the rapid growth stage (303) of the subject of measurement can be provided. Increasing the period of the growth stage means increasing the period of the rapid growth stage (303), and the rapid growth stage (303) can be generally defined as a period that begins when secondary sexual characteristics begin to appear and ends when the secondary sexual characteristics are completed as described above. Therefore, when the subject of measurement is in the rapid growth stage (303), a solution for delaying the point in time when the secondary sexual characteristics are completed can be provided. In other words, various solutions for expanding the range of the x-axis corresponding to the rapid growth stage (303) in FIG. 2 can be provided to the subject of measurement through the display unit (120).

[0080] If the subject of the measurement described above is in the general growth stage (301), it may include information on the physical information taken into consideration, including information on the adjustment of indicators that can alleviate cases in which sex hormones increase abnormally.

[0081] In addition, when the subject of measurement is in the decelerated growth stage (305), a solution for adjusting the period of the decelerated growth stage (305) of the subject of measurement can be provided. The growth stage period adjustment can be divided into a case where the subject's physical information is located at the beginning of the decelerated growth stage (305) and a case where the subject's physical information is located in the middle to late part of the decelerated growth stage (305) among the growth stages classified based on the input physical information of the subject of measurement.

[0082] The criteria for distinguishing between the beginning and the middle and late stages of the aforementioned deceleration growth stage (305) may be distinguished based on a predetermined range corresponding to the rapid growth stage (303) to the deceleration growth stage (305) based on the x-axis in FIG. 2, or based on the physical information of the input subject, if the secondary sexual characteristics are not completed, it may be distinguished as the beginning of the deceleration growth stage (305), and if completed, it may be distinguished as the middle and late stages of the deceleration growth stage (305).

[0083] Preferably, based on the input physical information of the subject, it is possible to determine whether secondary sexual characteristics have been completed, and thus determine whether the physical information of the subject is currently in the beginning or middle to late stage of the decelerated growth phase (305). If it is not possible to determine whether secondary sexual characteristics have been completed based on the input physical information of the subject, it is possible to determine whether the physical information of the subject is in the beginning or middle to late stage of the decelerated growth phase (305) based on a predetermined range corresponding to the rapid growth phase (303) to the decelerated growth phase (305).

[0084] Meanwhile, if the input physical information of the subject is located at the beginning of the decelerated growth phase (305), a period adjustment solution can be provided to delay the entry into the decelerated growth phase (305). As described above, since secondary sexual characteristics are being completed at the time of transition from the rapid growth phase (303) to the decelerated growth phase (305), a solution can be provided to delay the point in time when the secondary growth is completed, which can be similar to the solution provided when the subject is in the rapid growth phase (303). In other words, various solutions for moving the range of the x-axis corresponding to the decelerated growth phase (305) in FIG. 2 to the right can be provided to the subject through the display unit (120). In this case, the range of the decelerated growth phase (305) may increase depending on the physical information of the subject, or may decrease as the rapid growth phase (303) increases.

[0085] And when the input body information of the subject is located in the middle to late part of the decelerated growth phase (305), a solution for adjusting the period for increasing the period of the decelerated growth phase (305) can be provided. As described above, the decelerated growth phase (305) refers to the time when the growth plate of the subject is closed, and generally, after entering the decelerated growth phase (305), about 50% of the growth plate is closed after 6 months, and when the growth plate is closed and natural growth stops, the no-growth phase (307) is entered, so in this case, a solution for increasing the period of the decelerated growth phase (305) can be provided. In other words, various solutions for expanding the range of the x-axis corresponding to the decelerated growth phase (305) in FIG. 2 can be provided to the subject through the display unit (120).

[0086] If the subject of the measurement described above is in the general growth stage (301), the physical information taken into consideration may include, in particular, information on the adjustment of indicators that can alleviate the degree of closure of the growth plate.

[0087] In addition, in cases where the subject of measurement is in the slow growth phase (307), solutions for improving physical function through lifestyle habits, customized exercise, posture correction, nutrient intake, etc. can be provided based on the physical information of the subject of measurement entered.

