CN112614593A - Myopia development evolutionary tree establishing method and myopia development risk assessment device - Google Patents
Myopia development evolutionary tree establishing method and myopia development risk assessment device Download PDFInfo
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Abstract
The invention discloses a myopia development evolutionary tree building method, which comprises the steps of firstly obtaining a myopia parameter distribution diagram of a preset retina area of a sample individual, wherein the preset retina area is located in an area of a retina except a macular area, then dividing the sample individual into a plurality of groups, wherein each group corresponds to different myopia development degrees, obtaining a representative myopia parameter distribution diagram of the group for each group, further obtaining the similarity of any representative myopia parameter distribution diagram of any previous myopia development degree corresponding group and any representative myopia parameter distribution diagram of a next myopia development degree corresponding group, and building a myopia development evolutionary tree according to the representative myopia parameter distribution diagram and the similarity data of each group. The method realizes the establishment of the myopia development evolutionary tree according to the myopia parameter distribution of the peripheral retina, and can be used for guiding and evaluating the myopia development risk. The invention also discloses a myopia development risk assessment device.
Description
Technical Field
The invention relates to the technical field of ophthalmic medical treatment, in particular to a myopia development evolutionary tree building method. The invention also relates to a myopia development risk assessment device.
Background
The traditional clinical optometry method is to evaluate the risk degree of developing myopia by measuring the diopter of the fovea of the macula of an eyeball, however, researches show that the optical defocusing state of the peripheral retina (namely, the retina area except the macular area) is closely related to the visual imaging quality of human eyes and the myopia development risk. Therefore, the understanding of the optical defocusing state of the retina around the eyeball has very important significance for improving the imaging quality of human eyes, optimizing the optical design of a myopia correction optical product and guiding myopia prevention and control.
Disclosure of Invention
In view of this, the present invention aims to provide a method for building a myopia development evolutionary tree, which can be used for guiding and evaluating myopia development risks by building a myopia development evolutionary tree according to the myopia parameter distribution of peripheral retina. The invention also provides a myopia development risk assessment device.
In order to achieve the purpose, the invention provides the following technical scheme:
a myopia development evolutionary tree building method comprises the following steps:
acquiring a myopia parameter distribution map of a preset retina area of a sample individual, wherein the preset retina area is located in an area of the retina except a macular area;
dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees;
for each group, obtaining a representative myopia parameter distribution map of the group according to the myopia parameter distribution map of each sample individual belonging to the group;
and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
Preferably, the predetermined area of the retina includes at least an area of the retina between +20 ° to-16 ° of horizontal meridian and between +40 ° to-40 ° of vertical meridian other than the macular region.
Preferably, for each of said groups, obtaining a representative myopia parameter profile for the group from said myopia parameter profiles for respective ones of said sample individuals belonging to the group comprises:
clustering according to the myopia parameter distribution maps of the sample individuals belonging to the group, and dividing the sample individuals belonging to the group into one or more sub-groups;
and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
Preferably, obtaining the similarity of the two representative myopia parameter profiles comprises:
acquiring the difference root mean square of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
Preferably, the similarity of the two representative myopia parameter profiles is obtained according to the following formula:
DC＝S/RMS；
wherein DC represents the similarity of the two representative myopia parameter profiles, S represents the mean of the ratio of the two representative myopia parameter profiles, and RMS represents the root mean square of the difference of the two representative myopia parameter profiles.
A myopia progression risk assessment device, comprising:
the acquisition device is used for acquiring a myopia parameter distribution map of a preset region of the retina of the evaluation object;
and the evaluation device is used for obtaining the myopia risk result of the evaluation object according to the myopia parameter distribution graph of the evaluation object and a pre-established myopia development evolution tree, wherein the myopia development evolution tree reflects the development condition of the myopia parameter distribution graph of the retina preset area of the previous myopia development degree to the myopia parameter distribution graph of the retina preset area of the next myopia development degree.
