A method and device for processing corneal reshaping sample data

Abstract

The embodiment of the present invention discloses a method and device for processing corneal reshaping sample data, which relates to the technical field of corneal reshaping lenses. The method includes: obtaining original eye examination data to obtain an initial data set; performing basic processing on the initial data set to obtain a preliminary data set; inputting the preliminary data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model; inputting all original eye examination data in each preliminary data set into the corneal reshaping parameter prediction model, and rescreening the preliminary data set according to the prediction results and actual data to obtain the final corneal reshaping sample data set. The present invention can reduce the impact of hidden adverse case data on the prediction model, significantly reduce the loss function, and greatly improve the final performance effect of the model.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of orthokeratology lenses, and specifically to a method and device for processing orthokeratology sample data. Background Art

[0002] When training artificial neural networks for parameter prediction of orthokeratology lenses, a large amount of clinical case data is used for training. Due to the diversity of human eye parameters, the difficulty in standardizing and evaluating corneal biomechanical parameters, and the complexity of the combination of multi-dimensional eye examination parameters, the optimal fitting effect of orthokeratology lenses will generally vary to a certain extent. In other words, not all case experiences are optimal. Due to the particularity and unquantifiable characteristics of factors such as corneal biomechanics and eyelid tension, not all patients can achieve the best fit. At the same time, the fitting effect is also closely related to the scale of the hospital and the number and professionalism of professionals. Therefore, the data used for training requires very complex processing, and various extreme corneal morphologies, anti-rule and anti-regular fitting parameters, etc. need to be screened, and it is necessary to ensure that the proportion is not too high and the overall fitting data is not positively affected. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method and device for processing corneal reshaping sample data, so as to effectively screen the data to reduce the difficulty of fitting and ensure the prediction accuracy of the corneal reshaping parameter prediction model.

[0004] In order to solve the above problems, a first aspect of an embodiment of the present invention discloses a method for processing orthokeratology sample data, comprising: Acquire original eye examination data to obtain an initial data set; Performing basic processing on the initial data set to obtain a preliminary data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data; Inputting the preliminary selected data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model; All original eye examination data in each preliminary data set are respectively input into the orthokeratology parameter prediction model, and the preliminary data set is screened again according to the prediction results and actual data to obtain the final orthokeratology sample data set.

[0005] As a preferred embodiment, in the first aspect of the embodiment of the present invention, the initial data set is subjected to basic processing, including: The theoretical abnormal data in the initial data set are filtered out according to the flat K value, flat e value and positioning arc value in each original eye examination data; According to the relationship between the flat K value, flat e value and positioning arc value in each original eye examination data, regular abnormal data in the initial data set are filtered out.

[0006] As a preferred embodiment, in the first aspect of the embodiment of the present invention, filtering out theoretical abnormal data in the initial data set according to the flat K value, flat e value and positioning arc value in each original eye examination data includes: When the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: FK<38 or FK>48; Case 2: em<0 or em>1; Case 3: AC<38 or AC>48; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

[0007] As a preferred embodiment, in the first aspect of the embodiment of the present invention, when the target original eye examination data also contains one or more of intraocular pressure value, spherical power, steep K value, astigmatism degree, corneal diameter, drop amplitude, total diameter and toricity, and when one or more of the following situations exist, the target original eye examination data is filtered out: Situation 4: IOP ≤ 7 or IOP ≥ 24; Scenario 5: ; Case 6: SK<39 or SK>50; Case 7: ΔK<0 or ΔK>5; Scenario 8: HVID<9 or HVID>15; Case 9: RP<0 or RP>6; Case 10: DIV<9 or DIV>12; Scenario 11: CP<0 or CP>4; Among them, IOP is the intraocular pressure value, F is the spherical power, SK is the steep K value, ΔK is the astigmatism degree, HVID is the corneal diameter, RP is the drop, DIV is the total diameter, and CP is the toric volume.

