A personalized scleral lens intelligent fitting system based on biomechanics and its application method

Abstract

The present invention discloses a personalized scleral lens intelligent fitting system based on biomechanics and its application method. The system includes: analyzing the strain distribution of the anterior segment of the eye at different moments after wearing through the DIC method, and combining the mechanical inversion method to solve the material properties of the anterior segment of the eye; then, based on finite element analysis, simulating the wearing effect of the SL; optimizing the personalized fitting scheme of the SL based on the FR thickness and force distribution; finally, verifying the accuracy of the fitting scheme through clinical data; this system is applied to the SL fitting equipment to realize the recommendation of a scientific, efficient, accurate, and personalized SL fitting scheme.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medicine, in particular to a personalized scleral lens intelligent fitting system based on biomechanics and an application method thereof. Background Art

[0002] In ophthalmic clinics, the visual correction of complex corneal and ocular surface diseases has always been a difficult problem to be solved urgently. Irregular astigmatism, difficult-to-heal ocular surface wounds, and severe dry eye seriously affect the visual and quality of life of patients. Traditional correction methods are difficult to meet the needs of such patients: frame glasses cannot correct irregular astigmatism, conventional contact lenses may further irritate the cornea, and surgical treatment has a relatively high risk and limited applicability. Therefore, how to provide more accurate and efficient correction methods for patients with complex corneal and ocular surface diseases has become an urgent problem to be solved clinically.

[0003] In recent years, as a new type of diagnosis and treatment method, the scleral lens (SL) has provided a breakthrough solution for such patients. The SL covers the cornea through a large-diameter design, avoids direct contact with it, and forms a fluid reservoir (FR) between the lens and the cornea, which can not only effectively correct irregular astigmatism, but also relieve dry eye symptoms and promote the healing of the ocular surface. However, although the clinical efficacy of the SL has been widely recognized, its fitting process is relatively complex, highly dependent on the experience of doctors, and lacks scientific and standardized guidance. At present, the fitting of the SL mainly adjusts the trial fitting according to the corneal morphology and scleral contour. Regional Variations in Postlens Tear Layer Thickness During Scleral Lens Wear [J]. Eye Contact Lens, 2020, 46(6): 368-74 published by VINCENT S J et al. shows that the biomechanical properties of the cornea and sclera are also key factors affecting the fitting effect of the SL. Due to the significant differences in the biomechanical properties of the cornea and sclera among individuals, fitting based only on morphological parameters may not accurately predict the actual wearing effect of the SL. This fitting method is not only inefficient, increasing the number of trial fittings and discomfort of patients, but may also cause complications such as corneal hypoxia, abnormal tear dynamics, and corneal epithelial damage due to poor fitting, further leading to a decline in visual quality and affecting the long-term wearing safety of the SL.

[0004] In addition, the settling effect of the SL lens (i.e., the lens gradually adheres to the ocular surface after being worn for a period of time) takes 2-4 hours to reach a stable state, making it difficult to directly predict the fitting situation after stable wearing during the initial fitting. This further prolongs the fitting process, increases the burden on patients, and raises the learning curve for clinicians. To a certain extent, these factors have hindered the large-scale promotion of SL in clinical practice. There is an urgent need to develop a more scientific and efficient personalized fitting method to optimize the fitting process, improve the fitting accuracy, and reduce the discomfort and complication rate during patient trial fitting.

[0005] To solve the above problems, current scleral lens fitting mainly selects the initial trial lens based on the central sagittal height, without fully considering the force conditions in the SL-sclera contact area, the FR thickness change, and the long-term settling effect. The adjustment after the trial still relies on the physician's experience, and the fitting status is adjusted by observing through a slit lamp, lacking quantitative analysis methods and making it difficult to scientifically and accurately optimize the fitting parameters. In addition, the biomechanical properties of the eye may affect the final fitting status, and the existing research on methods for measuring the corneal and scleral material properties in vivo still needs to be further optimized. During the clinical fitting process, there is a lack of time-correlation analysis of the SL fitting situation, making it impossible to accurately predict the fitting trend over time, resulting in a long fitting process and a complex adjustment process.

[0006] The market needs a scleral lens intelligent fitting system that can solve the above problems, accurately predict the fitting effects of different models of SL, output the FR thickness distribution and the force conditions in the contact area, and realize the recommendation of a scientific, efficient, and personalized SL fitting plan. The present invention solves such problems. Summary of the Invention

[0007] To solve the deficiencies of the prior art, the purpose of the present invention is to provide a personalized scleral lens intelligent fitting system based on biomechanics and its application method, which solves the problems of cumbersome fitting process, low fitting accuracy, low patient wearing comfort, and complications in the prior art, and can predict the most suitable scleral lens model, thereby optimizing the wearing effect.

