Method for calculating predicted adult height and device for carrying out the same

Abstract

A method for calculating a predicted adult height according to one embodiment of the present application includes generating a growth prediction model; and calculating a predicted adult height using the generated growth prediction model, wherein the generating the growth prediction model further includes acquiring input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculating an adult height based on the input data and the growth statistical data; and generating the growth prediction model based on the input data and the calculated adult height.

Description

[Technical Field]

[0001] This application relates to growth prediction or growth analysis. In particular, this application relates to a method for calculating predicted adult height and an electronic device for performing the same. [Background technology]

[0002] As interest in the complete and correct growth of children increases, growth prediction techniques for predicting a child's future height are gaining attention.

[0003] Previous growth prediction techniques included genetic-based prediction, which reflects genetic characteristics and predicts adult height by inputting the heights of parents. However, genetic-based prediction techniques have limitations in that they do not reflect the growth and developmental status of the subject at the time of analysis, nor do they reflect statistical characteristics of growth that are affected by country, gender, race, and the historical environment, and simply calculate adult height based on the heights of the parents.

[0004] Another conventional growth prediction method is the bone age-based prediction method, which predicts adult height based on bone age. These methods include 1) the Bayley and Pinneau (BP) method (an adult height prediction method proposed by researchers at the GP bone age determination method), which calculates the growth rate based on the difference between chronological age and bone age, and then predicts adult height using current height, current age, and growth rate; and 2) the Adult Height Prediction (AHP) method (an adult height prediction method proposed by researchers at the TW3 bone age method), which calculates predicted adult height for boys aged 10 or older and girls aged 7 or older based on current height, Rus score based on bone age, and a constant based on chronological age; and for boys under 10 and girls under 7 years old, calculates predicted adult height based on current height, chronological age, and a constant based on chronological age. However, bone age-based prediction techniques have limitations, such as being unable to reflect genetic, temporal, regional, and racial characteristics, and being relatively inaccurate for subjects who require or are undergoing growth-related treatment, such as growth hormone treatment or treatment for precocious puberty, in addition to normal subjects.

[0005] Therefore, there is a need for a method of calculating a child's predicted adult height and a device for carrying out this method in order to more accurately calculate the child's final height. Summary of the Invention [Problem to be solved by the invention]

[0006] One problem to be solved by the present invention is to provide a method for calculating predicted adult height, which calculates predicted adult height by reflecting the growth and developmental state at the time of analysis and the characteristics of the era, region, and race, and an apparatus for performing the method.

[0007] An object of the present invention is to provide a method for calculating predicted adult height that reflects genetic characteristics and an apparatus for performing the method.

[0008] One problem to be solved by the present invention is to provide a method for calculating predicted adult height that can calculate predicted adult height for not only a normal group but also a growth-related treatment group, and an apparatus for carrying out the method.

[0009] The problems to be solved by the present invention are not limited to those described above, and problems not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from this specification and the accompanying drawings. [Means for solving the problem]

[0010] A method for calculating a predicted adult height according to one embodiment of the present application includes generating a growth prediction model; and calculating a predicted adult height using the generated growth prediction model. The generating the growth prediction model may further include acquiring input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculating an adult height based on the input data and the growth statistical data; acquiring an actual adult height of the child corresponding to the input data; and generating the growth prediction model based on the input data, the calculated adult height, and the actual adult height.

[0011] An electronic device according to an embodiment of the present application may include a processor configured to acquire input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information, calculate an adult height based on the input data and the growth statistical data, obtain an actual adult height of the child corresponding to the input data, generate a growth prediction model based on the input data, the calculated adult height, and the actual adult height, and calculate a predicted adult height using the generated growth prediction model.

[0012] The means for solving the problems of the present invention are not limited to the above-mentioned means, and any unmentioned means will be clearly understood by a person having ordinary skill in the art to which the present invention pertains from this specification and the accompanying drawings. [Effects of the Invention]

[0013] According to an embodiment of the present application, a method for calculating a predicted adult height and an electronic device for performing the method can calculate a predicted adult height by generating a growth prediction model using the adult height calculated based on growth statistical data that reflects historical, regional, and racial characteristics. This makes it possible to calculate a predicted adult height that reflects various characteristics that affect growth.

[0014] According to an embodiment of the present application, a method for calculating predicted adult height and an electronic device for performing the method can generate a growth prediction model for each age group and / or growth treatment group, and calculate the predicted adult height using the growth prediction model corresponding to the group to which the subject belongs, thereby making it possible to predict the predicted adult height with higher accuracy.

[0015] According to a method for calculating predicted adult height and an electronic device for performing the method, a growth prediction model reflecting genetic characteristics can be generated using a genetic predicted height as a data set, and a predicted adult height can be calculated by reflecting genetic characteristics through the growth prediction model reflecting genetic characteristics.

[0016] The effects of the present invention are not limited to those described above, and effects not mentioned will be clearly understood by those skilled in the art from this specification and the accompanying drawings.

[0017] [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a schematic diagram of an electronic device according to an embodiment of the present application;

[0019] [Figure 2] 1 is a diagram illustrating the operation of an electronic device according to an embodiment of the present application.

[0020] [Figure 3] 1 is a diagram illustrating an operation of generating a growth prediction model for an electronic device according to an embodiment of the present application.

[0021] [Figure 4] 1 is a diagram illustrating an example of growth statistical data according to an embodiment of the present application.

[0022] [Figure 5] 1 is a diagram illustrating an operation of calculating a predicted adult height using a growth prediction model of an electronic device according to an embodiment of the present application.

[0023] [Figure 6] 1 is a flowchart illustrating a method for calculating predicted adult height according to one embodiment of the present application.

