CN114239208A - Data processing method based on cornea shaping lens fitting and related equipment - Google Patents
Data processing method based on cornea shaping lens fitting and related equipment Download PDFInfo
- Publication number
- CN114239208A CN114239208A CN202110889537.8A CN202110889537A CN114239208A CN 114239208 A CN114239208 A CN 114239208A CN 202110889537 A CN202110889537 A CN 202110889537A CN 114239208 A CN114239208 A CN 114239208A
- Authority
- CN
- China
- Prior art keywords
- data
- cornea
- detected
- lens
- fitting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 210000004087 cornea Anatomy 0.000 title claims abstract description 81
- 238000003672 processing method Methods 0.000 title claims abstract description 21
- 238000007493 shaping process Methods 0.000 title claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000005259 measurement Methods 0.000 claims abstract description 21
- 238000011156 evaluation Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims description 23
- 238000012549 training Methods 0.000 claims description 23
- 238000013178 mathematical model Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 18
- 238000012216 screening Methods 0.000 claims description 11
- 238000004140 cleaning Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 8
- 230000001788 irregular Effects 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 12
- 238000003860 storage Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 5
- 230000004438 eyesight Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000000399 orthopedic effect Effects 0.000 description 3
- 201000009310 astigmatism Diseases 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 239000013078 crystal Substances 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000004323 axial length Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 208000001491 myopia Diseases 0.000 description 1
- 230000004379 myopia Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Abstract
The application discloses a data processing method and related equipment based on a cornea shaping lens, wherein the method comprises the following steps: an acquisition step, acquiring instantaneous refractive power characteristic data of a cornea to be measured and eye measurement parameters of a patient to be measured; an evaluation step, namely simulating a corrected cornea parameter map of the cornea to be detected, wherein the cornea parameter map is generated according to the instantaneous refractive power characteristic data and the eye to be detected measurement parameters and is used for predicting lens parameters adapted to the eye to be detected; and an output step, extracting corrected cornea parameter map data of the cornea to be detected, and outputting a fitting result according to a preset algorithm. The trained model is used for accurately predicting the prescription of the patient to be tested, and the technical problems of low prescription efficiency, large error and low reliability of the current artificial experience are solved.
Description
Technical Field
The application relates to the technical field of lens matching, in particular to a data processing method and related equipment based on a corneal plastic lens matching.
Background
Orthokeratology (OK) is a special RGP lens. Ordinary RGP lenses (myopic lenses) are used to correct vision, while shaping lenses are used for "orthotics", i.e. to improve vision by changing the corneal aggregate morphology. RGP lenses of the "corrective" type, in which the inner surface is parallel to the surface of the cornea and fits together, adjust the lens power by altering the outer surface of the lens. The opposite is true for "orthopedic" shaping mirrors. The outer surface is relatively simple and the inner surface is relatively complex. The inner surface of the shaping mirror is no longer parallel to or coincident with the cornea, but rather some gaps are created between the lens and the cornea, which takes advantage of the tear action to achieve an "orthopedic" effect.
In the traditional orthopedic diagnosis and treatment, a doctor can continuously estimate the worn OK mirror parameters for a myopic teenager by changing a try-on piece according to simple data of related ophthalmologic equipment and then by utilizing the experience of the doctor in the ophthalmologic diagnosis and treatment.
However, the above fitting procedure depends strongly on the experience of the doctor, and the accuracy of the fitting is low, has large error and is poor in reliability.
Disclosure of Invention
The embodiment of the application provides a data processing method and related equipment for lens fitting based on a corneal shaping lens, and the technical purposes of improving lens fitting efficiency, reducing lens fitting errors and improving lens fitting reliability are achieved.
A data processing method based on a cornea shaping lens comprises the following steps:
an acquisition step, acquiring instantaneous refractive power characteristic data of a cornea to be measured and eye measurement parameters of a patient to be measured;
an evaluation step of simulating a corrected cornea parameter map of the cornea to be measured, wherein the cornea parameter map is generated according to the instantaneous refractive power characteristic data and the eye measurement parameters to be measured and is used for predicting lens parameters adapted to the eye to be measured;
and an output step, extracting corrected cornea parameter map data of the cornea to be detected, and outputting a fitting result according to a preset algorithm.
