Myopia prevention and control optical intervention evaluation method, system and terminal based on OCT technology

Abstract

The application provides a myopia prevention and control optical intervention evaluation method, system and terminal based on OCT technology, which performs naked eye fundus scanning operation on both eyes of a myopia prevention and control target patient by using OCT technology, generates a naked eye refractive topography of both eyes, comprehensively and objectively measures the defocus distribution of the whole area of the retina, generates initial optical intervention parameters, and selects and formulates an initial target optical intervention scheme; and performs wearing eye fundus scanning operation on both eyes of the myopia prevention and control target patient wearing a trial optical intervention product, generates a wearing eye refractive topography of both eyes, so as to compare the changes of the refractive topographies of both eyes of the myopia prevention and control target patient before and after wearing the optical intervention product, predict the myopia progression of both eyes of the myopia prevention and control target patient based on a myopia development prediction model, assist doctors to provide a more suitable personalized target optical intervention scheme, and improve the fitting effect, fitting efficiency and myopia prevention and control effect of the optical intervention product.

Description

Technical Field

[0001] This application relates to the field of vision correction technology, and in particular to a method, system and terminal for myopia prevention and control optical intervention assessment based on OCT technology. Background Technology

[0002] Myopia is a common eye disease that threatens the vision health of children and adolescents worldwide. It has a high prevalence and progresses rapidly; if left uncontrolled, it can easily develop into high myopia, increasing the risk of irreversible eye diseases such as retinal degeneration and macular degeneration. Therefore, taking effective optical interventions to slow the progression of myopia has become an important issue in the field of vision correction. Currently, widely used optical interventions are mainly based on the theory of "peripheral myopic defocus." By wearing myopia control glasses, orthokeratology lenses, or defocusing soft lenses, while correcting central vision, peripheral light is focused in front of the retina, thereby transmitting signals to the eyeball to inhibit axial elongation and slow the progression of myopia.

[0003] Accurate assessment of peripheral retinal defocus is crucial for selecting appropriate optical intervention products and developing effective myopia control programs. Currently, the selection of myopia control optical intervention programs relies heavily on experience. Generally, subjective refraction is used to obtain the central refractive error of the fundus, combined with trial lenses for a rough assessment. This fitting method focuses primarily on central visual acuity correction, lacking sufficient consideration of the refractive state of the peripheral retina, i.e., lacking objective and comprehensive measurement and quantitative analysis of the defocus distribution in the peripheral retina. Consequently, standardized, serialized lens designs are used, lacking personalization. This results in poor matching between the defocus intervention signal and the individual retinal refractive state in some patients, leading to unsatisfactory myopia control effects, low fitting efficiency of optical intervention products, and even visual discomfort due to poorly adapted optical intervention programs.

[0004] Furthermore, this routine subjective refraction relies heavily on the optometrist's experience and the patient's subjective feedback, lacking a unified and objective evaluation standard for the effectiveness of optical intervention and myopia control, which leads to a high degree of subjectivity in the formulation of optical intervention plans.

[0005] Therefore, the selection of existing optical intervention assessment programs for myopia prevention and control suffers from defects such as lack of individualized quantitative basis, inaccurate matching with the actual defocus state of the fundus, poor fitting effect of optical intervention products, low fitting efficiency, and subjective evaluation. These defects restrict the full realization of their myopia prevention and control potential and affect their vision correction effect. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, system and terminal for myopia prevention and control optical intervention assessment based on OCT technology, in order to solve the technical problems of low personalization in the selection of existing myopia prevention and control optical intervention assessment schemes, poor fitting effect of optical intervention products, low fitting efficiency, and poor myopia prevention and control effect caused by subjective fitting evaluation.

[0007] To achieve the above and other related objectives, the first aspect of this application provides a method for evaluating optical interventions for myopia control based on OCT technology. This method includes: performing uncorrected fundus scanning on both eyes of a myopia control target patient to generate uncorrected retinal data for both eyes, and calculating the total fundus refractive values ​​for both eyes to generate uncorrected refractive topography; collecting pupil data from both eyes of the myopia control target patient and calculating their pupil diameters, while simultaneously establishing a fundus defocus model based on the uncorrected refractive topography to generate initial optical intervention parameters; selecting a trial optical intervention product based on the initial optical intervention parameters, and performing a fundus scanning with and without contact lenses on both eyes of the myopia control target patient to generate... The system generates binocular retinal data with glasses to calculate the total fundus refractive values ​​of both eyes in patients with myopia control goals, and generates binocular refractive topography maps with glasses. Based on the binocular retinal data with glasses, a fitting assessment is performed on the trial optical intervention product. If the fitting conditions are not met, the trial optical intervention product is updated, and the lens-guided fundus scanning and fitting assessment are repeated until the fitting conditions are met, in order to obtain optimized binocular refractive topography maps with glasses and optimized optical intervention parameters. Based on a pre-trained myopia development prediction model, the system predicts the change in total fundus refractive values ​​of patients with myopia control goals in the target future time period based on the binocular uncorrected refractive topography maps with glasses and the optimized binocular refractive topography maps with glasses, and updates the optimized optical intervention parameters to formulate a target optical intervention plan suitable for patients with myopia control goals.

[0008] In some embodiments of the first aspect of this application, the method of performing uncorrected or corrected fundus scanning on the eye of a patient with myopia control target includes: using an optical coherence tomography (OCT) device and a liquid zoom lens integrated into the OCT device to perform uncorrected or corrected fundus scanning on the eye, specifically including: initializing the OCT device and the liquid zoom lens, and having the eye of the patient with myopia control target fixate on a preset target; once the eye fixates well, cyclically adjusting the focal length of the liquid zoom lens and controlling the OCT scan. The device performs a fundus scan on the eye under test, acquiring monocular uncorrected retinal tomographic images or monocular wearing-lens retinal tomographic images under multiple refractive compensation values ​​in real time. Based on each refractive compensation value and the monocular uncorrected retinal tomographic images or monocular wearing-lens retinal tomographic images registered with each refractive compensation value, it generates monocular uncorrected retinal data or monocular wearing-lens retinal data for the eye under test. The binocular uncorrected retinal data includes: the uncorrected retinal data of the left eye and the uncorrected retinal data of the right eye of the patient with myopia control target. The binocular wearing-lens retinal data includes: the wearing-lens retinal data of the left eye and the wearing-lens retinal data of the right eye of the patient with myopia control target.

[0009] In some embodiments of the first aspect of this application, the optical coherence tomography device includes: a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit; the liquid zoom lens is placed in front of the XY scanning galvanometer of the sample arm scanning optical path, and its optical axis is coaxial with the optical axis of the XY scanning galvanometer.

[0010] In some embodiments of the first aspect of this application, when the eye under test fixates on a preset target, a monitoring camera can be used to track the eye movement changes of the eye under test in order to determine whether the eye under test is fixating well.

[0011] In some embodiments of the first aspect of this application, the binocular uncorrected refractive topography includes: uncorrected refractive topography of the left eye and uncorrected refractive topography of the patient with myopia control target; wherein the method for calculating the total fundus refractive value of the eye to be tested in the patient with myopia control target and generating the monocular uncorrected refractive topography of the eye to be tested includes: acquiring multiple refractive compensation values ​​in the monocular uncorrected retinal data of the eye to be tested and monocular uncorrected retinal tomographic images registered with each refractive compensation value; defining multiple key fundus locations and extracting the sharpness index of each key fundus location from each monocular uncorrected retinal tomographic image; calculating the measured refractive value of each key fundus location according to the maximum sharpness index of each key fundus location; fitting the total fundus refractive value of the eye to be tested using Zernike polynomials and generating the monocular uncorrected refractive topography of the eye to be tested.