[0008] As a preferred embodiment, in the first aspect of the embodiment of the present invention, filtering out regular abnormal data in the initial data set according to the relationship between the flat K value, the flat e value and the positioning arc value in each original eye examination data includes: When the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: When 0.55≤em≤1, ; Case 2: When 0≤em≤0.45, ; Case 3: When 0.45<em<0.55, ; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

[0009] As a preferred embodiment, in the first aspect of the embodiment of the present invention, when the target original eye examination data also contains spherical power and drop, or astigmatism and toric curvature, or corneal diameter and total diameter, and one or more of the following situations exist, the target original eye examination data is filtered out: Case 4: RP+FK-AC-F<-0.25 or RP+FK-AC-F>1.25; Case 5: HVID-DIV<0.4 or HVID-DIV>2.2; Scenario 6: ; Among them, F is the spherical power, RP is the reduction, HVID is the corneal diameter, DIV is the total diameter, CP is the toric curvature, and ΔK is the astigmatism degree.

[0010] As a preferred embodiment, in the first aspect of the embodiment of the present invention, all the original eye examination data in each preliminary selected data set are respectively input into the orthokeratology parameter prediction model, and the preliminary selected data set is screened again according to the prediction result and the actual data to obtain the final orthokeratology sample data set, including: The target original eye examination data is input into the corneal reshaping parameter prediction model to obtain the positioning arc prediction value of the target original eye examination data, and the actual value of the positioning arc in the target original eye examination data is compared with the size of the positioning arc prediction value. When the absolute value of the difference between the two is greater than 0.62, the target original eye examination data is filtered out.

[0011] As a preferred embodiment, in the first aspect of the embodiment of the present invention, when the target original eye examination data includes one or more of the actual value of the drop, the actual value of the total diameter and the actual value of the curvature, the target original eye examination data is input into the corneal reshaping parameter prediction model, and one or more of the predicted value of the drop, the predicted value of the total diameter and the predicted value of the curvature of the target original eye examination data is obtained. When one or more of the following situations exist, the target original eye examination data is filtered out: Scenario 1: The absolute value of the difference between the predicted value of the decline and the actual value of the decline is greater than 1; Case 2: The absolute value of the difference between the predicted total diameter and the actual total diameter is greater than 0.3; Case 3: The absolute value of the difference between the predicted value of the loop curvature and the actual value of the loop curvature is greater than 0.75.

[0012] A second aspect of an embodiment of the present invention discloses a corneal shaping sample data processing device, comprising: An acquisition unit, used for acquiring original eye examination data to obtain an initial data set; A preliminary selection unit, used for performing basic processing on the initial data set to obtain a preliminary selection data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data; A training unit, used for inputting the preliminary selected data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model; The final selection unit is used to input all the original eye examination data in each preliminary selection data set into the corneal reshaping parameter prediction model, and re-screen the preliminary selection data set according to the prediction results and actual data to obtain the final corneal reshaping sample data set.

[0013] A third aspect of an embodiment of the present invention discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the corneal shaping sample data processing method disclosed in the first aspect of the embodiment of the present invention are implemented.

[0014] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the steps of the corneal shaping sample data processing method disclosed in the first aspect of the embodiment of the present invention.

[0015] A fifth aspect of an embodiment of the present invention discloses a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the steps of the corneal shaping sample data processing method disclosed in the first aspect of the embodiment of the present invention.

[0016] A sixth aspect of an embodiment of the present invention discloses an application publishing platform, which is used to publish a computer program product. When the computer program product runs on a computer, the computer executes the steps of the corneal shaping sample data processing method disclosed in the first aspect of the embodiment of the present invention.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The embodiment of the present invention can reduce the impact of hidden adverse case data on the prediction model by performing theoretical abnormality screening, regular abnormality screening and fitted abnormality screening on the initial data set composed of original eye examination data, significantly reduce the loss function, and greatly improve the final performance of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1It is a flowchart of a corneal shaping sample data processing method disclosed in an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of a corneal shaping sample data processing device disclosed in an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] This specific implementation manner is merely an explanation of an embodiment of the present invention, and it is not a limitation of the embodiment of the present invention. After reading this specification, a person skilled in the art may make non-creative modifications to the embodiment as needed, but as long as it is within the scope of the claims of the embodiment of the present invention, it is protected by the patent law.