[0088] The slow growth period (307) is when the growth rate slows down and gradually converges to 0 before the growth plate closes, so a solution can be provided to slow down the rate at which the growth plate of the subject closes through lifestyle habits, customized exercise, posture correction, etc. based on the subject's weight, body fat, body water, muscle mass, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference.

[0089] In addition, in cases where the subject of measurement is in the growth plate stage (309), a solution for improving physical function through lifestyle habits, customized exercise, posture correction, nutrient intake, etc. can be provided based on the entered physical information of the subject of measurement.

[0090] In the case of the growth plate stage (309), the growth plate closes and natural growth stops, so a solution for improving physical function through lifestyle habits, customized exercise, and posture correction can be provided based on the subject's weight, body fat, body water, muscle mass, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference, or a solution for improving physical function through nutrient intake, etc. can be provided based on protein, mineral content, bone tissue (bone density), etc.

[0091] Figure 2 is a diagram showing predicted height and target height for each growth stage according to exemplary embodiments of the present invention.

[0092] Looking at the growth stages with reference to Figure 2, the childhood and adolescence period may include a general growth period (301), a rapid growth period (303), a decelerated growth period (305), a slow growth period, and a growth plate period (309).

[0093] Each growth stage can be categorized by growth rate, and the height grown each year varies depending on each growth stage, and even within the same growth stage, the actual height grown can vary depending on the growth type.

[0094] The general growth period (301) usually refers to the period before puberty when secondary sexual characteristics appear, and children and adolescents during this period generally have open growth plates, so they generally grow 4 to 5 cm per year in the case of a short growth type and 6 to 7 cm per year in the case of a tall growth type, depending on the growth environment. In exemplary embodiments, the general growth period (301) may be defined as a period before the onset of rapid pubertal growth in children and adolescents, and may be a growth stage that ends at a point in time that is earlier than the predicted APHV by the first growth period. In one embodiment, the first growth period may be a period of between 1 and 2 years, for example, about 1.5 years.

[0095] The rapid growth phase (303) is the period when secondary sexual characteristics begin to appear, in women, the breasts swell and pubic hair develops, and in men, the testicles enlarge and pubic hair begins to grow, and a change called the pubertal stage occurs. The rapid growth phase (303) generally lasts about 2 to 3 years after the normal growth phase (301), and grows at an average of 7 to 10 cm per year. In exemplary embodiments, the rapid growth phase (303) may be defined as a period when the rapid growth of puberty begins and the growth rate increases before APHV, and may be a growth stage that starts at a time prior to the predicted APHV by the first growth period and ends at the predicted APHV. In one embodiment, the first growth period may be a period of 1 to 2 years, for example, about 1.5 years.

[0096] The deceleration period (305) refers to the period when secondary sexual characteristics are fully developed. During this period, women can be distinguished by the onset of menstruation, and men can clearly see the changes in pubic hair, voice change, and armpit hair. When the deceleration period (305) begins, the growth rate rapidly decreases compared to the rapid growth period (303), and it generally lasts for about 2 to 3 years, and natural growth stops after growing at an average of 5 to 6 cm per year. The growth plate begins to close little by little after the rapid growth period (304), and about 50% of it is closed about 6 months after entering the deceleration period (305). In exemplary embodiments, the deceleration phase (305) may be defined as a period in which the growth rate gradually decreases from the APHV until the end of the adolescent growth spurt, and may be a growth phase that starts at the predicted APHV and ends at a time that is a second growth period after the predicted APHV. In one embodiment, the second growth period may be a period of between one and two years, for example, about 1.5 years.

[0097] The slow growth period (307) may refer to a period in which the growth velocity approaches 0 before entering the growth plate phase after the rapid growth of puberty. In exemplary embodiments, the slow growth period (307) may be defined as a period in which the growth velocity gradually converges to 0 after the end of the rapid growth of puberty in children and adolescents, and may be a growth stage that starts at a time point that is the second growth period longer than the predicted APHV and ends at a time point that is the third growth period longer than the predicted APHV. In one embodiment, the third growth period may be a period of between 2 and 4 years, for example, about 3 years.

