CN116665894A - Bone age monitoring system, method, electronic device and storage medium - Google Patents

Bone age monitoring system, method, electronic device and storage medium Download PDF

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CN116665894A
CN116665894A CN202310452369.5A CN202310452369A CN116665894A CN 116665894 A CN116665894 A CN 116665894A CN 202310452369 A CN202310452369 A CN 202310452369A CN 116665894 A CN116665894 A CN 116665894A
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bone age
information
age
bone
dysplasia
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刘莉
肖芦山
李则杨
伍成凯
洪畅
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Southern Hospital Southern Medical University
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Southern Hospital Southern Medical University
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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Abstract

The application provides a bone age monitoring system, a method, electronic equipment and a storage medium, wherein the bone age monitoring system comprises: the data acquisition module is used for acquiring bone age information and age information; the analysis and diagnosis module is used for analyzing the bone age information and the age information, determining whether the bone age information is abnormal, and if the bone age information is abnormal, further acquiring bone age dysplasia information which can cause bone age dysplasia, wherein the bone age dysplasia information comprises life style, genetic diseases and diet conditions; the health scheme making module is used for inputting the bone age dysplasia information and the bone age information into the classification regression tree algorithm model, wherein the classification regression tree algorithm model is used for generating a health scheme according to the bone age dysplasia information and the bone age information, can accurately diagnose and prevent bone age abnormality by comprehensively analyzing bone age and age conditions, customize a personalized health scheme, detect bone age dysplasia and ensure health growth.

Description

Bone age monitoring system, method, electronic device and storage medium
Technical Field
The present application relates to the field of health management technologies, and in particular, to a bone age monitoring system, a bone age monitoring method, an electronic device, and a storage medium.
Background
Bone age refers to the state of bone growth and development, usually assessed by imaging and clinical examination. In the fields of medicine and health management, understanding bone age is of great importance for judging physical development conditions of children, predicting future growth trend and making personalized health plans.
At present, the traditional bone age monitoring generally only provides bone age information, other related factors such as life style, genetic diseases, diet condition and the like cannot be comprehensively considered, and therefore, comprehensive health guidance and scheme cannot be provided for children with abnormal bone age. In addition, since conventional bone age monitoring is generally only performed in hospitals, it is difficult for some remote areas or people who are inconvenient to go to the hospitals to perform continuous bone age health monitoring.
Disclosure of Invention
In view of the foregoing, there is a need for a bone age monitoring system, method, electronic device and storage medium that overcome at least one of the above drawbacks.
In a first aspect, an embodiment of the present application provides a bone age monitoring system, including: the data acquisition module is used for acquiring bone age information and age information; the analysis and diagnosis module is used for analyzing the bone age information and the age information, determining whether the bone age information is abnormal, and if the bone age information is abnormal, further acquiring bone age dysplasia information which can cause bone age dysplasia, wherein the bone age dysplasia information comprises life style, genetic diseases and diet conditions; the health scheme making module is used for inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model, and the classification regression tree algorithm model is used for generating a health scheme according to the bone age dysplasia information and the bone age information.
According to one embodiment of the application, the classification regression tree algorithm model comprises: the data preprocessing module is used for preprocessing the bone age information and the bone age dysplasia information; the tree construction module is used for constructing a classification regression tree model, carrying out feature selection by applying a Gini index according to the bone age dysplasia information and the bone age information, splitting tree nodes and constructing a classification regression tree structure; and the health scheme output module is used for generating the health scheme according to the bone age dysplasia information and the classification regression tree structure.
According to one embodiment of the application, the bone age monitoring system further comprises: the health tracking module is used for generating a health file according to the bone age abnormality information and the health scheme; the health tracking module is also used for collecting new bone age information and updating the health scheme according to the new bone age information.
According to one embodiment of the application, the bone age monitoring system further comprises:
and the message notification module is in communication connection with a terminal and is used for sending the health plan generated by the health plan making module to the terminal.
According to one embodiment of the application, the health regimen comprises: diet adjustment regimen, exercise regimen, sleep adjustment regimen, medication regimen, and nutritional supplement regimen.
