Refractive development management method and related equipment

Abstract

The embodiment of the invention provides a refractive development management method and related equipment, and belongs to the technical field of computers. The method comprises the following steps: converting heterogeneous data of a current patient into structured data, wherein the structured data comprises an ocular axis length and a diopter; constructing a personal refractive development curve of the current patient according to the structured data; determining the risk level of the current patient based on the personal refractive development curve and the normal child growth and development curve library through an AI algorithm; and in response to the first instruction, displaying the personal refractive development curve and the risk level of the current patient on the result page. According to the embodiment of the invention, the refraction risk of the patient can be accurately evaluated in a personalized manner, the workload of a doctor is reduced, the accuracy of refraction development analysis is improved, the refraction development condition can be visually checked, the understanding of the health condition of the patient can be enhanced, and the formulation of a personalized medical scheme is facilitated.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and related equipment for managing refractive development. Background Technology

[0002] In related technologies, traditional refractive management methods often rely on single or limited data sources, which can easily lead to data inconsistencies and a lack of comprehensiveness, potentially resulting in inaccurate analysis. Furthermore, previous refractive development monitoring was often based on population average data, failing to fully consider individual differences, which may lead to inaccurate risk assessments for some patients.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for refractive development management, which aims to provide personalized and accurate assessment of patients' refractive risks and improve the accuracy of refractive development analysis.

[0005] To achieve the above objectives, one aspect of this application proposes a method for managing refractive development, the method comprising: The heterogeneous data of the current patient is converted into structured data, which includes axial length and refractive error; The patient's individual refractive development curve is constructed based on the structured data; The risk level of the current patient is determined by an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves. In response to the first instruction, the current patient's individual refractive development curve and risk level are displayed on the results page.

[0006] In some embodiments, the conversion of the current patient's heterogeneous data into structured data includes: Acquire heterogeneous data from different sources; Obtain a standardized dictionary, which includes mapping rule files, each of which corresponds to a type of data source; The heterogeneous data is mapped into structured data according to the mapping rule file.

[0007] In some embodiments, the method further includes: Rules for obtaining reasonable data; The structured data is identified using the data rationality rules to obtain suspicious data; Obtain the repair strategy corresponding to the suspicious data; The suspicious data in the structured data is repaired using the repair strategy to obtain the repaired structured data.

[0008] In some embodiments, the method further includes: Construct a time-series data model based on the structured data; The time-series data model of the current patient is stored in the refractive record database.

[0009] In some embodiments, determining the current patient's risk level using an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves includes: Calculate the instantaneous axial elongation rate and axial acceleration of the individual refractive development curve; Obtain the instantaneous growth rate threshold and acceleration threshold; If the instantaneous growth rate of the axial length exceeds the instantaneous growth rate threshold, an early warning and the current patient's risk level are generated based on the instantaneous growth rate of the axial length and the instantaneous growth rate threshold. If the axial acceleration exceeds the acceleration threshold, an early warning and the current patient's risk level are generated based on the axial acceleration and the acceleration threshold.

[0010] In some embodiments, determining the current patient's risk level using an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves includes: Obtain the normal data range from the normal children's growth and development curve database; The deviation is generated by comparing the individual's refractive development curve with the normal data range using an AI algorithm. The current patient's risk level is generated based on the deviation. An early warning is generated based on the current patient's risk level and the degree of deviation.

[0011] To achieve the above objectives, another aspect of this application provides a refractive development management device, the device comprising: The conversion module is used to convert the current patient's heterogeneous data into structured data, which includes axial length and refractive error. The curve construction module is used to construct the current patient's personal refractive development curve based on the structured data; The risk assessment module is used to determine the current patient's risk level based on the individual's refractive development curve and a database of normal children's growth and development curves using an AI algorithm. The display module is configured to, in response to a first instruction, display the current patient's individual refractive development curve and risk level on the results page.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method, device, electronic device, storage medium, and program product for refractive development management. This solution converts the heterogeneous data of the current patient into structured data; constructs the current patient's personal refractive development curve based on the structured data, which is conducive to generating personalized personal refractive development information, improving the accuracy and individuality of the analysis, and enhancing the user experience; determines the current patient's risk level based on the personal refractive development curve and a database of normal children's growth and development curves using AI algorithms, which can accurately and personally assess the patient's refractive risk, reduce the workload of doctors, improve the accuracy of refractive development analysis, and facilitate the formulation of personalized medical plans; responding to the first instruction, displays the current patient's personal refractive development curve and risk level on the results page, which is conducive to intuitively viewing the refractive development status and enhancing the understanding of the patient's health status. Attached Figure Description