[0008] To achieve the above objectives, the present invention adopts the following technical solutions:

[0009] A personalized scleral lens intelligent fitting system based on biomechanics, including the following:

[0010] Step 1, use an information acquisition module to acquire the information of the anterior segment of the eye;

[0011] Step 2, record the change process of the anterior segment information through a micro-excitation method, combine with DIC technology, extract the local strain characteristics of the cornea and sclera, and analyze the biomechanical response under micro-deformation;

[0012] Step 3: Based on the biomechanical response analysis under small deformations, use the mechanical inversion method, optical coherence elastography (OCE) technology, or finite element iteration method to perform inverse calculations of mechanical parameters to obtain the material properties of the cornea and sclera.

[0013] Step 4: Establish an SL finite element model database, establish a personalized full-eye finite element model, establish the contact simulation between the scleral lens and the human eye globe, simulate the overall fitting process of the scleral lens and the human eye globe, obtain the SL finite element simulation model, and finally output the simulated SL fitting results; the SL fitting results include: the FR thickness chart characterizing the SL settlement, and outputting the contact pressure and strain between the SL and the corneosclera to characterize the stress situation.

[0014] Step 5: Based on the SL fitting results, construct a personalized SL fitting optimization strategy and recommend the most suitable SL model and parameters.

[0015] For the aforementioned personalized scleral lens intelligent fitting system based on biomechanics, the information acquisition module includes: an OCT optical coherence tomography scanner, a pressure sensor, or a laser interferometer. The selection of the information acquisition module is not restricted. As long as it is a tool that can collect high-resolution images of the cornea and sclera, a pressure sensor that can measure the pressure changes at different parts of the eye surface in real time, or a laser interference device that can measure the small deformations on the eye surface is applicable to the present invention.

[0016] For the aforementioned personalized scleral lens intelligent fitting system based on biomechanics, further, the content of Step 2 includes: collecting a continuous image sequence of the cornea and sclera before and after a small excitation through the information acquisition module to obtain the displacement field information before and after deformation; then using the AL-DIC algorithm to perform registration analysis on the OCT images, matching the image feature points through cross-correlation calculation, extracting the pixel-level displacement field, calculating the strain, and constructing a strain distribution map.

[0017] For the aforementioned personalized scleral lens intelligent fitting system based on biomechanics, further, the content of Step 3 includes: using the mechanical inversion method combined with the finite element method to simulate the stress response of tissues under small excitations, and adjusting the material parameters through iterative optimization to minimize the error between the simulated displacement field and the experimentally measured displacement field; selecting a nonlinear elastic constitutive model suitable for soft tissues, calculating the elastic modulus, shear modulus, and Poisson's ratio of the cornea and sclera, and providing accurate biomechanical parameter support for personalized SL fitting.

[0018] For the aforementioned personalized scleral lens intelligent fitting system based on biomechanics, as an embodiment, the nonlinear elastic constitutive model suitable for soft tissues is the Ogden model. The Ogden model is only a preferred embodiment and is not an exhaustive one.

[0019] The above-mentioned personalized scleral lens intelligent fitting system based on biomechanics, further, the content of step four includes:

[0020] (1) Establish an SL finite element model database

[0021] Simulate the morphology and material properties of each model of SL:

[0022] Based on the scleral lens OCT images, obtain the three-dimensional coordinates of each model of SL, and obtain the function expression of the three-dimensional morphology of the SL surface through 10th-order Zernike polynomial fitting; then obtain the material parameters of the SL bending resistance performance to describe the material properties of each model of SL. Finally, use the C3D15H element to divide the SL model grid, and then establish the three-dimensional finite element model of each model of SL to form an SL finite element model database; the material parameter of the SL bending resistance performance is the bending modulus.

[0023] (2) Establish a personalized whole-eye finite element model

[0024] First, based on the classic Gullstrand eye model data, use the C3D15H element to divide the grid and establish an idealized whole-eye model; then, on the basis of the idealized model, obtain the three-dimensional coordinates of the cornea and sclera of each human eye according to the large-range anterior segment OCT images, and modify the node coordinates of the cornea and sclera parts in the model to obtain a morphologically personalized whole-eye finite element model.