According to one embodiment of the application, the bone age monitoring system further comprises: the data statistics module is used for acquiring a plurality of pieces of bone age information, a plurality of pieces of age information and a plurality of pieces of bone age dysplasia information; the data statistics module is also used for generating bone age abnormality statistics information according to the plurality of bone age information, the plurality of age information and the plurality of bone age dysplasia information.
According to one embodiment of the present application, the data statistics module is further configured to obtain region information and time information corresponding to the bone age dysplasia information; the bone age abnormality statistical information includes the region information and the time information.
In a second aspect, an embodiment of the present application provides a bone age monitoring method, where the bone age monitoring system in the first aspect includes: collecting bone age information and age information; analyzing the bone age information and the age information to determine whether the bone age information is abnormal; if the bone age information is abnormal, further acquiring bone age dysplasia information which can cause bone age dysplasia, wherein the bone age dysplasia information comprises life style, genetic diseases and diet conditions; inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model; and generating a health scheme according to the bone age dysplasia information and the bone age information.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the bone age monitoring method of the second aspect when executing the instructions.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium comprising instructions that instruct a device to perform the bone age monitoring method according to the second aspect.
According to the bone age monitoring system, the method, the electronic equipment and the storage medium, provided by the embodiment of the application, the bone age and age conditions can be comprehensively analyzed, the abnormal bone age can be accurately diagnosed and prevented, the personalized health scheme is customized, the abnormal bone age development can be detected, and the healthy growth can be ensured.
Drawings
Fig. 1 is a schematic diagram of a bone age monitoring system according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a bone age monitoring system according to another embodiment of the present application
Fig. 3 is a flowchart of a bone age monitoring method according to an embodiment of the present application.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the application.
Description of the main reference signs
Bone age monitoring system 10;10a
Data acquisition module 110
Analytical diagnostic module 120
A health plan formulation module 130;130a
Data preprocessing module 131
Tree construction module 132
Health plan output module 133
Health tracking module 140
Message notification module 150
Data statistics module 160
Electronic equipment 20
Processor 21
Memory 22
Method steps S100-S500
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application.
It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and a plurality refers to two or more. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It should be noted that, in the embodiments of the present application, the terms "first," "second," and the like are used for distinguishing between the descriptions and not necessarily for indicating or implying a relative importance, or for indicating or implying a sequence. Features defining "first", "second" may include one or more of the stated features, either explicitly or implicitly. In describing embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without any inventive effort, are intended to be within the scope of the present application.
Bone age refers to the state of bone growth and development, usually assessed by imaging and clinical examination. In the fields of medicine and health management, understanding bone age is of great importance for judging the physical development condition of a person, predicting future growth trend and making personalized health plans.
At present, the traditional bone age monitoring generally only provides bone age information, other relevant factors such as life style, genetic diseases, diet condition and the like cannot be comprehensively considered, and therefore, comprehensive health guidance and scheme cannot be provided for patients. In addition, since conventional bone age monitoring is generally only performed in hospitals, it is difficult for some remote areas or people who are inconvenient to go to the hospitals to perform continuous bone age health monitoring.
Furthermore, the dynamic monitoring and health management functions for bone age have not been fully implemented. Therefore, it is necessary to develop a bone age monitoring system that can dynamically monitor and manage bone ages and describe the distribution of bone age areas. Such a system would provide information support for government-and community-established related decisions and community-established related protocols, thereby helping to improve developmental conditions.
Therefore, the embodiment of the application provides a bone age monitoring system, a method, electronic equipment and a storage medium, which can accurately diagnose and prevent abnormal bone age by comprehensively analyzing the bone age and age, customize a personalized health scheme, detect abnormal bone age development and ensure healthy growth.
Some embodiments of the application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram of a bone age monitoring system according to an embodiment of the present application. The bone age monitoring system 10 shown in fig. 1 at least includes the following data acquisition module 110, analysis diagnosis module 120, and health plan formulation module 130.