[0024] [Figure 7] 1 is a flowchart embodying steps for generating a growth forecast model according to one embodiment of the present application.

[0025] [Figure 8] 1 is a flowchart embodying steps for calculating predicted adult height using a growth prediction model according to one embodiment of the present application.

[0026] DETAILED DESCRIPTION OF THE INVENTION

[0027] A method for calculating a predicted adult height according to one embodiment of the present application includes generating a growth prediction model; and calculating a predicted adult height using the generated growth prediction model. The generating the growth prediction model may further include acquiring input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculating an adult height based on the input data and the growth statistical data; acquiring an actual adult height of the child corresponding to the input data; and generating the growth prediction model based on the input data, the calculated adult height, and the actual adult height.

[0028] The above-mentioned objects, features, and advantages of the present application will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. However, since the present application can be modified in various ways and can have various embodiments, the following will describe in detail a specific embodiment by way of example in the drawings.

[0029] The same reference numerals will generally refer to the same elements throughout the specification. Furthermore, elements having the same function within the same concept shown in the drawings of each embodiment will be described using the same reference numerals, and redundant description thereof will be omitted.

[0030] If it is determined that a detailed description of a known function or configuration related to this application may unnecessarily obscure the gist of this application, the detailed description will be omitted. In addition, numbers (e.g., 1, 2, etc.) used in the description of this specification are merely identification symbols for distinguishing one component from another.

[0031] In addition, the suffixes "module" and "section" for components used in the following examples are given or used interchangeably only for the sake of ease of writing the specification, and do not have any meanings or roles that are distinct from each other in themselves.

[0032] In the following examples, the singular expression includes the plural expression unless the context clearly indicates otherwise.

[0033] In the following examples, terms such as "include" or "have" mean that the features or components described in the specification are present, and do not preclude the possibility that one or more other features or components may be added.

[0034] In the drawings, the dimensions of elements may be increased or decreased for the sake of convenience of explanation. For example, the size and thickness of each element shown in the drawings are arbitrarily shown for the sake of convenience of explanation, and the present invention is not necessarily limited to those shown in the drawings.

[0035] When an embodiment is implemented differently, the order of certain processes may be performed differently from the order described. For example, two processes described in succession may be performed substantially simultaneously or may be performed in the reverse order from that described.

[0036] In the following examples, when elements are said to be connected, this includes not only the case where the elements are directly connected, but also the case where the elements are indirectly connected through an intervening element.

[0037] For example, when it is stated in this specification that components are electrically connected, this includes not only cases where the components are directly electrically connected, but also cases where the components are indirectly electrically connected through an intervening component.

[0038] A method for calculating a predicted adult height according to one embodiment of the present application includes generating a growth prediction model; and calculating a predicted adult height using the generated growth prediction model. The generating the growth prediction model may further include acquiring input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculating an adult height based on the input data and the growth statistical data; acquiring an actual adult height of the child corresponding to the input data; and generating the growth prediction model based on the input data, the calculated adult height, and the actual adult height.

[0039] According to an embodiment of the present application, the step of calculating the adult's height may further include the steps of: obtaining a first percentile corresponding to the chronological age information and the height information from the growth statistical data based on the height information and chronological age information of the input data; and calculating a first height corresponding to the first percentile as the adult's height using growth statistical data of a group whose growth has stopped.

[0040] According to an embodiment of the present application, the step of calculating the adult's height may further include the steps of: obtaining a second percentile corresponding to the bone age information and the height information from the growth statistical data based on the height information and bone age information of the input data; and calculating a second height corresponding to the second percentile as the adult's height using growth statistical data of a group whose growth has stopped.

[0041] According to one embodiment of the present application, the step of generating the growth prediction model may further include the steps of: inputting the height information, chronological age information, bone age information, and gender information of the input data, and the calculated adult height as input values ​​of the growth prediction model; inputting the actual adult height as an output value of the growth prediction model; and calculating weights of the growth prediction model to output the output value from the input values.

[0042] According to one embodiment of the present application, the step of calculating the predicted adult height may further include the steps of: acquiring subject input data including height information at the time of analysis, chronological age information at the time of analysis, bone age information at the time of analysis, and sex information of the subject whose predicted adult height is to be analyzed, and growth statistical data of a group to which the subject belongs; and calculating the predicted adult height from the subject input data using the growth prediction model including the calculated weights.

[0043] According to one embodiment of the present application, the step of calculating the predicted adult height may further include the steps of: calculating an adult height from subject input data of the subject through the growth statistical data; and inputting the subject input data and the calculated adult height into the growth prediction model, and obtaining the predicted adult height output through the growth prediction model.

[0044] According to an embodiment of the present application, the step of generating the growth prediction model may further include the steps of: generating a first model including a first weight based on a dataset consisting of input data corresponding to the first age range and adult heights calculated through the growth statistical data, and the actual adult height, if the identified age range corresponds to a first age range; and generating a second model including a second weight based on a dataset consisting of input data corresponding to the second age range and adult heights calculated through the growth statistical data, and the actual adult height, if the identified age range corresponds to a second age range.

[0045] According to one embodiment of the present application, the step of calculating the predicted adult height may further include the steps of: identifying a predetermined age range based on chronological age information or bone age information of the subject input data; if the identified age range corresponds to the first range, calculating the predicted adult height from the adult height calculated through the subject input data and the growth statistical data using the first model; and if the identified age range corresponds to the second range, calculating the predicted adult height from the adult height calculated through the subject input data and the growth statistical data using the second model.

[0046] According to one embodiment of the present application, a computer-readable recording medium having a program recorded thereon for executing the method for calculating predicted adult height may be provided.