[0098] The growth plate sternum (309) refers to a period when the growth period is not completely over, but natural height growth becomes difficult, and the growth plate is closed. Generally, women enter the growth plate sternum (309) about 1 year and 6 months to 2 years after menarche, and men enter the growth plate sternum (309) about 1 year and 6 months to 2 years after the start of hair growth in the armpits. During the growth plate sternum (309), the growth plate closes and natural growth stops, but growth can be achieved in the range of about 1 to 3 cm by changing bad lifestyle habits and improving physical functions through customized exercise, posture correction, and nutrient intake. In exemplary embodiments, the growth plate sternum (309) can be defined as a period when the growth rate is close to 0 after the growth plate closes, and can be a growth stage starting at a point in time that is the third growth period longer than the predicted APHV. In one embodiment, the third growth period may be a period of between two and four years, for example, about three years.

[0099] FIG. 3 is a flowchart illustrating a method for providing growth prediction and solutions based on growth age using an artificial intelligence model according to exemplary embodiments of the present invention.

[0100] Referring to FIG. 3, a method for predicting growth based on growth age and providing a solution using an artificial intelligence model according to exemplary embodiments of the present invention may include a step of receiving biometric data of a subject of measurement (S10), a step of extracting predicted APHV (Age of Peak Height Velocity) data, which is an age at which growth velocity is expected to reach a maximum value, using the biometric data of the subject of measurement (S20), a step of classifying the growth stage of the subject of measurement into one of a plurality of growth stages based on the extracted predicted APHV data (S30), a step of inputting the extracted predicted APHV data into a trained neural network to predict final height (S40), and a step of providing a growth management solution based on the classified growth stage and the predicted final height (S50).

[0101] The biometric data input step (S10) can be performed by inputting physical information about the subject of measurement through the biometric data input unit (10). At this time, the physical information about the subject of measurement may include not only basic information such as grade (or age), gender, and height, but also additional information such as weight, protein, mineral content, body fat, body water, soft lean mass, fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, and hip circumference. However, such physical information is only one example to help understanding of the present invention, and the present embodiment is not limited thereto, and the types of information constituting the physical information may be varied depending on the embodiment.

[0102] In addition, the physical information of the subject of measurement may be a single physical information measured once, or may be physical information measured multiple times, i.e., information that is continuous in time series. However, the concept of the present invention is not necessarily limited to this, and the physical information of the subject of measurement may also be information that is discontinuous in time series. In other words, the physical information of the subject of measurement may be collected at various times and at various times.

[0103] In one embodiment, the biometric data of the subject may include at least two time-series biometric data measured at different age groups.

[0104] The predicted APHV extraction step (S20) can be performed using an artificial intelligence model, and the artificial intelligence model will be described first as follows.

[0105] Specifically, the artificial intelligence model used in the predicted APHV extraction step (S20) may be an artificial intelligence model trained using first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity of a plurality of sample subjects reaches its maximum, as target data, and the artificial intelligence model may be an artificial intelligence model constructed through a step of inputting time-series biometric data of a plurality of sample subjects, a step of estimating a plurality of first growth velocity curves representing the growth velocity of height of each of the plurality of sample subjects compared to their actual ages using the time-series biometric data of the sample subjects, a step of extracting first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches its maximum, for each of the plurality of first growth velocity curves, and a step of inputting the first APHV data into an artificial intelligence model using the target data and training the model.

[0106] At this time, the first APHV data may be a set of data obtained by inputting biometric data of the plurality of sample subjects, estimating a plurality of first growth velocity curves representing the growth velocity of height of each of the plurality of sample subjects compared to their actual age, and extracting the age at which the growth velocity reaches the maximum value for each of the plurality of first growth velocity curves.

[0107] Here, the estimation step of the first growth rate curve can be performed by extracting age data and height data from the time-series biometric data of the sample subjects and then applying the extracted age data and height data to a curve transformation model that uses the age data and height data as input variables.

[0108] In exemplary embodiments, the curve transformation model may be a model constructed using a polynomial function.

[0109] In one embodiment, the curve transformation model may be a model constructed using mathematical expression 1.

