CN117116495B - Fine classification method and system for keratoconus - Google Patents
Fine classification method and system for keratoconus Download PDFInfo
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- 201000002287 Keratoconus Diseases 0.000 title claims abstract description 193
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- 201000009310 astigmatism Diseases 0.000 claims description 50
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Abstract
The invention relates to the technical field of data processing, and provides a method and a system for finely classifying keratoconus, wherein the method comprises the following steps: obtaining corneal topography parameters of the keratoconus, and carrying out initial classification on the keratoconus according to the corneal topography parameters to obtain an initial cornea type; selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme; performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors; carrying out correlation analysis on cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of keratoconus according to the factor influence grades and the cornea influence factors; and carrying out secondary fine classification on the keratoconus according to the lesion degree to obtain the target cornea type. The invention can make the classification of the keratoconus finer, thereby improving the accuracy of the classification of the keratoconus.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for precisely classifying keratoconus.
Background
Keratoconus (keratoconus) is a common non-inflammatory, chronic, progressive and localized corneal dilatation disorder characterized by a thinning of the central or paracentral corneal stroma, a conical protruding deformation of the central apex, loss of normal curvature of the cornea, and irregular astigmatism and scarring, resulting in a severe reduction in the optical performance of the cornea, as if the lens of a camera were severely distorted, resulting in severe vision impairment. Keratoconus generally develops during adolescence and gradually stabilizes around age 40 with 1, usually involving both sides, but the progression of the eyes is mostly asymmetric.
The etiology of keratoconus has not been clarified until now, and has a certain family genetic tendency, and it has been found that it is related to collagen development disorder, endocrine and cell metabolic disorder, immunodeficiency and the like, and may be a multifactorial pathogenesis. Keratoconus is a contraindication for many cornea operations because surgical treatment can accelerate and aggravate pathological expansion of cornea, is one of serious complications of cornea refractive operation, and particularly, clinical manifestations of moderate and severe keratoconus are very typical, while early or subclinical keratoconus generally only presents as local mild anterior process, cornea thickness is normal and has no typical clinical sign, and diagnosis is difficult, because keratoconus has a prevalence of 0.2-2 per mill in the population and a prevalence of more than 5% in patients with refractive surgery. In summary, the etiology of keratoconus is related to various factors, so there are various classification methods for keratoconus, and thus the classification of keratoconus is not fine enough and has low accuracy.
Disclosure of Invention
The invention provides a precise classification method and a precise classification system for keratoconus, which mainly aim to solve the problems of insufficient precision and low accuracy of classification of the keratoconus.
In order to achieve the above object, the present invention provides a method for classifying keratoconus in detail, comprising:
obtaining cornea topography parameters of a keratoconus, and carrying out initial classification on the keratoconus according to the cornea topography parameters to obtain an initial cornea type;
selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme;
performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
and carrying out secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
Further, the initial classification of the keratoconus according to the corneal topography parameters, to obtain an initial cornea type, includes:
Determining cornea curvature and astigmatism degree according to the cornea topographic map parameters;
dividing the cornea grade of the keratoconus according to the cornea curvature, and dividing the astigmatism grade of the keratoconus according to the astigmatism grade;
and classifying the keratoconus according to the cornea grade and the astigmatism grade to obtain an initial cornea type.
Further, the determining the cornea curvature and the astigmatism degree according to the cornea topographic map parameters comprises:
extracting a cornea curvature radius in the cornea topographic map parameters, and measuring a first refractive index and a second refractive index corresponding to the cornea curvature radius;
calculating a corneal curvature according to the corneal curvature radius, the first refractive index and the second refractive index;
the corneal curvature was calculated using the following formula:
K＝(n 2 -n 1 /R
wherein K represents the corneal curvature, n 1 Represents the first refractive index, n 2 Representing the second refractive index, R representing the radius of curvature of the cornea;
and extracting the steep curvature and the flat curvature of the cornea curvature, and calculating the astigmatism degree according to the steep curvature and the flat curvature.
Further, the selecting the correction scheme of the keratoconus according to the initial cornea type comprises the following steps:
Determining a lesion time period from the initial cornea type;
matching calculation is carried out on the pathological change period and a correction period in a preset correction method library, so that matching degree is obtained;
the matching degree is calculated using the following formula:
wherein β represents the degree of matching, a represents the lesion time period, and b represents the correction time period;
and extracting a target correction method from the correction method library according to the matching degree, and taking the target correction method as a correction scheme of the keratoconus.