[0016] Figure 1 This is a flowchart of the refractive development management method provided in the embodiments of this application; Figure 2 yes Figure 1 The flowchart of step S101 in the text; Figure 3 This is a flowchart of the data repair steps in the refractive development management method provided in the embodiments of this application; Figure 4 yes Figure 1 A flowchart of step S103 in the process; Figure 5 yes Figure 1 Another flowchart for step S103 in the process; Figure 6 This is a structural diagram of the intelligent management system when the refractive development management method provided in the embodiments of this application is applied to an intelligent management system; Figure 7This is a schematic diagram of the refractive development management device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, 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 herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] 1) Axial length refers to the length of the anteroposterior diameter of the eyeball. It is an important parameter in refractive development and is closely related to the refractive state of the eye (such as myopia, hyperopia, astigmatism, etc.).

[0023] In related technologies, traditional refractive management methods often rely on single or limited data sources, which can easily lead to data inconsistencies and a lack of comprehensiveness, potentially resulting in inaccurate analysis. Furthermore, previous refractive development monitoring was often based on population average data, failing to fully consider individual differences, which may lead to inaccurate risk assessments for some patients.

[0024] In view of this, this application provides a method and related equipment for refractive development management. This method converts the heterogeneous data of the current patient into structured data; constructs the current patient's personal refractive development curve based on the structured data, which is conducive to generating personalized personal refractive development information, improving the accuracy and individuality of the analysis, and enhancing the user experience; determines the current patient's risk level based on the personal refractive development curve and a database of normal children's growth and development curves using AI algorithms, which can accurately assess the patient's refractive risk in a personalized manner, reduce the workload of doctors, improve the accuracy of refractive development analysis, and facilitate the formulation of personalized medical plans; responding to the first instruction, displays the current patient's personal refractive development curve and risk level on the results page, which is conducive to intuitively viewing the refractive development status and enhancing the understanding of the patient's health status.

[0025] The refractive development management method provided in this application relates to the field of computer technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the refractive development management method, but is not limited to the above forms.

[0026] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0027] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0028] Figure 1 This is an optional flowchart of the refractive development management method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0029] Step S101: Convert the heterogeneous data of the current patient into structured data.

[0030] Specifically, the structured data includes axial length and refractive power.

[0031] Understandably, the system receives heterogeneous data from current patients and performs data cleaning and standardization according to preset rules (such as unit unification, outlier removal, and key field mapping).

[0032] In some embodiments, heterogeneous data from different sources is automatically acquired; a standardized dictionary is obtained; and the heterogeneous data is mapped into structured data according to a mapping rule file. The standardized dictionary includes mapping rule files, with each mapping rule file corresponding to a specific type of data source.

[0033] Optionally, obtain data rationality rules; identify structured data through data rationality rules to obtain suspicious data; obtain the repair strategy corresponding to the suspicious data; repair the suspicious data in the structured data through the repair strategy to obtain the repaired structured data.

[0034] Furthermore, a time-series data model is constructed based on the structured data; the time-series data model of the current patient is stored in the refractive record database.

[0035] In this embodiment, the heterogeneous data of the current patient is converted into structured data to improve the effectiveness and consistency of the data, thus preparing for subsequent analysis and prediction.

[0036] Step S102: Construct the current patient's personal refractive development curve based on structured data.

[0037] Optionally, the curves showing the change in axial length and equivalent spherical power over time can be automatically generated. Here, refractive power includes the equivalent spherical power.