Preferably, the method further comprises the following steps:
the algorithm training step is specifically realized as follows:
extracting historical fitting data for a plurality of patients, the historical fitting data including at least: instantaneous refractive power characteristic data, eye measurement parameters of a patient to be tested and lens matching parameters which are successfully matched;
training the constructed mathematical model based on the historical fitting data to generate the preset algorithm.
Preferably, the data washing and screening step comprises:
obtaining historical fitting data of a plurality of patients, and performing data cleaning and screening to remove irregular visual light data, wherein the visual light data at least comprises instantaneous refractive power characteristic data;
and carrying out non-dimensionalization processing on the data obtained by data cleaning and screening to obtain historical fitting data of the patients.
Preferably, training the constructed mathematical model based on the historical fitting data comprises:
counting and calculating historical fitting data of the patients to generate training data;
and performing predictive training on the training data for the lens parameters on the mathematical model.
Preferably, the building of the completed mathematical model is implemented as:
in the XGboost mathematical model, an objective function is constructed
Where θ represents the parameter that the mathematical model needs to input, n represents the number of samples, L (y)i，yi) Representing the error function between the predicted value and the actual value, Ω (f)k) Representing the constructed residual terms and k representing the number of residual terms.
Preferably, the method further comprises the following steps:
and taking the fitting result of the current patient as a sample of algorithm training so as to update a database consisting of historical fitting data of a plurality of patients.
Preferably, before the obtaining step, the method further comprises: and carrying out dimensionless processing on the instantaneous refractive power characteristic data of the cornea to be measured and the eye measurement parameters of the patient to be measured.
Preferably, the patient is a population of adolescent patients between 4 and 18 years of age.
A data processing device based on a orthokeratology lens fitting, comprising:
the acquisition module is used for acquiring instantaneous refractive power characteristic data of the cornea to be detected and eye measurement parameters of the patient to be detected;
the evaluation module simulates a corrected cornea parameter map of the cornea to be detected, and the cornea parameter map is generated according to the instantaneous refractive power characteristic data and the eye to be detected measurement parameters and is used for predicting lens parameters adapted to the eye to be detected;
and the output module extracts the corrected cornea parameter map data of the cornea to be detected and outputs a fitting result according to a preset algorithm.
A computing device characterized by at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of orthokeratology-based lens fitting data processing as described above.
According to the data processing method and the related equipment based on the lens matching of the corneal shaping lens, instantaneous refractive power characteristic data of a cornea to be measured and eye measurement parameters of a patient to be measured are obtained through the obtaining step; an evaluation step, simulating a cornea parameter map of the cornea to be detected after correction; and an output step, extracting corrected cornea parameter map data of the cornea to be detected, and outputting a fitting result according to a preset algorithm. The trained model is used for accurately predicting the prescription of the patient to be tested, and the technical problems of low prescription efficiency, large error and low reliability of the current artificial experience are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a data processing method for a orthokeratology-based lens prescription in an embodiment of the present application;
FIG. 2 is a schematic view of a corneal parameter map of a data processing method based on a orthokeratology lens fitting in an embodiment of the present application;
FIG. 3 is a diagram of a corneal lens variation of a data processing method based on a orthokeratology lens fitting in an embodiment of the present application;
FIG. 4 is a schematic flow chart illustrating a data processing method for a orthokeratology-based lens prescription in an embodiment of the present application;
FIG. 5 is a schematic flow chart illustrating a data processing method for a orthokeratology-based lens prescription in an embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating a data processing method for a orthokeratology-based lens prescription in an embodiment of the present application;
FIG. 7 is a schematic structural diagram of a data processing device based on a orthokeratology lens fitting in an embodiment of the present application;
FIG. 8 is a schematic diagram of a data processing computing device based on a orthokeratology lens fitting in an embodiment of the present application;
fig. 9 is a schematic structural diagram of a computer-readable medium in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a data processing method and related equipment for lens fitting based on a corneal shaping lens, and the technical aims of improving lens fitting efficiency, reducing lens fitting errors and improving lens fitting reliability are achieved.