[0047] An electronic device according to an embodiment of the present application may include a processor configured to acquire input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information, calculate an adult height based on the input data and the growth statistical data, obtain an actual adult height of the child corresponding to the input data, generate a growth prediction model based on the input data, the calculated adult height, and the actual adult height, and calculate a predicted adult height using the generated growth prediction model.

[0048]

[0049] Hereinafter, a method for calculating predicted adult height according to the present invention and an electronic device (or server, hereinafter referred to as electronic device) for performing the method will be described with reference to FIGS. 1 to 8. FIG.

[0050] FIG. 1 is a schematic diagram of an electronic device 1000 according to one embodiment of the present application.

[0051] The electronic device 1000 according to one embodiment of the present application may include a transceiver 1100 , a memory 1200 and a processor 1300 .

[0052] The transceiver 1100 of the electronic device 1000 can communicate with a user terminal and / or any external device (or external server) including a database. For example, the electronic device 1000 can receive an input requesting calculation of a predicted adult height from a user terminal through the transceiver 1100. For example, the electronic device 1000 can receive any data necessary to calculate a predicted adult height, including height information at the time of analysis, chronological age information at the time of analysis, bone age information at the time of analysis, and / or gender information of a subject whose predicted adult height is to be analyzed, from the user terminal through the transceiver 1100. For example, the electronic device 1000 can receive growth statistics data categorized by age, gender, country, region, and / or race from a database through the transceiver 1100. For example, the electronic device 1000 can transmit the calculated predicted adult height information to any external device including a user terminal through the transceiver 1100.

[0053] The electronic device 1000 can connect to a network through the transceiver 1100 and transmit and receive various data. The transceiver 1100 can be broadly classified into a wired type and a wireless type. Since the wired type and the wireless type each have advantages and disadvantages, the electronic device 1000 may be provided with both a wired type and a wireless type depending on the case. Here, in the case of a wireless type, a communication method based on a Wireless Local Area Network (WLAN) such as Wi-Fi can be mainly used. Alternatively, in the case of a wireless type, a communication method based on cellular communication, for example, LTE or 5G, can be used. However, the wireless communication protocol is not limited to the above examples, and any appropriate wireless communication method can be used. In the case of a wired type, LAN (Local Area Network) and USB (Universal Serial Bus) communication are typical examples, but other methods are also possible.

[0054] The memory 1200 of the electronic device 1000 can store various information. Various data can be temporarily or semi-permanently stored in the memory 1200. Examples of the memory 1200 include a hard disk drive (HDD), a solid state drive (SSD), a flash memory, a read-only memory (ROM), and a random access memory (RAM). The memory 1200 can be provided in a form built into the electronic device 1000 or in a removable form. The memory 1200 can store various data necessary for the operation of the electronic device 1000, including an operating system (OS) for driving the electronic device 1000 and programs for operating each component of the electronic device 1000.

[0055] The processor 1300 may control the overall operation of the electronic device 1000. For example, the processor 1300 may control the overall operation of the electronic device 1000, including an operation of generating a growth prediction model (described below) and / or an operation of calculating a predicted adult height using the growth prediction model. Specifically, the processor 1300 may load and execute a program for the overall operation of the electronic device 1000 from the memory 1200. The processor 1300 may be embodied as hardware, software, or a combination thereof, such as an AP (Application Processor), CPU (Central Processing Unit), MCU (Microcontroller Unit), or similar device. In this case, the hardware may be provided in the form of an electronic circuit that processes electrical signals and performs control functions, and the software may be provided in the form of a program or code that drives a hardware circuit.

[0056]

[0057] Hereinafter, the operation of the electronic device 1000 according to an embodiment of the present application and the method for calculating the predicted adult height performed by the electronic device 1000 will be described in more detail with reference to FIGS.

[0058] FIG. 2 is a diagram illustrating the operation of the electronic device 1000 according to an embodiment of the present application.

[0059] The electronic device 1000 according to an embodiment of the present application can generate a growth prediction model for predicting a predicted adult height. Furthermore, the electronic device 1000 can be configured to use the generated growth prediction model to calculate a predicted adult height from subject input data including height information, age information (e.g., bone age, chronological age), and / or gender information of the subject at the time of analysis.

[0060] FIG. 3 is a diagram illustrating an operation of generating a growth prediction model of the electronic device 1000 according to an embodiment of the present application.

[0061] The electronic device 1000 according to an embodiment of the present application may acquire input data through the transceiver 1100. Here, the input data may include any information that may affect an adult's height, including height information at a specific point in time, age information (e.g., bone age information and chronological age information), gender information, and / or parental height information and growth treatment information.

[0062] Furthermore, the electronic device 1000 according to an embodiment of the present application can acquire growth statistical data through the transceiver 1100 .

[0063] FIG. 4 is a diagram showing an example of growth statistical data according to an embodiment of the present application.

[0064] The growth statistics may be grouped by age, sex, country, region, and / or race, and the grouped growth statistics may include statistics related to height percentiles by age (e.g., bone age or chronological age) for that group.

[0065] The electronic device 1000 according to one embodiment of the present application can calculate the height of an adult based on input data and growth statistics data.

[0066] As an example, the electronic device 1000 may acquire the 1st percentile corresponding to the chronological age information and height information from the growth statistical data based on the height information and chronological age information of the input data. For example, if the chronological age information of the input data includes 10 years and 120 months and the height information of the input data includes 135.0 cm, the electronic device 1000 may acquire the 1st percentile (height percentile 25 of FIG. 4 ) corresponding to the chronological age information (10 years and 120 months in FIG. 4 ) and height information (135.0 cm in FIG. 4 ) using the growth statistical data corresponding to the gender information of the input data. Furthermore, the electronic device 1000 may calculate a first height corresponding to the 1st percentile as an adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age). For example, the electronic device 1000 may calculate a first height corresponding to the 1st percentile (e.g., height percentile 25 obtained as described above) as an adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of an adult age group).

