Teenager temporomandibular joint disorder disease risk prediction method and device

Abstract

The invention provides a teenager temporomandibular joint disorder disease risk prediction method and device, and relates to the technical field of medical treatment, the method comprises the following steps: collecting basic information and health data of a target group, obtaining pathological data of temporomandibular joints of the target group through imaging examination, and preprocessing to generate comprehensive health data of a target patient; key factors related to the temporomandibular joint disorder disease are recognized, and feature variables influencing the temporomandibular joint disorder disease are screened out; and establishing a personalized TMD disease risk prediction model, performing TMD disease risk assessment on teenagers, performing risk grade division according to a prediction result, performing risk analysis according to different growth stages of the teenagers, and generating a personalized TMD risk assessment report. According to the method, the limitation of a traditional diagnosis means can be effectively overcome, earlier and more accurate TMD disease risk prediction is realized, and a more scientific health management tool is provided for clinic.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, in particular to the field of risk prediction technology related to adolescent temporomandibular joint disorder (TMD), and specifically to a method and device for predicting the risk of adolescent temporomandibular joint disorder (TMD). Background Art

[0002] Temporomandibular Disorder (TMD) is a common disease in oral and maxillofacial surgery. The main symptoms include pain in the temporomandibular joint area, abnormal mandibular movement and dysfunction. In severe cases, anxiety, depression and sleep disorders may occur, which greatly affects the quality of life of patients. Studies have shown that the incidence of TMD in adolescents is as high as 33.2%, and it is more common in women. Although the cause of TMD has not yet been clarified, studies have pointed out that age, gender, psychological factors, malocclusion, and bad oral habits may be important causes. In addition, lack of social support, strained family relationships, and stress in the living environment may cause or aggravate TMD symptoms. The prediction model analyzes historical data to identify key factors that affect disease progression and treatment efficacy, and uses these factors to predict future disease development.

[0003] Adolescence is a critical period of human growth and development. TMD not only affects chewing function, but may also have long-term effects on facial development and mental health. Therefore, it is crucial to establish a risk prediction model for adolescent TMD, which can reveal the risk factors of the disease, help identify high-risk groups at an early stage, and provide a scientific basis for the formulation of preventive measures, thereby reducing the incidence of TMD and promoting the healthy growth of adolescents.

[0004] However, the early diagnosis of adolescent TMD still faces many challenges. Traditional diagnostic methods usually rely on the observation of clinical symptoms and imaging examinations, which may not be sensitive enough in adolescents, and due to their strong subjectivity, it is difficult to achieve accurate early prediction. In addition, existing risk assessment tools often ignore the particularities of the adolescent population (such as growth and development, psychological factors, etc.) and cannot provide personalized prediction and intervention plans. Therefore, there is an urgent need for a new method and tool based on multifactor analysis that can effectively predict the risk of adolescent TMD. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose a method and device for predicting the risk of temporomandibular joint disorder in adolescents. By collecting comprehensive health data of adolescents and combining advanced machine learning algorithms, the risk of TMD in individuals can be accurately assessed, and personalized health intervention recommendations can be provided based on the prediction results. This method can effectively overcome the limitations of traditional diagnostic methods, achieve earlier and more accurate prediction of TMD risk, and provide more scientific health management tools for clinicians.

[0006] To achieve the above object, the present invention provides the following technical solutions: Based on the above objectives, in a first aspect, the present invention provides a method for predicting the risk of temporomandibular joint disorder in adolescents, comprising the following steps: Collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination; Pre-process the collected basic information, health data and pathological data and generate comprehensive health data of the target patient; Identify key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screen out characteristic variables affecting temporomandibular joint disorder based on feature selection; Based on the extracted characteristic variables, the model is trained through the collected historical data, and the model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model; Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, the risk level is divided according to the prediction results, and risk analysis is performed according to the different growth stages of adolescents to generate a personalized TMD risk assessment report.

[0007] As a further solution of the present invention, the comprehensive health data of the target patient are health data and pathological data divided and matched according to the basic information of the target patient, wherein the basic information of the target patient includes the adolescent's age, gender, genetic factors, body mass index and living habit data; the health data of the target patient includes the adolescent's psychological state data and movement posture data; the pathological data of the target patient includes imaging examination data of the temporomandibular joint.