[0012] In some embodiments of the first aspect of this application, the initial optical intervention parameters are used to select a trial optical intervention product suitable for patients with myopia control goals, including: lens optical parameters of at least one optical intervention product; the optimized optical intervention parameters are used to formulate a personalized target optical intervention plan suitable for patients with myopia control goals, including: lens optical parameters of the target optical intervention product.

[0013] In some embodiments of the first aspect of this application, the training method of the myopia development prediction model includes: acquiring the age of multiple myopic patients, binocular uncorrected refractive topography, binocular corrected refractive topography, and axial length from one or more historical clinical follow-ups; constructing training samples for each myopic patient; and labeling each training sample with the annual change in fundus refractive value or the annual increase in axial length to construct a training sample dataset; inputting the training sample dataset into a long short-term memory network for training, and introducing a time decay mechanism to weight each training sample according to the time interval between every two adjacent clinical follow-ups to obtain a converged myopia development prediction model.

[0014] To achieve the above and other related objectives, a second aspect of this application provides a myopia control optical intervention assessment system based on OCT technology. The OCT-based myopia control optical intervention assessment system includes: a naked-eye scanning module, used to perform naked-eye fundus scanning on both eyes of a myopia control target patient, generating naked-eye retinal data for both eyes, and calculating the total fundus refractive values ​​of both eyes based on this data to generate naked-eye refractive topography maps for both eyes; an optical intervention parameter generation module, connected to the naked-eye scanning module, used to collect pupil data of both eyes of the myopia control target patient, calculate the pupil diameter, and simultaneously establish a defocus model of the fundus for both eyes based on the naked-eye refractive topography maps to generate initial optical intervention parameters; and a spectacle scanning module, connected to the optical intervention parameter generation module, used to select a trial optical intervention product based on the initial optical intervention parameters, and perform spectacle fundus scanning on both eyes of the myopia control target patient to generate binocular... The system uses: 1) Lens-worn retinal data to calculate the total fundus refractive values ​​of both eyes in patients with myopia control goals, generating binocular lens-worn refractive topography; 2) Fit assessment module, connected to the lens-worn scanning module, to perform fit assessment operations on the trial optical intervention product based on the lens-worn retinal data, and to update the trial optical intervention product if the fit conditions are not met, repeating the lens-worn fundus scanning and fit assessment operations until the fit conditions are met, thereby obtaining optimized binocular lens-worn refractive topography and optimized optical intervention parameters; and 3) Correction effect prediction module, connected to the uncorrected eye scanning module, the lens-worn scanning module, and the fit assessment module, to predict the change in total fundus refractive values ​​of patients with myopia control goals in a target future time period based on a pre-trained myopia development prediction model, the binocular uncorrected refractive topography, and the optimized binocular lens-worn refractive topography, and to update the optimized optical intervention parameters, in order to formulate a target optical intervention plan suitable for patients with myopia control goals.

[0015] In some embodiments of the second aspect of this application, the myopia prevention and control optical intervention assessment system based on OCT technology further includes: an optical coherence tomography (OCT) device and a liquid zoom lens integrated into the OCT device, used to perform uncorrected fundus scanning or corrected fundus scanning on both eyes of the myopia prevention and control target patient; wherein, the OCT device includes: a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit; the liquid zoom lens is placed in front of the XY scanning galvanometer of the sample arm scanning optical path, and its optical axis is coaxial with the optical axis of the XY scanning galvanometer.

[0016] To achieve the above and other related objectives, a third aspect of this application provides a myopia prevention and control optical intervention assessment terminal based on OCT technology. The myopia prevention and control optical intervention assessment terminal based on OCT technology includes: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the terminal performs the myopia prevention and control optical intervention assessment method based on OCT technology according to any one of the above embodiments.

[0017] As described above, this application has the following beneficial effects: This application provides a method, system, and terminal for evaluating optical intervention for myopia control based on OCT technology. By using OCT technology to perform uncorrected fundus scanning on both eyes of a myopia control target patient, uncorrected refractive topography is generated for both eyes, comprehensively and objectively measuring the defocus distribution of the entire retina, generating initial optical intervention parameters to select and formulate an initial target optical intervention plan; and by performing corrected fundus scanning on both eyes of a myopia control target patient who has been fitted with trial optical intervention products, corrected refractive topography is generated for both eyes, thereby enabling comparison of the changes in refractive topography before and after wearing corrected lenses. Based on a myopia development prediction model, the myopia progression of both eyes of the myopia control target patient can be predicted, assisting doctors in providing more suitable personalized target optical intervention plans, improving the fitting effect, fitting efficiency, and myopia control effect of optical intervention products. Attached Figure Description

[0018] Figure 1 The diagram shown is a flowchart of an optical intervention assessment method for myopia prevention and control based on OCT technology in one embodiment of this application.

[0019] Figure 2 The diagram shows a flowchart of performing naked-eye fundus scanning or spectacle-guided fundus scanning in one embodiment of this application.

[0020] Figure 3 The diagram shown is a flowchart illustrating a method for generating a monocular uncorrected or monocular refractive topographic map using glasses, according to an embodiment of this application.

[0021] Figure 4 The diagram shown is a structural schematic of a myopia prevention and control optical intervention assessment system based on OCT technology in one embodiment of this application.

[0022] Figure 5 The diagram shown is a structural schematic of a myopia prevention and control optical intervention assessment system based on OCT technology in another embodiment of this application.

[0023] Figure 6 The diagram shown is a structural schematic of a myopia prevention and control optical intervention assessment terminal based on OCT technology in one embodiment of this application. Detailed Implementation

[0024] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0025] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect, without limiting their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0026] To address the problems mentioned above, this application provides a method, system, and terminal for myopia prevention and control optical intervention assessment based on OCT technology. The aim is to solve the technical problems of low personalization in the selection of existing myopia prevention and control optical intervention assessment schemes, poor fitting effects of optical intervention products, low fitting efficiency, and poor myopia prevention and control effects due to subjective fitting evaluation.

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0028] like Figure 1 The diagram illustrates a flowchart of an optical intervention assessment method for myopia prevention and control based on OCT technology, as described in this embodiment. The method mainly includes the following steps.

[0029] Step S1: Perform uncorrected fundus scanning on both eyes of the myopia control target patient to generate uncorrected retinal data for both eyes, and calculate the total fundus refractive values ​​of both eyes of the myopia control target patient based on the data, and generate uncorrected refractive topography maps for both eyes.

[0030] In one embodiment, an optical coherence tomography (OCT) device and a liquid zoom lens integrated into the OCT device can be used to perform uncorrected fundus scans on both eyes of a patient with myopia control target.

[0031] The optical coherence tomography (OCT) device is a precision imaging system based on the principle of low-coherence optical interference. It can acquire retinal tomographic images by scanning the fundus. It mainly includes a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit. Its working principle is as follows: the broadband light source emits low-coherence near-infrared light; this near-infrared light is split into two beams by the beam splitter; one beam is focused onto the eye under test (such as the left or right eye of a myopia control target patient) by the sample arm scanning optical path and is reflected; the other beam is focused onto a reference mirror in the reference arm scanning optical path and is reflected; the detector converts the two beams of light returning from the sample arm scanning optical path and interfering with each other into electrical signals, which are then sent to the image reconstruction unit for digital processing and analysis to reconstruct a retinal tomographic image of the eye under test.