The data processing method based on the orthokeratology lens fitting provided by the embodiment of the application is further explained with reference to the attached figure 1,
a data processing method based on a cornea shaping lens comprises the following steps:
an acquisition step S11, acquiring instantaneous refractive power characteristic data of the cornea to be measured and eye measurement parameters of the patient to be measured;
the step aims at inputting characteristic data of a subject (patient), instantaneous power characteristic data of a cornea to be measured and instantaneous power data characteristic at 4mm of the cornea, and comprises the following steps: mean, maximum, minimum, corneal e-value, corneal elevation difference, corneal elevation value, and corneal radius at four millimeters.
The measured eye parameters of the testee (patient) comprise the length of the eye axis, the sphere power, the naked eye vision, the crystal thickness, the amplitude reduction, the astigmatism and the axis rate ratio, and the age, the sex and the height of the patient are obtained simultaneously.
An evaluation step S12 of simulating a corrected cornea parameter map of the cornea to be measured, the cornea parameter map being generated according to the instantaneous refractive power characteristic data and the measured parameters of the eye to be measured, for predicting lens parameters adapted to the eye to be measured;
in the evaluation step, the vision condition of the eye of the patient with the cornea to be tested and the OK mirror worn by the patient is used, and the height and refractive power of the cornea are changed to a certain extent, the change of the cornea to be tested after different OK mirrors are worn by the user is graphically displayed, and the corresponding OK mirror prediction result is given, so that the user is assisted to obtain good OK mirror parameters more efficiently, and the aim of accurately matching the mirror is fulfilled.
Optionally, the instantaneous power value, the corneal height value and the corresponding corneal height difference value at 4mm of the cornea can be obtained by a formula lookup manner, a corneal topographer used by the patient can be a Medmont corneal topographer, and map data of the instrument can be obtained by analyzing the instantaneous power characteristic data and the measured eye parameters of the patient.
Simulating a corrected cornea parameter map of the cornea to be detected, wherein the cornea parameter map can display the cornea radius, the cornea curvature, the cornea e value, the sphere power, the cornea height value, the crystal thickness and the naked eye vision; meanwhile, on an output interface of the cornea parameter map, the following can be displayed: gender, age, height, amplitude, astigmatism, axial ratio, mean power, maximum power, minimum power.
And an output step S13, extracting the corrected cornea parameter map data of the cornea to be detected, and outputting a fitting result according to a preset algorithm.
The specific fitting result needs to be calculated by a preset algorithm, and the fitting parameters and the suggestions suitable for the cornea to be tested are output, wherein the fitting result includes but is not limited to a corneal flatness K value, a sex, an age, a corneal height difference, a corneal E value, an ocular axis length and a sphere power, and for example, the following fitting results are referred to:
referring to fig. 2-3, fig. 2 is a map generation display based on corneal parameters, and fig. 3 is a change map of a corneal lens with a certain corneal parameter worn by a patient, which can quantitatively estimate the position of the cornea forming a ring and the size of the ring. If the cornea of the patient is seen to have a obviously good circular ring after the patient wears the OK lens with the parameter model, the cornea correction parameters and the lens matching result suitable for the patient can be output by means of a preset algorithm. Preferably, the preset algorithm needs to be obtained through training, and the method further discloses an algorithm training step, which is specifically implemented by referring to fig. 4:
s41, historical fitting data of a plurality of patients is extracted, wherein the historical fitting data at least comprises: instantaneous refractive power characteristic data, eye measurement parameters of a patient to be tested and lens matching parameters which are successfully matched;
transient power profile data is obtained for a larger population (e.g., 1000 cases) including, but not limited to, corneal applanation K value, gender, age, corneal height difference, corneal E value, axial length, sphere power, etc. All data were subject to patient informed consent.