Preferably, the establishing the myopia progression evolution tree comprises:
acquiring a myopia parameter distribution map of a preset retina area of a sample individual, wherein the preset retina area is located in an area of the retina except a macular area;
dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees;
for each group, obtaining a representative myopia parameter distribution map of the group according to the myopia parameter distribution map of each sample individual belonging to the group;
and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
Preferably, for each of said groups, obtaining a representative myopia parameter profile for the group from said myopia parameter profiles for respective ones of said sample individuals belonging to the group comprises:
clustering according to the myopia parameter distribution maps of the sample individuals belonging to the group, and dividing the sample individuals belonging to the group into one or more sub-groups;
and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
Preferably, obtaining the similarity of the two representative myopia parameter profiles comprises:
acquiring the difference root mean square of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
The method specifically comprises the following steps: and establishing a similarity matrix of the corresponding grouping of the myopia development degree of the upper stage and the myopia development degree of the lower stage according to the similarity of any representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the upper stage and any representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the lower stage, wherein the similarity matrix comprises N rows and M columns, and the mth row and mth column of elements in the nth row represent the similarity of the nth representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the upper stage and the mth representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the lower stage.
According to the technical scheme, the myopia development evolutionary tree building method provided by the invention comprises the steps of firstly obtaining a myopia parameter distribution map of a preset retina area of a sample individual, wherein the preset retina area is positioned in an area of the retina except a macular area, then, according to the light deflection capability of the eyeballs of the sample individuals, dividing each sample individual into a plurality of groups, wherein each group respectively corresponds to different myopia development degrees, and for each group, obtaining a representative myopia parameter distribution map of the group according to the myopia parameter distribution maps of the sample individuals belonging to the group, further obtaining the similarity between any representative myopia parameter distribution map of any group corresponding to the previous myopia development degree and any representative myopia parameter distribution map of any group corresponding to the next myopia development degree, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution map of each group and the similarity data. The method realizes the establishment of the myopia development evolutionary tree according to the myopia parameter distribution of the peripheral retina, and can be used for guiding and evaluating the myopia development risk.
The myopia development risk assessment device provided by the invention realizes the myopia development risk assessment of the object according to the myopia parameter distribution of the preset retina area of the object.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a method for building a myopia progression evolution tree according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for obtaining a representative myopia parameter profile of the group based on the myopia parameter profiles of individual samples belonging to the group according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for obtaining similarity of two representative myopia parameter profiles in accordance with an embodiment of the present invention;
fig. 4 is a schematic view of a myopia progression risk assessment apparatus according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for building a myopia progression evolution tree according to an embodiment of the present invention, and as can be seen from the diagram, the method for building the myopia progression evolution tree includes the following steps:
s10: and acquiring a myopia parameter distribution map of a preset retina area of the sample individual, wherein the preset retina area is positioned in an area of the retina except the macular area.
The myopia parameter is a parameter in which the retina shows a change relative to the emmetropia when myopia occurs. The distribution graph of the myopia parameter of the preset area of the retina refers to the distribution of the myopia parameter value along with the position of the preset area of the retina.
The preset retina area is located in the area of the retina except the macular area, namely the preset retina area is selected from the area of the retina except the macular area, namely the peripheral retina, and the optical distribution characteristics of the peripheral retina have important influence on the visual imaging quality of eyeballs or the myopia risk assessment.
Preferably, the predetermined retinal region includes at least a region of the retina excluding the macular region between +20 ° to-16 ° of the horizontal meridian and +40 ° to-40 ° of the vertical meridian, wherein the sign of the horizontal meridian represents the upper retina (lower visual field) when positive and represents the lower retina (upper visual field) when negative. The sign of the vertical meridian is positive for the nasal retina (temporal field) and negative for the temporal retina (nasal field). The numerical value (°) of the horizontal meridian or the vertical meridian represents an angle deviating from the visual axis with the pupil center as a reference point and the visual axis as a center line.
Alternatively, the myopic parameter of the retina may be, but is not limited to, optical defocus, diopter, or equivalent spherical power. Accordingly, an optical defocus distribution map, a diopter distribution map or an equivalent sphere power distribution map of a preset region of the retina of the sample individual can be obtained. In practice, adults in refractive development (18 years old and over) and children in refractive development (8 years old-15 years old) can be selected as sample individuals, and relevant data of the sample individuals can be collected.
S11: and dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees.