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 part of the embodiments of the present invention, not all of them. Based on the embodiments in the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of the present invention.

[0021] The term "comprise" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0022] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0023] The embodiment of the present invention can reduce the impact of hidden adverse case data on the prediction model by performing theoretical abnormality screening, regular abnormality screening and fitted abnormality screening on the initial data set composed of original eye examination data, significantly reduce the loss function, and greatly improve the final performance of the model, which is described in detail below in conjunction with the accompanying drawings. Embodiment 1

[0024] See also Figure 1 , Figure 1FIG. 1 is a flow chart of a method for processing corneal orthokeratology sample data disclosed in an embodiment of the present invention. Figure 1 As shown, the corneal shaping sample data processing method includes: S110, acquiring original eye examination data to obtain an initial data set.

[0025] The original eye examination data may be data obtained from self-examination of several people, or data obtained through hospitals, research institutes or other institutions with corneal reshaping functions. Of course, it may also be data obtained from relevant Internet sharing platforms. No further limitation is made here on the source of the original eye examination data.

[0026] The acquired raw eye examination data can be directly used as the composition of the initial data set. Of course, the constructed initial data set can also be pre-processed by data cleaning before the next step. Data cleaning includes but is not limited to checking data consistency, processing invalid values ​​and missing values, etc.

[0027] The preprocessed initial data set constitutes a basic sample set for training the neural network model. The embodiment of the present invention screens each sample in the initial data set to improve the accuracy of the neural network model training.

[0028] S120, performing basic processing on the initial data set to obtain a preliminary selected data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data.

[0029] The basic processing is to filter the original eye examination data that exceeds the physiological indicators and the set regularity threshold to obtain the preliminary selected data set.

[0030] Please refer to Table 1, which shows the screening of theoretical abnormal data. When the target original eye examination data is within the unacceptable range of any item in Table 1, the target original eye examination data is discarded.

[0031] Table 1. Theoretical abnormal data screening table

[0032] In Table 1, if the remark shows that it cannot be empty, it means that the parameter must exist in the target original eye examination data, that is, the target original eye examination data must have a flat K value, a flat e value and a positioning arc value. If any one or more of the three does not exist, the target original eye examination data will be directly eliminated.

[0033] If the target original eye examination data have the above three parameters at the same time, the theoretical abnormal data in the initial data set are filtered out according to the flat K value, flat e value and positioning arc value in each original eye examination data.

[0034] Specifically, if the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: FK<38 or FK>48; Case 2: em<0 or em>1; Case 3: AC<38 or AC>48; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc (also known as the adaptation arc, used to fit with the cornea to position the lens) value.

[0035] In Table 1, if the remark is displayed as being empty, it means that the parameter is an optional item (optional parameter). If it exists, the original eye examination data is screened according to the existing parameter. If it does not exist, the original eye examination data is not screened using the parameter.

[0036] These optional parameters are intraocular pressure value, spherical power, steep K value, astigmatism, corneal diameter, drop amplitude, total diameter and toricity. If the target original eye examination data has one or more of these parameters and one or more of the following situations exist, the target original eye examination data is filtered out: Situation 4: IOP ≤ 7 or IOP ≥ 24; Scenario 5: ; Case 6: SK<39 or SK>50; Case 7: ΔK<0 or ΔK>5; Scenario 8: HVID<9 or HVID>15; Case 9: RP<0 or RP>6; Case 10: DIV<9 or DIV>12; Scenario 11: CP<0 or CP>4; Among them, IOP is the intraocular pressure value, F is the spherical power of the lens, SK is the steep K value, ΔK is the astigmatism degree (ΔK=SK-FK), HVID is the corneal diameter (that is, the horizontal diameter of the visible iris), RP is the reduction of the lens (that is, the degree of myopia reduction), DIV is the total diameter of the lens, and CP is the toric amount (also known as the astigmatism amount, which is positively correlated with the lens sagittal height).

[0037] Please refer to Table 2, which shows the screening of regular abnormal data. When the target original eye examination data is within the unacceptable range of any item in Table 2, the target original eye examination data is discarded.