[0032] The liquid zoom lens is an optical element that can continuously, rapidly, and precisely adjust the focal length by changing the radius of curvature of the liquid interface through the application of voltage. In this embodiment, integrating the liquid zoom lens into the optical coherence tomography (OCT) device facilitates rapid axial scanning and dynamic focusing, significantly improving the imaging depth of retinal tomographic images. Furthermore, it helps to simultaneously perform refractive compensation while scanning the eye under test, thereby correcting refractive errors (such as myopia, hyperopia, and astigmatism) in the eye under test in real time and dynamically, ensuring that the scanning beam is always precisely focused on the retinal layer of the eye under test. Therefore, it can significantly improve the imaging quality of retinal tomographic images and adapt to individual differences in the eyes under test.

[0033] In one embodiment, the liquid zoom lens is integrated into the sample arm scanning optical path of the optical coherence tomography device. Specifically, the liquid zoom lens is placed in front of the XY scanning galvanometer of the sample arm scanning optical path.

[0034] In this embodiment, the sample arm scanning optical path includes a collimator, an XY scanning galvanometer, a scanning lens, and a focusing objective. When a near-infrared beam split by the beam splitter is transmitted to the sample arm scanning optical path, it is first converted into a parallel beam by the collimator; then it is incident on the XY scanning galvanometer, so that the deflection of the parallel incident beam in the X and Y directions is controlled by two orthogonally placed galvanometers respectively; the deflected outgoing light is then relayed by the scanning lens and focused at a specific depth of the eye to be tested by the focusing objective, thus completing the fundus scan of the eye to be tested.

[0035] The optical axis of the liquid zoom lens is coaxial with the optical axis of the XY scanning galvanometer. Preferably, when integrating the liquid zoom lens into the optical coherence tomography device, a laser calibrator can be used to calibrate the optical axis, ensuring that the coaxiality error between the optical axis of the liquid zoom lens and the optical axis of the XY scanning galvanometer is below a preset error threshold, such as 0.1 mm, thereby avoiding the introduction of off-center aberrations and ensuring the imaging quality of the retinal tomographic image.

[0036] In one embodiment, such as Figure 2 As shown, the method of performing uncorrected fundus scanning on a single eye (such as the left or right eye) of a patient with myopia control target using the optical coherence tomography device and the liquid zoom lens includes the following steps.

[0037] Step S11: Initialize the optical coherence tomography device and the liquid zoom lens, and have the eye of the patient to be tested fixate on the preset target.

[0038] Specifically, during device initialization, a reset control signal can be used to control the optical coherence tomography (OCT) device to enter standby mode and to reset the focal length of the liquid zoom lens to infinity, simulating a relaxed state of the human eye. It should be understood that when performing a naked-eye fundus scan on the left eye of a patient targeting myopia control, the left eye is used as the eye to be tested; similarly, when performing a naked-eye fundus scan on the right eye of a patient targeting myopia control, the right eye is used as the eye to be tested.

[0039] In one embodiment, when the eye under test fixates on a preset target, a monitoring camera can be used to track the eye movement changes of the eye under test to determine whether the eye is fixating well. At this time, when initializing the optical coherence tomography device and the liquid zoom lens, it is also necessary to initialize the monitoring camera, including starting the monitoring camera into standby mode, and starting to track the eye movement changes of the eye under test after the eye of the patient with myopia prevention and control fixates on the preset target.

[0040] The monitoring camera is a high-speed camera sensitive to near-infrared light, which can be used to acquire grayscale images of the eye under test in real time, so as to identify the pupil outline of the eye under test, obtain the pupil center coordinates and pupil diameter, and determine that the eye under test has good fixation when the continuous change of the pupil center coordinates is less than a preset eye movement deviation threshold.

[0041] In one specific embodiment, the method for identifying the pupil contour of the eye under test and obtaining the pupil center coordinates based on the grayscale image of the eye includes: performing Gaussian filtering on the grayscale image of the eye to smooth the image and suppress noise, obtaining a corresponding smooth grayscale image of the eye; performing image segmentation using an adaptive thresholding algorithm to locate the pupil region in the smooth grayscale image of the eye, obtaining a binary image of the pupil; performing a closing operation on the binary image of the pupil to clean up noise, fill holes, and connect broken areas; performing edge detection on the processed binary image of the pupil using the Canny edge detection operator to obtain the pupil edge contour of the eye under test; and performing ellipse fitting on the pupil edge contour to obtain a corresponding fitted ellipse of the pupil, using the center point coordinates of the fitted ellipse of the pupil as the pupil center coordinates of the eye under test, and using the major axis diameter of the fitted ellipse of the pupil as the pupil diameter of the eye under test. It should be noted that the pupil center coordinates can be pixel coordinates, and the pupil diameter is also a pixel diameter.

[0042] Step S12: When the eye under test has good fixation, the focal length of the liquid zoom lens is cyclically adjusted, and the optical coherence tomography device is controlled to perform fundus scanning on the eye under test, and monocular uncorrected retinal tomographic images under multiple refractive compensation values ​​are acquired in real time.

[0043] In one embodiment, within each cycle, the focal length of the liquid zoom lens can be adjusted to a specified refractive compensation value via a voltage control signal, and the monitoring camera can be controlled to track the eye movement changes of the eye under test in real time. When the eye under test is still fixating well, the optical coherence tomography device is controlled to perform a fundus scan of the eye under test with a field of view of approximately 45° to 55° using a single-line scanning protocol to obtain a monocular uncorrected retinal tomographic image registered with the current refractive compensation value.

[0044] Preferably, the method of cyclically adjusting the liquid zoom lens includes: adjusting the lens focal length of the liquid zoom lens in increments based on a preset step size to simulate the human eye's adjustment process from distance to near vision. For example, based on a step size of 0.5D, the lens focal length of the liquid zoom lens is sequentially adjusted from 0D, 0.5D, 1.0D, 1.5D, 2.0D... to 10.0D. It should be noted that the lens focal length is the refractive compensation value, and the adjustment step size, initial refractive compensation value, and target refractive compensation value are not specifically limited in this application; users can set them according to their needs.

[0045] After completing all cycles of lens adjustment and fundus scanning, the optical coherence tomography (OCT) device, the monitoring camera, and the liquid zoom lens are reset, including: the OCT device and the monitoring camera re-entering standby mode, and the liquid zoom lens resetting its focal length to infinity.

[0046] Step S13: Generate monocular uncorrected retinal data of the eye under test based on each refractive compensation value and the monocular uncorrected retinal tomographic images registered with each refractive compensation value.

[0047] The monocular uncorrected retinal data includes: multiple refractive compensation values ​​and monocular uncorrected retinal tomographic images configured with each refractive compensation value.

[0048] According to steps S11-S13, after performing uncorrected fundus scanning on both eyes of the myopia control target patient, corresponding uncorrected retinal data for the left and right eyes are generated, thus generating uncorrected retinal data for both eyes of the myopia control target patient. The uncorrected retinal data for both eyes includes: uncorrected retinal data for the left and right eyes of the myopia control target patient, i.e., uncorrected retinal data for the left and right eyes respectively. The uncorrected retinal data for the left eye includes: multiple refractive compensation values ​​scanned in the left eye of the myopia control target patient and uncorrected retinal tomographic images of the left eye registered with each refractive compensation value; the uncorrected retinal data for the right eye includes: multiple refractive compensation values ​​scanned in the right eye of the myopia control target patient and uncorrected retinal tomographic images of the right eye registered with each refractive compensation value.