[0008] As a further solution of the present invention, when identifying the key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, the categorical variables in the comprehensive health data are judged by a chi-square test, and the relationship between the occurrence of adolescent TMD and the gender, living habits, and psychological stress of the target patient in the comprehensive health data is analyzed by a chi-square test, including the following steps: Collect basic information, health data, and imaging examination results of the target group; The data were converted into categorical variables and a contingency table was constructed. The contingency table contained the relationship between each factor and the occurrence of TMD. The contingency table was a two-dimensional table, where the rows represented different categorical variables and the columns represented the presence or absence of TMD. Use the chi-square test formula to calculate the chi-square statistic and degrees of freedom. Based on the calculated chi-square statistic and degrees of freedom, look up the chi-square distribution table and determine the p-value corresponding to the statistic. According to the results of the chi-square test, significant factors associated with TMD were screened out.

[0009] As a further solution of the present invention, when the chi-square test formula is used to calculate the chi-square statistic, the chi-square test formula is:

[0010] In the formula, For the The actual observed frequency of each category; For the The expected frequency of each category; When looking up the chi-square distribution table and determining the p-value corresponding to the statistic, if the p-value is less than the set significance level threshold, it is determined that there is a significant relationship between the factor and TMD. Factors with p-values ​​less than the significance level threshold are considered key factors. The p-value indicates the probability of observing the current sample mean difference or a more extreme difference when the null hypothesis (that is, the two sample means are equal) holds.

[0011] As a further solution of the present invention, when the characteristic variables affecting the temporomandibular joint disorder are screened out based on feature selection, the key characteristic variables affecting the disease are screened out based on LASSO, and feature selection and parameter estimation are performed by introducing an L1 regularization term, including the following steps: To collect data on characteristics related to TMJ disorders, including clinical indicators and imaging data; After cleaning, standardizing and preprocessing the collected feature data, the LASSO regression model is used to set the corresponding regularization parameters; The LASSO regression model is used to select the characteristic variables that have a significant impact on the model, and cross-validation is used to evaluate the model and analyze the selected characteristic variables.

[0012] As a further scheme of the present invention, establishing a personalized TMD risk prediction model includes: collecting historical data related to TMD, including the patient's clinical characteristics, living habits and psychological state, removing missing values ​​and outliers from the collected historical data, selecting characteristic variables related to TMD risk, and standardizing numerical features.

[0013] As a further scheme of the present invention, when establishing a personalized TMD disease risk prediction model, it also includes training the preprocessed historical data, dividing the data set into a training set and a test set, using the training set to train the model, dividing the training set into K subsets, training the model with K-1 subsets in turn, verifying with 1 subset, repeating K times, and finally taking the average value as the performance evaluation of the model.

[0014] As a further solution of the present invention, when the risk level is divided according to the prediction results, the adolescents are divided into different risk levels: Low risk: score between 0-30; Medium risk: score between 31-60; High risk: score between 61-100.

[0015] As a further solution of the present invention, a personalized TMD risk assessment report is generated based on the assessment results, and the contents of the TMD risk assessment report include: risk level, influencing factor analysis and health recommendations.

[0016] In a second aspect, the present invention provides a device for predicting the risk of temporomandibular joint disorder in adolescents, comprising: Data collection module: collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination; Data processing module: pre-processes the collected basic information, health data and pathological data and generates comprehensive health data of the target patient; Feature identification module: identifying key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screening out characteristic variables affecting temporomandibular joint disorder based on feature selection; Model building module: Based on the extracted characteristic variables, the model is trained through the collected historical data, and the model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model; Risk prediction module: Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, the risk level is divided according to the prediction results, and risk analysis is performed according to the different growth stages of adolescents to generate a personalized TMD risk assessment report.

[0017] In another aspect of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned methods for predicting the risk of temporomandibular joint disorder in adolescents according to the present invention is executed.

[0018] In another aspect of the present invention, a computer-readable storage medium is provided, storing computer program instructions, which, when executed, implement any of the above-mentioned methods for predicting the risk of temporomandibular joint disorder in adolescents according to the present invention.