[0110] [Mathematical Formula 1]

[0111]

[0112] In the above mathematical expression 1, y it is α i , β i and γ i are random correction values ​​for adjusting the vertical shift, horizontal shift and slope of the curve, respectively, h is a specific function related to the height data, and t may be age data.

[0113] Meanwhile, the artificial intelligence model according to the present invention is performed after the step of inputting time-series bio-data of the sample subjects, and may further include a step (not shown) of preprocessing the time-series bio-data of the sample subjects.

[0114] The above preprocessing step can be performed by determining as noise all biometric data of sample subjects for which biometric data collected at a specific growth period do not exist, and removing them.

[0115] In exemplary embodiments, the preprocessing step may be performed by dividing the growth phases of the plurality of sample subjects into a normal growth phase, a rapid growth phase, and a decelerated growth phase, and then removing as noise all data of the sample subjects for which there is no biometric data corresponding to each of the divided growth phases.

[0116] In one embodiment, the preprocessing step may be performed by further dividing the growth phase of the plurality of sample subjects into a slow growth phase and a growth plate phase, and then removing as noise all data of the sample subjects for which there is no biometric data corresponding to each of the divided growth phases.

[0117] For example, the above preprocessing step can be performed by dividing the age range of the plurality of sample subjects into 8 to 18 years, and then removing as noise all data of sample subjects whose input period difference between adjacent biometric data is 4 years or more.

[0118] Alternatively, the preprocessing step may be performed by removing as noise all data of sample subjects for whom biometric data entered during the period between the ages of 9 and 14 among the multiple sample subjects exists.

[0119] The predicted APHV extraction step (S20) may include a step of estimating a predicted growth rate pattern using the biological data of the subject of measurement, and a step of extracting a predicted AOGS (Age of Onset of Growth Spurt), which is an age at which the growth rate of the subject of measurement begins to increase, and a predicted APHV (Age of Peak Height Velocity), which is an age at which the growth rate of the subject of measurement has a maximum value.

[0120] In exemplary embodiments, in the predicted APHV extraction step (S20), the predicted growth rate estimation step of the subject of measurement may be performed by generating a graph (hereinafter referred to as a second growth rate curve) representing the predicted growth rate of the subject of measurement using single or multiple data, as illustrated in FIG. 4, and then extracting predicted AOGS data representing the predicted AOGS value and predicted APHV data representing the predicted APHV value from the second growth rate curve.

[0121] In the above, the predicted APHV extraction step (S20) has been described as being performed by generating the second growth rate curve based on the bio-data of the subject of measurement and then extracting the predicted AOGS data and the predicted APHV data from the second growth rate curve, but the concept of the present invention is not necessarily limited thereto. That is, the predicted APHV extraction step (S20) may be controlled to directly extract the predicted AOGS data and the predicted APHV data through a preset algorithm without the need to generate the second growth rate curve by simply inputting the bio-data of the subject of measurement into a growth age estimation model using an artificial intelligence model.

[0122] After the predicted APHV extraction step (S20) is performed, the growth stage classification step (S30) can be performed.

[0123] The growth stage classification step (S30) can be performed by classifying the growth stage of the subject of measurement into one of a plurality of growth stages, for example, one of a rapid growth phase and a decelerated growth phase, based on the predicted APHV data extracted through the predicted APHV extraction step (S20).

[0124] At this time, the rapid growth period can be defined as a period in which the growth rate gradually increases after the onset of rapid pubertal growth in children and adolescents until the predicted APHV, and the decelerating growth period can be defined as a period in which the growth rate gradually decreases after the predicted APHV until the end of rapid pubertal growth in children and adolescents.

[0125] In exemplary embodiments, the rapid growth phase may be a growth phase that starts at a point in time that is a first growth period before the predicted APHV and ends at the predicted APHV, and the decelerating growth phase may be a growth phase that starts at the predicted APHV and ends at a point in time that is a second growth period after the predicted APHV.

[0126] In one embodiment, the first growth period and the second growth period may be set to the same period, for example, the first growth period and the second growth period may each be a period between one year and two years.