[0049] Subsequently, based on the uncorrected retinal data of both eyes, the total fundus refractive values ​​of both eyes of the myopia control target patient are calculated, and uncorrected refractive topographic maps of both eyes of the myopia control target patient are generated. Specifically, this includes: calculating the total fundus refractive values ​​of the left and right eyes of the myopia control target patient based on the uncorrected retinal data of the left and right eyes, respectively; generating the uncorrected refractive topographic map of the left eye based on the total fundus refractive value of the left eye; generating the uncorrected refractive topographic map of the right eye based on the total fundus refractive value of the right eye; and obtaining the uncorrected refractive topographic maps of both eyes based on the uncorrected refractive topographic maps of the left and right eyes. That is, the uncorrected refractive topographic maps of both eyes include: the uncorrected refractive topographic map of the left eye and the uncorrected refractive topographic map of the right eye of the myopia control target patient.

[0050] In one embodiment, such as Figure 3 As shown, the method for calculating the total fundus refractive value of the corresponding eye to be tested and generating a monocular uncorrected refractive topography map of the eye to be tested based on the monocular uncorrected retinal data of the target patient for myopia prevention and control includes the following steps.

[0051] Step S14: Obtain multiple refractive compensation values ​​from the monocular uncorrected retinal data of the eye under test, as well as monocular uncorrected retinal tomographic images registered with each refractive compensation value.

[0052] If the eye to be tested is the left eye, acquire multiple refractive compensation values ​​from the uncorrected retinal data of the left eye and tomographic images of the left eye's uncorrected retina registered with each refractive compensation value; if the eye to be tested is the right eye, acquire multiple refractive compensation values ​​from the uncorrected retinal data of the right eye and tomographic images of the right eye's uncorrected retina registered with each refractive compensation value.

[0053] Step S15: Define multiple key fundus locations and extract the clarity index of each key fundus location from the uncorrected retinal tomographic images of each monocular eye.

[0054] Preferably, the fovea centralis, optic disc center, nasal region center, temporal region center, superior region center, and inferior region center of the retina can be defined as key fundus locations. It should be noted that these key fundus locations can be defined according to user needs, and this application does not specifically limit them.

[0055] In this embodiment, the local image contrast of the key fundus location in a monocular uncorrected retinal tomographic image can be used as a sharpness index of the key fundus location to evaluate the sharpness of the key fundus location in the monocular uncorrected retinal tomographic image. The local image contrast can be calculated based on the grayscale standard deviation or gradient amplitude of a small region of the key fundus location image. It should be noted that the sharpness index can be defined according to user needs, and this application is not specifically limited.

[0056] Step S16: Calculate the measured refractive value of each key fundus location based on the maximum clarity index of each key fundus location.

[0057] Specifically, a monocular uncorrected retinal tomographic image with the highest clarity index of the key fundus location is obtained (at this time, the key fundus location is most clearly represented on the monocular uncorrected retinal tomographic image), and the refractive compensation value registered with the monocular uncorrected retinal tomographic image is taken as the optimal refractive compensation value of the key fundus location; the opposite of the optimal refractive compensation value is taken to calculate the measured refractive value of the key fundus location.

[0058] Step S17: Fit the refractive values ​​of the entire fundus of the eye under test using Zernike polynomials and generate a monocular uncorrected refractive topography of the eye under test.

[0059] It should be understood that Zernike polynomial fitting essentially uses a set of basis functions orthogonal on the unit circle to decompose and approximate a two-dimensional wavefront or surface data. It can decompose complex wavefront aberrations (such as lens or human eye distortion) into "aberration components" (such as defocus, astigmatism, and coma) with clear physical meaning.

[0060] Specifically, the method of fitting the refractive values ​​of the entire fundus of the eye under test using Zernike polynomials mainly includes the following steps.

[0061] ① Obtain key locations in each fundus Measuring refractive values And the coordinates of each key location in the fundus Within the normalized unit circle. The specific calculation formula is:

[0062] ;

[0063] ;

[0064] in, The radius of the retina of the eye being tested; These are the polar coordinates of key locations in the fundus.

[0065] ② For each discrete measured refractive value Numerical integration was performed to obtain the key locations of each fundus. wavefront height .

[0066] It should be understood that, under the paraxial approximation, the refractive value and wavefront height satisfy the Poisson equation:

[0067] ;

[0068] in, It is a wavefront function used to describe the deviation between the actual wavefront and the ideal plane after light passes through an optical system (such as the eye under test); Refractive value; For the Laplace operator.

[0069] In a preferred embodiment, a least-squares integral algorithm can be used to calculate each discrete measured refractive value. Numerical integration is performed to solve for each discrete wavefront height. It should be noted that users can also directly utilize the Laplace property of the Zernike polynomial to measure the discrete refractive values ​​as needed. This application does not specifically limit the application to numerical integration.

[0070] ③ The height of each wavefront The fit is a Zernike polynomial, and the Zernike coefficients are solved.

[0071] wavefront function It can be expanded into a sum of Zernike polynomials, specifically expressed as:

[0072] ;

[0073] in, For the first The Xiang Zernik polynomial function describes the spatial distribution of a specific aberration (such as defocus, astigmatism, coma, etc.). For the first The Xiang Zernik polynomial coefficients represent the weights of the aberration mode.

[0074] In this embodiment, the number of Zernike terms can be selected (such as the first 10 terms under the OSA standard), and a design matrix can be constructed. ,in, Then, based on the height of each wavefront... The least squares fitting algorithm is used to solve for the coefficients of each Zernike polynomial. .

[0075] ④ Based on the Zernike coefficient, calculate the refractive value at any location in the fundus of the eye under test to generate a monocular uncorrected refractive topography map of the eye under test.

[0076] The formula for calculating the refractive value at any location in the fundus of the eye being tested is as follows:

[0077] ;

[0078] in, Fundus of the eye to be tested The refractive value at the location; For the first Xiang Zernike polynomial function; For the first Xiang Zernike polynomial coefficients; For the Laplace operator.

[0079] Step S2: Collect pupil data of both eyes of the target patient for myopia prevention and control, calculate the pupil diameter of both eyes, and at the same time, establish a defocus model of the fundus of both eyes based on the uncorrected refractive topography of both eyes to generate initial optical intervention parameters.

[0080] The initial optical intervention parameters can be used to select suitable trial optical intervention products for patients aiming to control myopia, including but not limited to: lens optical parameters of at least one optical intervention product. The types of optical intervention products include, but are not limited to: myopia control eyeglasses, orthokeratology lenses, and defocus soft lenses. The lens optical parameters of each optical intervention product include: the central optical zone diameter, central optical zone refractive value, peripheral defocus optical parameter matrix, and near-use adaptation zone parameters of the left and right lenses of the optical intervention product.

[0081] In this embodiment, step S2 involves, on the one hand, collecting pupil data from both eyes of the target patient for myopia control and calculating the pupil diameters to determine the central optical zone diameter of the lenses for each optical intervention product. Specifically, based on the left pupil data of the target patient, the left pupil diameter is calculated to determine the central optical zone diameter of the left lens of each optical intervention product; and based on the right pupil data of the target patient, the right pupil diameter is calculated to determine the central optical zone diameter of the right lens of each optical intervention product.