In an embodiment of the present application, the data acquisition module 110 is configured to acquire bone age information and age information. It will be appreciated that bone age information is typically obtained by radiographic techniques including wrist X-ray, hand X-ray, chest X-ray, and the like. By means of these techniques, the size, shape, density, etc. of human skeleton may be measured to determine the bone age. The data acquisition module 110 can read the bone age of the child by connecting to a hospital medical record system. In other possible embodiments, the data acquisition module 110 may also obtain the child's bone age information by reading the child's medical record information. It will be appreciated that age information refers to the actual age of the child, and is typically determined by reading the date of birth in an identification card or medical record.
In the embodiment of the present application, the analysis and diagnosis module 120 is configured to analyze the bone age information and the age information to determine whether the bone age information is abnormal, and if the bone age information is abnormal, further obtain bone age dysplasia information that may cause bone age dysplasia, where the bone age dysplasia information includes life style, genetic disease and diet condition.
It is understood that lifestyle, genetic disorders and eating conditions are all factors that may cause bone age dysplasia in children. Bad lifestyles such as lack of exercise, insufficient sleep, excessive use of electronic equipment, etc. can affect the bone age development of children. Genetic diseases are also important causes of bone age dysplasia in children, such as hypoplasia, hypothyroidism, etc. In addition, unbalanced diet, lack of nutrition, excessive intake of caffeine, etc. may also negatively affect bone age development. Thus, understanding these factors, which may lead to bone age dysplasia, helps to formulate corresponding health regimens and precautions.
It is understood that bone age refers to a method of assessing skeletal development by X-ray examination. In the growth and development process of human body, bones are one of the most varied organs, and the bone development degree of different age periods is obviously different. The doctor can evaluate the bone age by taking X-ray pictures and observing the development degree of ossification centers of the left palmar phalanges, the carpals, and the lower end of the radius ulna. Bone age assessment may reflect the level of growth and development, predict adult height, arrival time at puberty, and aid in diagnosing certain metabolic disorders, etc. The evaluation of bone age is generally performed by comparing a standard bone age tablet with a bone age tablet to be tested, and determining the bone age level by calculating the bone age difference.
Normally, the bone age of children is consistent with the development of their life age. However, due to the influence of factors such as diet, medicine, genetics, living environment and the like, some children develop in advance of bone age, and some children develop later. For the evaluation of the development level, a method of bone age difference (bone age minus life age) is generally used clinically. The difference value is positive, which indicates that the bone age development of the children is advanced in advance of the life age of the children; a negative difference indicates a relative delay in the bone age development of the child.
It is understood that the bone age development of children in advance of their life age may cause physical incompatibility, such as imbalance in the ratio of parts of the body, imbalance in the ratio of height and weight, etc., and thus affect the healthy growth of children. Furthermore, premature bone age development may lead to premature puberty in the child, destabilizing physical development and premature closure of the bones, thereby affecting adult height and health. In addition, premature bone age development may be associated with dyssynchrony of mental and social development, adversely affecting the mental health and social abilities of the child. In addition, relatively delayed bone age development can also present a number of problems for children. On the one hand, children may be caused to be lower in height than the same age, thereby affecting their self-esteem and mental health. On the other hand, bone age lag may also suggest certain potential health problems, such as growth hormone deficiency, malnutrition, etc. Therefore, it is also important to monitor and address the problem of relative retardation in the bone age development of children in a timely manner.
In an embodiment of the present application, the health plan formulation module 130 is configured to input the bone age dysplasia information and the bone age information into a classification regression tree algorithm model, where the classification regression tree algorithm model is configured to generate a health plan according to the bone age dysplasia information and the bone age information.
In the embodiment of the application, a classification regression tree algorithm (Classification and Regression Tree, CART) model is a decision model based on a tree structure. The CART model may be used to generate a health plan on information of bone age dysplasia and bone age data. Firstly, a classification regression tree algorithm model divides a data set into different age groups according to bone age data, and then training a classification regression tree is carried out on each age group to obtain a corresponding health scheme. During the training process, the classification regression tree algorithm model may use Gini indexes to evaluate the importance of the features, thereby determining the optimal features for splitting. The health plan generated by the classification regression tree algorithm model may include suggestions in sports, diet, medication, etc. to help children improve bone age dysplasia and promote healthy growth.
It can be understood that the Gini index is calculated as:
wherein D is a numberThe dataset, |y| represents the number of categories in the dataset, p k Representing the proportion of samples of class k in the dataset.