And dividing the sample individual into a plurality of groups according to the light deflection capacity of the eyeball of the sample individual, wherein each group corresponds to different myopia development degrees. Optionally, a plurality of groups may be divided according to the light deflection capability of the individual retina macular region, and each group corresponds to a different light deflection capability range.
Illustratively, the equivalent Spherical power (SER) of the fovea of the macular region can be obtained by visual acuity examination, and the sample individuals are classified into an emmetropic group (-0.5D < SER <0.5D), a low myopia group (-2D < SER < -0.5D), a medium myopia group (-4D < SER < -2D), a high myopia group (-6D < SER < -4D), and an ultra-high myopia group (SER < -6D).
S12: for each of the groups, obtaining a representative myopia parameter profile for the group from the myopia parameter profiles for the individual samples belonging to the group.
The representative myopia parameter distribution map of the group refers to representative data capable of reflecting the myopia parameter distribution of the preset area of the retina of the sample individuals of the group.
Optionally, for each group, the representative myopia parameter distribution map of the group may be obtained according to the myopia parameter distribution map of each sample individual belonging to the group through the following process, please refer to fig. 2, where fig. 2 is a flowchart of a method for obtaining the representative myopia parameter distribution map of the group according to the myopia parameter distribution map of each sample individual belonging to the group in this embodiment, and includes the following steps:
s120: and clustering according to the myopia parameter distribution map of each sample individual belonging to the group, and dividing each sample individual belonging to the group into one or more sub-groups.
And clustering according to the myopia parameter distribution map of each individual sample belonging to the group, performing cluster analysis by taking the myopia parameters of each position on the myopia parameter distribution map as variables, and dividing the grouped individual samples into one or more subgroups. Alternatively, but not limited to euclidean distances may be used in determining the distances between sample individuals in the cluster analysis process.
Alternatively, a weighted average method may be used in the construction of the clustering lineage diagram in the clustering analysis.
The most common G subgroups in the group can be searched according to the distance between the individual samples of the pedigree graph obtained by clustering, and the value of G is preferably 4. If the sample number of a certain sub-packet is less, discrete data can be eliminated, and the number of sub-packets is reduced to G-3 or less.
Preferably, before clustering is performed according to the myopia parameter distribution maps of the individual samples belonging to the group, the myopia parameter distribution maps of the individual samples belonging to the group can be subjected to standardization, which is beneficial to improving the accuracy of clustering results. For example, the data contained in the myopia parameter distribution map may be converted into a data form with a mean value of 0 and a standard deviation of 1.
S121: and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
The representative myopia parameter distribution map of the sub-group can be obtained according to the myopia parameter distribution map of each sample individual belonging to the sub-group by the following method: and superposing the myopia parameter distribution maps of the sample individuals belonging to the sub-group, and then averaging to obtain a representative myopia parameter distribution map corresponding to the sub-group. And superposing the data of the corresponding positions of the myopia parameter distribution maps of the sub-grouped sample individuals, then averaging, combining the data obtained at all the positions to obtain the myopia parameter distribution map again, and further obtaining the representative myopia parameter distribution map of the sub-group.
And obtaining the representative myopia parameter distribution map corresponding to each sub-group of the group according to the process, and further obtaining each representative myopia parameter distribution map of the group.
S13: and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
For each of the constructed groupings corresponding respectively to different degrees of myopia progression, each grouping includes a corresponding number of representative myopia parameter profiles. And for any adjacent two-stage myopia development degree corresponding grouping, acquiring the similarity of any representative myopia parameter distribution graph of the previous-stage myopia development degree corresponding grouping and any representative myopia parameter distribution graph of the next-stage myopia development degree corresponding grouping.
Optionally, the similarity between the two representative myopia parameter distribution maps may be obtained through the following processes, please refer to fig. 3, where fig. 3 is a flowchart of a method for obtaining the similarity between the two representative myopia parameter distribution maps in this embodiment, and includes the following steps:
s130: and acquiring the root mean square difference of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps.
And calculating the square of the difference value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution graphs, and then calculating the average posterior square of each position to obtain the Root Mean Square (RMS) of the difference of the two representative myopia parameter distribution graphs.
S131: and acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps.
