Method for evaluating effectiveness of myopia out-of-focus distribution

Abstract

The present invention relates to the technical field of myopia prevention and control, and relates to a method for evaluating the effectiveness of myopia out-of-focus distribution. In order to solve the problem that the effectiveness of myopia out-of-focus distribution is rarely evaluated at present, in the present invention, analysis and induction are carried out on the basis of a large amount of clinical sample data, related parameters are determined by means of multiple regression analysis from a plurality of factors affecting an out-of-focus form, a regression equation affecting eye axis growth is obtained by using the related parameters, and finally, the distance between an out-of-focus peak value and a cornea center point is used as a core indicator for evaluating the effectiveness of myopia out-of-focus distribution, thereby providing a new idea for design and evaluation of a myopia out-of-focus distribution position in clinical practice. By using the evaluation method of the present invention, quantitative evaluation can be carried out on the out-of-focus form of an individual, and personalized out-of-focus distribution design can be carried out on the basis of the individual condition of a patient, so that myopia out of focus is distributed in an optimal position of a lens to achieve an optimal myopia prevention and control effect, and the evaluation method has great significance in early myopia prevention and control.

Description

A method for evaluating the effectiveness of myopic defocus distribution Technical Field

[0001] The present invention relates to the technical field of myopia prevention and control, and in particular to a method for evaluating the effectiveness of myopia defocus distribution. Background Art

[0002] Myopia defocus distribution design is the core part of the design of myopia prevention and control products (such as defocus frame glasses, defocus soft lenses, orthokeratology lenses, and defocus RGPs). How to make the myopia defocus distributed in the best position of the lens to achieve the best myopia prevention and control effect is the key to the clinical design of defocus lenses.

[0003] At present, the widely used myopia prevention and control defocus designs mainly include concentric bifocal design, central optical multi-layer concentric ring design, peripheral multi-point distribution convex lens design and progressive multifocal design, among which the peripheral multi-point distribution convex lens design uses the multi-point convex lens design on the periphery of the lens to change the mid-peripheral retinal imaging from the original defocus to the myopic defocus state, thereby achieving the purpose of controlling the progression of myopia. Although the peripheral multi-point distribution convex lens can form a certain degree of myopic defocus, it is unknown whether the amount of defocus and the distribution of defocus shape can be further optimized to achieve the effect of improving myopia control. In addition, this design will have a greater impact on the visual quality of the peripheral field of view, especially for people with excessively large pupils. The central optical multi-layer concentric ring design is the basic design of various soft corneal contact lenses for myopia prevention and control. By adding positive power to the mid-peripheral lens, the mid-peripheral retinal imaging is changed from the original defocus to the myopic defocus state, thereby achieving myopia prevention and control. The purpose is to achieve this goal, but the disadvantage of the central optical multi-layer concentric ring design is that there is currently no unified standard for the size, number and defocus amount of the defocus rings in clinical practice. At present, there are large differences among various brands in clinical practice. There are peripheral myopia defocus designs with a central optical zone of 6mm, paracentral myopia defocus designs with a central optical zone of 3mm, double-ring designs, multi-ring designs, and progressive change designs. This is also the reason why the myopia control effects of the ring designs vary greatly; the central optics of the progressive multifocal design are normal myopia degrees, starting from the edge of the central optical zone, and gradually adding positive degrees to the periphery. By adding positive degrees to the mid-peripheral lenses, the mid-peripheral retinal imaging is changed from the original defocus to the myopic defocus state, thereby achieving the purpose of myopia prevention and control. The shortcomings of this method are similar to those of the central optical multi-layer concentric ring design. There is still no unified standard for the size, number and defocus amount of the defocus rings, and there are currently large differences among various brands in clinical practice, and the control effects are also uneven.

[0004] The distribution of myopic defocus on the lens will directly affect the user experience and the effectiveness of myopia prevention and control. The above three existing mainstream myopia prevention and control defocus designs and other types of defocus distribution are mostly designed based on experience or slightly adjusted. There is no unified quantitative and evaluation standard for the effectiveness of defocus distribution. This leads to poor defocus distribution design, large individual differences in myopia prevention and control effects, and poor myopia prevention and control effects at low degrees and low ages. It is impossible to design myopic defocus lenses with the best myopia prevention and control effects in a targeted manner according to patient needs.