In this example, the smaller the Gini index, the purer the data set, the better the classification effect. In the CART model, gini index of each feature is calculated when the division feature is selected, and the feature with the smallest Gini index is selected as the division feature. By calculating the Gini index, the CART model can find the optimal features to perform classification regression tree splitting, so that a corresponding classification regression tree model is generated, and a health scheme aiming at bone age dysplasia is generated based on the classification regression tree model.
It will be appreciated that this categorical regression tree model will contain a series of judgment conditions and corresponding conclusions to guide the generation of a health regimen. For example, if genetic disease factors are included in the bone age dysplasia information, the classification regression tree model will split at the corresponding nodes and generate the corresponding subtrees to generate the corresponding health plan. In this way, the classification regression tree model will generate different health schemes for different bone age dysplasia information and bone age information to meet the needs of different children.
Specifically, the classification regression tree model can find the optimal feature to perform classification regression tree splitting by calculating Gini indexes. For example, there is a set of children's bone age anomaly data that includes a plurality of characteristics, such as eating, genetic disease, etc., and corresponding labels, such as normal or abnormal. The classification regression tree model may determine which feature is best suited as a splitting condition by calculating the Gini index for each feature. For example, if the classification regression tree model finds that the Gini index for the diet is the smallest, the classification regression tree model can treat it as a condition for the first split.
Next, the classification regression tree model may divide the data set by the conditions of the first split and continue to calculate the Gini index for each subset to determine the next most appropriate split condition. By constantly iterating the calculations, a classification regression tree model containing a plurality of nodes may ultimately be generated.
After the classification regression tree model is generated, it can be used to formulate a health plan for bone age dysplasia. For each infant, the optimal health scheme can be obtained by gradually judging on the classification regression tree model according to the bone age dysplasia information and the age information of the infant. For example, if the infant's diet is abnormal, it may be recommended to make dietary adjustments and further develop detailed health regimens based on its age and bone age development.
In an embodiment of the present application, the health plan formulated by the health plan formulation module 130 includes, but is not limited to: diet adjustment regimen, exercise regimen, sleep adjustment regimen, medication regimen, nutritional supplement regimen, and the like. In particular, the health regimen formulation module 130 may customize the health regimen appropriate for an individual based on information collected in the etiology acquisition questionnaire, in combination with medical knowledge and clinical experience. In the process of formulating the health scheme, factors such as living habits, family environment, learning pressure and the like of the children can be considered, so that effective execution of the health scheme and comprehensive promotion of the health of the children are promoted.
In embodiments of the application, the dietary adjustment regimen may include a rational dietary adjustment regimen based on the child's dietary profile and the nutrients needed, such as increasing protein intake, controlling fat and sugar intake, etc., to promote bone health development.
In the embodiment of the application, the exercise scheme can be formulated according to the information of the bone age data, the age, the physical condition, the life habit and the like of the children, so as to promote bone development and health.
In the embodiment of the application, the sleep adjustment scheme can be formulated according to the sleep condition and living habit of the child, for example, enough sleep time is ensured, the sleep posture is adjusted, and the like, so as to promote the bone healthy development.
In the embodiment of the application, the drug treatment scheme can be formulated according to indexes such as bone age data and bone density of children and the like and combined with the result of etiology analysis, for example, hormone treatment for promoting bone development, drug treatment for promoting bone development by using calcium tablets and the like are used for promoting bone healthy development.
In the embodiment of the application, the nutrition supplementing scheme can comprise the steps of preparing a personalized nutrition supplementing scheme, such as supplementing nutrients of calcium, vitamin D and the like, according to the diet condition of children and the needed nutrients and combining the etiology analysis result so as to promote the bone healthy development.
Fig. 2 is a schematic diagram of a bone age monitoring system 10a according to another embodiment of the present application. The bone age monitoring system 10a shown in fig. 2 is identical to the bone age monitoring system 10 shown in fig. 1 and also includes a data acquisition module 110, an analysis and diagnosis module 120, and a health plan formulation module 130a. In addition, the bone age monitoring system 10a further includes: a health tracking module 140, a message notification module 150, and a data statistics module 160; in contrast to the bone age monitoring system 10, the health plan formulation module 130a also includes a data preprocessing module 131, a tree construction module 132, and a health plan output module 133.