And calculating the ratio of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps, and averaging the ratio of each position to obtain the average value of the ratio of the two representative myopia parameter distribution maps, which is expressed as a Slope (Slope). Preferably, the calculation is always performed by a smaller absolute value/a larger absolute value when calculating the myopia parameter value ratio at the corresponding position.
S132: and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
The similarity of two representative myopia parameter distribution maps can be obtained according to the following formula:
DC＝S/RMS；
wherein DC represents the similarity of the two representative myopia parameter profiles, S represents the mean of the ratio of the two representative myopia parameter profiles, and RMS represents the root mean square of the difference of the two representative myopia parameter profiles.
The similarity of the two representative myopia parameter distribution diagrams reflects the similarity of the two representative myopia parameter distribution diagrams, and the larger the similarity value is, the more similar the two representative myopia parameter distribution diagrams are.
Preferably, before the similarity between any representative myopia parameter distribution map grouped corresponding to the previous myopia development degree and any representative myopia parameter distribution map grouped corresponding to the next myopia development degree is obtained, the representative myopia parameter distribution maps of the respective groups may be standardized, and specifically, the parameter values of the respective positions of the respective representative myopia parameter distribution maps may be translated, or the parameter values of the respective positions of the respective representative myopia parameter distribution maps may be scaled, or the parameter values of the respective positions of the respective representative myopia parameter distribution maps may be translated, and the parameter values of the respective positions of the respective representative myopia parameter distribution maps may be scaled. The step of translating the representative myopia parameter distribution map refers to adding or subtracting the same value to the parameter value of each position, and the step of scaling the representative myopia parameter distribution map refers to multiplying the parameter value of each position by the same coefficient. By carrying out standardization processing on each representative myopia parameter distribution map, each representative myopia parameter distribution map has the same maximum value and minimum value, and the accuracy of the obtained result is improved.
In specific implementation, after the Root mean square difference between any representative myopia parameter distribution map of the group corresponding to the previous myopia development degree and any representative myopia parameter distribution map of the group corresponding to the next myopia development degree is obtained, a Root mean square relation matrix RMSM (RMSM) may be established. After obtaining the average value of the ratio of any representative myopia parameter distribution map grouped correspondingly to the previous myopia development degree to any representative myopia parameter distribution map grouped correspondingly to the next myopia development degree, a Slope relation matrix SM (Slope matrix, SM) can be established.
According to the similarity of any representative myopia parameter distribution graph correspondingly grouped with the previous myopia development degree and any representative myopia parameter distribution graph correspondingly grouped with the next myopia development degree, a similarity matrix of the corresponding grouping of the previous myopia development degree and the next myopia development degree can be established, the similarity matrix comprises N rows and M columns, and the element of the nth row and the mth column represents the similarity of the nth representative myopia parameter distribution graph correspondingly grouped with the previous myopia development degree and the mth representative myopia parameter distribution graph correspondingly grouped with the next myopia development degree. The established similarity matrix can be described as a Determination coefficient matrix DCM (DCM).
And further, establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graph of each group and the similarity data. The representative myopia parameter distribution graph of each group reflects the myopia parameter distribution of several representative retina preset regions corresponding to the myopia development degree of the group, and shows the change process which the myopia parameter distribution of the retina preset regions may experience when the emmetropia is developed into the ultra-high myopia by combining the similarity data between the groups corresponding to the myopia development degrees.
According to the myopia development evolutionary tree, the myopia parameter distribution of the next level of myopia development degree is developed from the myopia parameter distribution of the previous level of myopia development degree, namely the myopia development progress. The distribution of the myopia parameters of the previous myopia progression degree does not necessarily progress to the distribution of the myopia parameters of the next myopia progression degree, i.e., the myopia progression stops. Between the upper and lower myopia progression degrees, the two myopia parameter distributions with the highest similarity are the most probable progression routes between each other. And finding out one with the highest similarity with the myopia parameter distribution of the previous myopia development degree from the myopia parameter distribution of the next myopia development degree by taking the similarity data as a basis, and connecting the two myopia parameter distributions to form an evolution route.
The method realizes the establishment of the myopia development evolutionary tree according to the myopia parameter distribution of the peripheral retina, and the established myopia development evolutionary tree can be used for guiding the assessment of myopia development risks, the optimization of the optical design of myopia correction optical products and the guidance of myopia prevention and control.