[0038] In some embodiments, time-series analysis is performed based on key indicators of structured data to construct a personal refractive development curve for the current patient.

[0039] Understandably, this involves acquiring the current patient's historical data and constructing the current patient's individual refractive development curve based on the historical data and structured data.

[0040] Furthermore, machine learning models are used to predict and determine the current patient's risk level based on their individual refractive development curves, and to issue early warnings.

[0041] In this embodiment, constructing a personal refractive development curve for the current patient based on structured data is beneficial for generating personalized personal refractive development information, improving the accuracy and individuality of the analysis, and enhancing the user experience.

[0042] Step S103: Determine the current patient's risk level using an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves.

[0043] Optionally, dynamic change indicators of an individual's refractive development curve can be calculated. These dynamic change indicators include, but are not limited to, the instantaneous growth rate and acceleration of structured data such as axial length and equivalent spherical power.

[0044] In some embodiments, the instantaneous axial growth rate and axial acceleration of an individual's refractive development curve are calculated; instantaneous growth rate thresholds and acceleration thresholds are obtained; if the instantaneous axial growth rate exceeds the instantaneous growth rate threshold, an early warning and the current patient's risk level are generated based on the instantaneous axial growth rate and the instantaneous growth rate threshold; if the axial acceleration exceeds the acceleration threshold, an early warning and the current patient's risk level are generated based on the axial acceleration and the acceleration threshold.

[0045] In other embodiments, the normal data range of a normal children's growth and development curve database is obtained; the individual's refractive development curve is compared with the normal data range using an AI algorithm to generate a deviation; the current patient's risk level is generated based on the deviation; and an early warning is generated based on the current patient's risk level and deviation.

[0046] Understandably, a machine learning model is constructed by using a database of normal children's growth and development curves for comparison, extracting key features such as the rate of change of refractive power and the annual increase in axial length, inputting the patient's personal refractive development curve and key features, and outputting the corresponding risk level (such as low, medium, or high).

[0047] In this embodiment, the risk level of the current patient is determined by an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves. This enables personalized and accurate assessment of the patient's refractive risk, reduces the workload of doctors, improves the accuracy of refractive development analysis, and facilitates the development of personalized medical plans.

[0048] In step S104, in response to the first instruction, the patient's personal refractive development curve and risk level are displayed on the results page.

[0049] Specifically, the first instruction is triggered when the results page is opened or refreshed.

[0050] In some embodiments, an individual's refractive development curve and risk level are displayed.

[0051] Furthermore, explanations and medical advice are provided on the results page.

[0052] Optionally, the risk level, key characteristics, and individual refractive development curve can be input into the large language model, which will then output explanations and medical recommendations.

[0053] In this embodiment, in response to the first instruction, the current patient's personal refractive development curve and risk level are displayed on the results page, which is beneficial for intuitively viewing the refractive development status and can enhance the understanding of the patient's health status.

[0054] Steps S101 to S104 as illustrated in this embodiment convert the heterogeneous data of the current patient into structured data; construct the current patient's personal refractive development curve based on the structured data, which is beneficial for generating personalized personal refractive development information, improving the accuracy and individuality of the analysis, and enhancing the user experience; determine the current patient's risk level based on the personal refractive development curve and a database of normal children's growth and development curves using AI algorithms, which can accurately assess the patient's refractive risk in a personalized manner, reduce the workload of doctors, improve the accuracy of refractive development analysis, and facilitate the formulation of personalized medical plans; responding to the first instruction, display the current patient's personal refractive development curve and risk level on the results page, which is beneficial for intuitively viewing the refractive development status and enhancing the understanding of the patient's health status.

[0055] Please see Figure 2 In some embodiments, step S101 may include, but is not limited to, steps S201 to S203: Step S201: Obtain heterogeneous data from different sources.

[0056] In step S201 of some embodiments, an ETL tool is used to extract heterogeneous data from different sources.

[0057] Alternatively, a data integration platform can be used to automatically synchronize heterogeneous data from different sources, or custom scripts can be written to extract heterogeneous data from different sources.