[0038] Table 2. Regular abnormal data screening table

[0039] As can be seen from Table 2, when performing regular abnormal screening, the flat K value, flat e value and positioning arc value are still required parameters in the original eye examination data, and if one or more of the following situations exist between these parameters, the target original eye examination data is filtered out: Case 1: When 0.55≤em≤1, ; Case 2: When 0≤em≤0.45, ; Case 2: When 0.45<em<0.55, ; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

[0040] Similarly, it can be seen from Table 2 that if the target original eye examination data still contains spherical power and drop, or astigmatism and toric curvature, or corneal diameter and total diameter, and one or more of the following situations exist, the target original eye examination data is filtered out: Case 4: RP+FK-AC-F<-0.25 or RP+FK-AC-F>1.25; Case 5: HVID-DIV<0.4 or HVID-DIV>2.2; Scenario 6: ; Among them, F is the spherical power, RP is the reduction, HVID is the corneal diameter, DIV is the total diameter, CP is the toric curvature, and ΔK is the astigmatism degree.

[0041] Through the above-mentioned theoretical exception processing and regular exception processing, various extreme corneal morphologies, adaptation parameters that go against the rules and laws, etc. can be eliminated, so that they can be used to train the neural network model.

[0042] S130, inputting the preliminary data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model.

[0043] The neural network model can adopt existing conventional neural network models, such as perceptron neural network, linear neural network, BP neural network, radial basis function network, self-organizing network, feedback network and other machine learning models. Its basic principle is to define a neural network model containing multiple learnable parameters (generally weights), iteratively calculate the input data set, and use the multi-layer network structure corresponding to the neural network model to process the input data, thereby calculating the loss value, and completing the update of the parameters of the neural network model based on the back propagation function to the parameters of the neural network model.

[0044] During the training process, the preliminary data set can be divided into training samples and test samples, wherein the ratio of training samples to test samples can be set to 7:3 or 8:2, etc. The neural network model is trained by the training samples, and the trained neural network model is tested by the test samples. If the prediction result reaches the preset threshold, for example, the relationship between the prediction result and the actual value of the test sample can be characterized by the variance, etc. When the variance is less than or equal to the preset threshold, the neural network model training is considered to be completed, and the trained corneal shaping parameter prediction model is obtained. If the variance is greater than the preset threshold, the test samples and training samples are reshuffled, and the neural network model is continued to be trained using the new test samples and training samples until the requirements are met.

[0045] S140, inputting all the original eye examination data in each preliminary data set into the orthokeratology parameter prediction model respectively, and re-screening the preliminary data set according to the prediction results and actual data to obtain the final orthokeratology sample data set.

[0046] In order to ensure that the overall fitting data is not positively affected, in a preferred embodiment of the present invention, the preliminary data set is also screened to obtain the final orthokeratology sample data set. In this screening process, the data with large discreteness, that is, the fitting abnormal data, is mainly filtered.

[0047] Please refer to Table 3, which shows the screening of abnormal fitting data. When the original eye examination data in the primary data set is within any unacceptable range shown in Table 3, the target original eye examination data is discarded.

[0048] Table 3. Fitting abnormal data screening table

[0049] It can be seen from Table 3 that when performing fitting abnormality screening, the positioning arc value is still used as a required parameter for screening. The target original eye examination data is input into the corneal reshaping parameter prediction model to obtain the positioning arc prediction value of the target original eye examination data. The actual positioning arc value in the target original eye examination data and the positioning arc prediction value are compared. When the absolute value of the difference between the two is greater than 0.62, the target original eye examination data is filtered out.

[0050] The actual value of the drop, the actual value of the total diameter and the actual value of the curvature are optional. If the target original eye examination data includes one or more of the actual value of the drop, the actual value of the total diameter and the actual value of the curvature, the target original eye examination data is input into the corneal reshaping parameter prediction model, and one or more of the predicted value of the drop, the predicted value of the total diameter and the predicted value of the curvature of the target original eye examination data is obtained. When one or more of the following situations exist, the target original eye examination data is filtered out: Scenario 1: The absolute value of the difference between the predicted value of the decline and the actual value of the decline is greater than 1; Case 2: The absolute value of the difference between the predicted total diameter and the actual total diameter is greater than 0.3; Case 3: The absolute value of the difference between the predicted value of the loop curvature and the actual value of the loop curvature is greater than 0.75.