[0005] Summary of the Invention

[0006] In view of the defects existing in the above-mentioned prior art, the present invention aims to provide a method for evaluating the effectiveness of myopia defocus distribution, thereby solving the problems existing in the above-mentioned background technology.

[0007] The present invention conducts inductive analysis based on large clinical sample data, and uses a multivariate regression analysis method to ultimately obtain the most significant parameter for evaluating the effectiveness of the defocus distribution from multiple defocus parameters that may affect axial length growth. This parameter with the highest significance is used to quantify the myopic defocus distribution morphology and myopic defocus amount, which can effectively solve the problem of poor defocus distribution design and proposes a new concept for the difficult problem of how to design and evaluate the myopic defocus amount and myopic defocus position in clinical practice.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0009] The first aspect disclosed in the present invention is: a method for evaluating the effectiveness of myopia defocus distribution, comprising the following steps:

[0010] S1: Based on large sample data, determine the influencing parameters of axial length growth from multiple parameters of defocus morphology;

[0011] S2: Using the influencing parameters determined in step S1 as inputs to the axial length growth control regression model, the axial length growth control regression model is analyzed to obtain an evaluation result of the effectiveness of the myopic defocus distribution.

[0012] As a further preferred embodiment of the above solution, in step S1, the multiple parameters include previous parameters, baseline parameters, and new parameters, wherein:

[0013] Previous parameters included peak defocus, total defocus, optical zone decentration, and optical zone size;

[0014] Baseline parameters included baseline age and baseline degree;

[0015] The new parameter is the distance between the defocus peak and the center of the cornea.

[0016] As a further preferred embodiment of the above scheme, in step S1, the influencing parameters of axial length growth determined are baseline age, baseline degree and the distance between the defocus peak and the corneal center.

[0017] As a further preferred embodiment of the above scheme, in step S2, the influencing parameters are used as the input of the regression model for controlling axial eye growth to obtain the regression model for controlling axial eye growth, which is expressed as follows: ALG=A+B*Age+C*SE+D*3 / 4X

[0018] Where ALG is the amount of axial length growth, Age is the baseline age, SE is the baseline degree, X is the distance between the defocus peak and the center of the cornea, and A, B, C, and D are all constants.

[0019] As a further preferred solution of the above solution, after multiple regression analysis, the regression model for controlling axial length growth is: ALG=0.3197-0.0446*Age+0.03790*SE+0.0315*(3 / 4X).

[0020] As a further preferred embodiment of the above solution, in step S2, the effectiveness of the myopic defocus distribution is evaluated using the analysis results, as follows:

[0021] When 3 / 4X∈(0, 1.55mm), ALG∈(0.1mm, 0.15mm);

[0022] When 3 / 4X∈(1.55mm, 1.96mm), ALG∈(0.1mm, 0.18mm);

[0023] When 3 / 4X∈(1.96mm, 2.12mm), ALG∈(0.18mm, 0.28mm);

[0024] When 3 / 4X∈(2.12mm, 2.25mm), ALG∈(0.19mm, 0.29mm);

[0025] Taking ALG≤0.18mm as the standard, the obtained 3 / 4X was compared with 1.96mm to evaluate the effectiveness of the defocus morphology on myopia control.

[0026] The second aspect disclosed in the present invention is: an electronic device, including a processor and a memory, the memory storing computer instructions, the processor being used to run the computer instructions stored in the memory to implement the steps of a method for evaluating the effectiveness of myopia defocus distribution as described in any one of the above aspects.