In the embodiment of the present application, the data acquisition module 110 and the analysis and diagnosis module 120 in the bone age monitoring system 10a have the same or similar functions as the data acquisition module 110 and the analysis and diagnosis module 120 in fig. 1, and the detailed functions are shown in fig. 1 and the description thereof are omitted herein.
In the embodiment of the present application, the health plan formulation module 130a further includes a data preprocessing module 131, a tree construction module 132, and a health plan output module 133. In this embodiment, the data preprocessing module 131 is configured to preprocess the bone age information and the bone age dysplasia information. In this embodiment, the tree construction module 132 is configured to construct a classification regression tree model, apply Gini indexes to perform feature selection according to the bone age dysplasia information and the bone age information, split tree nodes and construct a classification regression tree structure; in this embodiment, the health plan output module 133 is configured to generate a health plan according to the bone age dysplasia information and the classification regression tree structure.
In this embodiment, the preprocessing module 131 reads the images in the medical record or directly reads the bone age data in the medical record. If the preprocessing module 131 reads the images in the medical record, image processing, such as denoising, graying, binarization, edge detection, image segmentation, etc., is required to obtain bone regions and extract bone features, thereby obtaining bone age data. If the preprocessing module 131 directly reads the bone abnormal data in the medical record, the data needs to be cleaned to remove the missing value, the abnormal value, and the like. Feature extraction is then required to convert the raw data into feature vectors that can be used for model training. For a bone age monitoring system, features including bone age, gender, genetic history, eating habits, lifestyle, etc. may be extracted. For features of continuity, normalization or normalization processes may be performed to facilitate classification regression tree model training.
In an embodiment of the present application, the tree construction module 132 is used to construct a classification regression tree model. Specifically, the construction of the classification regression tree model comprises the steps of selecting optimal characteristics to split the classification regression tree by calculating indexes such as Gini indexes or information gains according to the characteristic values of training data; repeating the above process for each child node after splitting until a stopping condition is met, such as that the number of node samples reaches a certain threshold or the depth of the tree reaches a certain limit; finally, a classification regression tree model consisting of decision nodes and leaf nodes is obtained.
It will be appreciated that in the classification task, each leaf node represents a class, and when predicting new sample data, the classification regression tree is traversed from the root node according to the feature value, and finally reaches a leaf node, where the class of the leaf node is the prediction result. In the regression task, the leaf node represents a numerical value, and the prediction result is the average or median of the leaf nodes reached from the root node.
In the embodiment of the present application, the health plan output module 133 may input the acquired bone age dysplasia information and bone age information into the constructed classification regression tree model for calculation, and generate a corresponding health plan according to the conclusions on the branches and leaf nodes of the classification regression tree. In the process, the judging conditions and conclusions of the branches and the leaf nodes of the classification regression tree play a key role. For example, if it is determined from the conclusions on the branches and leaf nodes of the classification regression tree that the child's bone age dysplasia is primarily due to vitamin D deficiency, the health regimen output module 133 may formulate a specific targeted health regimen, such as increasing intake of vitamin D-containing foods, proper sun exposure, etc., for the purpose of improving bone age dysplasia.
In the embodiment of the application, the health tracking module 140 can continuously track the bone age health of the children, discover and process abnormal conditions in time, and ensure the bone age health development of the children. Specifically, the health tracking module 140 may obtain the bone age information and the age information of the child through regular data collection and analysis, and evaluate the bone age health status of the child in combination with the previously established classification regression tree model. If the evaluation result shows that an abnormal situation exists, the health tracking module 140 can generate a new health scheme in time to help children adjust life style, eating habit and the like, and suggest proper treatment methods to achieve the effect of promoting healthy development of bone age. It can be appreciated that the health tracking module 140 can also record the bone age health development condition of the child, form a complete health file, and support the doctor and the parents to share and view data. Thus, doctors and parents can know the bone age health condition of children at any time and make adjustment and treatment in time.