Referring to fig. 5, after S41, a data cleansing and screening step is further included, referring to fig. 6:
s51: obtaining historical fitting data of a plurality of patients, and performing data cleaning and screening to remove irregular visual light data, wherein the visual light data at least comprises instantaneous refractive power characteristic data;
it should be noted that the patients in fig. 1-5 refer to a population of teenager patients between 4 and 18 years old who are more suitable for wearing OK glasses.
And S52, carrying out non-dimensionalization processing on the data obtained by data cleaning and screening to obtain historical fitting data of the patients.
The data cleaning and screening step refers to removing irregular visual light data, performing dimensionless processing on partial data, screening effective characteristics based on more standard data, and referring to the following historical prescription data information tables of a plurality of patients as an example:
| feature(s) | Classification/data |
| Age (age) | 4-12 |
| Sex | Male/female |
| Height of a person | 110-175(cm) |
| Length of eye axis | 21-29(mm) |
| Corneal FK | 39-49(D) |
| Corneal E value | 0.3-1 |
| Refractive power of 4mm | 38-49(D) |
| Height difference | 0-50(um) |
| Maximum refractive power of 4mm | 40-47(D) |
| Minimum refractive power of 4mm | 39-40(D) |
| Radius of cornea | 7-9(mm) |
| Axial ratio | 3.1-4 |
| Maximum central 8mm height | 30-50(um) |
And S42, training the constructed mathematical model based on the historical fitting data to generate the preset algorithm. Part of the population characteristic data used for modeling is shown in the following table:
referring to fig. 6, training based on the historical fitting data is performed on the constructed mathematical model, including:
s61, counting and calculating the historical fitting data of the patients to generate training data;
in the explanation correspondence table of the step S52, statistical calculation is performed on the related group history data, and intermediate calculation values such as each feature average value and variation range are obtained to participate in algorithm training.
And S62, performing predictive training on the training data in the mathematical model aiming at the lens parameters.
Optionally, as an example, the building of the completed mathematical model is specifically implemented as:
XGBin the oost mathematical model, an objective function is constructed
Wherein theta represents various parameters required to be input by the mathematical model, such as the number of predicted leaf nodes, the depth of the tree and other superparameters, n represents the number of samples, and L (y) representsi，yi) Representing the error function between the predicted value and the actual value, Ω (f)k) Representing the constructed residual items, and k represents the number of the residual items, namely when the prediction of one tree is established to be inaccurate enough, one tree is constructed again to evaluate the difference value between the predicted value and the accurate value of the previous tree, and the construction is continued until the optimal prediction effect is achieved.
The right side of the objective function equation contains two parts of calculation, the first part is empirical risk calculation, namely the difference between a predicted value and a true value, and the second part is structural risk calculation adopted for preventing model overfitting, and generally uses a two-norm. However, the form of the objective function and the choice of the objective function are not unique. From the output, minimization of the difference between the predicted outcome and the actual time of myopia onset is the criterion for selecting the objective function.
And solving the segmentation points and the tree leaf nodes for matching the lens parameters under the objective function.
Based on the target function, the target function is converted into a formula capable of solving a unique solution by means of a Taylor expansion formula to obtain a unique segmentation point and a tree leaf node number;
preferably, the method further comprises the following steps:
and taking the fitting result of the current patient as a sample of algorithm training so as to update a database consisting of historical fitting data of a plurality of patients.
After the OK mirror parameter prediction, the OK mirror parameter prediction method is further used for updating the database according to the instantaneous refractive power characteristic data of the cornea to be measured of the patient, the eye measurement parameter to be measured of the patient and the fitting result of the patient to form a new database. When the next prediction is carried out, a new preset algorithm can be generated based on a new database to optimize and improve the accuracy of the prediction model.
Preferably, before the obtaining step, the method further comprises: and carrying out dimensionless processing on the instantaneous refractive power characteristic data of the cornea to be measured and the eye measurement parameters of the patient to be measured.