[0027] The third aspect disclosed in the present invention is: a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the steps of a method for evaluating the effectiveness of myopia defocus distribution as described in any one of the above aspects.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The evaluation method of the present invention is based on large clinical sample data. It combines the topographic morphological analysis method with multiple regression analysis to simplify the complex. The relevant parameters are finally determined from multiple parameters affecting axial growth through multiple regression analysis, and then a regression model for evaluating axial growth is obtained. The regression model can be used to make a quantitative evaluation of the individual's defocus morphology. At the same time, the defocus distribution can be designed in a targeted manner according to the individual situation of the patient, so that the myopia defocus distribution has a better myopia control effect, which is a breakthrough in the determination of the clinical myopia defocus amount and the design of the myopia defocus distribution.

[0030] 2. The evaluation method of the present invention is beneficial for better early myopia prevention and control effects for people with low degrees and young age groups. The defocus distribution design is based on the quantitative results, so that the myopia defocus distribution is distributed at the optimal position of the lens to achieve the best myopia prevention and control effect, which is of great significance for early myopia prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments or the prior art.

[0032] FIG1 shows the detailed process of statistical analysis of univariate regression and multivariate regression using R language in the present invention, and the final influencing parameters are obtained from a large number of influencing factors.

[0033] Figure 2 shows the statistical results of variance analysis among the four groups of people with the same baseline, when 3 / 4X∈(0, 1.55mm), ALG∈(0.1mm, 0.15mm); when 3 / 4X∈(1.55mm, 1.96mm), ALG∈(0.1mm, 0.18mm); when 3 / 4X∈(1.96mm, 2.12mm), ALG∈(0.18mm, 0.28mm); when 3 / 4X∈(2.12mm, 2.25mm), ALG∈(0.19mm, 0.29mm); the statistical differences were significant.

[0034] FIG3 is the overall defocus curve of the 5 mm BOZD group and the 6.2 mm BOZD group of the present invention;

[0035] FIG4 is a diagram showing the detection of optical zone decentration and optical zone size in the 5 mm BOZD group and the 6.2 mm BOZD group of the present invention;

[0036] FIG5 is a pupil detection diagram of the 5 mm BOZD group and the 6.2 mm BOZD group of the present invention;

[0037] Figure 6 is a myopia defocus distribution curve fitted by the present invention using the topography of wearing OK glasses as an example, wherein AF represents the original axial topography (A, D) before wearing the glasses, the tangential topography (B, E) after wearing the glasses, and the tangential difference (C, F) of the topography after wearing the glasses of the 5mmBOZD group and the 6.2mmBOZD group, and G represents the comprehensive curves fitted by the 5mmBOZD group and the 6.2mmBOZD group respectively. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0039] Previous studies on myopia prevention and control have found that after wearing orthokeratology lenses, a smaller central optical zone (COZ) and appropriate treatment zone decentration (TZD) both help slow the growth of the ocular axis and play a role in myopia control. The topographic morphology after wearing the lenses shows that the COZ and TZD have similar effects on corneal morphology. That is, after adjusting vision through the COZ or TZD, the myopic defocus ring formed on the patient's cornea is closer to the center of the cornea (i.e., closer to the pupil). At the same time, the COZ and TZD can produce a larger total defocus (sum), a steeper defocus morphology (higher defocus asphericity), and a larger myopic defocus peak (Vmax). These parameters are all closely related to myopia control. However, whether such a large number of complex parameters can be unified by a simple parameter, and whether this single parameter can directly evaluate the effectiveness of myopia defocus design, these two points remain unresolved in clinical practice and by myopia prevention and control agencies.

[0040] Based on this, the present invention provides a method for evaluating the effectiveness of myopia defocus distribution, aiming to provide a quantitative evaluation of the myopia defocus distribution, so that the determined results can be used as a basis for judging which defocus distribution is more effective for myopia control. Referring to Figures 1-6, the method comprises at least the following steps:

[0041] S1: Based on large sample data, determine the influencing parameters of axial length growth from multiple defocus morphological parameters;

[0042] S2: Using the influencing parameters determined in step S1 as inputs to the axial length growth control regression model, the axial length growth control regression model is analyzed to obtain an evaluation result of the effectiveness of the myopic defocus distribution.