[0019] Compared with the prior art, the method and device for predicting the risk of temporomandibular joint disorder in adolescents proposed by the present invention have the following beneficial effects: 1. The present invention conducts a comprehensive assessment through comprehensive health data, combines statistical analysis and feature selection, accurately identifies key factors related to TMD, trains the model by screening out the most influential feature variables, and optimizes the model parameters through cross-validation to ensure the generalization ability of the model. The established personalized TMD risk prediction model is not only highly adaptable, but also able to provide targeted risk assessment for each adolescent. Through personalized risk prediction, tailored health management recommendations can be provided for different groups based on the specific circumstances of adolescents, such as growth stage, gender, genetic background and other factors, thereby improving the accuracy of prevention and intervention.

[0020] 2. The present invention can also analyze the risk of TMD according to different growth stages of adolescents. The TMD risk factors faced by adolescents at different stages may be different, and multi-dimensional risk assessment and hierarchical management can be achieved. Therefore, through staged analysis, the special risk points of each stage can be accurately identified, providing a scientific basis for preventive intervention. And by grading the risk scores output by the model, it helps parents, doctors and educators to identify potential high-risk groups in the adolescent group, take timely intervention measures, and prevent further deterioration of the disease.

[0021] 3. The TMD risk prediction model provided by the present invention can provide early warning in adolescent groups. Through intelligent analysis of health data, high-risk individuals can be identified in advance to avoid further deterioration and complication of TMD. Timely warnings can allow doctors and parents to take intervention measures early, such as lifestyle adjustments, psychological counseling, occlusal correction, etc., thereby effectively reducing the chances of adolescents getting sick. Based on personalized assessment reports, adolescents and parents can get tailored health advice to help them improve their daily living habits and avoid bad habits (such as incorrect chewing methods, lack of sleep, etc.) that cause TMD, thereby achieving the goal of health management with prevention as the main focus.

[0022] 4. The present invention can greatly improve the work efficiency of medical personnel in adolescent TMD risk assessment through automated risk assessment and report generation processes, avoiding the cumbersome manual analysis and evaluation processes in traditional methods. Data processing and analysis can effectively reduce the workload of doctors, allowing doctors to focus more on the intervention and treatment of high-risk individuals without having to spend a lot of time on data collation and preliminary analysis, helping medical institutions make more scientific and reasonable decisions in adolescent health management, improving the overall level of medical services, and promoting the development of personalized medicine.

[0023] In summary, the method and device for predicting the risk of temporomandibular joint disorder in adolescents provided by the present invention can effectively promote the health management of adolescents through comprehensive analysis of the individual health status of adolescents, personalized TMD risk assessment and intervention. It not only improves the accuracy of early warning of TMD, but also provides reliable decision-making support for clinicians and parents, thereby reducing the prevalence of TMD in adolescents and promoting the all-round development of adolescents' physical and mental health. At the same time, with the support of intelligent systems, it can also improve the efficiency and quality of medical services, promote the development of personalized medicine, and meet the current trends in medical technology and health management.

[0024] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the exemplary embodiments or related technical descriptions. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 This is a flow chart of a method for predicting the risk of temporomandibular joint disorder in adolescents according to an embodiment of the present invention.

[0026] Figure 2 The present invention is a flowchart of a method for predicting the risk of temporomandibular joint disorder in adolescents using a chi-square test to determine categorical variables in comprehensive health data.

[0027] Figure 3 The present invention is a flowchart of an example of predicting the risk of temporomandibular joint disorder in adolescents according to a method for predicting the risk of temporomandibular joint disorder in adolescents according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0029] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0030] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product or device that includes a series of steps or units.

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0033] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0034] Existing risk assessment tools often ignore the particularity of adolescent groups and are unable to provide personalized prediction and intervention plans. The present invention proposes a method and device for predicting the risk of temporomandibular joint disorder in adolescents. By collecting comprehensive health data of adolescents and combining advanced machine learning algorithms, it can accurately assess the risk of individuals suffering from TMD and provide personalized health intervention recommendations based on the prediction results. This method can effectively overcome the limitations of traditional diagnostic methods, achieve earlier and more accurate prediction of TMD risk, and provide more scientific health management tools for clinicians.