[0058] Understandably, it supports multiple formats, connectors, and protocols, and supports both incremental and full fetch mechanisms.

[0059] Step S202: Obtain a standardized dictionary.

[0060] Specifically, the standardized dictionary includes mapping rule files, with each mapping rule file corresponding to a different type of data source.

[0061] In step S202 of some embodiments, a standardized dictionary is pre-configured in the data fusion engine. This dictionary defines the standard field names, standard units (e.g., axial length is uniformly set to millimeters (mm), and diopter is uniformly set to diopters (D)) and valid value ranges for all core refractive indicators (such as axial length, spherical power, cylindrical power, corneal curvature, etc.).

[0062] Specifically, for each type of data source that is accessed, a dedicated mapping rule file is configured for it.

[0063] Optionally, a mechanism can be developed to support dynamically updated mapping rules to adapt to changes in the source data structure or business rules.

[0064] Furthermore, version control of the mapping rule files enables the tracking and management of different versions of the mappings.

[0065] Step S203: Map heterogeneous data into structured data according to the mapping rule file.

[0066] In step S203 of some embodiments, when data flows in, the engine maps the fields of the source data to standard fields according to the rules of the file, and performs unit conversion and value range verification.

[0067] For example, the rule would specify: IF Data source = 'School screening system' AND Field name = 'Axis axis' THEN Target field = 'Axis axis length_mm', Conversion formula = 'Original value * 10'.

[0068] Understandably, data cleaning and validation are performed during the mapping process to improve the accuracy and completeness of structured data.

[0069] Please see Figure 3 In some embodiments, the refractive development management method provided in this application further includes a data repair step, which may include, but is not limited to, steps S301 to S304: Step S301: Obtain the data rationality rules.

[0070] In step S301 of some embodiments, strict data rationality rules are set based on medical common sense about children's refractive development.

[0071] For example, rule 1: reasonable range of axial length [15mm, 35mm]; rule 2: reasonable range of spherical power [-35.00D, +15.00D]; rule 3: if the "corrected visual acuity" field is empty, it will be automatically filled with the "uncorrected visual acuity" value (a common case of missing data).

[0072] Step S302: Identify structured data through data rationality rules to obtain suspicious data.

[0073] In step S302 of some embodiments, the system marks the data that triggers the rule as "suspicious" and can automatically repair or notify manual review according to the policy.

[0074] Step S303: Obtain the repair strategy corresponding to the suspicious data.

[0075] In step S303 of some embodiments, a rule-repair strategy mapping table is established, and the repair strategy corresponding to the suspicious data or the repair strategy of the data rationality rule is determined according to the mapping table.

[0076] For example, if the "corrected visual acuity" field is empty, it will be automatically filled with the "uncorrected visual acuity" value; if the axial length exceeds a reasonable range, it will be set to NULL or the user will be prompted for confirmation.

[0077] Step S304: Repair the suspicious data in the structured data using the repair strategy to obtain the repaired structured data.

[0078] In step S304 of some embodiments, the cleaned and standardized data is uniformly organized into a time-series data model and stored in the refractive archive database.

[0079] Please see Figure 4 In some embodiments, step S103 may include, but is not limited to, steps S401 to S404: Step S401: Calculate the instantaneous axial length growth rate and axial acceleration of the individual refractive development curve.

[0080] In step S401 of some embodiments, the instantaneous axial length growth rate of the individual refractive development curve is derived by the finite difference method.

[0081] Furthermore, the axial acceleration is calculated based on the instantaneous growth rate of the axial length.

[0082] Step S402: Obtain the instantaneous growth rate threshold and acceleration threshold.

[0083] Optionally, the normal range and threshold of axial length growth rate and acceleration can be determined.

[0084] It is understandable that the instantaneous growth rate threshold includes the instantaneous growth rate threshold of structured data such as axial length and equivalent spherical power; the acceleration includes the acceleration of structured data such as axial length and equivalent spherical power.