[0127] In addition, the growth stage may further include a general growth stage, a slow growth stage, and a growth plate stage, and the growth stage classification step may be performed to classify the growth stage of the subject of measurement into any one of a general growth stage, a rapid growth stage, a slow growth stage, a slow growth stage, and a growth plate stage based on the predicted APHV data.

[0128] At this time, the general growth period can be defined as a period before the onset of rapid pubertal growth in children and adolescents, the slowed growth period can be defined as a period after the end of rapid pubertal growth in children and adolescents in which the growth rate gradually converges to 0, and the no-growth plate period can be defined as a period in which the growth plate in children and adolescents closes and the growth rate approaches 0.

[0129] In exemplary embodiments, the normal growth phase may be a growth phase whose end point is a time point preceding the predicted APHV by the first growth period, the slow growth phase may be a growth phase whose start point is a time point preceding the predicted APHV by the second growth period, and whose end point is a time point preceding the predicted APHV by the third growth period, and the no-growth phase may be a growth phase whose start point is a time point preceding the predicted APHV by the third growth period.

[0130] In one embodiment, the third growth period may be set to a different period from the first growth period and the second growth period, and for example, the third growth period may be a period between two and four years.

[0131] Meanwhile, although not shown in the drawing, a growth age estimation step (not shown) may be performed between the predicted APHV extraction step (S20) and the growth stage classification step (S30).

[0132] In this case, the growth age calculation step can be performed using the following mathematical formula 2:

[0133] [Equation 2]

[0134] GA i '= (CA i - APHV i ') + APHV G

[0135] Here, GA i ' is the estimated growth age of the subject, and CA i is the chronological age / calendar age of the subject, and APHV i ' is the predicted APHV data including the estimated and predicted APHV values ​​of the subject of the measurement, and APHV G The second APHV data may be a representative value or average value of the first APHV data derived from the learning data group.

[0136] That is, the growth age (E) of the subject of measurement calculated through the above growth age calculation step ga ) is the second APHV data (S AA ) That is, for the average value of the first APHV data, the actual age (E) of the subject of measurement ra ) and predicted APHV data of the above measurement subjects (E PA ) can be calculated by compensating for the difference.

[0137] Therefore, the biological growth age of the subject of measurement can be estimated by applying all of the actual age of the subject of measurement, that is, the temporal actual age uniformly calculated from the time of birth, the predicted APHV data calculated using the tracked growth rate of the subject of measurement, and the second APHV data corresponding to the group average APHV of the sample subjects, and the biological growth age can be utilized to construct a growth stage classification model, and the reliability of various prediction models such as the integrated height growth prediction model, the precocious puberty prediction model, and the obesity prediction model constructed based on the classified growth stages can be maximized.

[0138] The final height prediction step (S40) can be performed by inputting the first APHV data, the second APHV data, and the predicted APHV data of the subject into a learned neural network using multiple models to predict the final height.

[0139] In exemplary embodiments, the final height prediction step (S40) may include a first model and a second model, and may construct a pipeline in which at least a portion of the output of the second model is input to the first model.

[0140] More specifically, the first model may be a model that learns body information corresponding to at least one growth stage among a plurality of growth stages as learning data based on time-series body information of a plurality of sample subjects.

[0141] The first model comprises an LSTM neural network for learning time-series data, and trains the LSTM neural network using the first APHV data and / or the second APHV data among the past physical information of the plurality of sample subjects. Then, the predicted APHV data among the physical information of the current measurement subject is input into the trained LSTM neural network, and the predicted growth rate for each growth stage is output.

[0142] Meanwhile, the above LSTM neural network can be trained for each growth stage. Accordingly, it can be trained with past physical information of the corresponding growth stage, such as the normal growth stage (301), rapid growth stage (303), decelerating growth stage (305), slowing growth stage (307), and growth plate stage (309).

[0143] For example, in this embodiment, time-series physical information of multiple sample subjects is sequentially input as learning data according to age or an arbitrary period, and the result of calculating a predicted value at a past point in time or growth rate, for example, the first APHV data and / or the second APHV data, can be transmitted to predict growth rate at the next age or an arbitrary period.