Further, the extracting the cornea parameters corresponding to the correction scheme includes:
correcting the keratoconus according to the correction scheme to obtain correction parameters;
and carrying out parameter screening on the correction parameters by utilizing the pre-acquired eye axis increment to obtain target correction parameters, and taking the target correction parameters as cornea parameters.
Further, the multiple linear regression analysis is performed on the cornea parameters to obtain cornea influence factors, including:
taking the pre-acquired eye axis increment as a dependent variable, and constructing a regression equation by taking the cornea parameter as the independent variable;
the regression equation is expressed as:
y＝2.013-0.414c+0.115d-0.085e-0.132f
wherein y represents the eye axis increment, c represents a baseline age in the cornea parameter, d represents an initial diopter in the cornea parameter, e represents a corresponding eccentricity on a flat meridian in the cornea parameter, and f represents an initial eye axis length in the cornea parameter;
And carrying out multiple parallel regression analysis on the cornea parameters according to the regression equation to obtain cornea influence factors.
Further, the multiple parallel regression analysis is performed on the cornea parameters according to the regression equation to obtain cornea influence factors, including:
judging whether the cornea parameters accord with normal distribution or not by using a preset checking method;
when the cornea parameters accord with normal distribution, carrying out first correlation test on the cornea parameters by utilizing the regression equation to obtain a first influence factor;
when the cornea parameters do not accord with normal distribution, performing a second correlation test on the cornea parameters by using the regression equation to obtain a second influence factor;
and summarizing the first influence factors and the second influence factors to obtain cornea influence factors.
Further, the performing correlation analysis on the cornea influence factors to obtain a factor influence grade includes:
randomly selecting a target influence factor from the cornea influence factors, and grouping the target influence factors to obtain a plurality of factor groups;
generating a factor change graph by taking the plurality of factor groups as abscissa and the eye axis increment as ordinate;
And sorting the influence grades of the plurality of factor groups according to the factor change diagram to obtain the factor influence grade.
Further, the analyzing the keratoconus lesion degree according to the factor influence grade and the cornea influence factor comprises:
carrying out weight assignment on the cornea influence factors according to the factor influence grade to obtain factor weights;
carrying out integration calculation according to the factor weight and the cornea influence factor to obtain a factor influence value;
the integration calculation was performed using the following formula:
wherein H represents the factor influence value, G i Represents the ith cornea influencing factor, g i Represents the factor weight corresponding to the ith cornea influencing factor, I representsThe total number of cornea influencing factors;
and dividing the keratoconus degree according to the factor influence value to obtain the pathological change degree of the keratoconus.
In order to solve the above problems, the present invention also provides a refined classification system for keratoconus, the system comprising:
the keratoconus initial classification module is used for acquiring corneal topography parameters of the keratoconus, and carrying out initial classification on the keratoconus according to the corneal topography parameters to obtain an initial cornea type;
The cornea parameter extraction module is used for selecting a correction scheme of the keratoconus according to the initial cornea type and extracting cornea parameters corresponding to the correction scheme;
the cornea parameter regression analysis module is used for performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
the pathological change degree analysis module is used for carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
and the keratoconus secondary classification module is used for carrying out secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
According to the embodiment of the invention, the keratoconus is initially classified by the corneal topography parameters, so that the initial cornea type can be accurately obtained, and the computer processing efficiency can be accelerated; the correction scheme of the keratoconus can be accurately selected through the initial cornea type, and cornea parameters corresponding to the correction scheme are accurately extracted, so that the accuracy of parameter analysis can be improved; by performing multiple linear regression analysis on cornea parameters, cornea influence factors can be accurately obtained; by carrying out correlation analysis on the cornea influencing factors, the factor influencing grade can be accurately obtained, the pathological change degree of the keratoconus can be accurately analyzed according to the factor influencing grade and the cornea influencing factors, the efficiency and the accuracy of data analysis are improved, and a data basis is better provided for the subsequent classification of the keratoconus; the keratoconus is subjected to secondary fine classification through the lesion degree, so that the type of the target cornea can be accurately obtained, the classification of the keratoconus is more accurate, and the classification accuracy can be improved. Therefore, the refined classification method and system for keratoconus provided by the invention can solve the problems of insufficient refinement and lower accuracy of keratoconus classification.