[0051] The embodiment of the present invention eliminates various extreme corneal morphologies and adaptation parameters that go against rules and regulations through theoretical abnormality screening and regular abnormality screening, thereby reducing the impact of hidden adverse case data on the prediction model, and can preliminarily ensure the accuracy of the training neural network model, and then eliminate data with large discreteness, so that the neural network model can be free from the positive influence of the fitting data or have a smaller positive influence.

[0052] After obtaining the final corneal reshaping sample data set, the neural network model can be retrained, which significantly reduces the loss function and greatly improves the final performance of the model. At the same time, it can also reduce the difficulty of fitting and achieve the purpose of fast fitting. Embodiment 2

[0053] Please refer to Figure 2 As shown, Figure 2 Schematic diagram of a corneal shaping sample data processing system disclosed in an embodiment of the present invention. Figure 2 As shown, the corneal reshaping sample data processing system includes: An acquisition unit 210 is used to acquire original eye examination data to obtain an initial data set; A preliminary selection unit 220 is used to perform basic processing on the initial data set to obtain a preliminary selection data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data; A training unit 230, configured to input the preliminary selected data set into a neural network model, train the neural network model, and obtain a trained orthokeratology parameter prediction model; The final selection unit 240 is used to input all the original eye examination data in each preliminary selection data set into the corneal reshaping parameter prediction model, and re-screen the preliminary selection data set according to the prediction results and actual data to obtain the final corneal reshaping sample data set.

[0054] Preferably, the preliminary selection unit 220 may include: The theoretical abnormal data in the initial data set are filtered out according to the flat K value, flat e value and positioning arc value in each original eye examination data; Specifically, when the target original eye examination data has one or more of the following situations, the target original eye examination data is filtered out: Case 1: FK<38 or FK>48; Case 2: em<0 or em>1; Case 3: AC<38 or AC>48; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

[0055] When the target original eye examination data also contains one or more of intraocular pressure value, spherical power, steep K value, astigmatism degree, corneal diameter, drop amplitude, total diameter and toricity, and one or more of the following situations exist, the target original eye examination data is filtered out: Situation 4: IOP ≤ 7 or IOP ≥ 24; Scenario 5: ; Case 6: SK<39 or SK>50; Case 7: ΔK<0 or ΔK>5; Scenario 8: HVID<9 or HVID>15; Case 9: RP<0 or RP>6; Case 10: DIV<9 or DIV>12; Scenario 11: CP<0 or CP>4; Among them, IOP is the intraocular pressure value, F is the spherical power, SK is the steep K value, ΔK is the astigmatism degree, HVID is the corneal diameter, RP is the drop amplitude, DIV is the total diameter, and CP is the toric volume; According to the relationship between the flat K value, the flat e value and the positioning arc value in each original eye examination data, the regular abnormal data in the initial data set are filtered out; Specifically, When the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: When 0.55≤em≤1, ; Case 2: When 0≤em≤0.45, ; Case 3: When 0.45<em<0.55, ; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

[0056] When the target original eye examination data still contains spherical power and drop, or astigmatism and toric curvature, or corneal diameter and total diameter, and one or more of the following situations exist, the target original eye examination data is filtered out: Case 4: RP+FK-AC-F<-0.25 or RP+FK-AC-F>1.25; Case 5: HVID-DIV<0.4 or HVID-DIV>2.2; Scenario 6: ; Among them, F is the spherical power, RP is the reduction, HVID is the corneal diameter, DIV is the total diameter, CP is the toric curvature, and ΔK is the astigmatism degree.