[0025] Secondly, set the boundary conditions of the model, restrict the displacement of the scleral equator in the Z direction and the displacement of the whole-eye center node in the XY plane; simulate the intraocular pressure by controlling the pressure degree of freedom of the fluid cavity enclosed by the inner surface of the model. Each personalized model uses the biomechanical corrected intraocular pressure measurement value provided by Corvis ST, and uses the Ogden material model to simulate the material properties of the cornea and sclera. The equation expression of the Ogden material model is shown as the following formula:

[0026] ,

[0027] where U is the strain potential energy, is the principal stretch, μ and α represent the material parameters of the tissue. The μ and α of the personalized human eye are set according to the biomechanical parameters measured by each subject in step three;

[0028] (3) Establish the contact simulation between the scleral lens and the human eye globe

[0029] Set the mechanical contact between the SL model and the whole-eye model. Set the frictional contact mode between the SL and the human eye, with the friction coefficient FRICTION being 0.05 and the damping coefficient STABILIZE for the contact between the SL and the human eye being 1.0e-5. Set the gravity according to the material and density of the SL. Use the non-uniform pressure method to simulate the tear film tension between the SL and the anterior surface of the cornea, and use the non-uniform pressure on the anterior surface of the SL to simulate the pressure of the eyelid on the SL;

[0030] (4) Simulate the whole process of wearing the scleral lens on the human eye globe

[0031] The three-dimensional coordinates of the anterior segment collected clinically are the shape of the eyeball under the action of intraocular pressure. The geometric shape of the eyeball under the condition of no intraocular pressure, that is, no stress, is obtained through reverse iteration. The solution method is as follows: First, obtain the three-dimensional coordinates from the anterior segment image measured by OCT, that is, the shape under the action of intraocular pressure IOP, and establish a finite element model. Record this shape as X0. Apply IOP in the finite element model and calculate the nodal displacement vector u1 = X0 – x0 caused by the action of IOP. Then the first estimated shape in the stress-free form can be expressed as X1 = X0 – u1; Next, in the second step, reapply IOP to the stress-free form X1, calculate the nodal displacement vector u2 = X0 – x1 between the loaded shape x1 and the target shape X0, and then calculate the shape X2 = X1 – u2 for subsequent iterative analysis. Repeat this process, and at the same time monitor the nodal displacement vector and distribution u k = X0 – x k-1 ,X k = X k-1 – u k ,until the error is less than 10 -5 ,then it is considered to have converged to the stress-free state;

[0032] Under the stress-free state of the whole eye globe, take the load and contact settings during the contact between the SL and the human eye as input conditions, and finally output the simulated SL fitting results; The load includes: the positive loading of the intraocular pressure on the whole eye globe simulated by the fluid cavity, the tear film tension between the SL and the cornea, and the pressure of the eyelid on the SL; The contact settings include: the friction, damping, and adhesive contact between the SL and the eyeball; The SL fitting results include: the FR thickness chart characterizing the SL settlement, and outputting the contact pressure and strain between the SL and the corneosclera to characterize its stress condition.

[0033] For the aforementioned personalized scleral lens intelligent fitting system based on biomechanics, further, the content of step five includes:

[0034] Use finite element analysis to simulate the influence of various SL parameters on the FR thickness and stress distribution, extract the FR thickness gradient and contact pressure, and establish a standardized database;

[0035] Optimize the SL fitting parameters by setting the optimal FR thickness range and force threshold;

[0036] Based on the simulation data, establish a regression model of FR thickness - force distribution using multiple regression analysis, analyze the relationship between SL parameters, FR thickness, and force distribution, and provide a quantitative basis for parameter optimization;

[0037] The FR thickness - force distribution regression model predicts the FR thickness and corneal - scleral force under each SL parameter;

[0038] Use an AI optimization algorithm for multi - objective parameter optimization, comprehensively balance factors such as FR thickness fitting, force on the contact area, and wearing stability, and automatically recommend the optimal SL parameter combination;

[0039] Output a personalized FR thickness map and a force evaluation report to provide a scientific basis for doctors' prescriptions.

[0040] Furthermore, the SL parameters include: diameter, base curve, and elevation of each zone; furthermore, the AI optimization algorithm includes: genetic algorithm GA or Bayesian optimization BO. The listing here is only an example of an embodiment and is not an exhaustive list.

[0041] The aforementioned personalized scleral lens intelligent fitting system based on biomechanics further includes: Step Six, clinically verify the prediction scheme.

[0042] An application method of a personalized scleral lens intelligent fitting system based on biomechanics, applied to a computer - readable storage medium equipped with the aforementioned personalized scleral lens intelligent fitting system, and a device provided with the computer - readable storage medium.