[0035] See also Figure 1 As shown, an embodiment of the present invention provides a method for predicting the risk of adolescent temporomandibular joint disorder. The present invention aims to deeply explore the epidemiological characteristics, causes, prediction models and prevention strategies of adolescent TMD. The method includes the following steps: Step S10: Collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination.

[0036] In this step, the comprehensive health data of the target patient is composed of health data and pathological data divided and matched according to the basic information of the target patient, wherein the basic information of the target patient includes the adolescent's age, gender, genetic factors, body mass index and living habit data; the health data of the target patient includes the adolescent's psychological state data and movement posture data; the pathological data of the target patient includes imaging examination data of the temporomandibular joint.

[0037] When collecting basic information and health data of the target group, epidemiological surveys can also be used: a combination of questionnaires, clinical examinations, and imaging examinations can be used to collect epidemiological data on adolescent TMD. When collecting data, oral health data of adolescents can be collected from multiple schools and medical institutions.

[0038] In this embodiment, when the pathological data of the temporomandibular joint of the target group is obtained by imaging examination, the examination means include clinical examination and imaging examination, wherein: 1) Clinical examination: including palpation of the temporomandibular joint, range of motion examination, and pain assessment.

[0039] 2) Imaging examination: Use CBCT and MRI to perform a detailed examination of the temporomandibular joint.

[0040] Step S20: pre-process the collected basic information, health data and pathological data and generate comprehensive health data of the target patient.

[0041] In this step, the collected data is cleaned, missing values ​​and outliers are preprocessed, and the data is divided according to the target patients to generate the comprehensive health data of the target patients.

[0042] Step S30: identifying key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screening out characteristic variables affecting temporomandibular joint disorder based on feature selection.

[0043] In this step, when the key factors related to temporomandibular joint disorder in the comprehensive health data are identified based on statistical analysis, the categorical variables in the comprehensive health data are judged by the chi-square test, and the relationship between the occurrence of adolescent TMD and the gender, living habits, and psychological stress of the target patient in the comprehensive health data is analyzed by the chi-square test, including the following steps: Step S301, collecting basic information, health data and imaging examination results of the target group; Step S302, converting the data into categorical variables and constructing a contingency table, which contains the relationship between each factor and the occurrence of TMD, wherein the contingency table is a two-dimensional table, in which rows represent different categorical variables and columns represent the presence or absence of TMD; Step S303: Calculate the chi-square statistic and degrees of freedom using the chi-square test formula, and search the chi-square distribution table based on the calculated chi-square statistic and degrees of freedom to determine the p-value corresponding to the statistic; Step S304: Screen out significant factors related to TMD based on the chi-square test results.

[0044] Among them, when the chi-square test formula is used to calculate the chi-square statistic, the chi-square test formula is:

[0045] In the formula, For the The actual observed frequency of each category; For the The expected frequency of each category; When looking up the chi-square distribution table and determining the p-value corresponding to the statistic, if the p-value is less than the set significance level threshold, it is determined that there is a significant relationship between the factor and TMD, and the factor with a p-value less than the significance level threshold is regarded as a key factor.

[0046] Step S40: Based on the extracted characteristic variables, model training is performed through the collected historical data, and model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model.

[0047] In this step, when the characteristic variables affecting the temporomandibular joint disorder are screened out based on feature selection, the key characteristic variables affecting the disease are screened out based on LASSO, and feature selection and parameter estimation are performed by introducing the L1 regularization term, including the following steps: To collect data on characteristics related to TMJ disorders, including clinical indicators and imaging data; After cleaning, standardizing and preprocessing the collected feature data, the LASSO regression model is used to set the corresponding regularization parameters; The LASSO regression model is used to select the characteristic variables that have a significant impact on the model, and cross-validation is used to evaluate the model and analyze the selected characteristic variables.

[0048] Among them, establishing a personalized TMD risk prediction model includes: collecting historical data related to TMD, including the patient's clinical characteristics, living habits and psychological state, removing missing values ​​and outliers from the collected historical data, selecting characteristic variables related to TMD risk, and standardizing numerical features.