Referring to fig. 4, fig. 4 is a schematic view of a myopia progression risk assessment apparatus according to an embodiment of the present invention, in which the myopia progression risk assessment apparatus includes:
the acquisition device 20 is used for acquiring a myopia parameter distribution map of a preset area of the retina of the evaluation object;
and the evaluation device 21 is used for obtaining the myopia risk result of the evaluation object according to the myopia parameter distribution diagram of the evaluation object and a pre-established myopia development evolution tree, wherein the myopia development evolution tree reflects the development condition of the myopia parameter distribution diagram of the retina preset area of the previous myopia development degree to the myopia parameter distribution diagram of the retina preset area of the next myopia development degree.
The myopia parameter is a parameter in which the retina shows a change relative to the emmetropia when myopia occurs. The distribution graph of the myopia parameter of the preset area of the retina refers to the distribution of the myopia parameter value along with the position of the preset area of the retina. Preferably, the predetermined retinal area is located in an area of the retina other than the macular area, i.e., the predetermined retinal area is selected from an area other than the macular area of the retina, i.e., the peripheral retina.
The myopia development evolutionary tree comprises myopia parameter distribution maps of preset retina areas with different myopia development degrees and development conditions of the myopia parameter distribution map of the preset retina area reflecting the previous myopia development degree to the myopia parameter distribution map of the preset retina area reflecting the next myopia development degree.
The myopia development risk assessment device of the embodiment assesses the myopia risk result of the object by acquiring the myopia parameter distribution diagram of the preset region of the retina of the assessment object and combining the pre-established myopia development evolution tree, and achieves the purpose of assessing the myopia development risk of the object according to the myopia parameter distribution of the preset region of the retina of the assessment object.
Optionally, the obtaining device 20 may be based on a wavefront aberration principle, and establish a myopia parameter distribution map of the retina by measuring a wavefront aberration of an eye dioptric system of the evaluation subject, so as to obtain a myopia parameter distribution map of a preset region of the retina.
Specifically referring to fig. 1, the step of establishing the myopia progression evolution tree includes the following steps:
s10: and acquiring a myopia parameter distribution map of a preset retina area of the sample individual, wherein the preset retina area is positioned in an area of the retina except the macular area.
S11: and dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees.
And dividing the sample individual into a plurality of groups according to the light deflection capacity of the eyeball of the sample individual, wherein each group corresponds to different myopia development degrees. Optionally, a plurality of groups may be divided according to the light deflection capability of the individual retina macular region, and each group corresponds to a different light deflection capability range.
S12: for each of the groups, obtaining a representative myopia parameter profile for the group from the myopia parameter profiles for the individual samples belonging to the group.
The representative myopia parameter distribution map of the group refers to representative data capable of reflecting the myopia parameter distribution of the preset area of the retina of the sample individuals of the group.
Alternatively, for each group, a representative myopia parameter profile for the group may be obtained from the myopia parameter profiles of the individual samples belonging to the group by the following process, with reference to fig. 2, including the following steps:
s120: and clustering according to the myopia parameter distribution map of each sample individual belonging to the group, and dividing each sample individual belonging to the group into one or more sub-groups.
And clustering according to the myopia parameter distribution map of each individual sample belonging to the group, performing cluster analysis by taking the myopia parameters of each position on the myopia parameter distribution map as variables, and dividing the grouped individual samples into one or more subgroups.
The most common G subgroups in the group can be searched according to the distance between the individual samples of the pedigree graph obtained by clustering, and the value of G is preferably 4. If the sample number of a certain sub-packet is less, discrete data can be eliminated, and the number of sub-packets is reduced to G-3 or less.
S121: and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
The representative myopia parameter distribution map of the sub-group can be obtained according to the myopia parameter distribution map of each sample individual belonging to the sub-group by the following method: and superposing the myopia parameter distribution maps of the sample individuals belonging to the sub-group, and then averaging to obtain a representative myopia parameter distribution map corresponding to the sub-group. And superposing the data of the corresponding positions of the myopia parameter distribution maps of the sub-grouped sample individuals, then averaging, combining the data obtained at all the positions to obtain the myopia parameter distribution map again, and further obtaining the representative myopia parameter distribution map of the sub-group.