[0057] Preferably, the final selection unit 240 may include: Inputting the target original eye examination data into the corneal reshaping parameter prediction model to obtain the positioning arc prediction value of the target original eye examination data, comparing the actual positioning arc value in the target original eye examination data with the positioning arc prediction value, and filtering out the target original eye examination data when the absolute value of the difference between the two is greater than 0.62; When the target original eye examination data includes one or more of the actual value of the drop, the actual value of the total diameter, and the actual value of the curvature, the target original eye examination data is input into the corneal reshaping parameter prediction model, and one or more of the predicted value of the drop, the predicted value of the total diameter, and the predicted value of the curvature of the target original eye examination data is obtained. When one or more of the following situations exist, the target original eye examination data is filtered out: Scenario 1: The absolute value of the difference between the predicted value of the decline and the actual value of the decline is greater than 1; Case 2: The absolute value of the difference between the predicted total diameter and the actual total diameter is greater than 0.3; Case 3: The absolute value of the difference between the predicted value of the loop curvature and the actual value of the loop curvature is greater than 0.75. Embodiment 3

[0058] See also Figure 3 , Figure 3A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present invention described herein or / and claimed.

[0059] like Figure 3 As shown, the electronic device includes at least one processor 310, and a memory connected to the at least one processor 310, such as a ROM (read-only memory) 320, a RAM (random access memory) 330, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 310 can perform various appropriate actions and processes according to the computer program stored in the ROM 320 or the computer program loaded from the storage unit 380 to the random access memory RAM 330. In the RAM 330, various programs and data required for the operation of the electronic device can also be stored. The processor 310, the ROM 320, and the RAM 330 are connected to each other via a bus 340. The I / O (input / output) interface 350 is also connected to the bus 340.

[0060] A number of components in the electronic device are connected to the I / O interface 350, including: an input unit 360, such as a keyboard, a mouse, etc.; an output unit 370, such as various types of displays, speakers, etc.; a storage unit 380, such as a disk, an optical disk, etc.; and a communication unit 390, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 390 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet or / and various telecommunication networks.

[0061] The processor 310 may be any general or / and dedicated processing component with processing and computing capabilities. Some examples of the processor 310 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 310 executes one or more steps of a corneal shaping sample data processing method described in the first embodiment above.

[0062] In some embodiments, a method for processing corneal reshaping sample data may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 380. In some embodiments, part or all of the computer program may be loaded into or / and installed on an electronic device via the ROM 320 and / or the communication unit 390. When the computer program is loaded into the RAM 330 and executed by the processor 310, one or more steps of the method for processing corneal reshaping sample data described in the first embodiment above may be executed. Alternatively, in other embodiments, the processor 310 may be configured to execute a method for processing corneal reshaping sample data in any other appropriate manner (e.g., by means of firmware).

[0063] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, or / and combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed or / and interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general purpose programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0064] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0065] In the context of an embodiment of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0066] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0067] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0068] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0069] The above is a detailed introduction to a corneal reshaping sample data processing method and device disclosed in the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for processing orthokeratology sample data, characterized in that: It includes: Acquire original eye examination data to obtain an initial data set; Performing basic processing on the initial data set to obtain a preliminary data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data; Inputting the preliminary selected data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model; All original eye examination data in each preliminary data set are respectively input into the orthokeratology parameter prediction model, and the preliminary data set is screened again according to the prediction results and actual data to obtain the final orthokeratology sample data set.

2. The method for processing orthokeratology sample data according to claim 1, characterized in that: The initial data set is subjected to basic processing, including: The theoretical abnormal data in the initial data set are filtered out according to the flat K value, flat e value and positioning arc value in each original eye examination data; According to the relationship between the flat K value, flat e value and positioning arc value in each original eye examination data, regular abnormal data in the initial data set are filtered out.