[0082] It should be noted that, in the above steps, when tracking the eye movement changes of the target patient's eye using a monitoring camera, the process includes: acquiring a grayscale image of the eye under test using the monitoring camera, identifying the pupil outline of the eye under test, and obtaining the pupil center coordinates and pupil diameter. Step S2 can directly use the pupil diameter obtained in this step for further optical correction and determine the corresponding central optical zone diameter.

[0083] ;

[0084] in, Based on the grayscale images of the eyes captured by the monitoring camera, an image processing algorithm is used to calculate the diameter of the left or right pupil of the target patient for myopia prevention and control. The actual pupil diameter of the left or right eye of the patient with myopia control target is the actual physical size of both pupils; The optical magnification factor of the surveillance camera is given. Considering the subtle influence of corneal curvature on this optical magnification factor, a curvature compensation factor is also introduced. It should be noted that the optical magnification factor... curvature compensation factor This can be obtained by calibrating the eye of a standard model.

[0085] Based on optical constraints and defocus theory, a mathematical model can be established between the pupil diameter and the central optical zone diameter of each optical intervention product, thereby calculating the central optical zone diameter of the left or right lens of each optical intervention product:

[0086] ;

[0087] in, The diameter of the central optical zone of the left or right lens of an optical intervention product; This is a proportionality coefficient, taking into account the distance between the optical intervention product and the cornea during wear, as well as the influence of the tear film. Usually less than 1; This is a compensation constant used to compensate for minor displacements, lens misalignment, or eye aberrations during the wearing of optical intervention products. It should be noted that different scaling factors are used for different types of optical intervention products. and compensation constant The specific values ​​can be set by the user according to their needs; this application does not limit them.

[0088] After obtaining the diameter of the central optical zone of the left or right lens of each optical intervention product, the refractive value of the central optical zone can be determined based on the refractive value of the fovea of ​​the left or right eye of the patient with myopia control target.

[0089] In step S2, on the other hand, based on the uncorrected refractive topography of the two eyes of the target myopia control patient, a defocus model of the fundus of both eyes is established to determine the peripheral defocus matrix and near adaptation zone parameters of the defocus optical zone of the left and right lenses of each optical intervention product. Specifically, based on the uncorrected refractive topography of the left eye of the target myopia control patient, a defocus model of the fundus of the left eye is established to determine the peripheral defocus matrix and near adaptation zone parameters of the defocus optical zone of the left lens of each optical intervention product; similarly, based on the uncorrected refractive topography of the right eye of the target myopia control patient, a defocus model of the fundus of the right eye is established to determine the peripheral defocus matrix and near adaptation zone parameters of the defocus optical zone of the right lens of each optical intervention product.

[0090] In one embodiment, the monocular fundus defocus model of the left eye fundus defocus model and the right eye fundus defocus model can be expressed as:

[0091] ;

[0092] in, A point on the retina of the left or right eye Defocus amount, This is the radial distance from the point to the fovea of ​​the macula. This is the azimuth angle of that point; For this point The baseline defocus amount can be calculated from the refractive value at that point and the refractive value at the fovea of ​​the macula. The refractive values ​​at that point and the fovea of ​​the macula can be obtained from the uncorrected refractive topography of the left eye or the uncorrected refractive topography of the right eye. Quadrant weighting coefficients, and azimuth angle Related; The duration of near-vision use for patients with myopia prevention and control targets; This is a weighting coefficient for near-field eye use duration; The rate of axial length increase for patients with myopia prevention and control targets; This is the feedback coefficient.

[0093] In one embodiment, the retina of each eye under test can be divided into defocus zones. Using the pupil center as a circle, multiple rings are divided outwards from the pupil outline. Each ring is further subdivided according to the temporal quadrant, superior quadrant, nasal quadrant, and inferior quadrant, ultimately obtaining multiple defocus zones. Preferably, the retina of each eye under test can be divided into 4 rings, and each ring is further subdivided according to 4 quadrants, resulting in 16 defocus zones. Combined with the central pupil area, this results in 17 zones. The baseline defocus amount of each defocus zone is then used to determine the defocus zones. Determine the defocus distribution type of each defocus zone and the defocus difference between each quadrant in order to configure the quadrant weight coefficients for each defocus zone. This generates a quadrant partition coefficient matrix. Furthermore, the quadrant weight coefficients for each defocus partition can be adjusted based on the age of the target patient for myopia control. For example, for myopia control target patients under the age of 12, due to the high plasticity of children's retina, the weighting coefficients for all quadrants can be adjusted. This is used to enhance intervention.

[0094] Therefore, based on each point Defocus amount A personalized fundus defocus model for the left eye and the right eye of the target patient for myopia prevention and control is constructed to describe the distribution of defocus differences in the left and right eyes of the target patient for myopia prevention and control. This model is then used to generate the peripheral defocus optical parameter matrix and near-use adaptation zone parameters of the defocus optical zone of the binocular lenses of various optical intervention products.

[0095] Specifically, the peripheral defocus optical parameter matrix includes: additional defocus parameters, gradient rate of change, aspherical coefficient, and channel width for each defocus zone; the near-field adaptation zone parameters include: downward line of sight angle, near-field zone horizontal offset, gradient band length, and astigmatism control zone.

[0096] Step S3: Based on the initial optical intervention parameters, select a trial optical intervention product and perform a lens-worn fundus scan on both eyes of the myopia control target patient to generate lens-worn retinal data for both eyes. Based on this, calculate the total fundus refractive value of both eyes of the myopia control target patient and generate a lens-worn refractive topography map for both eyes.

[0097] Specifically, based on the lens optical parameters of various optical intervention products, a trial product of one type of optical intervention product (such as myopia control eyeglasses) can be selected. The lenses of this trial product are then adjusted according to the corresponding lens optical parameters to obtain the trial optical intervention product. Next, a fundus scan is performed on the left and right eyes of the target myopia control patient wearing the selected trial optical intervention product, generating corresponding left and right eye retinal data. Based on this data, the total fundus refractive values ​​of the left and right eyes of the target myopia control patient are calculated, generating corresponding left and right eye refractive topographic maps.

[0098] The binocular retinal data includes: monocular retinal data for the left and right eyes of the myopia control target patient, i.e., left-eye retinal data and right-eye retinal data. The left-eye retinal data includes: multiple refractive compensation values ​​scanned in the left eye of the myopia control target patient and tomographic images of the left eye retina registered with each refractive compensation value; the right-eye retinal data includes: multiple refractive compensation values ​​scanned in the right eye of the myopia control target patient and tomographic images of the right eye retina registered with each refractive compensation value. The binocular refractive topography includes: the left-eye and right-eye refractive topography of the myopia control target patient.

[0099] In one embodiment, the method of performing the spectacle-guided fundus scanning operation is the same as the method of performing the uncorrected fundus scanning operation provided in the above embodiments. The optical coherence tomography (OCT) device and the liquid zoom lens can be used to perform spectacle-guided fundus scanning operations on the left or right eye of the patient with myopia control who is wearing trial optical intervention products. The specific method includes the following steps, such as... Figure 2 as well as Figure 3 As shown.

[0100] Step S11: Initialize the optical coherence tomography device and the liquid zoom lens, and have the eye of the patient wearing glasses, which is the target of myopia prevention and control, fixate on the preset target.

[0101] Step S12: When the eye under test has good fixation, the focal length of the liquid zoom lens is cyclically adjusted, and the optical coherence tomography device is controlled to perform fundus scanning on the eye under test, and monocular lens-worn retinal tomographic images under multiple refractive compensation values ​​are acquired in real time.