[0085] In step S402 of some embodiments, the instantaneous growth rate threshold and the acceleration threshold can be default values, can be manually configured by the user, or can be dynamically determined by the system.

[0086] Step S403: If the instantaneous growth rate of axial length exceeds the instantaneous growth rate threshold, an early warning and the current patient's risk level are generated based on the instantaneous growth rate of axial length and the instantaneous growth rate threshold.

[0087] In step S403 of some embodiments, if the instantaneous growth rate of the axial length exceeds the instantaneous growth rate threshold of the axial length, an early warning is generated and marked as high risk level; otherwise, no early warning is generated and it is marked as low risk.

[0088] Optionally, a classification standard (such as green for low risk, yellow for medium risk, and red for high risk) can be formulated based on the instantaneous growth rate threshold and the acceleration threshold.

[0089] Furthermore, different explanations, medical advice, and interventions are set for different risk levels.

[0090] Step S404: If the axial acceleration exceeds the acceleration threshold, an early warning and the current patient's risk level are generated based on the axial acceleration and the acceleration threshold.

[0091] In step S404 of some embodiments, if the axial acceleration exceeds the axial acceleration threshold, an early warning is generated and marked as high risk; otherwise, no early warning is generated and the risk level is low.

[0092] Please see Figure 5 In some embodiments, step S103 may also include, but is not limited to, steps S501 to S504: Step S501: Obtain the normal data range from the normal children's growth and development curve library. In step S501 of some embodiments, the normal range and threshold of each data in the normal children's growth and development curve library are determined.

[0093] Optionally, maintain a library of normal growth and development curves for children's refractive parameters, categorized by age and gender.

[0094] Step S502: The individual's refractive development curve is compared with the normal data range using an AI algorithm to generate the deviation.

[0095] In step S502 of some embodiments, the system compares the patient's structured data and its growth trend with the normal range for the same age group in the curve library.

[0096] Among them, time-series comparison algorithms or deep learning models (such as LSTM, Transformer) are used to model individual refractive development curves and normal children's growth and development curves.

[0097] In some embodiments, the distance between the current patient's individual curve and the curves of other patients of the same sex and age is calculated, and this distance is used as the deviation. The distance includes Euclidean distance and Mahalanobis distance.

[0098] Optionally, the overall deviation can be calculated by combining multiple indicators (axial length, refractive power, corneal curvature) with distance weighting.

[0099] The AI ​​algorithm also generates explanations, medical advice, and interventions.

[0100] Step S503: Generate the current patient's risk level based on the deviation.

[0101] In some embodiments, pre-trained models such as logistic regression and random forest are used, taking an individual's refractive development curve as input, and outputting the current patient's risk level. The model is trained using a database of normal children's growth and development curves.

[0102] Optionally, a mapping relationship between deviation and risk level can be established, and the current patient's risk level can be generated based on the deviation.

[0103] Step S504: Generate an early warning based on the current patient's risk level and deviation.

[0104] In some embodiments, an alert is triggered when the risk level reaches medium / high risk or when the deviation suddenly increases.

[0105] Understandably, when an alert is triggered, the alert page displays the patient's current refractive development curve, risk level, explanation, medical advice, and intervention measures.

[0106] Figure 6 This is a structural diagram of the intelligent management system when the refractive development management method provided in this application is applied to an intelligent management system. Figure 6 The system may include, but is not limited to: The first layer is the multi-source data acquisition layer.

[0107] 1) Data acquisition module: used to acquire multi-source refractive data.

[0108] 2) In-hospital terminals: Terminal equipment is installed in the examination room, and medical staff can quickly enter data such as visual acuity, axial length, intraocular pressure, and refractive error by scanning a code or by automatically collecting data through the device interface.

[0109] 3) External data interface: used to connect with school screening systems, physical examination center systems, and partner optical shop systems to achieve automatic data import.

[0110] 4) User self-service entry: Allows parents to supplement and enter their child's examination data from other hospitals or in the past through the mini-program.