And carrying out non-dimensionalization processing on the characteristic data of the subject, and enabling the characteristic data to be in accordance with the data type in the database or a preset data standard so as to be conveniently input into an input end of the cornea parameter map equipment.
The technical effects of the invention obtained by the above embodiments are:
(1) the invention utilizes the historical corneal topography and the visual data of a large group, considers the corneal morphology and the self static data of the testee, establishes a computer system based on a mathematical model to predict the parameters of the OK mirror, and has the advantages of convenience, rapidness and high accuracy.
(2) The computer system adopts a machine learning algorithm to automatically predict the parameters of the near OK mirror, firstly selects the characteristics influencing the parameters of the OK mirror according to the processed data, then establishes a mathematical model based on a large amount of historical fitting excellent data, thus avoiding the traditional estimation and different fitting modes, simultaneously establishing an accurate verifiable mathematical model directly based on the data, and finally calculating the parameters of the OK mirror of a patient through a solidified model.
(3) The computer system of the invention can skillfully avoid the traditional blind selection method depending on doctor experience and continuous trial wearing, and can predict the parameters of the OK mirror more accurately, thereby helping the doctor to make the selection work of the parameters of the OK mirror and having great clinical significance.
By utilizing the computer system, 200 subjects in OK mirror fitting in the last year are predicted, good and excellent subjects reach 180 subjects, the accuracy rate reaches 80%, the accuracy is far higher than the traditional fitting accuracy, the efficiency is incomparable to the traditional method, and the system is proved to have very high prediction accuracy.
Referring to fig. 7, the invention discloses a data processing device based on a corneal plastic lens, comprising:
an obtaining module 71, configured to obtain instantaneous refractive power characteristic data of a cornea to be measured and a measurement parameter of an eye of a patient to be measured;
an evaluation module 72 for simulating a corrected cornea parameter map of the cornea to be measured, the cornea parameter map being generated according to the instantaneous refractive power characteristic data and the eye measurement parameters to be measured, and being used for predicting lens parameters suitable for the eye to be measured;
the output module 73 extracts the corrected cornea parameter map data of the cornea to be detected, and outputs the fitting result according to a preset algorithm.
Fig. 8 illustrates a computing device matching the processing method of fig. 1-6, including:
it should be noted that the computing device 80 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
As shown in fig. 8, computing device 80 is embodied in the form of a general purpose computing device. Components of computing device 80 may include, but are not limited to: the at least one processor 81, the at least one memory 82, and a bus 83 connecting the various system components including the memory 82 and the processor 81.
The memory 82 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 821 and/or cache memory 822, and may further include Read Only Memory (ROM) 823.
In some possible implementations, a computing device according to the present application may include at least one processor, and at least one memory (e.g., a first server). The memory stores program codes, and when the program codes are executed by the processor, the program codes cause the processor to execute the steps of the system permission opening method according to the various exemplary embodiments of the present application described above in the specification.
Referring to fig. 9, the methods illustrated in fig. 1-6 and corresponding embodiments can also be implemented by a computer-readable medium 91, and referring to fig. 9, computer-executable instructions, i.e., program instructions corresponding to the method of the present invention, are stored, and the computer-executable instructions or high-speed chip-executable instructions are used for executing the data processing method based on the orthokeratology lens fitting described in the above embodiments.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Programming code for carrying out operations for the subject application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., over the internet using an internet service provider).
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, control device, or apparatus, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product for system privilege opening of embodiments of the present application may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, control apparatus, or device.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A data processing method based on a cornea shaping lens is characterized by comprising the following steps:
an acquisition step, acquiring instantaneous refractive power characteristic data of a cornea to be measured and eye measurement parameters of a patient to be measured; an evaluation step, namely simulating a corrected cornea parameter map of the cornea to be detected, wherein the cornea parameter map is generated according to the instantaneous refractive power characteristic data and the eye to be detected measurement parameters and is used for predicting lens parameters adapted to the eye to be detected;
and an output step, extracting corrected cornea parameter map data of the cornea to be detected, and outputting a fitting result according to a preset algorithm.