[0043] The advantages of the present invention are as follows:

[0044] The present invention first applies the DIC technology (Digital Image Correlation Analysis Technology) to the fitting analysis of SL (scleral lens), filling the gap in the measurement of existing scleral material properties; applying the strain characteristics extracted by DIC to the fitting of SL expands the mechanical information for SL fitting and reduces the complications of SL wearing;

[0045] The present invention combines the strain information extracted by DIC with an inversion algorithm to inversely calculate the material properties of ocular tissues (such as the cornea, sclera, and corneosclera); by establishing a database of finite element models of the eyeball and scleral lens and combining individualized parameters for mechanical simulation and prediction, ensure that the scleral lens fitting scheme for each patient can best meet the specific needs of their eyes;

[0046] The personalized SL finite element model established in the present invention simulates the contact, load action, and deformation process between the scleral lens and the eyeball; by simulating the wearing stability of scleral lenses of different models, it analyzes the thickness of the tear film under the lens, the stress and deformation of the corneosclera, and realizes predicting the most suitable scleral lens model based on biomechanical changes, so as to optimize the wearing effect;

[0047] The method for fitting and evaluating scleral lenses established in the present invention helps to improve the fitting success rate, reduce the waiting time and lens replacement rate of patients, which is of great significance to both doctors and patients, and is also beneficial to the popularization of SL in China, with profound social and economic benefits.

[0048] Explanation of proprietary terms:

[0049] Augmented Lagrangian-DIC (AL-DIC) is a hybrid DIC algorithm that combines the advantages of Local Subset DIC and Global DIC. It uses the Augmented Lagrangian multiplier method to ensure global kinematic consistency while maintaining the fast calculation speed and parallel computing ability of Local Subset DIC.

[0050] Scleral lens, also known as rigid gas-permeable scleral contact lens, is a large-diameter lens that arches over the cornea and corneoscleral limbus like a bridge, spanning from above the cornea and finally fitting onto the conjunctiva and sclera.

[0051] Optical Coherence Tomography (OCT) uses the principle of low-coherence interference to perform high-resolution imaging of biological tissues with single-scattered light and weakly scattered light. When the optical path difference between the single-scattered light and the forward-scattered light returned from the scattering medium is within the coherence length of the light source, they interfere with the reference light to form a signal that can be used to calculate the tomographic image. Since the multiple-scattered light (diffuse light) has an optical path difference exceeding the coherence length and cannot form an interference signal, it will not be recorded by OCT. In this way, OCT can achieve non-invasive, high-resolution tomographic imaging of biological tissues and is widely used in fields such as ophthalmology (such as retinal and corneal imaging), cardiovascular, neuroscience, dermatology, and dentistry.

[0052] The anterior segment of the eye includes the cornea, iris, ciliary body, anterior chamber, posterior chamber, anterior chamber angle, and lens zonule. Some definitions also include the anterior part of the lens. In some cases, it may also involve the anterior vitreous, conjunctiva, anterior sclera, and the attachment points of the extraocular muscles. Description of the drawings

[0053] Figure 1It is a schematic diagram of the OCT image when the lens is first worn;

[0054] Figure 2 It is a schematic diagram of the image 4 hours after wearing the lens;

[0055] Figure 3 It is a schematic diagram of the segmented image;

[0056] Figure 4 It is a schematic diagram of the segmented image;

[0057] Figure 5 It is a schematic diagram of the displacement in the x - direction;

[0058] Figure 6 It is a schematic diagram of the displacement in the y - direction;

[0059] Figure 7 It is to construct a full - field strain distribution map;

[0060] Figure 8 It is a schematic diagram of the scleral lens coordinate (left) based on the swept - source OCT image and a schematic diagram of the scleral lens three - dimensional finite - element model (right) of the present invention;

[0061] Figure 9 It is a schematic diagram of the corneal and scleral coordinates (left) based on the swept - source OCT image of the present invention; a schematic diagram of the whole - eye finite - element model (right);

[0062] Figure 10 It is a schematic diagram of the contact simulation between the scleral lens and the human eye globe of the present invention;

[0063] Figure 11 It is the FR thickness map of the present invention;

[0064] Figure 12 It is the simulated contact pressure and the eyeball strain results in the contact area between the scleral lens and the eyeball of the present invention;

[0065] Figure 13 It is the clinical FR thickness map (left) when the lens is first worn and the model FR thickness map (right) in the verification test of the present invention;

[0066] Figure 14 It is the horizontal - direction von Mises strain field (left) from 0 hour to 4 hours after wearing the lens and the vertical - direction von Mises strain field (right) from 0 hour to 4 hours after wearing the lens in the verification test of the present invention. Specific embodiments

[0067] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0068] A personalized scleral lens intelligent fitting system based on biomechanics includes the following:

[0069] Step 1: Use an information acquisition module to collect information on the eyeball and sclera. As a preferred embodiment, the information acquisition module is an OCT optical coherence tomography scanner. The choice of the information acquisition module is not limited. It can also be a wide-field anterior segment OCT, CLARUSM 500, high-resolution fundus camera, Pentacam, etc., which can collect high-resolution images of the cornea and sclera and are applicable to the present invention.