[0111] In some embodiments, the child's parents scan a QR code at the entrance of the examination room to retrieve the child's information. After the examiner completes the examination, they select the child on the terminal, and the data (such as axial length 23.5mm, refractive error -0.50D) is submitted to the cloud data fusion engine via the mini-program front end. The engine integrates this data with the child's previous two historical data (axial length 22.8mm, 23.2mm).

[0112] In other embodiments, school screening data is uploaded to the platform in batches via a data interface.

[0113] The second layer is the cloud processing and analysis layer (i.e., the cloud data processing and analysis module): 1) Data Fusion Engine: Receives raw data and performs data cleaning and standardization according to preset rules (such as unit unification, outlier removal, and key field mapping).

[0114] 2) Refractive profile database: Stores standardized time-series data of individual refractive development.

[0115] 3) AI Early Warning Engine: (1) Trend Analysis Unit: Automatically generates curves showing the changes in axial length and equivalent spherical power over time.

[0116] (2) Risk assessment unit: The child's axial length growth rate and refractive error changes are compared with the growth and development curves of normal children of the same age and gender; at the same time, based on their own historical data, predictive models (such as linear regression, ARIMA model, etc.) are used to predict the development trend in the future.

[0117] (3) Risk level output: Based on the comparison and prediction results, output the levels of "low risk", "medium risk" and "high risk", and set the threshold to trigger automatic warning.

[0118] In some embodiments, the AI ​​early warning engine analysis found that the child's axial length had increased too rapidly in the past six months, exceeding that of 95% of children of the same age, and predicted a high risk of myopia within the next year. The system automatically marked the child as "high risk" (yellow warning) on ​​the medical care end.

[0119] In other embodiments, the platform creates or updates a profile for each student, and the AI ​​engine initially screens out students suspected of having refractive errors.

[0120] The third layer is the user interaction application layer (user interaction application module).

[0121] 1) Healthcare worker interface: Displays patient list, detailed records, AI alerts, and supports one-click generation of visual reports.

[0122] 2) Parent / Patient side: Display the child's refractive development trend and risk assessment results in intuitive forms such as charts, and receive follow-up reminders and health education information.

[0123] In some embodiments, after reviewing the warning and trend graphs, doctors provide parents with intervention suggestions such as increasing outdoor activities and regular check-ups.

[0124] In other embodiments, the system automatically sends a notification message to the parents of these students via a parent-facing mini-program: "Your child was found to have a suspected refractive error during the school's vision screening. We recommend that you take your child to a medical institution for a detailed examination as soon as possible." The message also includes an appointment link for the nearest partner hospital.

[0125] The inventive point that distinguishes this invention from the prior art is: 1) Innovative multi-source data fusion and standardization method: A unified data interface and standardized cleaning rules were designed to automatically transform heterogeneous data from different standards and formats (such as hospital HIS systems, school screening equipment, and manually entered data) into unified and analyzable structured data.

[0126] 2) Dynamic risk assessment model based on time series data: Creatively construct key parameters such as axial length and refractive power into time series, and use AI algorithms (such as time series prediction and growth curve model) to dynamically assess the deviation of an individual from the normal group of the same age and their own historical data, so as to achieve early and quantitative warning of myopia risk.

[0127] 3) Closed-loop integration of diagnosis and treatment and management processes: Data collection, AI analysis, early warning prompts, doctor-patient interaction, follow-up visits and other independent processes are integrated into a seamless closed loop, which significantly improves management efficiency and user compliance.

[0128] Understandably, this application addresses the challenge of integrating children's refractive data across multiple institutions and scenarios, creating valuable lifelong electronic health records. Through AI models analyzing time-series data, this application shifts the model from "post-treatment" to "pre-emptive warning," providing more scientific and timely alerts and offering a critical window for clinical intervention. Specifically, automated data entry and analysis significantly reduce the workload of medical staff; intuitive visualization reports and timely reminders significantly improve parental understanding and compliance. Furthermore, the high-quality, structured big data accumulated by this application's platform provides valuable resources for regional myopia prevention and control policy development and scientific research on children's refractive development.