[0102] Step S13: Generate monocular lens-guided retinal data of the eye under test based on each refractive compensation value and the monocular lens-guided retinal tomographic images registered with each refractive compensation value.

[0103] Step S14: Obtain multiple refractive compensation values ​​from the monocular lens-worn retinal data of the eye under test, as well as monocular lens-worn retinal tomographic images registered with each refractive compensation value.

[0104] Step S15: Define multiple key fundus locations and extract the clarity index of each key fundus location from each monocular lens-guided retinal tomographic image.

[0105] Step S16: Calculate the measured refractive value of each key fundus location based on the maximum clarity index of each key fundus location.

[0106] Step S17: Fit the total fundus refractive value of the eye under test using Zernike polynomials and generate a monocular refractive topography map of the eye under test.

[0107] For the sake of brevity, I will not repeat myself.

[0108] Step S4: Based on the binocular retinal data, perform a fitting evaluation on the trial optical intervention product. If the fitting conditions are not met, update the trial optical intervention product and repeat the lens-worn fundus scan and fitting evaluation until the fitting conditions are met, so as to obtain optimized binocular refractive topography and optimized optical intervention parameters.

[0109] In one embodiment, the method for performing a fitting evaluation operation on the trial optical intervention product based on the binocular contact lens retinal data includes: analyzing the lens center positioning deviation, defocus range, and alignment accuracy between the defocus optical area and the retinal target area of ​​the trial optical intervention product based on the binocular contact lens retinal data, i.e., the left eye contact lens retinal data and the right eye contact lens retinal data, to determine whether preset fitting conditions are met. The fitting conditions include at least: a lens center positioning deviation threshold, a defocus range coverage threshold, and an alignment accuracy threshold.

[0110] If any of the following parameters of the trial optical intervention product—the lens center positioning deviation, the defocus range, or the alignment accuracy between the defocus optical area and the retinal target area—exceeds the preset corresponding threshold, then the trial optical intervention product is determined not to meet the adaptation conditions; otherwise, the trial optical intervention product is determined to meet the adaptation conditions.

[0111] Furthermore, if the trial optical intervention product is determined not to meet the fitting criteria, it needs to be updated. This includes changing the type of trial optical intervention product (e.g., replacing myopia control glasses with orthokeratology lenses) or changing the lenses of the trial optical intervention product. The patient with myopia control needs then wears the new trial optical intervention product, and a new fundus scan is performed on both eyes of the patient to generate new binocular retinal data and new binocular refractive topography. Based on the new binocular retinal data, a fitting assessment is performed again to determine if the new trial optical intervention product meets the fitting criteria. This process is repeated until the trial optical intervention product meets the fitting criteria. At this point, the binocular refractive topography corresponding to the trial optical intervention product is used as the optimized binocular refractive topography, and optimized optical intervention parameters are obtained based on the trial optical intervention product.

[0112] The optimized optical intervention parameters are used to formulate personalized target optical intervention plans tailored to patients with myopia control needs, including but not limited to: the lens optical parameters of the target optical intervention product. Based on the trial optical intervention product that meets the fitting criteria, the type of the target optical intervention product and its lens optical parameters are determined, thereby obtaining the optimized optical intervention parameters. The type of the target optical intervention product is consistent with the type of the trial optical intervention product that meets the fitting criteria, and the lens optical parameters of the target optical intervention product are obtained by optimizing the lens optical parameters of that type of optical intervention product in the initial optical intervention parameters.

[0113] Step S5: Based on the pre-trained myopia development prediction model, predict the change in total fundus refractive value of the target myopia control patient in the target future time period according to the binocular uncorrected refractive topography and the optimized binocular corrected refractive topography, and update the optimized optical intervention parameters to formulate a target optical intervention plan suitable for the target myopia control patient.

[0114] The target optical intervention scheme includes, but is not limited to: the lens optical parameters of the target optical intervention product, and / or the joint intervention requirements.

[0115] In one embodiment, the myopia progression prediction model can be trained using a Long Short-Term Memory (LSTM) network. This model leverages the advantage of LSTM in effectively addressing the gradient vanishing or exploding problems that easily occur when processing long-sequence data. It can process irregular time intervals of clinical follow-up data from myopia control target patients, capturing nonlinear fluctuations and long-term trends. This helps to accurately predict the changes in total fundus refractive values ​​of myopia control target patients over the target future period, such as the annual change in total fundus refractive values. This assists doctors in deciding whether to replace the target optical intervention product, adjust the lens optical parameters of the target optical intervention product, and whether to combine it with drug intervention, thus providing a more suitable target optical intervention plan.

[0116] In this embodiment, the training method of the myopia development prediction model includes: acquiring the age of multiple myopic patients, binocular uncorrected refractive topography from one or more historical clinical follow-ups, the type of optical intervention product worn, the lens optical parameters of the optical intervention product, binocular corrected refractive topography, and axial length; constructing training samples for each myopic patient; and labeling each training sample with the annual change in total fundus refractive value or the annual increase in axial length, thereby constructing a training sample dataset; inputting the training sample dataset into a long short-term memory network for training, and introducing a time decay mechanism to weight each training sample according to the time interval between every two adjacent clinical follow-ups, thereby obtaining a converged myopia development prediction model, which is used to predict the annual change in total fundus refractive value of myopia control target patients based on the binocular uncorrected refractive topography and the optimized binocular corrected refractive topography, establish a mapping relationship between the current refractive value characteristics and the myopia progression rate, and assess the possibility of myopia progression in the target future period (e.g., within one year). In one embodiment, the myopia development prediction model can also be used to predict the annual increase in axial length of myopia control target patients.

[0117] In one embodiment, the baseline axial length and corneal curvature of each myopic patient can also be obtained as auxiliary features to train the myopia development prediction model, thereby improving the model's prediction accuracy.

[0118] This application utilizes an optical coherence tomography (OCT) device and a liquid zoom lens integrated into the OCT device to perform uncorrected or corrected fundus scans on both eyes of patients with myopia control goals. This enables objective and quantitative assessment of the refractive values ​​of the fundus in both eyes of patients with myopia control goals using OCT technology. It allows for objective and comprehensive measurement of the defocus distribution in the central and peripheral areas of the retina, which helps in selecting and developing more suitable target optical intervention plans for individual patients with myopia control goals. This improves the accuracy and efficiency of fitting optical intervention products, reduces the number of trial fittings of optical intervention products, and optimizes the patient experience.

[0119] Furthermore, this application also compared the changes in refractive topography of patients before and after wearing glasses, i.e., their defocus changes, and based on a pre-trained myopia development prediction model, predicted the binocular progression of the target in the future, such as within one year. This can assist doctors in deciding whether to change the target optical intervention product and whether to combine it with drug intervention, providing a more suitable target optical intervention plan, and further ensuring the fitting effect, fitting efficiency and myopia control effect of the target optical intervention product.

[0120] like Figure 4 The diagram illustrates the structure of a myopia prevention and control optical intervention assessment system based on OCT technology, as shown in this embodiment. The myopia prevention and control optical intervention assessment system based on OCT technology in this embodiment mainly includes: a naked-eye scanning module, an optical intervention parameter generation module, a glasses-wearing scanning module, an adaptation assessment module, and a correction effect prediction module.

[0121] The naked-eye scanning module is used to perform naked-eye fundus scanning on both eyes of the myopia control target patient, generate naked-eye retinal data for both eyes, and calculate the total fundus refractive value of both eyes of the myopia control target patient based on the data, and generate naked-eye refractive topography maps for both eyes.