2. The method for processing data based on the orthokeratology lens prescription of claim 1, further comprising:
the algorithm training step is specifically realized as follows:
extracting historical fitting data for a plurality of patients, the historical fitting data including at least: instantaneous refractive power characteristic data, eye measurement parameters of a patient to be tested and lens matching parameters which are successfully matched;
training the constructed mathematical model based on the historical fitting data to generate the preset algorithm.
3. The method of claim 2, wherein the step of data cleaning and screening comprises:
obtaining historical fitting data of a plurality of patients, and performing data cleaning and screening to remove irregular visual light data, wherein the visual light data at least comprises instantaneous refractive power characteristic data;
and carrying out non-dimensionalization processing on the data obtained by data cleaning and screening to obtain historical fitting data of the patients.
4. The method for processing data of a orthokeratology-based lens prescription according to any of claims 1 or 2, wherein training the constructed mathematical model based on the historical lens prescription data comprises:
counting and calculating historical fitting data of the patients to generate training data;
and performing predictive training on the training data for the lens parameters on the mathematical model.
5. The method for processing data based on a keratoplastic lens prescription according to claim 2, wherein the mathematical model is constructed by:
in the XGboost mathematical model, an objective function is constructed
Where theta represents the parameter that the mathematical model needs to input, n represents the number of samples,
representing the error function between the predicted value and the actual value, Ω (f)k) Representing the constructed residual terms and k representing the number of residual terms.
6. The method for processing data based on the orthokeratology lens fitting of claim 2, further comprising:
and taking the fitting result of the current patient as a sample of algorithm training so as to update a database consisting of historical fitting data of a plurality of patients.
7. The orthokeratology-based lens fitting data processing method according to claim 1 or 5,
before the obtaining step, the method further comprises: and carrying out dimensionless processing on the instantaneous refractive power characteristic data of the cornea to be detected and the eye measurement parameters of the patient to be detected.
8. The method for data processing based on the fitting of a orthokeratology lens according to claim 1 or 4, characterized in that the patients are a population of adolescent patients between 4 and 18 years of age.
9. A data processing device based on a cornea shaping mirror is characterized by comprising:
the acquisition module is used for acquiring instantaneous refractive power characteristic data of the cornea to be detected and eye measurement parameters of the patient to be detected;
the evaluation module simulates a corrected cornea parameter map of the cornea to be detected, and the cornea parameter map is generated according to the instantaneous refractive power characteristic data and the eye to be detected measurement parameters and is used for predicting lens parameters adapted to the eye to be detected;
and the output module extracts the corrected cornea parameter map data of the cornea to be detected and outputs a fitting result according to a preset algorithm.
10. A computing device characterized by at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the orthokeratology-based data processing method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110889537.8A CN114239208A (en) | 2021-08-04 | 2021-08-04 | Data processing method based on cornea shaping lens fitting and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110889537.8A CN114239208A (en) | 2021-08-04 | 2021-08-04 | Data processing method based on cornea shaping lens fitting and related equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114239208A true CN114239208A (en) | 2022-03-25 |
Family
ID=80742509
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110889537.8A Pending CN114239208A (en) | 2021-08-04 | 2021-08-04 | Data processing method based on cornea shaping lens fitting and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114239208A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114967176A (en) * | 2022-06-15 | 2022-08-30 | 潍坊眼科医院有限责任公司 | Method and device for manufacturing orthokeratology lens based on cornea shape and refraction data |