Drawings
FIG. 1 is a flow chart of a method for classifying keratoconus according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a factor change diagram according to an embodiment of the present invention;
FIG. 3 is a flow chart of analyzing the degree of keratoconus according to the factor influence level and the cornea influence factor according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a refined classification system for keratoconus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the method for classifying keratoconus according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a refined classification method of keratoconus. The execution subject of the keratoconus refinement classification method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the refined classification method of keratoconus may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for classifying keratoconus according to an embodiment of the invention is shown. In this embodiment, the method for classifying keratoconus includes:
s1, acquiring corneal topography parameters of the keratoconus, and carrying out initial classification on the keratoconus according to the corneal topography parameters to obtain an initial cornea type.
In the embodiment of the invention, keratoconus refers to a common non-inflammatory, chronic, progressive and cornea local expansion disease, and is mainly a binocular and asymmetric lesion; the parameters of the cornea topographic map refer to parameters such as the shape of the cornea surface, the mean diopter of the cornea with different diameter circumferences, the surface rule coefficient, the cornea predicted vision and the like.
In the embodiment of the present invention, the initial classification of the keratoconus according to the corneal topography parameter to obtain an initial cornea type includes:
determining cornea curvature and astigmatism degree according to the cornea topographic map parameters;
dividing the cornea grade of the keratoconus according to the cornea curvature, and dividing the astigmatism grade of the keratoconus according to the astigmatism grade;
And classifying the keratoconus according to the cornea grade and the astigmatism grade to obtain an initial cornea type.
In an embodiment of the present invention, the determining the corneal curvature and the astigmatism degree according to the corneal topography parameter includes:
extracting a cornea curvature radius in the cornea topographic map parameters, and measuring a first refractive index and a second refractive index corresponding to the cornea curvature radius;
calculating a corneal curvature according to the corneal curvature radius, the first refractive index and the second refractive index;
and extracting the steep curvature and the flat curvature of the cornea curvature, and calculating the astigmatism degree according to the steep curvature and the flat curvature.
In the embodiment of the invention, the cornea curvature refers to the bending degree of the cornea, and one parameter of the cornea shape is the refractive index difference of refractive media at two sides of the cornea curved surface, and has the capacity of converging or dispersing light rays, which is also called cornea refractive power; the cornea curvature radius refers to the anterior-posterior diameter of the cornea, and can be measured by a cornea curvature instrument; the first refractive index refers to the refractive index of air against the cornea; the second refractive index refers to the refractive index of the aqueous humor of the cornea for the cornea.
In the embodiment of the invention, the cornea curvature is calculated by using the following formula:
K(n 2 -n 1 /R
wherein K represents the corneal curvature, n 1 Represents the first refractive index, n 2 And R represents the radius of curvature of the cornea.
In the embodiment of the invention, the steep curvature refers to the maximum value of the corneal curvature; the flat curvature refers to the minimum of the corneal curvatures; the astigmatism degree can be more accurately determined from the maxima and minima in the corneal curvature.
In the embodiment of the invention, the astigmatism degree is calculated by using the following formula:
A＝K 1 -K 2
wherein A represents the astigmatism degree, K 1 Represents the maximum value, K, in the corneal curvature 2 Representing the minimum of the corneal curvatures.
In the embodiment of the present invention, the cornea grade of the keratoconus may be classified into five grades according to the magnitude of the corneal curvature and a preset curvature threshold, for example, the cornea grade of the keratoconus may be classified into a suspicious keratoconus (the magnitude of the corneal curvature is in the range of > 47.0D), a mild keratoconus (the magnitude of the corneal curvature is in the range of 48-52.0D), a moderate keratoconus (the magnitude of the corneal curvature is in the range of 53-57.0D), a accentuated keratoconus (the magnitude of the corneal curvature is in the range of 58-62.0D), and a severe keratoconus (the magnitude of the corneal curvature is in the range of > 62.0D).