In the embodiment of the present application, the message notification module 150 is communicatively connected to the terminal, and the message notification module 150 is configured to send the health plan generated by the health plan making module 130a to the terminal. Specifically, the message notification module 150 may send the health scenario to the terminal for the user to view by means of a short message notification, a mail notification, an applet reminder notification, an APP reminder notification, a voice notification, and the like.
In the embodiment of the present application, the data statistics module 160 is configured to obtain a plurality of bone age information, a plurality of age information, and a plurality of bone age dysplasia information; the data statistics module 160 is further configured to generate bone age abnormality statistics according to the plurality of bone age information, the plurality of age information, and the plurality of bone age dysplasia information. The data statistics module 160 is further configured to obtain region information and time information corresponding to the bone age dysplasia information; the bone age abnormality statistical information includes region information and time information.
In an embodiment of the present application, the data statistics module 160 may provide data providing a child's bone age distribution. The bone age information and the age information of the children are collected, and data processing and analysis are carried out, so that the bone age distribution condition of the children can be obtained. The data can provide references for doctors, researchers, parents and the like, is helpful for understanding the trend and change of the bone age development of children, timely discovers abnormal conditions and establishes corresponding health schemes.
It will be appreciated that the data statistics module 160 needs to rely on a large amount of child bone age data. By collecting and arranging bone age data of places such as hospitals, schools and the like and establishing a child bone age database, sufficient data support can be provided for the data statistics module. In actual use, the data statistics module can provide various data reports, such as age and bone age distribution curves, age and bone age correlation analysis, bone age offset value distribution and the like, so that a user can analyze data more intuitively and comprehensively.
In some embodiments of the present application, the data statistics module 160 may enable a user to more intuitively understand and grasp the characteristics and rules of the data by graphically and visually presenting the data. Common data visualization means include scatter plots, line plots, bar graphs, pie charts, thermodynamic diagrams, and the like. For the statistical data of the bone age development condition of the children, the data statistical module can be presented in a bar graph or a line graph and the like. Through the charts, a user can intuitively know the bone age distribution conditions of different age groups and find abnormal values or abnormal trends so as to take measures to intervene in time.
In some embodiments of the application, the data statistics module 160 may present relationships between different factors and bone age development by thermodynamic diagrams or the like. For example, factors such as diet condition, genetic disease and the like can be related and analyzed with the bone age development condition, so that factors possibly causing bone age development abnormality are found, and scientific basis is provided for the establishment of a health scheme.
In some embodiments of the present application, the data statistics module 160 may perform bone age statistics in units of communities, and may help to understand the bone age development of children in different communities. In the process of statistics, the data statistics module 160 firstly needs to collect bone age data of children in different communities, and then generates corresponding bone age distribution charts and statistics through data processing and analysis so as to perform visual data analysis and display. The data statistics module 160 may statistically calculate the average, median, standard deviation, etc. for the bone age data of different communities to compare and analyze the differences and trends between the different communities. In addition, the data statistics module 160 can also perform classification analysis according to different factors such as age, gender, etc., so as to more comprehensively understand the bone age development condition of children in different communities. The data statistics module 160 can provide visual data charts and analysis results for communities to help communities know the bone age development condition of children and provide corresponding health education and intervention suggestions to promote the healthy growth of children through visual data analysis.
Fig. 3 is a flowchart of a bone age monitoring method according to an embodiment of the present application. The bone age monitoring method shown in fig. 3 at least comprises the following steps: s100: collecting bone age information and age information; s200: analyzing the bone age information and the age information, and determining whether the bone age information is abnormal; s300, if the bone age information is abnormal, further acquiring bone age dysplasia information possibly causing bone age dysplasia; s400, inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model; s500, generating a health scheme according to the bone age dysplasia information and the bone age information.
S100: and collecting bone age information and age information.
In the embodiment of the present application, the data acquisition module 110 is configured to acquire bone age information and age information, and the specific acquisition mode is shown in fig. 1 and 2 and the corresponding description thereof, which are not repeated herein.
S200: and analyzing the bone age information and the age information to determine whether the bone age information is abnormal.
In the embodiment of the present application, the analysis and diagnosis module 120 is configured to analyze the bone age information and the age information to determine whether the bone age information is abnormal, and the specific analysis method is shown in fig. 1 and 2 and the corresponding description thereof, which are not repeated herein.