3. The method for processing orthokeratology sample data according to claim 2, characterized in that: The theoretical abnormal data in the initial data set are filtered out according to the flat K value, flat e value and positioning arc value in each original eye examination data, including: When the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: FK<38 or FK>48; Case 2: em<0 or em>1; Case 3: AC<38 or AC>48; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

4. The method for processing orthokeratology sample data according to claim 3, characterized in that: When the target original eye examination data also contains one or more of intraocular pressure value, spherical power, steep K value, astigmatism degree, corneal diameter, drop amplitude, total diameter and toricity, and one or more of the following situations exist, the target original eye examination data is filtered out: Situation 4: IOP ≤ 7 or IOP ≥ 24; Scenario 5: ; Case 6: SK<39 or SK>50; Case 7: ΔK<0 or ΔK>5; Scenario 8: HVID<9 or HVID>15; Case 9: RP<0 or RP>6; Case 10: DIV<9 or DIV>12; Scenario 11: CP<0 or CP>4; Among them, IOP is the intraocular pressure value, F is the spherical power, SK is the steep K value, ΔK is the astigmatism degree, HVID is the corneal diameter, RP is the drop, DIV is the total diameter, and CP is the toric volume.

5. The method for processing orthokeratology sample data according to claim 2, characterized in that: According to the relationship between the flat K value, flat e value and positioning arc value in each original eye examination data, regular abnormal data in the initial data set are filtered out, including: When the target original eye examination data has one or more of the following conditions, the target original eye examination data is filtered out: Case 1: When 0.55≤em≤1, ; Case 2: When 0≤em≤0.45, ; Case 3: When 0.45<em<0.55, ; Among them, FK is the flat K value, em is the flat e value, and AC is the positioning arc value.

6. The method for processing orthokeratology sample data according to claim 5, characterized in that: When the target original eye examination data still contains spherical power and drop, or astigmatism and toric curvature, or corneal diameter and total diameter, and one or more of the following situations exist, the target original eye examination data is filtered out: Case 4: RP+FK-AC-F<-0.25 or RP+FK-AC-F>1.25; Case 5: HVID-DIV<0.4 or HVID-DIV>2.2; Scenario 6: ; Among them, F is the spherical power, RP is the reduction, HVID is the corneal diameter, DIV is the total diameter, CP is the toric curvature, and ΔK is the astigmatism degree.

7. The orthokeratology sample data processing method according to claim 1, characterized in that: All the original eye examination data in each preliminary data set are respectively input into the orthokeratology parameter prediction model, and the preliminary data set is screened again according to the prediction results and actual data to obtain the final orthokeratology sample data set, including: The target original eye examination data is input into the corneal reshaping parameter prediction model to obtain the positioning arc prediction value of the target original eye examination data, and the actual value of the positioning arc in the target original eye examination data is compared with the size of the positioning arc prediction value. When the absolute value of the difference between the two is greater than 0.62, the target original eye examination data is filtered out.

8. The method for processing orthokeratology sample data according to claim 7, characterized in that: When the target original eye examination data includes one or more of the actual value of the drop, the actual value of the total diameter, and the actual value of the curvature, the target original eye examination data is input into the corneal reshaping parameter prediction model, and one or more of the predicted value of the drop, the predicted value of the total diameter, and the predicted value of the curvature of the target original eye examination data is obtained. When one or more of the following situations exist, the target original eye examination data is filtered out: Scenario 1: The absolute value of the difference between the predicted value of the decline and the actual value of the decline is greater than 1; Case 2: The absolute value of the difference between the predicted total diameter and the actual total diameter is greater than 0.3; Case 3: The absolute value of the difference between the predicted value of the loop curvature and the actual value of the loop curvature is greater than 0.

75.

9. A corneal reshaping sample data processing device, characterized in that: It includes: An acquisition unit, used for acquiring original eye examination data to obtain an initial data set; A preliminary selection unit, used for performing basic processing on the initial data set to obtain a preliminary selection data set, wherein the basic processing includes filtering out theoretical abnormal data and regular abnormal data; A training unit, used for inputting the preliminary selected data set into a neural network model, training the neural network model, and obtaining a trained corneal reshaping parameter prediction model; The final selection unit is used to input all the original eye examination data in each preliminary selection data set into the corneal reshaping parameter prediction model, and re-screen the preliminary selection data set according to the prediction results and actual data to obtain the final corneal reshaping sample data set.

10. A computer-readable storage medium, characterized in that: It stores a computer program, wherein the computer program enables the computer to execute the steps of the corneal shaping sample data processing method described in any one of claims 1-8.

Images