And obtaining the representative myopia parameter distribution map corresponding to each sub-group of the group according to the process, and further obtaining each representative myopia parameter distribution map of the group.
S13: and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
For each of the constructed groupings corresponding respectively to different degrees of myopia progression, each grouping includes a corresponding number of representative myopia parameter profiles. And for any adjacent two-stage myopia development degree corresponding grouping, acquiring the similarity of any representative myopia parameter distribution graph of the previous-stage myopia development degree corresponding grouping and any representative myopia parameter distribution graph of the next-stage myopia development degree corresponding grouping.
Alternatively, the similarity between the two representative myopia parameter profiles may be obtained by the following process, referring to fig. 3, including the following steps:
s130: and acquiring the root mean square difference of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps.
And calculating the square of the difference value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution graphs, and then calculating the average posterior square of each position to obtain the Root Mean Square (RMS) of the difference of the two representative myopia parameter distribution graphs.
S131: and acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps.
And calculating the ratio of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps, and averaging the ratio of each position to obtain the average value of the ratio of the two representative myopia parameter distribution maps, which is expressed as a Slope (Slope). Preferably, the calculation is always performed by a smaller absolute value/a larger absolute value when calculating the myopia parameter value ratio at the corresponding position.
S132: and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
The similarity of two representative myopia parameter distribution maps can be obtained according to the following formula:
DC＝S/RMS；
wherein DC represents the similarity of the two representative myopia parameter profiles, S represents the mean of the ratio of the two representative myopia parameter profiles, and RMS represents the root mean square of the difference of the two representative myopia parameter profiles.
The similarity of the two representative myopia parameter distribution diagrams reflects the similarity of the two representative myopia parameter distribution diagrams, and the larger the similarity value is, the more similar the two representative myopia parameter distribution diagrams are.
In specific implementation, after the Root mean square difference between any representative myopia parameter distribution map of the group corresponding to the previous myopia development degree and any representative myopia parameter distribution map of the group corresponding to the next myopia development degree is obtained, a Root mean square relation matrix RMSM (RMSM) may be established. After obtaining the average value of the ratio of any representative myopia parameter distribution map grouped correspondingly to the previous myopia development degree to any representative myopia parameter distribution map grouped correspondingly to the next myopia development degree, a Slope relation matrix SM (Slope matrix, SM) can be established.
According to the similarity of any representative myopia parameter distribution graph correspondingly grouped with the previous myopia development degree and any representative myopia parameter distribution graph correspondingly grouped with the next myopia development degree, a similarity matrix of the corresponding grouping of the previous myopia development degree and the next myopia development degree can be established, the similarity matrix comprises N rows and M columns, and the element of the nth row and the mth column represents the similarity of the nth representative myopia parameter distribution graph correspondingly grouped with the previous myopia development degree and the mth representative myopia parameter distribution graph correspondingly grouped with the next myopia development degree. The established similarity matrix can be described as a Determination coefficient matrix DCM (DCM).
And further, establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graph of each group and the similarity data. The representative myopia parameter distribution graph of each group reflects the myopia parameter distribution of several representative retina preset regions corresponding to the myopia development degree of the group, and shows the change process which the myopia parameter distribution of the retina preset regions may experience when the emmetropia is developed into the ultra-high myopia by combining the similarity data between the groups corresponding to the myopia development degrees.
According to the myopia development evolutionary tree, the myopia parameter distribution of the next level of myopia development degree is developed from the myopia parameter distribution of the previous level of myopia development degree, namely the myopia development progress. The distribution of the myopia parameters of the previous myopia progression degree does not necessarily progress to the distribution of the myopia parameters of the next myopia progression degree, i.e., the myopia progression stops. Between the upper and lower myopia progression degrees, the two myopia parameter distributions with the highest similarity are the most probable progression routes between each other. And finding out one with the highest similarity with the myopia parameter distribution of the previous myopia development degree from the myopia parameter distribution of the next myopia development degree by taking the similarity data as a basis, and connecting the two myopia parameter distributions to form an evolution route.