In the embodiment of the invention, the keratoconus is classified into four grades according to the magnitude of the astigmatism degree and a preset astigmatism threshold, for example, the astigmatism degree is less than 100 and is used as light astigmatism, the astigmatism degree is between 100 and 200 and is used as moderate astigmatism, the astigmatism degree is between 200 and 300 and is used as severe astigmatism, and the astigmatism degree is above 300 and is used as high astigmatism.
Further, the astigmatism degree can be classified into three types of forward astigmatism in the range of 180 ° ± 30 ° in the axial direction, reverse astigmatism in the range of 90 ° ± 30 ° in the axial direction, and oblique astigmatism in the remaining range according to the difference of the axial positions in the corneal topography parameters.
In the embodiment of the invention, the keratoconus is firstly classified by using the cornea grade, then classified by using the astigmatism grade, and finally the keratoconus exists in a tree form to obtain a plurality of initial cornea types, for example, the keratoconus is a suspicious keratoconus mild astigmatism type.
According to the embodiment of the invention, the keratoconus can be accurately classified according to the corneal topography parameters, so that the initial cornea type is accurately obtained, and the classification of the keratoconus is more accurate.
S2, selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme.
In the embodiment of the present invention, the selecting the correction scheme of keratoconus according to the initial cornea type includes:
determining a lesion time period from the initial cornea type;
matching calculation is carried out on the pathological change period and a correction period in a preset correction method library, so that matching degree is obtained;
and extracting a target correction method from the correction method library according to the matching degree, and taking the target correction method as a correction scheme of the keratoconus.
In the embodiment of the invention, since the keratoconus is a keratopathy characterized by unknown etiology, bilateral, progressive corneal dilatation and irregular astigmatism, the choice of the correction scheme of the keratoconus depends on the severity of the keratoconus.
Further, the lesion time of the keratoconus is determined according to the cornea grade and the astigmatism grade in the initial cornea type, wherein the higher the cornea grade and the astigmatism grade, the longer the lesion time of the keratoconus is; the lesion stage includes an initial stage, a middle stage and a later stage, wherein the initial stage symptoms may be expressed as a suspicious keratoconus, a mild keratoconus and a mild astigmatism in the cornea grade, and a combined symptom when the cornea grade and the astigmatism grade are initial stages may be expressed as initial stage symptoms.
In the embodiment of the invention, the matching degree is calculated by using the following formula:
wherein β represents the degree of matching, a represents the lesion time period, and b represents the correction time period.
Specifically, since the correction schemes corresponding to the pathological changes of the keratoconus are different when the pathological changes of the keratoconus are different, the matching calculation is performed according to the pathological changes and the correction times in the correction method library, and the correction method corresponding to the correction time with the highest matching degree is used as the target correction method, namely the correction scheme of the keratoconus, wherein the correction method library comprises a plurality of correction times and the correction methods corresponding to the correction times, and the correction methods comprise spectacle correction, contact lens, limbal ring implantation, cornea transplantation operation and the like.
In an embodiment of the present invention, the extracting the cornea parameter corresponding to the correction scheme includes:
correcting the keratoconus according to the correction scheme to obtain correction parameters;
and carrying out parameter screening on the correction parameters by utilizing the pre-acquired eye axis increment to obtain target correction parameters, and taking the target correction parameters as cornea parameters.
In the embodiment of the invention, the parameter position, the length, the width and the like of the keratoconus are compared according to the first correction scheme, whether the keratoconus is abnormal or not is judged according to the comparison result, and when the keratoconus is abnormal, the keratoconus is corrected according to the correction scheme to obtain correction parameters, wherein the correction parameters comprise an astigmatism axis position, a cornea diameter, a pupil diameter, an eye axis length, an endothelial cell number, a cornea Surface Asymmetry Index (SAI), a cornea Surface Rule Index (SRI), an intraocular pressure, a flat meridian refractive power, a flat K axis, a steep meridian refractive power, a curved K axis and the like.
Further, the eye axis increment refers to an increased length of the eye axis length compared to a standard eye axis length, i.e., a difference between the currently measured eye axis length and the standard eye axis length; judging whether the keratoconus is related to the correction parameter according to the eye axis increment, taking the correction parameter related to the keratoconus as a cornea parameter, specifically, analyzing the degree of correlation between the correction parameter and the keratoconus by using a simulation experiment, and taking the correction parameter of which the degree of correlation reaches a preset degree threshold as the cornea parameter.