[0043] In the present invention, in order to determine the parameters of multiple defocus morphologies, the present invention conducts an inductive analysis based on a large sample of clinical data, uses an innovative topographic morphological analysis method, and analyzes the different myopic defocus morphologies produced by wearing OK lenses with different back surface optical diameters (BOZD). As shown in Figure 6, taking the topographic maps of OK lenses with 5mm BOZD and OK lenses with 6.2mm BOZD as examples, the parameters of multiple defocus morphologies are analyzed, thereby determining the parameters with differences as a parameter that affects the defocus morphology.

[0044] As shown in Figures 3-5, through examination of the optical zone decentration (TZd), central optical zone size (TZr), and pupil of the 5mm BOZD group and the 6.2mm BOZD group, it was found that there was basically no difference in the size and direction of TZd between the two groups, but there was a difference in TZr. There was basically no difference in pupil size between the two groups, but there was a difference in the total amount of defocus (sum) within the pupil range.

[0045] The parameters of multiple defocus morphologies finally determined include previous parameters, baseline parameters and new parameters. Among them, the previous parameters include defocus peak Vmax, total defocus sum, optical zone eccentricity, and optical zone size; the baseline parameters include baseline age Age and baseline degree SE; the new parameters are 3 / 4X the distance between the defocus peak and the center point of the cornea.

[0046] The finally determined parameters of multiple defocus morphologies were subjected to univariate correlation analysis with axial eye growth (ALG). All parameters related to ALG were brought into the multivariate linear regression equation (stepwise regression) for analysis, and the distance X between the defocus peak and the corneal center was divided into 1 / 4X, 2 / 4X, 3 / 4X, and X for correlation analysis. Through stepwise regression, the influencing parameters of axial eye growth were finally determined. The determined related parameters were used as the input of the regression model for controlling axial eye growth, and the regression model for controlling axial eye growth (model.final) was obtained, which is expressed as follows: ALG = A + B*Age + C*SE + D*3 / 4X

[0047] Among them, ALG is the amount of axial length growth, and A, B, C, and D are all constants.

[0048] The significantly correlated parameters in this model are the influencing parameters of axial length growth (ALG). From the above regression model (model.final) combined with Figure 1, it can be seen that the previous optical zone decentration (TZD), optical zone size (TZr), total defocus (sum) and defocus peak (Vmax) were not included in the regression model. Baseline age (Age), baseline power (SE) and 3 / 4X were the most relevant parameters that ultimately affected axial length growth.

[0049] Typically, corneal topography is performed on patients before and after wearing contact lenses (OK lenses, defocused soft lenses, defocused RGP lenses), and the defocus morphology is determined by analyzing the difference maps of the topography. When determining 3 / 4X, the present invention analyzes the myopic defocus morphology of the topography after two groups of people wear OK lenses with different BOZDs, and fits to obtain a distribution curve of myopic defocus (as shown in Figure 3). The obtained myopic defocus distribution curve is fit using the polyfit function and the polyval function in Matlab to obtain a 4th-order fitting curve, and the value of X is obtained, which can be obtained by 3 / 4X. Combined with Figure 6, it can be seen from the results of the study population of the present invention that the red curve (5mm BOZD) is steeper than the black curve (6.2mm BOZD). It can be seen that the axial length growth of the 5mm BOZD group is significantly less than that of the 6.2mm BOZD group, indicating that wearing OK lenses with 5mm BOZD can achieve better myopia control effects.

[0050] According to the results of multiple regression, the regression model for controlling axial length growth is finally: ALG = 0.3197-0.0446*Age+0.03790*SE+0.0315*(3 / 4X).

[0051] From this, we can see that X can be used as the only indicator to evaluate the effectiveness of the defocus distribution, and 3 / 4X is the core parameter. 3 / 4X is used as the final indicator to evaluate the effectiveness of the defocus distribution.