[0067] As an example, the electronic device 1000 may acquire the bone age information and the second percentile corresponding to the height information from the growth statistical data based on the height information and bone age information of the input data. For example, if the bone age information of the input data includes 10 years and 128 months and the height information of the input data includes 135.0 cm, the electronic device 1000 may acquire the second percentile (height percentile 10 in FIG. 4 ) corresponding to the bone age information (10 years and 128 months in FIG. 4 ) and the height information (135.0 cm in FIG. 4 ) using the growth statistical data corresponding to the gender information of the input data. Furthermore, the electronic device 1000 may calculate the second height corresponding to the second percentile as the adult height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age). For example, the electronic device 1000 may calculate the second height corresponding to the second percentile (e.g., height percentile 10 obtained as described above) as the adult height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of an adult age group).

[0068] For example, the electronic device 1000 may calculate the adult's height based on a first height calculated using the chronological age information and a second height calculated using the bone age information. For example, the electronic device 1000 may assign a first weight to the first height and a second weight to the second height, and calculate the adult's height based on the first height to which the first weight is assigned and the second height to which the second weight is assigned. For example, the electronic device 1000 may be embodied to calculate the adult's height as a weighted average of the first height and the second height.

[0069] As an example, the electronic device 1000 may be configured to calculate an adult's height based on a weighted average value of bone age information and chronological age information of the input data. For example, if the bone age information of the input data includes 12 years old and the chronological age information includes 10 years old, the electronic device 1000 may acquire weighted average age information corresponding to the weighted average of the two values ​​and acquire a percentile from the growth statistical data using the weighted average age information and the height information of the input data. For example, if the weights are set to be the same, the electronic device 1000 may calculate a third percentile corresponding to the weighted average age information including a value of 11 years old and the height information of the input data using the growth statistical data. In this case, the electronic device 1000 may calculate a third height corresponding to the third percentile as the adult's height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age), as described above.

[0070] However, the above calculation of the height of an adult is merely an example, and the electronic device 1000 may be implemented to calculate the height of an adult from input data using any suitable method.

[0071] Furthermore, the electronic device 1000 according to an embodiment of the present application can generate a growth prediction model based on the input data and the calculated adult height. More specifically, the electronic device 1000 can obtain the child's actual adult height corresponding to the input data. In this case, the electronic device 1000 can generate a growth prediction model based on the input data, the calculated adult height, and / or the actual adult height (Ground Truth).

[0072] For example, the electronic device 1000 may generate a growth forecast model using a regression analysis technique. Specifically, the electronic device 1000 may generate a growth forecast model including optimal weights using the following mathematical formula:

[0073]

[0074] -Math formula

[0075] TIFF2025537342000002.tif2551

[0076] k: The kth dataset consisting of actual adult height and dependent variables (values ​​of information contained in the input data and / or calculated adult height through growth statistics data).

[0077] JPEG2025537342000003.jpg14102

[0078] JPEG2025537342000004.jpg20166

[0079] JPEG2025537342000005.jpg1481

[0080]

[0081] Specifically, the electronic device 1000 may be configured to input an adult height calculated from input data such as height information (e.g., a child's height information), chronological age information, bone age information, gender information, and / or growth statistical data as an input value (x) of the growth prediction model, and input an actual adult height as an output value (y) of the growth prediction model. In this case, the electronic device 1000 may be configured to calculate a weight value (w) of the growth prediction model for outputting an output value from the input value.

[0082]

[0083] As an example, the electronic device 1000 may generate a growth prediction model using machine learning techniques. Specifically, the electronic device 1000 may train the growth prediction model using a learning dataset including input data including height information, chronological age information, bone age information, gender information, and / or an adult's height calculated using growth statistical data, and label information consisting of actual adult heights. More specifically, the electronic device 1000 may input the adult's height calculated using height information, chronological age information, bone age information, gender information, and / or growth statistical data into the growth prediction model, obtain an output value output through the growth prediction model, and update parameters included in the growth prediction model based on the difference between the output value and the actual adult's height included in the label information, thereby training the growth prediction model. For example, the electronic device 1000 may update the parameters of the growth prediction model so that the output value of the growth prediction model approximates the actual adult's height included in the label information.

[0084] Meanwhile, the electronic device 1000 according to an embodiment of the present application may generate a growth prediction model for each age range. Specifically, the electronic device 1000 may identify a predetermined age range based on age information (e.g., chronological age information or bone age information) of input data. For example, the predetermined age range may be set to a pre-pubertal age range and a post-pubertal age range. For example, for girls, the predetermined age range may be set to an age range from 4 to 8 years old and an age range over 8 years old.

[0085] In this case, when the identified age range corresponds to the first range, the electronic device 1000 may generate a first model including the first weighting value as described above based on the actual adult height and a dataset including the adult height calculated through the input data and growth statistical data corresponding to the first range. On the other hand, when the identified age range corresponds to the second range, the electronic device 1000 may generate a second model including the second weighting value as described above based on the actual adult height and a dataset including the adult height calculated through the input data and growth statistical data corresponding to the second range. In other words, the electronic device 1000 may be embodied to generate a growth prediction model optimized for each age range.

[0086] Meanwhile, the above-mentioned preset age ranges are merely examples for the convenience of explanation, and any appropriate age ranges may be set.