And S300, if the bone age information is abnormal, further acquiring bone age dysplasia information possibly causing bone age dysplasia.
In the embodiment of the present application, the analysis and diagnosis module 120 is configured to further obtain the bone age dysplasia information that may cause the bone age dysplasia if the bone age information is abnormal, and the specific obtaining manner is shown in fig. 1 and 2 and the corresponding description thereof, which are not repeated herein.
S400, inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model.
In the embodiment of the present application, the health plan making module 130 or the health plan making module 130a is configured to input the bone age dysplasia information and the bone age information into the classification regression tree algorithm model, and the specific input manner is shown in fig. 1 and 2 and the corresponding description thereof, which are not repeated here.
S500, generating a health scheme according to the bone age dysplasia information and the bone age information.
In the embodiment of the present application, the health plan making module 130 or the health plan making module 130a is configured to generate a health plan according to the bone age dysplasia information and the bone age information, and the specific generation manner is shown in fig. 1 and 2 and the corresponding description thereof, which are not repeated herein.
Fig. 4 is an electronic device 20 according to an embodiment of the present application. As shown in fig. 4, the electronic device 20 includes at least the following: a processor 21 and a memory 22.
In an embodiment of the present application, the memory 22 is configured to store instructions executable by the processor 21, and the processor 21 is configured to implement the bone age monitoring method as shown in fig. 3 when executing the instructions.
In an embodiment of the application, a computer-readable storage medium includes instructions that instruct a device to perform the cross-platform remote assistance method according to the first aspect. For example, the instruction instructs the apparatus to execute the bone age monitoring method as shown in steps S100 to S500 in fig. 3.
The program to be executed in the electronic device 20 according to an embodiment of the present application may be a program (a program for causing a computer to function) for controlling a central processing unit (Central Processing Unit, CPU) or the like to realize the functions of the above-described embodiment according to an aspect of the present application. Information processed by these devices is temporarily stored in a random access Memory (Random Access Memory, RAM) when the processing is performed, and then stored in various ROMs such as a Read Only Memory (Flash ROM) and a Hard Disk Drive (HDD), and Read, corrected, and written by a CPU as necessary.
Note that, a part of the electronic device 20 of the above embodiment may be implemented by a computer. In this case, the program for realizing the control function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed.
The term "computer system" as used herein refers to a computer system built into the electronic device 20, and uses hardware including an OS and peripheral devices. The term "computer-readable recording medium" refers to a removable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, and a storage device such as a hard disk incorporated in a computer system.
Also, the "computer-readable recording medium" may include: a medium for dynamically storing a program in a short time, such as a communication line in the case of transmitting the program via a network such as the internet or a communication line such as a telephone line; a medium storing a program for a fixed time, such as a volatile memory in a computer system, which is a server or a client in this case. The program may be a program for realizing a part of the functions described above, or may be a program capable of realizing the functions described above by being combined with a program recorded in a computer system.
The electronic device 20 in the above embodiment may be realized as an aggregate (device group) composed of a plurality of devices. Each device constituting the device group may include a part or all of each function or each functional block of the electronic apparatus 20 according to the above embodiment. The device group may have all the functions or functional blocks of the electronic apparatus 20.
It will be appreciated that the bone age monitoring system 10, method, electronic device 20 and storage medium provided by embodiments of the present application have various beneficial effects on the development and health of children. Firstly, early detection and diagnosis of children dysplasia can promote early intervention and treatment, avoid further development of diseases and improve treatment effect. And secondly, the biological age and sexual maturity of the children can be accurately and objectively estimated, a scientific basis is provided for individuation treatment, and the development and health of the children are guided. Meanwhile, the height of the adult of the child is predicted, whether the child develops normally or not is judged, and scientific basis is provided for the next treatment and correction. In addition, bone age monitoring can also select athletes with potential for the sports field, improve player quality and cultivate more excellent athletes. Finally, the bone age distribution data of the children are provided, scientific references are provided for data demand parties such as governments, communities and enterprises, and the monitoring and improvement of the health of the children are promoted.