[0049] The present invention will collect the incidence and distribution of TMD through large-scale epidemiological surveys, analyze risk factors such as gender, genetics, lifestyle, oral health and psychological factors. At the same time, statistical and machine learning algorithms are used to build a prediction model, and the accuracy and stability of the model are verified and optimized through independent samples. According to the epidemiological characteristics of adolescent TMD, the main risk factors are revealed, an accurate and reliable prediction model is established, and disease management is optimized and the disease burden is reduced.

[0050] The present invention can also analyze the risk of TMD according to different growth stages of adolescents. The TMD risk factors faced by adolescents at different stages may be different, and multi-dimensional risk assessment and hierarchical management can be achieved. Therefore, through staged analysis, the special risk points of each stage can be accurately identified, providing a scientific basis for preventive intervention. And by grading the risk scores output by the model, it helps parents, doctors and educators to identify potential high-risk groups in the adolescent group, take timely intervention measures, and prevent further deterioration of the disease.

[0051] Step S50: Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, risk levels are divided according to the prediction results, and risk analysis is performed according to different growth stages of adolescents to generate a personalized TMD risk assessment report.

[0052] In this step, when classifying risk levels based on the prediction results, adolescents are divided into different risk levels: Low risk: score between 0-30; Medium risk: score between 31-60; High risk: score between 61-100.

[0053] Among them, a personalized TMD risk assessment report is generated based on the assessment results. The contents of the TMD risk assessment report include: risk level, influencing factor analysis and health recommendations.

[0054] The TMD risk prediction model provided by the present invention can provide early warning in adolescent groups, identify high-risk individuals in advance through intelligent analysis of health data, and avoid further deterioration and complication of TMD. Timely warnings can allow doctors and parents to take intervention measures early, such as lifestyle adjustments, psychological counseling, occlusal correction, etc., thereby effectively reducing the chances of adolescents getting sick. Based on personalized assessment reports, adolescents and parents can get tailored health advice to help them improve their daily living habits and avoid bad habits (such as incorrect chewing methods, lack of sleep, etc.) that cause TMD, thereby achieving the goal of health management with prevention as the main focus.

[0055] For example, see Figure 3 As shown in the figure, assuming that the risk prediction analysis of temporomandibular joint disorder is conducted for adolescents in three schools in Siming District, Huli District, and Tongan District, then, combined with the sample size of the Affiliated Stomatological Hospital of Xiamen Medical College (three campuses), the steps of the temporomandibular joint disorder risk prediction method are as follows: (1) Sample collection: School 1 (Siming District); collected approximately 1,000 samples; School 2 (Huli District); collected approximately 1,000 samples; School 3 (Tong'an District); collected approximately 1,000 samples; The Stomatological Hospital Affiliated to Xiamen Medical College (three campuses) collected approximately 2,000 samples; (2) Model construction and evaluation: Approximately 2,000 samples will be collected and divided into: 80% for training and 20% for testing; through 80% sample training, model construction: training the complete model and simplifying the model; through 20% sample testing, the model will be internally verified; and combined with the 3,000 samples collected from schools 1-3, evaluation (AUC, DCA, Calibration) will be performed for external validation.

[0056] The method for predicting the risk of temporomandibular joint disorder in adolescents of the present invention conducts a comprehensive assessment through comprehensive health data, combines statistical analysis and feature selection, accurately identifies key factors related to TMD, trains the model by screening out the most influential feature variables, and optimizes the model parameters through cross-validation to ensure the generalization ability of the model. The established personalized TMD risk prediction model is not only highly adaptable, but also can provide targeted risk assessment for each adolescent. Through personalized risk prediction, tailored health management suggestions can be provided for different groups based on the specific circumstances of adolescents, such as growth stage, gender, genetic background and other factors, thereby improving the accuracy of prevention and intervention.