[0087] Meanwhile, although not shown in FIG. 3, the electronic device 1000 may acquire parental height information as input data to calculate the predicted adult height reflecting genetic factors. For example, the electronic device 1000 may acquire father's height information and mother's height information, calculate the genetic predicted height using the following genetic predicted height formula, and acquire the calculated genetic predicted height as input data. Furthermore, the electronic device 1000 may be embodied to input the calculated genetic predicted height as an input value (x) of a data set of a growth prediction model.

[0088]

[0089] -Genetic height prediction formula

[0090] Boys: (Father's height + Mother's height) / 2 + 6.5cm

[0091] Girls: (Father's height + Mother's height) / 2-6.5cm

[0092] The weights (or parameters) of a growth prediction model calculated by inputting genetically predicted height as a data set can reflect genetic characteristics, and a growth prediction model including weights (or parameters) that reflect genetic characteristics can provide the effect of calculating predicted adult height by reflecting genetic characteristics.

[0093] Furthermore, the electronic device 1000 may generate a growth prediction model for each growth treatment group. Specifically, the electronic device 1000 may acquire, as input data, a group identifier for identifying the growth treatment group. For example, the electronic device 1000 may acquire, as input data, a group identifier indicating whether the input data corresponds to the normal group, the growth hormone treatment group, the precocious puberty suppression treatment group, and / or the growth hormone-precocious puberty suppression treatment group. In this case, the electronic device 1000 may generate a growth prediction model for each group based on the group identifier. Specifically, when the input data includes a first group identifier (e.g., an identifier indicating the normal group), the electronic device 1000 may generate a third model including a third weight for the first group based on the input data (e.g., a data set associated with the normal group), adult height calculated through growth statistical data, and / or actual adult height. On the other hand, if the input data includes a second group identifier (e.g., an identifier indicating one of the growth hormone treatment group, the precocious puberty suppression treatment group, and / or the growth hormone-precocious puberty suppression treatment group), the electronic device 1000 can generate a fourth model including a fourth weighting value for the second group based on the input data (e.g., a dataset related to the treatment group), adult height calculated through growth statistical data, and / or actual adult height.

[0094] FIG. 5 is a diagram illustrating an operation of calculating a predicted adult height using a growth prediction model in the electronic device 1000 according to an embodiment of the present application.

[0095] The electronic device 1000 according to an embodiment of the present application may acquire subject input data including height information, chronological age information, bone age information, and / or gender information of a subject whose predicted adult height is to be analyzed at the time of analysis, via the transceiver 1100. Furthermore, the electronic device 1000 may acquire growth statistical data via the transceiver 1100. For example, the electronic device 1000 may acquire growth statistical data corresponding to a group to which the subject belongs (e.g., a group corresponding to the subject's gender, age, and / or treatment information) via the transceiver 1100.

[0096] Furthermore, the electronic device 1000 according to an embodiment of the present application may be configured to calculate an adult's height from subject input data including the subject's height information, chronological age information, bone age information, and / or gender information through growth statistical data.

[0097] For example, the electronic device 1000 may acquire percentiles corresponding to chronological age information and height information from growth statistical data based on height information and chronological age information in the target input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0098] For example, the electronic device 1000 may acquire percentiles corresponding to bone age information and height information from growth statistical data based on height information and bone age information in the subject input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0099] For example, the electronic device 1000 may acquire weighted average age information and percentiles corresponding to height information from growth statistical data based on weighted average age information of bone age information and chronological age information in the subject input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0100] Furthermore, the electronic device 1000 according to an embodiment of the present application can calculate a predicted adult height from an adult height calculated through the subject input data and / or growth statistical data using the generated growth prediction model. Specifically, the electronic device 1000 can calculate a predicted adult height from an adult height calculated through the subject input data and / or growth statistical data using the growth prediction model including the weights (or parameters) derived through the artificial intelligence model as described above. For example, the electronic device 1000 can input the subject input data height information, chronological age information, bone age information, gender information, and / or adult height calculated through the growth statistical data into the generated growth prediction model, and obtain a predicted adult height based on the calculated weights (or updated parameters) of the generated growth prediction model.

[0101] Furthermore, the electronic device 1000 according to an embodiment of the present application may be configured to calculate a predicted adult height using a growth prediction model generated for each age range. Specifically, the electronic device 1000 may identify a predetermined age range based on age information (e.g., chronological age information or bone age information) of the target input data. If the identified age range corresponds to a first range (e.g., an age range before puberty), the electronic device 1000 may calculate a predicted adult height from the adult height calculated based on the target input data and / or growth statistical data using a first model including a first weighting value. On the other hand, if the identified age range corresponds to a second range (e.g., an age range after puberty), the electronic device 1000 may calculate a predicted adult height from the adult height calculated based on the target input data and / or growth statistical data using a second model including a second weighting value.

[0102] Furthermore, the electronic device 1000 according to an embodiment of the present application may be configured to calculate a predicted adult height using a growth prediction model generated for each treatment group. Specifically, the electronic device 1000 may be configured to acquire, as subject input data, a group identifier indicating whether the subject input data corresponds to the normal group, the growth hormone treatment group, the precocious puberty suppression treatment group, and / or the growth hormone-precocious puberty suppression treatment group. The electronic device 1000 may select a model for calculating a predicted adult height based on the group identifier, and use the selected model to calculate a predicted adult height from the adult height calculated using the bone age information, chronological age information, height information, gender information, and / or growth statistical data of the subject input data. For example, if the subject input data includes a first group identifier (e.g., an identifier indicating the normal group), the electronic device 1000 may input the adult height calculated using the subject input data and / or growth statistical data into a third model including a third weighting value to obtain a predicted adult height. For example, if the subject input data includes a second group identifier (e.g., an identifier indicating one of the growth hormone treatment group, the precocious puberty suppression treatment group, and / or the growth hormone-precocious puberty suppression treatment group), the electronic device 1000 can input the adult height calculated through the subject input data and / or the growth statistical data into a fourth model including a fourth weighting value to obtain a predicted adult height.