It will be appreciated that the bone age monitoring system 10/10a, method, electronic device 20 and storage medium according to the embodiments of the present application may perform the functions of early detection and diagnosis of childhood dysplasia, assessment of biological age and sexual maturity, prediction of adult height, selection of excellent athletes, etc., bone age monitoring to identify genetic and metabolic diseases, assessment of nutritional status, assisted assessment of puberty development, promotion of parental health education, etc. Early diagnosis of genetic diseases or metabolic disorders through bone age monitoring improves the probability of successful treatment; judging whether the growth and development of the children are affected by the nutrition condition or not, and providing intervention advice in the nutrition aspect; evaluating the relationship between pubertal development and bone age, judging whether the child develops normally, and assisting early intervention and treatment; the knowledge and information about the health of the child are provided to parents, and the health education of parents is promoted, so that the parents can better care and protect the health of the child.
It will be appreciated by persons skilled in the art that the above embodiments have been provided for the purpose of illustrating the application and are not to be construed as limiting the application, and that suitable modifications and variations of the above embodiments are within the scope of the application as claimed.

Claims (10)

1. A bone age monitoring system, the bone age monitoring system comprising:
the data acquisition module is used for acquiring bone age information and age information;
the analysis and diagnosis module is used for analyzing the bone age information and the age information, determining whether the bone age information is abnormal, and if the bone age information is abnormal, further acquiring bone age dysplasia information which can cause bone age dysplasia, wherein the bone age dysplasia information comprises life style, genetic diseases and diet conditions;
the health scheme making module is used for inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model, and the classification regression tree algorithm model is used for generating a health scheme according to the bone age dysplasia information and the bone age information.
2. The bone age monitoring system of claim 1, wherein the categorical regression tree algorithm model comprises:
the data preprocessing module is used for preprocessing the bone age information and the bone age dysplasia information;
the tree construction module is used for constructing a classification regression tree model, carrying out feature selection by applying a Gini index according to the bone age dysplasia information and the bone age information, splitting tree nodes and constructing a classification regression tree structure;
and the health scheme output module is used for generating the health scheme according to the bone age dysplasia information and the classification regression tree structure.
3. The bone age monitoring system of claim 1, wherein the bone age monitoring system further comprises:
the health tracking module is used for generating a health file according to the bone age abnormality information and the health scheme;
the health tracking module is also used for collecting new bone age information and updating the health scheme according to the new bone age information.
4. The bone age monitoring system of claim 1, wherein the bone age monitoring system further comprises:
and the message notification module is in communication connection with a terminal and is used for sending the health plan generated by the health plan making module to the terminal.
5. The bone age monitoring system of claim 1, wherein the health regimen comprises:
diet adjustment regimen, exercise regimen, sleep adjustment regimen, medication regimen, and nutritional supplement regimen.
6. The bone age monitoring system of claim 1, wherein the bone age monitoring system further comprises:
the data statistics module is used for acquiring a plurality of pieces of bone age information, a plurality of pieces of age information and a plurality of pieces of bone age dysplasia information;
the data statistics module is also used for generating bone age abnormality statistics information according to the plurality of bone age information, the plurality of age information and the plurality of bone age dysplasia information.
7. The bone age monitoring system of claim 6, wherein the data statistics module is further configured to obtain regional information and temporal information corresponding to the bone age dysplasia information;
the bone age abnormality statistical information includes the region information and the time information.
8. A bone age monitoring method applied to the bone age monitoring system according to any one of claims 1 to 7, comprising:
collecting bone age information and age information;
analyzing the bone age information and the age information to determine whether the bone age information is abnormal;
if the bone age information is abnormal, further acquiring bone age dysplasia information which can cause bone age dysplasia, wherein the bone age dysplasia information comprises life style, genetic diseases and diet conditions;
inputting the bone age dysplasia information and the bone age information into a classification regression tree algorithm model;
and generating a health scheme according to the bone age dysplasia information and the bone age information.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the bone age monitoring method of claim 8 when executing the instructions.
10. A computer-readable storage medium comprising instructions that instruct a device to perform the bone age monitoring method of claim 8.
CN202310452369.5A 2023-04-24 2023-04-24 Bone age monitoring system, method, electronic device and storage medium Pending CN116665894A (en)

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