[0070] Step 2: Use optical coherence tomography (OCT) to obtain high-resolution images of the cornea and sclera under the action of a small excitation (external piezoelectric loading or physiological regulatory response), and combine the Augmented Lagrangian digital image correlation AL-DIC or DIC algorithm technology and the mechanical inversion method to calculate the individualized material properties of the cornea and sclera. First, use a wide-field anterior segment OCT to collect a continuous image sequence of the cornea and sclera before and after a small excitation to obtain the displacement field information before and after deformation. Subsequently, use the AL-DIC algorithm to perform registration analysis on the OCT images, match the image feature points through cross-correlation calculation, extract the pixel-level displacement field, and calculate the strain to construct a strain distribution map.

[0071] Figure 1 is the OCT image when just wearing the lens, Figure 2 is the image after wearing the lens for 4 hours. First, segment the cornea and sclera parts ( Figure 3 , 4 are the segmented images), perform zero-mean normalized cross-correlation calculation through fast Fourier transform, perform pixel-level matching in the subset area based on multi-scale search of the image pyramid to obtain the initial displacement estimation before and after deformation ( Figure 5 , 6 are the displacements in the x and y directions respectively), and then, in the local inverse combined Gauss-Newton iteration optimization process, use the alternating direction multiplier method for global optimization. The strain calculation can adopt the finite difference or finite element method, perform gradient operation on the displacement field, obtain the Green-Lagrange strain tensor, and construct a full-field strain distribution map ( Figure 7 ).

[0072] It should be noted that: The information acquisition module includes: an OCT optical coherence tomograph, a pressure sensor or a laser interferometer. The information acquisition module can be an OCT optical coherence tomograph. The image correlation method extracts the strain data on the surface of the eyeball by tracking the feature points in the image. Sensor technologies such as pressure sensors can also be used as alternative solutions. By installing micro pressure sensors on the scleral lens and the surface of the eyeball, the pressure changes at different parts of the surface of the eyeball are measured in real time, and then the strain distribution is indirectly deduced, which is applicable to situations requiring high-frequency sampling and real-time monitoring. In addition, a laser interferometer can also be used as an alternative technology. By measuring the minute deformation on the surface of the eyeball, the corresponding strain condition is deduced, which is applicable to research scenarios with relatively high precision requirements. Any alternative solution adopting a design concept of the present invention is inspired by the present invention and within the protection scope of the present invention.

[0073] Step 3: Use the mechanical inversion method combined with the finite element method to simulate the force response of the tissue under a minute excitation, and iteratively optimize and adjust the material parameters to minimize the error between the simulated displacement field and the displacement field measured experimentally; Select a nonlinear elastic constitutive model (Ogden model) applicable to soft tissues, and calculate the elastic modulus, shear modulus, and Poisson's ratio of the cornea and sclera to provide accurate biomechanical parameter support for personalized SL fitting. During the mechanical inversion process, first, based on the AL-DIC displacement field data measured experimentally, establish a finite element model of the cornea and sclera, and use the Ogden hyperelastic constitutive model to describe the nonlinear mechanical behavior of the tissue. The initial parameters set the material parameters μ and α of the cornea and sclera according to age. Subsequently, apply the same load as in the experiment in the finite element model, and calculate the displacement field obtained from the numerical simulation. By calculating the error between the displacement field measured experimentally and the simulated displacement field, use the Levenberg-Marquardt optimization method to adjust the material parameters to gradually converge the error. During the optimization process, update the shear modulus and exponential parameters each time, and use a step size = 0.1 for correction, and the update method is , . After 5 - 8 iterations, when the error meets the convergence condition, obtain the optimized material parameters of the cornea and sclera.

[0074] The mechanical inversion method can also be replaced by optical coherence elastography (OCE) technology. By performing depth scans on the high-resolution images of the eye surface, the geometric deformation of the eye can be observed in real time, and the mechanical properties can be indirectly calculated. In addition, the mechanical parameters can be inversely deduced by the finite element iteration method. In this method, the known geometric shape, load, and boundary conditions are input into the finite element model, and through multiple iterative calculations, the material constitutive parameters are gradually optimized until the simulation results match the experimental data. This method is applicable to the case where the material parameters are not fully known and has good prediction ability under certain conditions. Any alternative solution that adopts one of the design concepts of the present invention is inspired by the present invention and is within the protection scope of the present invention.