[0103] Meanwhile, although not specifically described for convenience of explanation, the electronic device 1000 may be embodied to generate a growth prediction model for each of the growth hormone treatment group, the precocious puberty suppression treatment group, and / or the growth hormone-precocious puberty suppression treatment group, and to calculate predicted adult height optimized for each group using the growth prediction model generated for each group.

[0104]

[0105] Hereinafter, a method for calculating the predicted adult height according to one embodiment of the present application will be described in more detail with reference to Figures 6 to 8. In describing the method for calculating the predicted adult height, overlapping examples described with reference to Figures 2 to 5 may be omitted, but this is for convenience of explanation only and should not be construed as limiting.

[0106] FIG. 6 is a flowchart illustrating a method for calculating predicted adult height according to one embodiment of the present application.

[0107] A method for calculating a predicted adult height according to one embodiment of the present application may include a step of generating a growth prediction model (S1000) and a step of calculating a predicted adult height using the growth prediction model (S2000).

[0108] In the step of generating a growth prediction model (S1000), the electronic device 1000 can generate a growth prediction model for predicting the expected adult height based on age information (e.g., bone age information or chronological age information), height information, and / or gender information.

[0109] FIG. 7 is a flowchart embodying the step of generating a growth prediction model (S1000) according to one embodiment of the present application.

[0110] The step of generating a growth prediction model (S1000) according to one embodiment of the present application may further include a step of acquiring input data (S1100), a step of calculating an adult height based on the input data and growth statistical data (S1200), and a step of generating a growth prediction model based on the input data and the calculated adult height (S1300).

[0111] In the step of acquiring input data (S1100), the electronic device 1000 may acquire input data through the transceiver 1100. Here, the input data may include any information that may affect an adult's height, including height information at a specific point in time, age information (e.g., bone age information and chronological age information), gender information, and / or parental height information and growth treatment information.

[0112] Furthermore, in the step of acquiring input data (S1100), the electronic device 1000 may acquire growth statistical data through the transceiver 1100. The growth statistical data may be grouped by age, sex, country, region, and / or race, and the grouped growth statistical data may include statistical data related to height percentiles by age for the corresponding group.

[0113] In the step of calculating the adult's height based on the input data and the growth statistical data (S1200), the electronic device 1000 can calculate the adult's height based on the input data and the growth statistical data.

[0114] As an example, the electronic device 1000 may acquire the 1st percentile corresponding to the chronological age information and height information from the growth statistical data based on the height information and chronological age information of the input data. For example, if the chronological age information of the input data includes 10 years and 120 months and the height information of the input data includes 135.0 cm, the electronic device 1000 may acquire the 1st percentile (height percentile 25 of FIG. 4 ) corresponding to the chronological age information (10 years and 120 months in FIG. 4 ) and height information (135.0 cm in FIG. 4 ) using the growth statistical data corresponding to the gender information of the input data. Furthermore, the electronic device 1000 may calculate a first height corresponding to the 1st percentile as an adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age). For example, the electronic device 1000 may calculate a first height corresponding to the 1st percentile (e.g., height percentile 25 obtained as described above) as an adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of an adult age group).

[0115] As an example, the electronic device 1000 may acquire the bone age information and the second percentile corresponding to the height information from the growth statistical data based on the height information and bone age information of the input data. For example, if the bone age information of the input data includes 10 years and 128 months and the height information of the input data includes 135.0 cm, the electronic device 1000 may acquire the second percentile (height percentile 10 in FIG. 4 ) corresponding to the bone age information (10 years and 128 months in FIG. 4 ) and the height information (135.0 cm in FIG. 4 ) using the growth statistical data corresponding to the gender information of the input data. Furthermore, the electronic device 1000 may calculate the second height corresponding to the second percentile as the adult height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age). For example, the electronic device 1000 may calculate the second height corresponding to the second percentile (e.g., height percentile 10 obtained as described above) as the adult height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of an adult age group).

[0116] For example, the electronic device 1000 may calculate the adult's height based on a first height calculated using the chronological age information and a second height calculated using the bone age information. For example, the electronic device 1000 may assign a first weight to the first height and a second weight to the second height, and calculate the adult's height based on the first height to which the first weight is assigned and the second height to which the second weight is assigned. For example, the electronic device 1000 may be embodied to calculate the adult's height as a weighted average of the first height and the second height.

[0117] As an example, the electronic device 1000 may be configured to calculate an adult's height based on a weighted average value of bone age information and chronological age information of the input data. For example, if the bone age information of the input data includes 12 years old and the chronological age information includes 10 years old, the electronic device 1000 may acquire weighted average age information corresponding to the weighted average of the two values ​​and acquire a percentile from the growth statistical data using the weighted average age information and the height information of the input data. For example, if the weights are set to be the same, the electronic device 1000 may calculate a third percentile corresponding to the weighted average age information including a value of 11 years old and the height information of the input data using the growth statistical data. In this case, the electronic device 1000 may calculate a third height corresponding to the third percentile as the adult's height using the growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age), as described above.

[0118] In the step of generating a growth prediction model based on the input data and the calculated adult height (S1300), the electronic device 1000 can generate the growth prediction model based on the input data and the calculated adult height. More specifically, the electronic device 1000 can obtain the actual adult height of the child corresponding to the input data. At this time, the electronic device 1000 can generate the growth prediction model based on the input data, the adult height calculated through the growth statistical data, and / or the actual adult height.