According to the embodiment of the invention, the correction scheme of the keratoconus can be accurately selected according to the initial cornea type, and the cornea parameters corresponding to the correction scheme are accurately extracted, so that the processing efficiency of a computer is improved, and the accuracy of the subsequent data analysis of the cornea parameters is ensured.
S3, performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors.
In the embodiment of the present invention, the performing multiple linear regression analysis on the cornea parameter to obtain a cornea influence factor includes:
taking the pre-acquired eye axis increment as a dependent variable, and constructing a regression equation by taking the cornea parameter as the independent variable;
And carrying out multiple parallel regression analysis on the cornea parameters according to the regression equation to obtain cornea influence factors.
In the embodiment of the present invention, the regression equation is expressed as:
y＝2.013-0.414c+0.115d-0.085e-0.132f
wherein y represents the eye axis growth, c represents a baseline age in the cornea parameter, d represents an initial diopter in the cornea parameter, e represents a corresponding eccentricity on a flat meridian in the cornea parameter, and f represents an initial eye axis length in the cornea parameter.
In the embodiment of the present invention, the multiple parallel regression analysis is performed on the cornea parameters according to the regression equation to obtain cornea influence factors, including:
judging whether the cornea parameters accord with normal distribution or not by using a preset checking method;
when the cornea parameters accord with normal distribution, carrying out first correlation test on the cornea parameters by utilizing the regression equation to obtain a first influence factor;
when the cornea parameters do not accord with normal distribution, performing a second correlation test on the cornea parameters by using the regression equation to obtain a second influence factor;
and summarizing the first influence factors and the second influence factors to obtain cornea influence factors.
In the embodiment of the invention, the test method refers to a Shapiro-Wilk test (a Charpy-Wilker test), the test method is used for judging whether the cornea parameters accord with normal distribution, when the cornea parameters accord with normal distribution, the regression equation is used for carrying out correlation test on the cornea parameters by adopting a pairing T test method, and the cornea parameters which are successfully paired are used as a first influencing factor; and when the cornea parameters do not accord with normal distribution, carrying out correlation test on the cornea parameters by using the regression equation and adopting a non-parameter test method, wherein the non-parameter test method can be independent sample non-parameter test or related sample non-parameter test, and taking the cornea parameters with P less than 0.05 as a second influencing factor.
In the embodiment of the invention, the first influence factor and the second influence factor which are subjected to the correlation test are integrated to obtain cornea influence factors, wherein the change amount of the length of the eye axis in the cornea parameters has no correlation with the increase amount of the eye axis, such as gender, initial astigmatism degree, cornea diameter, pupil diameter, and eccentricity on a steep meridian, so that the cornea influence factors cannot be used; the cornea parameter such as the base line age, the initial diopter, the eccentricity on the flat meridian, and the initial eye axis length is correlated with the eye axis increment, and thus can be used as a cornea influencing factor.
In the embodiment of the invention, the regression results of the specific cornea influence parameters after the cornea parameters are subjected to linear regression analysis include regression constants, standard errors, regression coefficients, significance values and the like, as shown in table 1, wherein the specific parameters include constants, baseline ages, initial diopters, eccentricity and initial eye axis lengths in the cornea parameters.
TABLE 1
In the embodiment of the invention, the cornea parameters are subjected to multiple linear regression analysis, so that cornea influence factors can be accurately obtained, and classification of keratoconus is finer.
S4, carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors.
In the embodiment of the present invention, the performing correlation analysis on the cornea influence factor to obtain a factor influence level includes:
randomly selecting a target influence factor from the cornea influence factors, and grouping the target influence factors to obtain a plurality of factor groups;
generating a factor change graph by taking the plurality of factor groups as abscissa and the eye axis increment as ordinate;
And sorting the influence grades of the plurality of factor groups according to the factor change diagram to obtain the factor influence grade.
In the embodiment of the invention, since the cornea influencing factors are several, one-to-one analysis is required for the cornea influencing factors, one target influencing factor is selected randomly from the cornea influencing factors for analysis, for example, the initial eye axis length in the cornea influencing factors is selected for analysis, the initial eye axis length is divided into three groups, specifically, the initial eye axis length is divided into short eye axis groups with the length smaller than 24.5mm, the initial eye axis length is divided into medium eye axis groups with the length within the range of 24.5mm-26mm, and the initial eye axis length is divided into long eye axis groups with the length greater than 26 m.