[0129] Please see Figure 7 This application also provides a refractive development management device that can implement the above-described method. The device includes: The conversion module 701 is used to convert the current patient's heterogeneous data into structured data, which includes axial length and refractive power. Curve construction module 702 is used to construct the current patient's personal refractive development curve based on structured data; Risk determination module 703 is used to determine the current patient's risk level based on an AI algorithm and a database of personal refractive development curves and normal children's growth and development curves. Display module 704 is used to display the current patient's personal refractive development curve and risk level on the results page in response to the first instruction.

[0130] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0131] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0132] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0133] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0134] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0135] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0136] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0137] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] The refractive development management method, device, electronic device, storage medium, and program product provided in this application convert heterogeneous data of the current patient into structured data; constructs a personal refractive development curve for the current patient based on the structured data, which is conducive to generating personalized personal refractive development information, improving the accuracy and individuality of analysis, and enhancing user experience; determines the current patient's risk level based on the personal refractive development curve and a database of normal children's growth and development curves using AI algorithms, which can accurately assess the patient's refractive risk in a personalized manner, reduce the workload of doctors, improve the accuracy of refractive development analysis, and facilitate the formulation of personalized medical plans; responds to a first instruction and displays the current patient's personal refractive development curve and risk level on the results page, which is conducive to intuitively viewing the refractive development status and enhancing the understanding of the patient's health status.

[0140] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0141] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0144] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0145] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0147] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for managing refractive development, characterized in that, The method includes the following steps: The heterogeneous data of the current patient is converted into structured data, which includes axial length and refractive error; The patient's individual refractive development curve is constructed based on the structured data; The risk level of the current patient is determined by an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves. In response to the first instruction, the current patient's individual refractive development curve and risk level are displayed on the results page.

2. The method according to claim 1, characterized in that, The process of converting the heterogeneous data of the current patient into structured data includes: Acquire heterogeneous data from different sources; Obtain a standardized dictionary, which includes mapping rule files, each of which corresponds to a type of data source; The heterogeneous data is mapped into structured data according to the mapping rule file.

3. The method according to claim 1, characterized in that, The method further includes: Rules for obtaining reasonable data; The structured data is identified using the data rationality rules to obtain suspicious data; Obtain the repair strategy corresponding to the suspicious data; The suspicious data in the structured data is repaired using the repair strategy to obtain the repaired structured data.

4. The method according to claim 1, characterized in that, The method further includes: Construct a time-series data model based on the structured data; The time-series data model of the current patient is stored in the refractive record database.

5. The method according to claim 1, characterized in that, The process of determining the current patient's risk level using an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves includes: Calculate the instantaneous axial elongation rate and axial acceleration of the individual refractive development curve; Obtain the instantaneous growth rate threshold and acceleration threshold; If the instantaneous growth rate of the axial length exceeds the instantaneous growth rate threshold, an early warning and the current patient's risk level are generated based on the instantaneous growth rate of the axial length and the instantaneous growth rate threshold. If the axial acceleration exceeds the acceleration threshold, an early warning and the current patient's risk level are generated based on the axial acceleration and the acceleration threshold.

6. The method according to claim 1, characterized in that, The process of determining the current patient's risk level using an AI algorithm based on the individual's refractive development curve and a database of normal children's growth and development curves includes: Obtain the normal data range from the normal children's growth and development curve database; The deviation is generated by comparing the individual's refractive development curve with the normal data range using an AI algorithm. The current patient's risk level is generated based on the deviation. An early warning is generated based on the current patient's risk level and the degree of deviation.

7. A refractive development management device, characterized in that, The device includes: The conversion module is used to convert the current patient's heterogeneous data into structured data, which includes axial length and refractive error. The curve construction module is used to construct the current patient's personal refractive development curve based on the structured data; The risk assessment module is used to determine the current patient's risk level based on the individual's refractive development curve and a database of normal children's growth and development curves using an AI algorithm. The display module is configured to, in response to a first instruction, display the current patient's individual refractive development curve and risk level on the results page.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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