[0119] For example, the electronic device 1000 may generate a growth forecast model using a regression analysis technique. Specifically, the electronic device 1000 may generate a growth forecast model including optimal weights using the following mathematical formula:

[0120]

[0121] -Math formula

[0122] TIFF2025537342000006.tif2551

[0123] k: The kth dataset consisting of actual adult height and dependent variables (values ​​of information contained in the input data and / or calculated adult height through growth statistics data).

[0124] JPEG2025537342000007.jpg14102

[0125] JPEG2025537342000008.jpg20166

[0126] JPEG2025537342000009.jpg1481

[0127] Specifically, the electronic device 1000 may be configured to input an adult height calculated from input data such as height information (e.g., a child's height information), chronological age information, bone age information, gender information, and / or growth statistical data as an input value (x) of the growth prediction model, and input an actual adult height as an output value (y) of the growth prediction model. In this case, the electronic device 1000 may be configured to calculate a weight value (w) of the growth prediction model for outputting an output value from the input value.

[0128] As an example, the electronic device 1000 may generate a growth prediction model using machine learning techniques. Specifically, the electronic device 1000 may train the growth prediction model using a learning dataset including input data including height information, chronological age information, bone age information, gender information, and / or an adult's height calculated using growth statistical data, and label information consisting of actual adult heights. More specifically, the electronic device 1000 may input the adult's height calculated using height information, chronological age information, bone age information, gender information, and / or growth statistical data into the growth prediction model, obtain an output value output through the growth prediction model, and update parameters included in the growth prediction model based on the difference between the output value and the actual adult's height included in the label information, thereby training the growth prediction model. For example, the electronic device 1000 may update the parameters of the growth prediction model so that the output value of the growth prediction model approximates the actual adult's height included in the label information.

[0129] Meanwhile, the electronic device 1000 according to an embodiment of the present application may generate a growth prediction model for each age range. Specifically, the electronic device 1000 may identify a predetermined age range based on age information (e.g., chronological age information or bone age information) of input data. In this case, if the identified age range corresponds to a first range, the electronic device 1000 may generate a first model including a first weighting value as described above based on the input data corresponding to the first range, a dataset including adult heights calculated through growth statistical data, and actual adult heights. On the other hand, if the identified age range corresponds to a second range, the electronic device 1000 may generate a second model including a second weighting value as described above based on the input data corresponding to the second range, a dataset including adult heights calculated through growth statistical data, and actual adult heights.

[0130] In the step of calculating the predicted adult height using the growth prediction model (S2000), the electronic device 1000 can calculate the predicted adult height from the subject input data including height information, age information (e.g., bone age, chronological age), and / or gender information of the subject at the time of analysis using the growth prediction model generated through step S1000.

[0131] FIG. 8 is a flowchart embodying the step of calculating predicted adult height (S2000) using a growth prediction model according to one embodiment of the present application.

[0132] The step of calculating the predicted adult height using a growth prediction model according to one embodiment of the present application (S2000) may further include the step of acquiring subject input data (S2100) and the step of calculating the predicted adult height from the subject input data using a growth prediction model (S2200).

[0133] In the step of acquiring subject input data (S2100), the electronic device 1000 may acquire, via the transceiver 1100, subject input data including height information at the time of analysis, chronological age information at the time of analysis, bone age information at the time of analysis, and / or gender information of the subject whose predicted adult height is to be analyzed.

[0134] Furthermore, in the step of acquiring subject input data (S2100), the electronic device 1000 may acquire growth statistical data through the transceiver 1100. For example, the electronic device 1000 may acquire growth statistical data corresponding to a group to which the subject belongs (e.g., a group corresponding to the subject's gender, age, and / or treatment information) through the transceiver 1100.

[0135] In the step of calculating the predicted adult height from the subject input data using the growth prediction model (S2200), the electronic device 1000 may be embodied to calculate the adult height from the subject input data including the subject's height information, chronological age information, bone age information, and / or gender information through growth statistical data.

[0136] For example, the electronic device 1000 may acquire percentiles corresponding to chronological age information and height information from growth statistical data based on height information and chronological age information in the target input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0137] For example, the electronic device 1000 may acquire percentiles corresponding to bone age information and height information from growth statistical data based on height information and bone age information in the subject input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0138] For example, the electronic device 1000 may acquire weighted average age information and percentiles corresponding to height information from growth statistical data based on weighted average age information of bone age information and chronological age information in the subject input data. Furthermore, the electronic device 1000 may calculate the height corresponding to the percentile as the adult height using growth statistical data of a group whose growth has stopped (e.g., growth statistical data of a preset age).

[0139] Furthermore, in step S2200 of calculating a predicted adult height from the subject input data using a growth prediction model, the electronic device 1000 can calculate a predicted adult height from the adult height calculated through the subject input data and / or growth statistical data using the growth prediction model including the weights (or parameters) calculated in step S1300. For example, the electronic device 1000 can input the subject input data's height information, chronological age information, bone age information, gender information, and / or adult height calculated through the growth statistical data into the generated growth prediction model, and obtain a predicted adult height calculated based on the calculated weights (or updated parameters) of the generated growth prediction model.

[0140] In the step S2200 of calculating the predicted adult height from the subject input data using the growth prediction model, the electronic device 1000 may be embodied to calculate the predicted adult height using the growth prediction model generated for each age range. Specifically, the electronic device 1000 may identify a predetermined age range based on age information (e.g., chronological age information or bone age information) of the subject input data. At this time, if the identified age range corresponds to a first range (e.g., an age range before puberty), the electronic device 1000 may calculate the predicted adult height from the adult height calculated from the subject input data and / or growth statistical data using a first model including a first weighting value. On the other hand, if the identified age range corresponds to a second range (e.g., an age range after puberty), the electronic device 1000 may calculate the predicted adult height from the adult height calculated from the subject input data and / or growth statistical data using a second model including a second weighting value.