Further, as shown in fig. 2, the factor change graph shows that as the initial eye axis length of the plurality of factor groups increases, the eye axis growth amount is continuously decreasing, and thus, the factor influence level of the plurality of factor groups is decreasing, the plurality of factor groups may be classified into three levels, for example, the short eye axis group may be classified into a high level, the middle eye axis group may be classified into a medium level, and the long eye axis group may be classified into a low level.
Specifically, the remaining cornea influencing factors may also be classified into four groups A, B, C, D, specifically denoted as a group a, by analyzing the eye axis increment by groups, and classifying the cornea influencing factors according to changes in the eye axis increment, for example, by baseline age and initial diopter: low age (8-12 years old) low grade group (-0.5D to-3.00D); group B: middle-high group (-3.00D or more) at low age (8-12 years); group C: high age (12-22 years old) low grade group (-0.5D to-3.00D); group D: the high group (-3.00D or more) in the high age (12-22 years) is classified according to the change of the eye axis increment, and the factor influence grade is obtained.
In the embodiment of the present invention, the eye axis variation amounts of the plurality of factor groups under the same baseline age but different initial diopter are shown in table 2, wherein Z represents the value of the hypothesis test statistic of the regression coefficient, and P represents the significance value (significance between independent variable and dependent variable); the amount of change in the axis of the eye for the several factor groups at different ages of the base line but the initial diopter was the same is shown in Table 3.
TABLE 2
TABLE 3 Table 3
Referring to fig. 3, in an embodiment of the present invention, the analyzing the keratoconus lesion according to the factor influence level and the cornea influence factor includes:
S31, carrying out weight assignment on the cornea influence factors according to the factor influence grade to obtain factor weights;
s32, carrying out integration calculation according to the factor weight and the cornea influence factor to obtain a factor influence value;
and S33, dividing the keratoconus according to the factor influence value to obtain the pathological change degree of the keratoconus.
In the embodiment of the invention, different weights are given to the cornea influence factors according to the influence levels of the factors, wherein the higher the influence level of the factors is, the higher the factor weight is.
In the embodiment of the invention, the following formula is utilized for carrying out integration calculation:
wherein H represents the factor influence value, G i Represents the ith cornea influencing factor, g i And (3) representing the factor weight corresponding to the ith cornea influence factor, wherein I represents the total number of the cornea influence factors.
In the embodiment of the present invention, the keratoconus is classified according to the factor influence value and a preset influence threshold to obtain the lesion degree, for example, the lesion degree of the keratoconus may be classified into five classes, i.e., five lesion degrees of early, middle and later stages, wherein the higher the lesion degree is, the more serious the lesion of the keratoconus is.
In the embodiment of the invention, the cornea influence factors are subjected to correlation analysis, so that the factor influence grade can be accurately obtained, and the pathological change degree of the keratoconus can be accurately analyzed according to the factor influence grade and the cornea influence factors, so that the accuracy of data analysis can be improved.
S5, performing secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
In the embodiment of the present invention, the secondary fine classification of the keratoconus according to the lesion degree refers to further classifying the initial cornea types of the keratoconus according to the lesion degree to obtain a target cornea type, i.e., classifying a plurality of initial cornea types into five groups according to the lesion degree, for example, the initial cornea types of the keratoconus are suspicious keratoconus mild astigmatism types, and classifying the suspicious keratoconus mild astigmatism types into five types of suspicious keratoconus mild astigmatism type early, suspicious keratoconus mild astigmatism type early and medium, suspicious keratoconus mild astigmatism type middle and later according to the lesion degree, thereby obtaining the target cornea type.
In the embodiment of the invention, the keratoconus is subjected to secondary fine classification according to the pathological change degree, so that the type of the target cornea can be accurately obtained, and the classification of the keratoconus is finer and more accurate.