[0075] Step 4: Construct an SL-personalized full-eye finite element model and simulate the wearing process

[0076] (1) Establish an SL finite element model database

[0077] To establish an SL finite element model database, it is necessary to simulate the morphology and material properties of different models of SL. First, based on the scleral lens OCT images, the three-dimensional coordinates of different models of SL are obtained ( Figure 8 left), and the function expression of the three-dimensional morphology of the SL surface is obtained by fitting with the 10th-order Zernike polynomial. Secondly, SL generally uses highly oxygen-permeable hard materials, and the material parameters of its bending resistance - bending modulus are obtained to describe the material properties of different models of SL. Finally, the SL model mesh is divided by C3D15H elements, and then the three-dimensional finite element models of different models of SL are established ( Figure 8 right), forming an SL finite element model database.

[0078] (2) Establish a personalized full-eye finite element model

[0079] First, based on the classic Gullstrand eye model data, the mesh is divided by C3D15H elements to establish an idealized full-eye model. On the basis of the idealized model, according to the three-dimensional coordinates of the cornea and sclera of different human eyes obtained from the large-range anterior segment OCT images ( Figure 9 left), the node coordinates of the cornea and sclera parts in the model are modified to obtain a morphology-personalized full-eye finite element model ( Figure 9 right).

[0080] Secondly, set the boundary conditions of the model to restrict the displacement in the Z direction at the equator of the sclera and the displacement of the central node of the full eye in the XY plane; simulate the intraocular pressure by controlling the pressure degrees of freedom of the fluid cavity enclosed by the inner surface of the model. Each personalized model uses the biomechanically corrected intraocular pressure measurement value provided by Corvis ST, and the Ogden material model is used to simulate the material properties of the cornea and sclera. The equation expression of the Ogden material model is shown as the following formula:

[0081] ,

[0082] in U is the strain potential energy, The main stretch amount, μ and α Represents the material parameters of tissue, personalizing the human eye μ and α Set according to the biomechanical parameters measured in step three for each subject;

[0083] (3) Establish contact simulation between scleral lens and human eyeball

[0084] Set up the mechanical contact between the SL model and the whole eyeball model, such as Figure 10 As shown in the figure, a friction contact mode (Cohesive Contact) is set between SL and the human eye, the friction coefficient FRICTION is 0.05, and the damping coefficient STABILIZE of the contact between the two is set to 1.0e-5. In addition, the gravity is set according to the material and density of SL. There is tear tension between SL and the anterior surface of the cornea. This study intends to simulate it using a non-homogeneous pressure method, that is, the pressure value takes into account the influence of tear gravity. Similarly, the pressure of the eyelid on SL is simulated as a non-homogeneous pressure on the anterior surface of SL, taking into account the difference in tension between the upper and lower eyelids.

[0085] (4) Simulation of wearing process of scleral lens and human eyeball

[0086] The three-dimensional coordinates of the anterior segment collected clinically are the shape of the eyeball under the action of intraocular pressure. The geometric shape of the eyeball under the condition of no intraocular pressure, that is, no stress, is obtained through reverse iteration. The solution is as follows: first, the three-dimensional coordinates are obtained based on the anterior segment image measured by OCT, that is, the shape under the action of intraocular pressure IOP, and a finite element model is established. The shape is recorded as X0, and IOP is applied to the finite element model. The node displacement vector u1=X0–x0 caused by IOP is calculated. The shape of the stress-free form estimated for the first time can be expressed as X1=X0–u1; then in the second step, IOP is reloaded on the stress-free form X1, and the displacement error u2=X0–x1 between the loaded shape x1 and the target shape X0 is calculated, and then the shape X2=X1–u2 for subsequent iterative analysis is calculated. Repeat this process while monitoring the node error value and distribution u relative to the target shape. k =X0–x k-1 , X k =X k-1 –u k , until the error is less than 10 -5 , it is considered to have converged to a stress-free state;

[0087] Under the stress-free state of the entire eyeball, the load and contact settings during the contact between the SL and the human eye are used as input conditions, and finally the simulated SL fitting results are output; the load includes: the positive loading of the intraocular pressure of the fluid chamber on the entire eyeball, the tear film tension between the SL and the cornea, and the pressure of the eyelid on the SL; the contact settings include: the friction, damping, and adhesive contact between the SL and the eyeball; the SL fitting results include: the FR thickness graph characterizing the SL settlement situation ( Figure 11 ), and the output of the contact pressure and strain between the SL and the corneosclera to characterize the stress situation ( Figure 12 ). Step Five, based on the situation of the SL fitting results, construct a personalized SL fitting optimization strategy and recommend the most suitable SL model and parameters:

[0088] Use finite element analysis to simulate the influence of various SL parameters on the FR thickness and stress distribution, extract the FR thickness gradient and contact pressure, and establish a standardized database;

[0089] By setting the optimal FR thickness range and stress threshold, optimize the SL fitting parameters:

[0090] First, based on finite element analysis, simulate the FR thickness distribution under different SL parameters, anterior segment morphology, and material parameters, extract the thickness gradients in the central and peripheral regions, and combine clinical experience to set the optimal range. The recommended thickness in the central region is 150 - 300 μm, and in the peripheral region is 50 - 100 μm to maintain good optical quality and tear exchange. Subsequently, calculate the 360° contact stress distribution between the SL and the sclera, and establish a personalized stress threshold through parametric learning to define the stress intervals of different fitting states in a data-driven manner. First, collect the stress distributions under different SL parameters and ocular conditions, use regression modeling (such as XGBoost, random forest) to construct the mapping relationship between SL parameters and the maximum contact stress, and delimit a reasonable stress range based on clinical practice and statistical analysis. The comfortable wearing range corresponds to the 25% - 50% quantile of the contact stress, the warning range falls within the 50% - 90% quantile, and the area exceeding the 90% quantile may cause corneal hypoxia or scleral tissue stress concentration and is not suitable for long-term wearing. Based on this optimization plan, when the FR thickness exceeds the optimal range and affects the optical quality, the SL diameter or sagittal height can be adjusted, and when the contact stress exceeds the warning or critical range, the SL edge design can be optimized to ensure that the personalized SL fitting reaches a balance between the optimal FR thickness and biomechanical stability.

[0091] Based on the simulation data, use multiple regression analysis to establish a FR thickness - stress distribution regression model, analyze the relationship between SL parameters and FR thickness, stress distribution, and provide a quantitative basis for parameter optimization;

[0092] The FR thickness - force distribution regression model predicts the FR thickness and the corneal - scleral forces under each SL parameter;

[0093] An AI optimization algorithm is used for multi - objective parameter optimization, comprehensively balancing the factors of FR thickness adaptation, contact area force, and wearing stability, and automatically recommending the optimal SL parameter combination; The SL parameters include: diameter, base curve, and elevation of each zone; The AI optimization algorithms include: Genetic Algorithm GA or Bayesian Optimization BO;

[0094] As an example, Bayesian optimization is used for multi - objective parameter optimization of scleral lens SL. A surrogate model is constructed through Gaussian Process Regression (GPR), and the Expected Improvement (EI) strategy is used to optimize the SL parameter combination to achieve the best balance among FR thickness adaptation, contact stress equilibrium, and wearing stability. First, the optimization objective function is defined, including the FR thickness adaptation error in the central and peripheral zones, the deviation of the maximum stress value in the contact area, and the wearing stability deviation, with the SL diameter, base curve, and elevation of each zone as the optimization variables. During the optimization process, BO randomly selects the initial SL parameter combination for finite - element calculation, trains the GPR surrogate model to predict the objective function values under different parameters, and selects the parameter combination most likely to improve the results through the EI strategy for iterative update. After each iteration, new experimental data is added to the dataset, and the optimization model is continuously adjusted and gradually converges to the optimal solution. Finally, after about 30 iterations, BO can find the optimal SL parameter combination, keeping the central - zone FR thickness between 150 - 300 μm, the peripheral - zone thickness between 50 - 100 μm, while keeping the contact stress within the comfortable wearing range, ensuring the comfort and biomechanical stability of long - term wearing.

[0095] Output a personalized FR thickness map and a force assessment report, providing a scientific basis for doctors' fitting;

[0096] Step six, clinically verify the prediction scheme, optimize the fitting strategy, improve the feasibility of SL in clinical applications, and achieve precise fitting and intelligent promotion.

[0097] An application method of a personalized scleral lens intelligent fitting system based on biomechanics, applied to a computer - readable storage medium equipped with the aforementioned personalized scleral lens intelligent fitting system, and a device equipped with the computer - readable storage medium.

[0098] The device includes: a memory, a processor, an input - output device, and a communication interface.

[0099] The memory includes: high-speed random access memory (RAM), non-volatile memory, etc. Any memory with a computer-readable storage medium can be applied to the present invention. The communication interface can be the Internet, wide area network, local area network, metropolitan area network, etc.

[0100] The processor includes a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0101] Accuracy experiment verification:

[0102] Anterior scleral lens images and a patient database with an established SL fitting model have been established, and currently, the SL-whole eye finite element modeling has been basically completed. The FR central thickness and gradient distribution trends output by the finite element model at the initial wearing of the lens are highly consistent with the clinical observation results ( Figure 13 ). The contact pressure in the contact area and the strain distribution of the eyeball simulated by the model are as Figure 12 shown. The results show that the force in the contact area presents a non-uniform distribution characteristic. Currently, the AL-DIC method has been developed to analyze clinical OCT images and it is found that from 0 hours to 4 hours after wearing the lens until stabilization, the strain in the nasal region is higher than that in the temporal region, and the strain in the upper region is higher than that in the lower region. This trend is consistent with the force distribution results output by the finite element model, verifying the accuracy and applicability of the model in predicting the eyeball strain characteristics during the SL fitting process ( Figure 14 ). The SL fitting simulation model has taken initial shape, laying a model foundation for the implementation of the project.