| CN116990450A (en) * | 2023-07-18 | 2023-11-03 | 欧几里德(苏州)医疗科技有限公司 | Defect detection method and system for cornea shaping mirror |
| CN116990450B (en) * | 2023-07-18 | 2024-04-26 | 欧几里德(苏州)医疗科技有限公司 | Defect detection method and system for cornea shaping mirror |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832581A (en) * | 2017-12-15 | 2018-03-23 | 百度在线网络技术（北京）有限公司 | Trend prediction method and device |
| CN111000525A (en) * | 2019-11-21 | 2020-04-14 | 明灏科技（北京）有限公司 | Corneal plastic lens fitting method and system based on big data |
| CN111134613A (en) * | 2019-11-21 | 2020-05-12 | 明灏科技（北京）有限公司 | Image recognition-based orthokeratology lens fitting method and system |
| CN111553402A (en) * | 2020-04-22 | 2020-08-18 | 首都医科大学附属北京同仁医院 | Intelligent orthokeratology lens selecting system and method based on big data and deep learning |
-
2021
- 2021-08-04 CN CN202110889537.8A patent/CN114239208A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832581A (en) * | 2017-12-15 | 2018-03-23 | 百度在线网络技术（北京）有限公司 | Trend prediction method and device |
| CN111000525A (en) * | 2019-11-21 | 2020-04-14 | 明灏科技（北京）有限公司 | Corneal plastic lens fitting method and system based on big data |
| CN111134613A (en) * | 2019-11-21 | 2020-05-12 | 明灏科技（北京）有限公司 | Image recognition-based orthokeratology lens fitting method and system |
| CN111553402A (en) * | 2020-04-22 | 2020-08-18 | 首都医科大学附属北京同仁医院 | Intelligent orthokeratology lens selecting system and method based on big data and deep learning |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114967176A (en) * | 2022-06-15 | 2022-08-30 | 潍坊眼科医院有限责任公司 | Method and device for manufacturing orthokeratology lens based on cornea shape and refraction data |
| CN116990450A (en) * | 2023-07-18 | 2023-11-03 | 欧几里德(苏州)医疗科技有限公司 | Defect detection method and system for cornea shaping mirror |
| CN116990450B (en) * | 2023-07-18 | 2024-04-26 | 欧几里德(苏州)医疗科技有限公司 | Defect detection method and system for cornea shaping mirror |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8388137B2 (en) | System and method for analyzing wavefront aberrations | |
| US10426551B2 (en) | Personalized refractive surgery recommendations for eye patients | |
| EP3395233B1 (en) | Ametropia treatment tracking methods and system | |
| US7862176B2 (en) | Method of fitting rigid gas-permeable contact lenses from high resolution imaging | |
| AU2018202725B2 (en) | Ametropia treatment tracking methods and system | |
| CN113806908A (en) | Parameter processing method based on cornea shaping lens fitting and related equipment | |
| CN111553402B (en) | Intelligent cornea shaping lens selecting system and method based on big data and deep learning | |
| JP2007531559A5 (en) | ||
| CN111000525B (en) | Corneal plastic lens fitting method and system based on big data | |
| CN112700858B (en) | Early warning method and device for myopia of children and teenagers | |
| CN110575134A (en) | method for calculating myopia degree based on eccentric photography | |
| CN111134613B (en) | Image recognition-based orthokeratology lens fitting method and system | |
| CN115881300A (en) | System and method for determining and analyzing ocular biometric status of a patient | |
| CN114239208A (en) | Data processing method based on cornea shaping lens fitting and related equipment | |
| US20240082059A1 (en) | Intelligent corneal procedure advisor | |
| US20240041653A1 (en) | Intelligent topographic corneal procedure advisor | |
| CN110989199A (en) | Calculating device for cornea moulding mirror power | |
| KR102208508B1 (en) | Systems and methods for performing complex ophthalmic tratment | |
| CN211627958U (en) | Calculating device for cornea moulding mirror power | |
| JP2002345755A (en) | Corneal shape analyzing method and corneal shape analyzing device | |
| CN110908140A (en) | Production device of orthokeratology mirror | |
| CN114924427B (en) | Lens matching method and device of cornea shaping lens, electronic equipment and storage medium | |
| WO2021173843A1 (en) | Epithelial thickness mapping and corneal procedure advisor | |
| Gong et al. | Application of machine learning method in clinical fitting of Ortho-K lens for myopia correction | |
| CN211786441U (en) | Production device of orthokeratology mirror |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220325 |