According to the embodiment of the invention, the keratoconus is initially classified by the corneal topography parameters, so that the initial cornea type can be accurately obtained, and the computer processing efficiency can be accelerated; the correction scheme of the keratoconus can be accurately selected through the initial cornea type, and cornea parameters corresponding to the correction scheme are accurately extracted, so that the accuracy of parameter analysis can be improved; by performing multiple linear regression analysis on cornea parameters, cornea influence factors can be accurately obtained; by carrying out correlation analysis on the cornea influencing factors, the factor influencing grade can be accurately obtained, the pathological change degree of the keratoconus can be accurately analyzed according to the factor influencing grade and the cornea influencing factors, the efficiency and the accuracy of data analysis are improved, and a data basis is better provided for the subsequent classification of the keratoconus; the keratoconus is subjected to secondary fine classification through the lesion degree, so that the type of the target cornea can be accurately obtained, the classification of the keratoconus is more accurate, and the classification accuracy can be improved. Therefore, the refined classification method of the keratoconus provided by the invention can solve the problems of insufficient refinement and lower accuracy of the classification of the keratoconus.
Fig. 4 is a functional block diagram of a keratoconus refinement classification system according to an embodiment of the present invention.
The refined classification system 400 of keratoconus of the present invention may be installed in an electronic device. Depending on the functions implemented, the refined classification system 400 for keratoconus may include an initial classification module 401 for keratoconus, a cornea parameter extraction module 402, a cornea parameter regression analysis module 403, a lesion degree analysis module 404, and a secondary classification module 405 for keratoconus. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the keratoconus initial classification module 401 is configured to obtain a corneal topography parameter of a keratoconus, and perform initial classification on the keratoconus according to the corneal topography parameter to obtain an initial cornea type;
the cornea parameter extraction module 402 is configured to select a correction scheme of the keratoconus according to the initial cornea type, and extract a cornea parameter corresponding to the correction scheme;
The cornea parameter regression analysis module 403 is configured to perform multiple linear regression analysis on the cornea parameter to obtain a cornea influence factor;
the lesion degree analysis module 404 is configured to perform correlation analysis on the cornea influencing factor to obtain a factor influencing grade, and analyze the lesion degree of the keratoconus according to the factor influencing grade and the cornea influencing factor;
the keratoconus secondary classification module 405 is configured to perform secondary fine classification on the keratoconus according to the lesion degree, so as to obtain a target cornea type.
In detail, each module in the keratoconus refinement and classification system 400 in the embodiment of the present invention adopts the same technical means as the keratoconus refinement and classification method in the drawings, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for classifying keratoconus according to an embodiment of the present invention.
The electronic device 500 may comprise a processor 501, a memory 502, a communication bus 503 and a communication interface 504, and may further comprise a computer program stored in the memory 502 and executable on the processor 501, such as a refined classification program for keratoconus.
The processor 501 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 502 (for example, executes a keratoconus refinement sorting program or the like), and invokes data stored in the memory 502 to perform various functions of the electronic device and process data.
The memory 502 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 502 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 502 may also be an external storage device of the electronic device in other embodiments, for example, a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used not only to store application software installed in an electronic device and various types of data, such as codes of a refined classification program of keratoconus, but also to temporarily store data that has been output or is to be output.
The communication bus 503 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory 502 and the at least one processor 501 etc.
The communication interface 504 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Further, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and further the user interface may be a standard wired interface, a wireless interface. Further, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 illustrates only an electronic device having components, and it will be appreciated by those skilled in the art that the configuration illustrated in fig. 5 is not limiting of the electronic device 500 and may include fewer or more components than illustrated, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 501 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The refined classification program of keratoconus stored in the memory 502 of the electronic device 500 is a combination of instructions that, when executed in the processor 501, may implement:
Obtaining cornea topography parameters of a keratoconus, and carrying out initial classification on the keratoconus according to the cornea topography parameters to obtain an initial cornea type;
selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme;
performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
and carrying out secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
In particular, the specific implementation method of the above instruction by the processor 501 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated with the electronic device 500 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
obtaining cornea topography parameters of a keratoconus, and carrying out initial classification on the keratoconus according to the cornea topography parameters to obtain an initial cornea type;
selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme;
performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
and carrying out secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
Claims (6)
1. A method for refined classification of keratoconus, the method comprising:
obtaining cornea topography parameters of a keratoconus, and carrying out initial classification on the keratoconus according to the cornea topography parameters to obtain an initial cornea type;
Selecting a correction scheme of the keratoconus according to the initial cornea type, and extracting cornea parameters corresponding to the correction scheme;
performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
performing secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type;
and performing correlation analysis on the cornea influence factors to obtain factor influence grades, wherein the method comprises the following steps of:
randomly selecting a target influence factor from the cornea influence factors, and grouping the target influence factors to obtain a plurality of factor groups;
generating a factor change graph by taking the plurality of factor groups as abscissa and the eye axis increment as ordinate;
according to the factor change diagram, sorting the influence grades of the plurality of factor groups to obtain factor influence grades;
the analyzing the pathological change degree of the keratoconus according to the factor influence grade and the cornea influence factor comprises the following steps:
Carrying out weight assignment on the cornea influence factors according to the factor influence grade to obtain factor weights;
carrying out integration calculation according to the factor weight and the cornea influence factor to obtain a factor influence value;
the integration calculation was performed using the following formula:
；
wherein,representing the factor influence value,/->Indicate->Individual cornea influencing factors, ->Indicate->Factor weights corresponding to individual cornea influencing factors, +.>Representing the total number of cornea influencing factors;
and dividing the keratoconus degree according to the factor influence value to obtain the pathological change degree of the keratoconus.