[0122] The optical intervention parameter generation module is connected to the naked-eye scanning module and is used to collect pupil data of both eyes of patients with myopia prevention and control targets, calculate the diameter of their pupils, and establish a defocus model of their fundus based on the naked-eye refractive topography of both eyes to generate initial optical intervention parameters.

[0123] The lens-wearing scanning module is connected to the optical intervention parameter generation module. It is used to select a trial optical intervention product based on the initial optical intervention parameters, and to perform lens-wearing fundus scanning on both eyes of the myopia control target patient to generate lens-wearing retinal data for both eyes. Based on this, the total fundus refractive value of both eyes of the myopia control target patient is calculated, and a lens-wearing refractive topography map of both eyes is generated.

[0124] The adaptation evaluation module is connected to the lens-worn scanning module. It is used to perform an adaptation evaluation operation on the trial optical intervention product based on the binocular lens-worn retinal data. If the evaluation does not meet the adaptation conditions, the trial optical intervention product is updated, and the lens-worn fundus scanning operation and adaptation evaluation operation are repeated until the adaptation conditions are met, so as to obtain optimized binocular lens-worn refractive topography and optimized optical intervention parameters.

[0125] The correction effect prediction module is connected to the uncorrected eye scanning module, the corrected eye scanning module, and the adaptation evaluation module, respectively. It is used to predict the change in the total fundus refractive value of the target myopia prevention and control patient in the target future time period based on the pre-trained myopia development prediction model, the uncorrected eye refractive topography of both eyes, and the optimized corrected eye refractive topography of both eyes, and update the optimized optical intervention parameters to prepare a target optical intervention plan adapted to the target myopia prevention and control patient.

[0126] In one embodiment, such as Figure 5 As shown, the myopia prevention and control optical intervention assessment system based on OCT technology further includes: an optical coherence tomography (OCT) scanner and a liquid zoom lens integrated into the OCT scanner. The OCT scanner and the system are used to perform uncorrected or corrected fundus scanning operations on both eyes of the target patient for myopia prevention and control.

[0127] Specifically, the optical coherence tomography (OCT) device includes: a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit. Its working principle is as follows: the broadband light source emits low-coherence near-infrared light; this near-infrared light is split into two beams by the beam splitter; one beam is focused onto the eye under test (such as the left or right eye of a myopia control target patient) by the sample arm scanning optical path and is reflected; the other beam is focused onto a reference mirror in the reference arm scanning optical path and is reflected; the detector converts the two light signals returning from the sample arm scanning optical path and interfering with each other into electrical signals, and sends them to the image reconstruction unit for digital processing and analysis to reconstruct a retinal tomographic image of the eye under test.

[0128] In one embodiment, the sample arm scanning optical path includes a collimator, an XY scanning galvanometer, a scanning lens, and a focusing objective. When a near-infrared beam split by the beam splitter is transmitted to the sample arm scanning optical path, it is first converted into a parallel beam by the collimator; then it is incident on the XY scanning galvanometer, so that the deflection of the parallel incident beam in the X and Y directions is controlled by two orthogonally placed galvanometers respectively; the deflected outgoing light is then relayed by the scanning lens and focused at a specific depth of the eye to be tested by the focusing objective, thus completing the fundus scan of the eye to be tested.

[0129] The liquid zoom lens is integrated into the sample arm scanning optical path of the optical coherence tomography (OCT) device. Specifically, the liquid zoom lens is placed in front of the XY scanning galvanometer of the sample arm scanning optical path. Furthermore, the optical axis of the liquid zoom lens is coaxial with the optical axis of the XY scanning galvanometer.

[0130] It should be understood that the myopia prevention and control optical intervention assessment system based on OCT technology described in this application and the various myopia prevention and control optical intervention assessment methods based on OCT technology provided in the above method embodiments belong to the same inventive concept, and will not be described again here for the sake of brevity.

[0131] It should also be understood that the module division in the system embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor or functional module, exist as separate physical entities, or be divided into more functional modules. The integrated modules or units described above can be implemented in hardware or as software functional modules.

[0132] The myopia prevention and control optical intervention assessment method based on OCT technology in this application can be implemented on the terminal side or the server side. Regarding the hardware structure of the myopia prevention and control optical intervention assessment terminal based on OCT technology, please refer to [link to relevant documentation]. Figure 6 This is a schematic diagram of an optional hardware structure of a myopia prevention and control optical intervention assessment terminal 600 based on OCT technology provided in an embodiment of this application. The myopia prevention and control optical intervention assessment terminal 600 based on OCT technology can be a mobile phone, computer device, tablet device, personal digital processing device, factory back-end processing device, etc. The myopia prevention and control optical intervention assessment terminal 600 based on OCT technology includes: at least one processor 601, a memory 602, at least one network interface 604, and a user interface 606. The various components in the device are coupled together through a bus system 605. It is understood that the bus system 605 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 605 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general will label all buses as bus systems.

[0133] The user interface 606 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0134] It is understood that memory 602 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable categories of memory.

[0135] In this embodiment, the memory 602 is used to store various types of data to support the operation of the OCT-based myopia prevention and control optical intervention assessment terminal 600. Examples of this data include: any executable program that operates on the OCT-based myopia prevention and control optical intervention assessment terminal 600, such as the operating system 6021 and application program 6022; the operating system 6021 includes various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 6022 may include various applications, such as media players, browsers, etc., for implementing various application services. The implementation of the OCT-based myopia prevention and control optical intervention assessment method provided in this embodiment can be included in the application program 6022.

[0136] The myopia prevention and control optical intervention assessment method based on OCT technology disclosed in the above embodiments of this application can be applied to, or implemented by, processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-mentioned myopia prevention and control optical intervention assessment method based on OCT technology can be completed by the integrated logic circuits in the hardware of processor 601 or by instructions in software form. Processor 601 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 601 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. General-purpose processor 601 may be a microprocessor or any conventional processor, etc. The steps of the myopia prevention and control optical intervention assessment method based on OCT technology provided in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory. The processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the aforementioned optical intervention assessment method for myopia prevention and control based on OCT technology.

[0137] In an exemplary embodiment, the myopia prevention and control optical intervention assessment terminal 600 based on OCT technology can be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned myopia prevention and control optical intervention assessment method based on OCT technology.

[0138] In summary, this application provides a method, system, and terminal for evaluating optical interventions for myopia control based on OCT technology. By utilizing OCT technology to perform uncorrected fundus scans on both eyes of a myopia control target patient, uncorrected refractive topography is generated, comprehensively and objectively measuring the defocus distribution across the entire retina, generating initial optical intervention parameters to select and formulate an initial target optical intervention plan. Furthermore, by performing corrected fundus scans on both eyes of a myopia control target patient wearing trial optical intervention products, corrected refractive topography is generated, allowing comparison of changes in refractive topography before and after wearing corrected lenses. Based on a myopia progression prediction model, this allows for prediction of the myopia progression in both eyes of the myopia control target patient, assisting doctors in providing more suitable personalized target optical intervention plans, and improving the fitting effect, fitting efficiency, and myopia control effectiveness of optical intervention products.