[0144] Therefore, the LSTM neural network can not only predict growth based on the current subject's physical information, but also learn the extent to which prediction results for various indicators from the past affect the current growth prediction, and through this, items that have a large influence on the change in growth based on age or an arbitrary period among the indicators can be extracted and reflected in the growth prediction.

[0145] Furthermore, for time-series learning, it is necessary to obtain regular, time-based physical data for multiple sample subjects. However, as described above, it can be difficult to regularly obtain physical data for multiple sample subjects, such as age or a random period of time. Therefore, outliers or discontinuous physical data can be removed for each unit period, allowing for temporal normalization.

[0146] Meanwhile, the second model can derive bone maturity (age) from a bone image using a convolutional neural network trained with bone maturity data of the subject as learning data.

[0147] More specifically, the convolutional neural network includes a plurality of convolution layers that create a features map for features in the target image of the analysis among the bone images, and a pooling layer that performs sub-sampling between the plurality of convolution layers, so that features at different levels for the target area of ​​analysis can be extracted, and the features can be probabilistically inferred through an activation function, or the bone maturity can be derived through weight learning between nodes through regression analysis.

[0148] The bone maturity extracted through the second model can be input into the LSTM neural network together with at least a portion of the subject's physical information, for example, predicted APHV data, to increase the accuracy of predicting the subject's growth.

[0149] In exemplary embodiments, the growth management solution providing step (S50) may provide a solution for increasing the growth prediction value of the subject of measurement when the subject of measurement is in the general growth period.

[0150] In exemplary embodiments, the growth management solution providing step (S50) may provide a solution for increasing the period of the rapid growth period when the subject of measurement is in the rapid growth period.

[0151] In exemplary embodiments, the growth management solution providing step (S50) may provide a solution for adjusting the period of the decelerating growth period when the subject of measurement is in the decelerating growth period.

[0152] In exemplary embodiments, the growth management solution providing step (S50) may provide a solution for slowing down the closing speed of the growth plate of the subject of measurement when the subject of measurement is in a slow growth period.

[0153] Figure 4 is a diagram showing multiple first growth rate curves that represent the growth rate of height compared to actual age using time-series biometric data of sample subjects.

[0154] FIG. 4 illustrates a plurality of first growth velocity curves generated through a first growth velocity curve estimation unit (30), and a first APHV data extraction unit (40) can be controlled to extract first APHV (Age of Peak Height Velocity) data, which is an age at which the growth velocity reaches a maximum value, for each of the plurality of first growth velocity curves. The first APHV data may mean data indicating a point at which the growth velocity reaches a maximum during rapid pubertal growth, i.e., an age at which the growth acceleration is 0.

[0155] Meanwhile, the first APHV data extraction unit (40) may be controlled to extract additional data in addition to the first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches the maximum value for each of the plurality of first growth velocity curves.

[0156] For example, the first APHV data extraction unit (40) can be controlled to further extract AOGS (Age of onset of growth spurt) data, which is the point at which pubertal growth spurt begins, AEGS (Age of end of growth spurt) data, which is the point at which pubertal growth spurt ends, etc.

[0157] The above AOGS data may refer to a point where the growth rate slope increases rapidly and the growth acceleration reaches a maximum, and may refer to data indicating age approximately 1.5 years prior to the first APHV data.

[0158] The above AEGS data may refer to the point at which the adolescent growth spurt ends, that is, the point at which the growth velocity after APHV becomes equal to the growth velocity (Onset of Growth Spurt Velocity) in AOGS, and may refer to data indicating the age approximately 1.5 years after the first APHV data.

[0159] Figures 5 and 6 are diagrams showing the APHV estimation error according to the age of the girl and the boy, respectively.

[0160] Referring to FIGS. 5 and 6, predicted APHV data of subjects of various ages were extracted using an APHV estimation model based on biometric data using artificial intelligence according to exemplary embodiments of the present invention, and then the predicted APHV data and the actual APHV data of the subjects were compared.