2. The method for refined classification of keratoconus as claimed in claim 1, wherein said initially classifying said keratoconus based on said corneal topography parameters to obtain an initial cornea type, comprising:
determining cornea curvature and astigmatism degree according to the cornea topographic map parameters;
dividing the cornea grade of the keratoconus according to the cornea curvature, and dividing the astigmatism grade of the keratoconus according to the astigmatism grade;
and classifying the keratoconus according to the cornea grade and the astigmatism grade to obtain an initial cornea type.
3. A method of fine classification of keratoconus as claimed in claim 2, wherein said determining corneal curvature and astigmatism degree from said corneal topography parameters comprises:
extracting a cornea curvature radius in the cornea topographic map parameters, and measuring a first refractive index and a second refractive index corresponding to the cornea curvature radius;
calculating a corneal curvature according to the corneal curvature radius, the first refractive index and the second refractive index;
the corneal curvature was calculated using the following formula:
；
wherein,representing the cornea curvature->Representing the first refractive index, +.>Representing the second refractive index, +.>Representing the saidRadius of cornea curvature;
and extracting the steep curvature and the flat curvature of the cornea curvature, and calculating the astigmatism degree according to the steep curvature and the flat curvature.
4. The method for refined classification of keratoconus as claimed in claim 1, wherein said selecting a correction scheme for said keratoconus based on said initial cornea type comprises:
determining a lesion time period from the initial cornea type;
matching calculation is carried out on the pathological change period and a correction period in a preset correction method library, so that matching degree is obtained;
The matching degree is calculated using the following formula:
；
wherein,representing the degree of matching,/->Representing the period of said lesions, < > F >>Representing the corrective time period;
and extracting a target correction method from the correction method library according to the matching degree, and taking the target correction method as a correction scheme of the keratoconus.
5. The method for refined classification of keratoconus according to claim 1, wherein said extracting the cornea parameters corresponding to the correction scheme comprises:
correcting the keratoconus according to the correction scheme to obtain correction parameters;
and carrying out parameter screening on the correction parameters by utilizing the pre-acquired eye axis increment to obtain target correction parameters, and taking the target correction parameters as cornea parameters.
6. A classification system applying the refined classification method of keratoconus according to any one of claims 1-5, characterized in that the system comprises:
the keratoconus initial classification module is used for acquiring corneal topography parameters of the keratoconus, and carrying out initial classification on the keratoconus according to the corneal topography parameters to obtain an initial cornea type;
the cornea parameter extraction module is used for selecting a correction scheme of the keratoconus according to the initial cornea type and extracting cornea parameters corresponding to the correction scheme;
The cornea parameter regression analysis module is used for performing multiple linear regression analysis on the cornea parameters to obtain cornea influence factors;
the pathological change degree analysis module is used for carrying out correlation analysis on the cornea influence factors to obtain factor influence grades, and analyzing the pathological change degree of the keratoconus according to the factor influence grades and the cornea influence factors;
and the keratoconus secondary classification module is used for carrying out secondary fine classification on the keratoconus according to the pathological change degree to obtain a target cornea type.
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