[0139] Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0140] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for assessing optical interventions for myopia prevention and control based on OCT technology, characterized in that, include: Uncorrected fundus scanning was performed on both eyes of patients with myopia control targets to generate uncorrected retinal data for both eyes. Based on this data, the total fundus refractive values ​​of both eyes of patients with myopia control targets were calculated, and uncorrected refractive topography maps of both eyes were generated. Collect pupil data of both eyes of patients with myopia control targets and calculate their pupil diameter. At the same time, based on the uncorrected refractive topography of both eyes, establish a defocus model of the fundus of both eyes to generate initial optical intervention parameters. Based on the initial optical intervention parameters, select a trial optical intervention product and perform a lens-worn fundus scan on both eyes of the myopia control target patient to generate lens-worn retinal data for both eyes. Based on this, calculate the total fundus refractive value of both eyes of the myopia control target patient and generate a lens-worn refractive topography map for both eyes. Based on the binocular retinal data, an adaptation assessment is performed on the trial optical intervention product. If the adaptation conditions are not met, the trial optical intervention product is updated, and the lens-worn fundus scan and adaptation assessment are repeated until the adaptation conditions are met, so as to obtain optimized binocular refractive topography and optimized optical intervention parameters. Based on the pre-trained myopia development prediction model, the changes in total fundus refractive values ​​of patients with myopia control targets are predicted in the target future time period according to the binocular uncorrected refractive topography and the optimized binocular corrected refractive topography, and the optimized optical intervention parameters are updated to formulate a target optical intervention plan suitable for patients with myopia control targets.

2. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 1, characterized in that, The methods for performing uncorrected or corrected fundus scanning on the eyes of patients targeting myopia prevention and control include: using an optical coherence tomography (OCT) device and a liquid zoom lens integrated into the OCT device to perform uncorrected or corrected fundus scanning on the eyes of the patients to be tested, specifically including: Initialize the optical coherence tomography device and the liquid zoom lens, and have the eye of the patient being tested, which is the target of myopia control, fixate on a preset target; When the eye under test has good fixation, the focal length of the liquid zoom lens is cyclically adjusted, and the optical coherence tomography device is controlled to perform fundus scanning on the eye under test, and monocular uncorrected retinal tomographic images or monocular lens-corrected retinal tomographic images under multiple refractive compensation values ​​are acquired in real time. Based on each refractive compensation value and the monocular uncorrected or monocular corrected retinal tomographic images registered with each refractive compensation value, monocular uncorrected or monocular corrected retinal data of the eye under test are generated. The binocular uncorrected retinal data includes: uncorrected retinal data of the left and right eyes of the patient with myopia control target; the binocular corrected retinal data includes: corrected retinal data of the left and right eyes of the patient with myopia control target.

3. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 2, characterized in that, The optical coherence tomography device includes: a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit; the liquid zoom lens is placed in front of the XY scanning galvanometer of the sample arm scanning optical path, and its optical axis is coaxial with the optical axis of the XY scanning galvanometer.

4. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 2, characterized in that, When the eye under test fixates on a preset target, a monitoring camera can be used to track the eye movement changes of the eye under test in order to determine whether the eye under test is fixating well.

5. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 2, characterized in that, The binocular uncorrected refractive topography includes: the uncorrected refractive topography of the left eye and the uncorrected refractive topography of the right eye of the patient with myopia control goals; wherein the method for calculating the total fundus refractive value of the eye to be tested in the patient with myopia control goals and generating the unilateral uncorrected refractive topography of the eye to be tested includes: Acquire multiple refractive compensation values ​​from the monocular uncorrected retinal data of the eye under test, as well as monocular uncorrected retinal tomographic images registered with each refractive compensation value; Multiple key locations in the fundus are defined, and the sharpness index of each key location is extracted from the uncorrected retinal tomographic images of each monocular eye. Based on the maximum clarity index of each key fundus location, the measured refractive value of each key fundus location was calculated separately; The Zernike polynomial was used to fit the total fundus refractive values ​​of the eye under test, and a monocular uncorrected refractive topography of the eye under test was generated.

6. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 1, characterized in that, The initial optical intervention parameters are used to select suitable optical intervention products for trial use by patients with myopia control goals, including: lens optical parameters of at least one optical intervention product; the optimized optical intervention parameters are used to formulate personalized target optical intervention plans for patients with myopia control goals, including: lens optical parameters of the target optical intervention product.

7. The myopia prevention and control optical intervention assessment method based on OCT technology according to claim 1, characterized in that, The training methods for the myopia development prediction model include: We obtained the age, binocular uncorrected refractive topography, binocular corrected refractive topography and axial length of one or more historical clinical follow-ups of multiple myopic patients, constructed training samples for each myopic patient, and labeled the annual change of fundus refractive value or the annual increase of axial length of each training sample to construct the training sample dataset. The training sample dataset is input into a long short-term memory network for training, and a time decay mechanism is introduced to weight each training sample according to the time interval between two adjacent clinical follow-ups to obtain a converged myopia development prediction model.

8. A myopia prevention and control optical intervention assessment system based on OCT technology, characterized in that, include: The naked-eye scanning module is used to perform naked-eye fundus scanning on both eyes of patients with myopia control targets, generate naked-eye retinal data for both eyes, and calculate the total fundus refractive values ​​of both eyes of patients with myopia control targets, and generate naked-eye refractive topography maps for both eyes. The optical intervention parameter generation module is connected to the naked-eye scanning module and is used to collect pupil data of both eyes of patients with myopia control targets and calculate their pupil diameter. At the same time, based on the naked-eye refractive topography of both eyes, a defocus model of the fundus of both eyes is established to generate initial optical intervention parameters. The lens-wearing scanning module is connected to the optical intervention parameter generation module. It is used to select a trial optical intervention product based on the initial optical intervention parameters, and to perform lens-wearing fundus scanning on both eyes of the myopia control target patient to generate lens-wearing retinal data for both eyes. Based on this, the total fundus refractive value of both eyes of the myopia control target patient is calculated, and a lens-wearing refractive topography map of both eyes is generated. The adaptation evaluation module is connected to the lens-wearing scanning module. It is used to perform an adaptation evaluation operation on the trial optical intervention product based on the lens-wearing retinal data of both eyes. If the evaluation does not meet the adaptation conditions, the trial optical intervention product is updated, and the lens-wearing fundus scanning operation and adaptation evaluation operation are repeated until the adaptation conditions are met, so as to obtain optimized lens-wearing refractive topography and optimized optical intervention parameters. The correction effect prediction module is connected to the uncorrected eye scanning module, the corrected eye scanning module, and the adaptation assessment module, respectively. Based on the pre-trained myopia development prediction model, and according to the binocular uncorrected eye refractive topography and the optimized binocular corrected eye refractive topography, it predicts the change in total fundus refractive value of the target myopia control patient in the target future time period, and updates the optimized optical intervention parameters to formulate a target optical intervention plan adapted to the target myopia control patient.

9. The myopia prevention and control optical intervention assessment system based on OCT technology according to claim 8, characterized in that, Also includes: An optical coherence tomography (OCT) device and a liquid zoom lens integrated in the OCT device are used to perform uncorrected fundus scanning or corrected fundus scanning on both eyes of a patient with myopia control target. The optical coherence tomography device includes: a broadband light source, a beam splitter, a sample arm scanning optical path, a reference arm scanning optical path, a detector, and an image reconstruction unit. The liquid zoom lens is placed in front of the XY scanning galvanometer in the scanning optical path of the sample arm, and its optical axis is coaxial with the optical axis of the XY scanning galvanometer.

10. A myopia prevention and control optical intervention assessment terminal based on OCT technology, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory, so that the terminal performs the myopia prevention and control optical intervention assessment method based on OCT technology as described in any one of claims 1 to 7.

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