The myopia development evolutionary tree establishing method and the myopia development risk assessment device provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (10)
1. A myopia development evolutionary tree building method is characterized by comprising the following steps:
acquiring a myopia parameter distribution map of a preset retina area of a sample individual, wherein the preset retina area is located in an area of the retina except a macular area;
dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees;
for each group, obtaining a representative myopia parameter distribution map of the group according to the myopia parameter distribution map of each sample individual belonging to the group;
and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
2. The method of claim 1, wherein the predetermined area of the retina includes at least an area of the retina between +20 ° horizontal meridian and-16 ° horizontal meridian and between +40 ° vertical meridian and-40 ° vertical meridian other than the macular region.
3. A myopia progression evolution tree building method according to claim 1, wherein for each of said groups, obtaining a representative myopia parameter map for the group from said myopia parameter maps for respective ones of said sample individuals belonging to the group comprises:
clustering according to the myopia parameter distribution maps of the sample individuals belonging to the group, and dividing the sample individuals belonging to the group into one or more sub-groups;
and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
4. The method of claim 1, wherein obtaining a similarity of two representative myopia parameter profiles comprises:
acquiring the difference root mean square of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
5. The method of claim 4, wherein the similarity between the two representative myopia parameter profiles is obtained according to the following formula:
DC＝S/RMS；
wherein DC represents the similarity of the two representative myopia parameter profiles, S represents the mean of the ratio of the two representative myopia parameter profiles, and RMS represents the root mean square of the difference of the two representative myopia parameter profiles.
6. A myopia progression risk assessment device, comprising:
the acquisition device is used for acquiring a myopia parameter distribution map of a preset region of the retina of the evaluation object;
and the evaluation device is used for obtaining the myopia risk result of the evaluation object according to the myopia parameter distribution graph of the evaluation object and a pre-established myopia development evolution tree, wherein the myopia development evolution tree reflects the development condition of the myopia parameter distribution graph of the retina preset area of the previous myopia development degree to the myopia parameter distribution graph of the retina preset area of the next myopia development degree.
7. A myopia progression risk assessment device according to claim 6, wherein establishing the myopia progression evolution tree comprises:
acquiring a myopia parameter distribution map of a preset retina area of a sample individual, wherein the preset retina area is located in an area of the retina except a macular area;
dividing each sample individual into a plurality of groups according to the light deflection capacity of the eyeballs of the sample individual, wherein each group corresponds to different myopia development degrees;
for each group, obtaining a representative myopia parameter distribution map of the group according to the myopia parameter distribution map of each sample individual belonging to the group;
and acquiring the similarity between any representative myopia parameter distribution graph grouped corresponding to the development degree of any previous myopia and any representative myopia parameter distribution graph grouped corresponding to the development degree of the next myopia, and establishing a myopia development evolutionary tree according to the representative myopia parameter distribution graphs and the similarity data of all the groups.
8. A myopia progression risk assessment device according to claim 7, wherein for each said group, obtaining a representative myopia parameter profile for the group from said myopia parameter profiles for respective said sample individuals belonging to the group comprises:
clustering according to the myopia parameter distribution maps of the sample individuals belonging to the group, and dividing the sample individuals belonging to the group into one or more sub-groups;
and for each sub-group, obtaining a representative myopia parameter distribution map corresponding to the sub-group according to the myopia parameter distribution map of each sample individual belonging to the sub-group.
9. A myopia progression risk assessment device according to claim 7, wherein obtaining the similarity of two representative myopia parameter profiles comprises:
acquiring the difference root mean square of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
acquiring the ratio average value of the myopia parameter values of the corresponding positions of the two representative myopia parameter distribution maps;
and obtaining the similarity of the two representative myopia parameter distribution graphs according to the difference root mean square and the ratio average value of the two representative myopia parameter distribution graphs.
10. A myopia progression risk assessment device according to claim 7, comprising in particular:
and establishing a similarity matrix of the corresponding grouping of the myopia development degree of the upper stage and the myopia development degree of the lower stage according to the similarity of any representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the upper stage and any representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the lower stage, wherein the similarity matrix comprises N rows and M columns, and the mth row and mth column of elements in the nth row represent the similarity of the nth representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the upper stage and the mth representative myopia parameter distribution map of the corresponding grouping of the myopia development degree of the lower stage.
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