[0141] According to the method for calculating predicted adult height and the electronic device for performing the method according to the embodiment of the present application, a growth prediction model is generated using the adult height calculated based on growth statistical data that reflects historical, regional, and racial characteristics, thereby making it possible to calculate predicted adult height while reflecting various characteristics that affect growth.

[0142] According to the method for calculating predicted adult height and the electronic device for performing the method according to the embodiments of the present application, a growth prediction model can be generated for each age group and / or growth treatment group, and the predicted adult height can be calculated using the growth prediction model corresponding to the group to which the subject belongs, thereby making it possible to predict the predicted adult height with higher accuracy.

[0143] According to the method for calculating predicted adult height and the electronic device for performing the method according to the embodiments of the present application, a growth prediction model reflecting genetic characteristics can be generated using genetic predicted height as a data set, and predicted adult height can be calculated by reflecting genetic characteristics through the growth prediction model reflecting genetic characteristics.

[0144] The various operations of the electronic device 1000 described above may be stored in a memory 1200 of the electronic device 1000 , and a processor 1300 of the electronic device 1000 may be configured to perform the operations stored in the memory 1200 .

[0145] The features, structures, effects, etc. described in the above embodiments are included in at least one embodiment of the present invention and are not necessarily limited to only one embodiment. Furthermore, the features, structures, effects, etc. exemplified in each embodiment can be combined or modified in other embodiments by a person skilled in the art to which the embodiments belong. Therefore, content related to such combinations and modifications should be interpreted as being included in the scope of the present invention.

[0146] Furthermore, although the above description focuses on the embodiments, these are merely examples and do not limit the present invention. Those skilled in the art will recognize that various modifications and applications not exemplified above are possible within the scope of the essential characteristics of the present invention. In other words, each component specifically illustrated in the embodiments can be modified and implemented. Differences related to such modifications and applications should be construed as being included within the scope of the present invention as defined by the appended claims.

Claims

1. 1. A method for calculating a predicted adult height by an electronic device, comprising: generating a growth prediction model; and Calculating a predicted adult height using the generated growth prediction model, The step of generating the growth prediction model comprises: acquiring input data and growth statistics data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and sex information; calculating adult height based on the input data and the growth statistical data; obtaining the child's actual adult height corresponding to the input data; and The method for calculating a predicted adult height further includes generating the growth prediction model based on the input data, the calculated adult height, and the actual adult height.

2. The step of calculating the height of an adult includes: obtaining a first percentile corresponding to the chronological age information and the height information from the growth statistical data based on the height information and chronological age information of the input data; and 2. The method of claim 1, further comprising the step of calculating a first height corresponding to the first percentile as the adult height using growth statistical data of a group whose growth has stopped.

3. The step of calculating the height of an adult includes: obtaining a second percentile corresponding to the bone age information and the height information from the growth statistical data based on the height information and the bone age information of the input data; and 2. The method of claim 1, further comprising the step of calculating a second height corresponding to the second percentile as the adult height using growth statistical data of a group whose growth has stopped.

4. The step of generating the growth prediction model comprises: inputting the height information, chronological age information, bone age information, sex information, and the calculated adult height of the input data as input values ​​of the growth prediction model; inputting the actual adult height as an output value of the growth prediction model; and 3. The method of claim 2, further comprising the step of calculating weights of the growth prediction model for outputting the output value from the input value.

5. The step of calculating the predicted adult height includes: A step of acquiring subject input data including height information at the time of analysis, chronological age information at the time of analysis, bone age information at the time of analysis, and sex information of the subject whose predicted adult height is to be analyzed, and growth statistical data of the group to which the subject belongs; and The method of claim 4 , further comprising: calculating the predicted adult height from the subject input data using the growth prediction model including the calculated weights.

6. The step of calculating the predicted adult height includes: calculating an adult height from the subject's subject input data through the growth statistical data; and 6. The method of claim 5, further comprising inputting the subject input data and the calculated adult height into the growth prediction model, and obtaining the predicted adult height output through the growth prediction model.

7. The step of generating the growth prediction model comprises: identifying a predetermined age range based on the chronological age information or bone age information of the input data; and 7. The method of claim 6, further comprising: generating a first model including a first weight based on the actual adult height and a dataset of adult heights calculated through input data corresponding to the first age range and the growth statistical data, if the identified age range corresponds to a first age range; and generating a second model including a second weight based on the actual adult height and a dataset of adult heights calculated through input data corresponding to the second age range and the growth statistical data, if the identified age range corresponds to a second age range.

8. The step of calculating the predicted adult height includes: identifying a predetermined age range based on chronological age information or bone age information of the subject input data; 8. The method of claim 7, further comprising: calculating the predicted adult height from the adult height calculated through the subject input data and the growth statistical data using the first model when the identified age range corresponds to the first range; and calculating the predicted adult height from the adult height calculated through the subject input data and the growth statistical data using the second model when the identified age range corresponds to the second range.

9. A computer-readable recording medium having recorded thereon a program for causing a computer to execute the method according to any one of claims 1 to 8.

10. 1. An electronic device for predicting adult height, comprising:

1. An electronic device comprising: a processor configured to acquire input data and growth statistical data, the input data including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculate an adult height based on the input data and the growth statistical data; obtain an actual adult height of the child corresponding to the input data; generate a growth prediction model based on the input data, the calculated adult height, and the actual adult height; and calculate a predicted adult height using the generated growth prediction model.

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