[0161] Specifically, through the above experimental results, it was confirmed that the average error between the predicted APHV data value and the actual APHV data value was 0.35 years for girls aged 10 years or older, and that the average error between the predicted APHV data value and the actual APHV data value was 0.39 years for boys aged 12 years or older. That is, as a result, in the case of the APHV estimation model based on biometric data using artificial intelligence according to the exemplary embodiments of the present invention, it was confirmed that the average error between the predicted data value and the actual data value was within 0.5 years in the average age at which secondary sexual characteristics appear for both girls and boys, and thus the APHV prediction accuracy was very high.

[0162] In addition, through the above experiment, it was confirmed that the prediction accuracy of the APHV estimation model improves as the actual age of the measurement subjects approaches the actual APHV data value.

[0163] Figures 7 and 8 are diagrams showing the importance of predictive variables for APHV in women and men, respectively.

[0164] Referring to FIGS. 7 and 8, various variables for constructing an APHV estimation model based on biometric data using artificial intelligence according to exemplary embodiments of the present invention are classified according to importance.

[0165] Specifically, as shown in Fig. 7, the variable with the highest importance in predicting APHV in women was Previously Velocity, whereas as shown in Fig. 8, the variable with the highest importance in predicting APHV in men was Height Quantile.

[0166] Meanwhile, in predicting APHV in women, current growth velocity was the fourth most important variable, and in predicting APHV in men, previous growth velocity and current growth velocity were the second and sixth most important variables, respectively.

[0167] That is, it was confirmed that the previous growth velocity (Previously Velocity) and the current growth velocity (Velocity) were very important variables for predicting the APHV of women and men, respectively. In particular, it was confirmed that the previous growth velocity (Previously Velocity) was the most important variable for women and the second most important variable for men. Consequently, it was confirmed that the accuracy of the predicted APHV data extracted using the APHV estimation model can be greatly improved when there are two or more biometric data measured at different times of the subject so that the previous growth velocity can be confirmed.

[0168] In addition, in order to increase the accuracy of the predicted APHV data extracted through the APHV estimation model based on biometric data using artificial intelligence according to exemplary embodiments of the present invention, it was confirmed that additional variables such as height quantile, waist to hip ratio (WHR), and body fat mass are required.

[0169] Figure 9 is a diagram showing variable importance in a conventional growth prediction model for women and men, and Figure 10 is a diagram showing variable importance in a growth prediction model of the present invention.

[0170] Referring to FIGS. 9 and 10, the most important variable in both the conventional female and male growth prediction models and the growth prediction model of the present invention was the growth rate, whereas in the conventional growth prediction model, the second most important variable was the height quantile, and in the growth prediction model of the present invention, the second most important variable was the age of peak height velocity (APHV).

[0171] That is, in the conventional growth prediction model before the concept of APHV was applied, the height quantile was shown to be the second most important variable after the growth rate, but in the growth prediction model according to the present invention after the concept of APHV (Age of Peak Height Velocity) was applied, the age of peak height velocity (APHV) was calculated to be higher in importance than the height quantile, and was shown to be the second most important variable after the growth rate, and accordingly, it was confirmed that the importance of APHV was very high among the variables affecting the predicted final height.

[0172] As described above, the method for providing a growth prediction and solution based on biometric data using artificial intelligence according to exemplary embodiments of the present invention estimates a growth velocity curve by applying a statistical methodology to a traced growth velocity extracted from past information, and extracts APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity reaches the maximum value for the growth velocity curve, and then utilizes the APHV data for growth stage classification, so that the reliability of various prediction models, such as an integrated height growth prediction model, a precocious puberty prediction model, and an obesity prediction model, constructed using the classified growth stages can be maximized.

[0173] In addition, according to various embodiments of the present invention, it is possible to accurately predict height by considering the growth stage of children and adolescents through a growth stage classification method constructed through artificial intelligence learning, and to provide a solution necessary for height growth by considering each growth stage.

[0174] While various embodiments of the present invention have been described in detail above, those skilled in the art will appreciate that various modifications to the above-described embodiments are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined not only by the claims set forth below but also by equivalents thereof.