[0052] The 3 / 4X obtained above is used to evaluate the axial length growth (ALG), and then to evaluate the control effect of myopic defocus distribution. The corresponding relationship between 3 / 4X and ALG in the present invention is as follows:

[0053] When 3 / 4X∈(0, 1.55mm), ALG∈(0.1mm, 0.15mm);

[0054] When 3 / 4X∈(1.55mm, 1.96mm), ALG∈(0.1mm, 0.18mm);

[0055] When 3 / 4X∈(1.96mm, 2.12mm), ALG∈(0.18mm, 0.28mm);

[0056] When 3 / 4X∈(2.12mm, 2.25mm), ALG∈(0.19mm, 0.29mm);

[0057] Generally, in clinical practice, ALG≤0.18mm is used as the basis for better control of axial growth. Taking ALG≤0.18mm as the standard, the obtained 3 / 4X is compared with 1.96mm. Only when 3 / 4X is less than 1.96mm, it indicates that the defocus shape is in a better myopia control shape.

[0058] The detailed description of the above embodiments is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, they are only used to illustrate the technical solution of the present invention and are not limiting. Other modifications or equivalent substitutions made by ordinary technicians in this field to the technical solution of the present invention should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. An evaluation method for the effectiveness of myopic defocus distribution, characterized in that, It includes the following steps: S1: Based on large-sample data, determine the influencing parameters of axial length growth from multiple parameters of defocus morphology; S2: Use the influencing parameters determined in step S1 as the input of the regression model for controlling axial length growth, and analyze the regression model for controlling axial length growth to obtain the evaluation result of the effectiveness of myopic defocus distribution.

2. The evaluation method for the effectiveness of myopic defocus distribution according to claim 1, characterized in that In step S1, the multiple parameters include past parameters, baseline parameters, and new parameters, where: The past parameters include defocus peak value, total defocus amount, optical zone eccentricity, and optical zone size; The baseline parameters include baseline age and baseline refractive power; The new parameter is the distance between the defocus peak value and the corneal center point.

3. The evaluation method for the effectiveness of myopic defocus distribution according to claim 2, wherein In step S1, the determined influencing parameters of axial length growth are baseline age, baseline refractive power, and the distance between the defocus peak value and the corneal center point.

4. An evaluation method for the effectiveness of myopic defocus distribution according to claim 3, characterized in that, In step S2, using the influencing parameters as the input of the regression model for controlling axial length growth, the regression model for controlling axial length growth is obtained, and its expression is as follows: ALG = A + B * Age + C * SE + D * 3 / 4X Where, ALG is the amount of axial length growth, Age represents baseline age, SE represents baseline refractive power, X represents the distance between the defocus peak value and the corneal center point, and A, B, C, and D are all constants.

5. The evaluation method for the effectiveness of myopic defocus distribution according to claim 4, characterized in that, After multiple regression analysis, the regression model for controlling axial length growth is: ALG = 0.3197 - 0.0446 * Age + 0.03790 * SE + 0.0315 * (3 / 4X).

6. The evaluation method for the effectiveness of myopic defocus distribution according to claim 5, wherein In step S2, use the analysis result to evaluate the effectiveness of myopic defocus distribution, specifically as follows: when 3 / 4X ∈ (0, 1.55 mm), ALG ∈ (0.1 mm, 0.15 mm); when 3 / 4X ∈ (1.55 mm, 1.96 mm), ALG ∈ (0.1 mm, 0.18 mm); when 3 / 4X ∈ (1.96 mm, 2.12 mm), ALG ∈ (0.18 mm, 0.28 mm); when 3 / 4X ∈ (2.12 mm, 2.25 mm), ALG ∈ (0.19 mm, 0.29 mm); Taking ALG ≤ 0.18 mm as the standard, compare the obtained 3 / 4X with 1.96 mm to evaluate the effectiveness of defocus morphology on myopia control.

7. An electronic device, comprising a processor and a memory, characterized in that, Computer instructions are stored on a memory, and a processor is used to run the computer instructions stored on the memory to implement the steps of an evaluation method for the effectiveness of myopic defocus distribution as described in any one of claims 1-6.

8. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause a computer to execute the steps of an evaluation method for the effectiveness of myopic defocus distribution as described in any one of claims 1-6.

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