[0057] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0058] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0059] In a second aspect of the embodiments of the present invention, the present invention further provides a device for predicting the risk of temporomandibular joint disorder in adolescents, comprising: Data collection module: collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination; Data processing module: pre-processes the collected basic information, health data and pathological data and generates comprehensive health data of the target patient; Feature identification module: identifying key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screening out characteristic variables affecting temporomandibular joint disorder based on feature selection; Model building module: Based on the extracted characteristic variables, the model is trained through the collected historical data, and the model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model; Risk prediction module: Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, the risk level is divided according to the prediction results, and risk analysis is performed according to the different growth stages of adolescents to generate a personalized TMD risk assessment report.

[0060] Through the above detailed steps, the adolescent temporomandibular joint disorder risk prediction device of the present invention is used to execute the steps of the adolescent temporomandibular joint disorder risk prediction method in the above embodiment, which will not be repeated here. The adolescent temporomandibular joint disorder risk prediction device of the present invention can greatly improve the work efficiency of medical personnel in adolescent TMD risk assessment through automated risk assessment and report generation processes, and avoid the cumbersome manual analysis and evaluation processes in traditional methods. Through data processing and analysis, the workload of doctors can be effectively reduced, allowing doctors to pay more attention to the intervention and treatment of high-risk individuals without having to spend a lot of time on data collation and preliminary analysis, helping medical institutions make more scientific and reasonable decisions in adolescent health management, improving the overall level of medical services, and promoting the development of personalized medicine.

[0061] In summary, the method and device for predicting the risk of temporomandibular joint disorder in adolescents provided by the present invention can effectively promote the health management of adolescents through comprehensive analysis of the individual health status of adolescents, personalized TMD risk assessment and intervention. It not only improves the accuracy of early warning of TMD, but also provides reliable decision-making support for clinicians and parents, thereby reducing the prevalence of TMD in adolescents and promoting the all-round development of adolescents' physical and mental health. At the same time, with the support of intelligent systems, it can also improve the efficiency and quality of medical services, promote the development of personalized medicine, and meet the current trends in medical technology and health management.

[0062] According to a third aspect of an embodiment of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[0063] The computer device includes a processor and a memory, and may also include an input system and an output system. The processor, memory, input system and output system may be connected via a bus or other means, and the input system may receive input digital or character information, and generate signal input related to the migration of the risk prediction of juvenile temporomandibular joint disorder. The output system may include display devices such as display screens.

[0064] As a non-volatile computer-readable storage medium, the memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the method for predicting the risk of temporomandibular joint disorders in adolescents in the embodiment of the present application. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of the method for predicting the risk of temporomandibular joint disorders in adolescents, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0065] The processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data. The processors of the multiple computer devices of the computer device of this embodiment execute various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, that is, implementing the steps of the method for predicting the risk of temporomandibular joint disorder in adolescents of the above method embodiment.

[0066] It should be understood that, to the extent that they do not conflict with each other, all embodiments, features and advantages described above for the method for predicting the risk of temporomandibular joint disorder in adolescents according to the present invention are also applicable to the prediction and storage medium for the risk of temporomandibular joint disorder in adolescents according to the present invention.

[0067] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0068] Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. As an example and not by way of limitation, a non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. A volatile memory may include a random access memory (RAM), which may act as an external cache memory. As an example and not by way of limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to include, but are not limited to, these and other suitable types of memory.

[0069] The various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure herein may be implemented or executed using the following components designed to perform the functions herein: a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and / or any other such configuration.

[0070] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0071] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0072] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for predicting the risk of temporomandibular joint disorder in adolescents, characterized in that: The method comprises the following steps: Collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination; Pre-process the collected basic information, health data and pathological data and generate comprehensive health data of the target patient; Identify key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screen out characteristic variables affecting temporomandibular joint disorder based on feature selection; Based on the extracted characteristic variables, the model is trained through the collected historical data, and the model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model; Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, the risk level is divided according to the prediction results, and risk analysis is performed according to the different growth stages of adolescents to generate a personalized TMD risk assessment report.

2. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 1, characterized in that: The comprehensive health data of the target patient are health data and pathological data divided and matched according to the basic information of the target patient, wherein the basic information of the target patient includes the adolescent's age, gender, genetic factors, body mass index and living habit data; the health data of the target patient includes the adolescent's psychological state data and movement posture data; the pathological data of the target patient includes imaging examination data of the temporomandibular joint.

3. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 2, characterized in that: When identifying the key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, the categorical variables in the comprehensive health data are judged by the chi-square test, and the relationship between the occurrence of adolescent TMD and factors including the gender, living habits, and whether there is psychological pressure of the target patient in the comprehensive health data is analyzed by the chi-square test, including the following steps: Collect basic information, health data, and imaging examination results of the target group; The data were converted into categorical variables and a contingency table was constructed. The contingency table contained the relationship between each factor and the occurrence of TMD. The contingency table was a two-dimensional table, where the rows represented different categorical variables and the columns represented the presence or absence of TMD. Use the chi-square test formula to calculate the chi-square statistic and degrees of freedom. Based on the calculated chi-square statistic and degrees of freedom, look up the chi-square distribution table and determine the p-value corresponding to the statistic. According to the results of the chi-square test, significant factors associated with TMD were screened out.

4. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 3, characterized in that: When the chi-square test formula is used to calculate the chi-square statistic, the chi-square test formula is: In the formula, For the The actual observed frequency of each category; For the The expected frequency of each category; When looking up the chi-square distribution table and determining the p-value corresponding to the statistic, if the p-value is less than the set significance level threshold, it is determined that there is a significant relationship between the factor and TMD, and the factor with a p-value less than the significance level threshold is regarded as a key factor.

5. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 1, characterized in that: When the characteristic variables affecting the temporomandibular joint disorder are screened out based on feature selection, the key characteristic variables affecting the disease are screened out based on LASSO, and feature selection and parameter estimation are performed by introducing the L1 regularization term, including the following steps: To collect data on characteristics related to TMJ disorders, including clinical indicators and imaging data; After cleaning, standardizing and preprocessing the collected feature data, the LASSO regression model is used to set the corresponding regularization parameters; The LASSO regression model is used to select the characteristic variables that have a significant impact on the model, and cross-validation is used to evaluate the model and analyze the selected characteristic variables.

6. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 1, characterized in that: When establishing a personalized TMD risk prediction model, it includes: collecting historical data related to TMD, including the patient's clinical characteristics, living habits and psychological state, removing missing values ​​and outliers from the collected historical data, selecting characteristic variables related to TMD risk, and standardizing numerical features.

7. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 6, characterized in that: When establishing a personalized TMD risk prediction model, it also includes training the preprocessed historical data, dividing the data set into a training set and a test set, using the training set to train the model, dividing the training set into K subsets, training the model with K-1 subsets in turn, validating with 1 subset, repeating K times, and finally taking the average value as the performance evaluation of the model.

8. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 1, characterized in that: When classifying risk levels based on the prediction results, adolescents are divided into different risk levels: Low risk: score between 0-30; Medium risk: score between 31-60; High risk: score between 61-100.

9. The method for predicting the risk of temporomandibular joint disorder in adolescents according to claim 8, characterized in that: Based on the assessment results, a personalized TMD risk assessment report is generated. The contents of the TMD risk assessment report include: risk level, influencing factor analysis and health recommendations.

10. A device for predicting the risk of temporomandibular joint disorder in adolescents, characterized in that: The device is used to implement the method for predicting the risk of juvenile temporomandibular joint disorder according to any one of claims 1 to 9, comprising: Data collection module: collect basic information and health data of the target group, and obtain pathological data of the temporomandibular joint of the target group through imaging examination; Data processing module: pre-processes the collected basic information, health data and pathological data and generates comprehensive health data of the target patient; Feature identification module: identifying key factors related to temporomandibular joint disorder in the comprehensive health data based on statistical analysis, and screening out characteristic variables affecting temporomandibular joint disorder based on feature selection; Model building module: Based on the extracted characteristic variables, the model is trained through the collected historical data, and the model parameters are optimized through cross-validation to establish a personalized TMD risk prediction model; Risk prediction module: Based on the trained personalized TMD risk prediction model, the TMD risk of adolescents is assessed, the risk level is divided according to the prediction results, and risk analysis is performed according to the different growth stages of adolescents to generate a personalized TMD risk assessment report.

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