Claims

1. Step of entering the subject’s biometric data; A step of extracting predicted APHV (Age of Peak Height Velocity) data, which is the age at which growth velocity is expected to reach its maximum value, using the biometric data of the subject of the measurement; A step of classifying the growth stage of the subject of measurement into one of multiple growth stages based on the extracted predicted APHV data; A step of predicting the final height by inputting the extracted predicted APHV data into a learned neural network; and A method for providing growth prediction and solution based on growth age using an artificial intelligence model, comprising: a step of providing a growth management solution based on the classified growth stages and the predicted final height.

2. In paragraph 1, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, wherein the plurality of growth stages include a general growth period, a rapid growth period, a decelerating growth period, a slowing growth period, and a no-growth period.

3. In paragraph 2, The above general growth period is defined as the period before the onset of rapid pubertal growth in children and adolescents. A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the general growth period is a growth stage whose end point is a point in time that is earlier than the predicted APHV by the first growth period.

4. In paragraph 3, A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the first growth period is a period of between 1 and 2 years.

5. In paragraph 3, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, characterized in that it provides a solution for increasing the growth prediction value of the subject of measurement when the subject of measurement is in the general growth period.

6. In paragraph 4, The above rapid growth period is defined as the period when secondary sexual characteristics begin to appear in children and adolescents. A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the rapid growth period is a growth stage that starts at a point in time earlier than the predicted APHV by the first growth period and ends at the predicted APHV.

7. In paragraph 6, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, characterized in that it provides a solution for increasing the period of the rapid growth period when the subject of the measurement is in the rapid growth period.

8. In paragraph 2, The above growth deceleration period is defined as the period when secondary sexual characteristics of children and adolescents are completed. A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the above-mentioned decelerating growth period is a growth stage that starts at the first APHV and ends at a point in time that has elapsed by the second growth period from the first APHV.

9. In paragraph 8, A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the second growth period is a period of between 1 and 2 years.

10. In paragraph 8, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, characterized in that it provides a solution for adjusting the period of the decelerating growth period when the subject of the measurement is in the decelerating growth period.

11. In paragraph 2, The above slow growth period is defined as the period in which the growth rate gradually converges to 0 after the end of rapid growth during puberty in children and adolescents. A method for providing growth prediction and solutions based on growth age using an artificial intelligence model, characterized in that the above-mentioned slow growth period is a growth stage that starts at a point in time that has passed the second growth period from the above-mentioned predicted APHV and ends at a point in time that has passed the third growth period from the above-mentioned predicted APHV.

12. In paragraph 11, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, characterized in that the second growth period is a period of between 1 and 2 years, and the third growth period is a period of between 2 and 4 years.

13. In paragraph 11, A method for predicting growth based on growth age and providing a solution using an artificial intelligence model, characterized in that it provides a solution for slowing down the closing speed of the growth plate of the subject of the measurement when the subject of the measurement is in a slowing growth period.

14. A program stored on a computer-readable recording medium including a program code for executing a method for predicting growth at each growth stage and providing a solution using artificial intelligence as described in any one of paragraphs 1 to 13.

15. A computer-readable recording medium having recorded thereon a program for executing a method for predicting growth at each growth stage and providing a solution using artificial intelligence as described in any one of claims 1 to 14.

16. Input section for entering biometric data of the subject of measurement; A predicted APHV (Age of Peak Height Velocity) extraction unit that extracts predicted APHV data, which is the age at which the growth rate is expected to reach its maximum value, based on the biological data of the subject of measurement entered above; A growth stage determination unit that classifies the growth stage of the subject of measurement into one of multiple growth stages based on the extracted predicted APHV data; A growth prediction unit that inputs the extracted predicted APHV data into a learned neural network to predict growth; A solution generation unit that generates a growth management solution based on the above-mentioned classified growth stages and the above-mentioned predicted final height; and A device for predicting growth by growth stage and providing a solution using artificial intelligence, including a display unit displaying the above-mentioned generated growth management solution.

17. In paragraph 16, The above growth prediction unit includes first APHV (Age of Peak Height Velocity) data, which is the age at which the growth velocity extracted based on time-series biometric data of a plurality of sample subjects reaches the maximum value, and an artificial intelligence model learned using the final height of the sample subjects as target data. A device for providing growth prediction and solutions by using artificial intelligence.

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