CN-121983306-A - Refractive development management method and related equipment

CN121983306ACN 121983306 ACN121983306 ACN 121983306ACN-121983306-A

Abstract

The embodiment of the application provides a refraction development management method and related equipment, and belongs to the technical field of computers. The method comprises the steps of converting heterogeneous data of a current patient into structural data, wherein the structural data comprises an eye axis length and diopter, constructing a personal refraction development curve of the current patient according to the structural data, determining the risk level of the current patient through an AI algorithm based on the personal refraction development curve and a normal child growth development curve library, and displaying the personal refraction development curve and the risk level of the current patient on a result page in response to a first instruction. The embodiment of the application can individually and accurately evaluate the refraction risk of the patient, reduce the workload of doctors, improve the accuracy of refraction development analysis, be favorable for visually checking refraction development conditions, enhance the understanding of the health condition of the patient and be favorable for the establishment of personalized medical treatment schemes.

Inventors

HUANG JING
Xiong Yinshi
LIN YAN
GONG GUIFANG
ZHONG JUN

Assignees

广州医科大学附属妇女儿童医疗中心

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. A method of refractive development management, the method comprising the steps of: converting heterogeneous data of a current patient into structured data, the structured data comprising an eye axis length and diopter; Constructing a personal refractive development curve of the current patient from the structured data; Determining the risk level of the current patient based on the personal refractive development curve and a normal child growth development curve library through an AI algorithm; In response to a first instruction, the personal refractive development curve of the current patient and the risk level are displayed on a results page.
2. The method of claim 1, wherein the converting the heterogeneous data of the current patient into structured data comprises: Heterogeneous data of different sources are obtained; obtaining a standardized dictionary, wherein the standardized dictionary comprises mapping rule files, and each mapping rule file corresponds to each type of data source; and mapping the heterogeneous data into structured data according to the mapping rule file.
3. The method according to claim 1, wherein the method further comprises: Acquiring a data rationality rule; Identifying the structured data through the data rationality rule to obtain suspicious data; acquiring a repair strategy corresponding to the suspicious data; And repairing the suspicious data of the structured data through the repairing strategy to obtain repaired structured data.
4. The method according to claim 1, wherein the method further comprises: Constructing a time sequence data model according to the structured data; And storing the time sequence data model of the current patient into a refraction archive database.
5. The method of claim 1, wherein said determining, by AI algorithm, the risk level of the current patient based on the personal refractive development curve and a library of normal child growth development curves comprises: calculating an eye axis instantaneous growth rate and an eye axis acceleration of the individual refractive development curve; acquiring an instantaneous growth rate threshold and an acceleration threshold; if the eye axis instantaneous growth rate exceeds the instantaneous growth rate threshold, generating an early warning and a risk level of the current patient based on the eye axis instantaneous growth rate and the instantaneous growth rate threshold; And if the eye axis acceleration exceeds the acceleration threshold, generating early warning and the risk level of the current patient based on the eye axis acceleration and the acceleration threshold.
6. The method of claim 1, wherein said determining, by AI algorithm, the risk level of the current patient based on the personal refractive development curve and a library of normal child growth development curves comprises: Acquiring a normal data range of a normal child growth curve library; comparing the personal refraction development curve with the normal data range through an AI algorithm to generate deviation degree; Generating a risk level of the current patient according to the deviation degree; and generating early warning according to the risk level of the current patient and the deviation degree.
7. A refractive development management apparatus, the apparatus comprising: The conversion module is used for converting heterogeneous data of the current patient into structural data, and the structural data comprises an eye axis length and diopter; The curve construction module is used for constructing a personal refraction development curve of the current patient according to the structural data; a risk determination module for determining a risk level of the current patient based on the personal refractive development curve and a normal child growth development curve library by an AI algorithm; and the display module is used for responding to a first instruction and displaying the personal refraction development curve and the risk level of the current patient on a result page.
8. An electronic device comprising a memory storing a computer program and a processor implementing the method of any of claims 1 to 6 when the computer program is executed by the processor.
9. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 6.

Description

Refractive development management method and related equipment Technical Field The application relates to the technical field of computers, in particular to a refraction development management method and related equipment. Background In the related art, conventional refractive management methods often rely on a single or limited data source, which is prone to data inconsistency and lack of comprehensiveness, and may lead to inaccurate analysis, and conventional refractive development monitoring often is based on population average data, and may lead to inaccuracy in risk assessment of certain patients due to failure to fully take individual differences into account. In summary, the technical problems in the related art are to be improved. Disclosure of Invention The embodiment of the application mainly aims to provide a refraction development management method and related equipment, aiming at individually and accurately evaluating the refraction risk of a patient and improving the accuracy of refraction development analysis. To achieve the above object, an aspect of an embodiment of the present application provides a refractive development management method, including: converting heterogeneous data of a current patient into structured data, the structured data comprising an eye axis length and diopter; Constructing a personal refractive development curve of the current patient from the structured data; Determining the risk level of the current patient based on the personal refractive development curve and a normal child growth development curve library through an AI algorithm; In response to a first instruction, the personal refractive development curve of the current patient and the risk level are displayed on a results page. In some embodiments, the converting the heterogeneous data of the current patient into structured data includes: Heterogeneous data of different sources are obtained; obtaining a standardized dictionary, wherein the standardized dictionary comprises mapping rule files, and each mapping rule file corresponds to each type of data source; and mapping the heterogeneous data into structured data according to the mapping rule file. In some embodiments, the method further comprises: Acquiring a data rationality rule; Identifying the structured data through the data rationality rule to obtain suspicious data; acquiring a repair strategy corresponding to the suspicious data; And repairing the suspicious data of the structured data through the repairing strategy to obtain repaired structured data. In some embodiments, the method further comprises: Constructing a time sequence data model according to the structured data; And storing the time sequence data model of the current patient into a refraction archive database. In some embodiments, the determining, by the AI algorithm, the risk level of the current patient based on the personal refractive development curve and a library of normal child growth development curves comprises: calculating an eye axis instantaneous growth rate and an eye axis acceleration of the individual refractive development curve; acquiring an instantaneous growth rate threshold and an acceleration threshold; if the eye axis instantaneous growth rate exceeds the instantaneous growth rate threshold, generating an early warning and a risk level of the current patient based on the eye axis instantaneous growth rate and the instantaneous growth rate threshold; And if the eye axis acceleration exceeds the acceleration threshold, generating early warning and the risk level of the current patient based on the eye axis acceleration and the acceleration threshold. In some embodiments, the determining, by the AI algorithm, the risk level of the current patient based on the personal refractive development curve and a library of normal child growth development curves comprises: Acquiring a normal data range of a normal child growth curve library; comparing the personal refraction development curve with the normal data range through an AI algorithm to generate deviation degree; Generating a risk level of the current patient according to the deviation degree; and generating early warning according to the risk level of the current patient and the deviation degree. To achieve the above object, another aspect of an embodiment of the present application provides a refractive development management apparatus, including: The conversion module is used for converting heterogeneous data of the current patient into structural data, and the structural data comprises an eye axis length and diopter; The curve construction module is used for constructing a personal refraction development curve of the current patient according to the structural data; a risk determination module for determining a risk level of the current patient based on the personal refractive development curve and a normal child growth development curve library by an AI algorithm; and the display module is used for responding to a