[0103] In summary, the present invention proposes a personalized scleral lens intelligent fitting method based on biomechanics. The present invention integrates digital image correlation (DIC), mechanical inversion method, and finite element analysis. Starting from the patient's personalized biomechanics, it accurately predicts the fitting effects of different models of SL, outputs the FR thickness distribution and the force conditions in the contact area, and realizes the recommendation of a scientific, efficient, and personalized SL fitting scheme. The present invention promotes the development of SL fitting from experience-driven to data-driven and precise, improving the objectivity, accuracy, and scientific nature of the fitting.

[0104] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any way. Any technical solutions obtained by using equivalent replacements or equivalent transformations fall within the protection scope of the present invention.

Claims

1. A personalized scleral lens intelligent fitting system based on biomechanics, characterized in that, It includes the following contents: Step 1: Use an information acquisition module to acquire information of the anterior segment of the eye. Step 2: Record the change process of the anterior segment information through a micro excitation method, combine with the digital image correlation (DIC) technology, extract the local strain characteristics of the cornea and sclera, and analyze the biomechanical response under micro deformation. Step 3: Calculate the material properties of the cornea and sclera by using a mechanical inversion method and optical coherence elastography (OCE) technology based on the biomechanical response analysis under micro deformation. Step 4: Establish a scleral lens (SL) finite element model database, establish a personalized full-eye finite element model, establish the contact simulation between the scleral lens and the human eye globe, simulate the overall wearing process of the scleral lens and the human eye globe, obtain the SL finite element simulation model, and finally output the simulated SL fitting result; the SL fitting result includes: the FR thickness chart characterizes the SL settlement situation, and outputs the contact pressure and strain between the SL and the corneosclera to characterize its stress situation. Step 5: Based on the situation of the SL fitting result, construct a personalized SL fitting optimization strategy and recommend the most suitable SL model and parameters. The content of Step 5 includes: Use finite element analysis to simulate the influence of each SL parameter on the FR thickness and stress distribution, extract the FR thickness gradient and contact pressure, and establish a standardized database. Optimize the SL fitting parameters by setting the optimal FR thickness range and stress threshold. Based on the simulation data, adopt multiple regression analysis to establish a FR thickness - stress distribution regression model, analyze the relationship between the SL parameters and the FR thickness and stress distribution, and provide a quantitative basis for parameter optimization. The FR thickness - stress distribution regression model predicts the FR thickness and the stress situation of the corneosclera under each SL parameter. Adopt an AI optimization algorithm for multi-objective parameter optimization, comprehensively balance the factors of FR thickness fitting, contact area stress, and wearing stability, and automatically recommend the optimal SL parameter combination. Output a personalized FR thickness map and a stress evaluation report to provide a scientific basis for doctors' fitting.

2. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, wherein The information acquisition module includes: an OCT optical coherence tomography scanner, a pressure sensor, or a laser interferometer.

3. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, wherein, The content of Step 2 includes: Use the information acquisition module to collect continuous image sequences of the cornea and sclera before and after micro excitation to obtain the displacement field information before and after deformation; then use the AL-DIC algorithm to register and analyze the OCT images, match the image feature points through cross-correlation calculation, extract the pixel-level displacement field, calculate the strain, and construct a strain distribution map.

4. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, wherein The content of Step 3 includes: Use a mechanical inversion method to simulate the stress response of tissues under micro excitation, and adjust the material parameters through iterative optimization to minimize the error between the simulated displacement field and the experimentally measured displacement field; select a nonlinear elastic constitutive model suitable for soft tissues, and calculate the elastic modulus, shear modulus, and Poisson's ratio of the cornea and sclera to provide accurate biomechanical parameter support for personalized SL fitting.

5. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 4, characterized in that, The nonlinear elastic constitutive model suitable for soft tissues is the Ogden model.

6. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, wherein The content of Step 4 includes: (1) Establish an SL finite element model database Simulate the morphology and material properties of each model of SL: Based on the scleral lens OCT images, the three-dimensional coordinates of each model of SL are obtained, and the functional expression of the three-dimensional shape of the SL surface is obtained by fitting with the 10th-order Zernike polynomial; then, the material parameters of the SL bending resistance performance are obtained to describe the material properties of each model of SL. Finally, the SL model mesh is divided by C3D15H elements, and then the three-dimensional finite element models of each model of SL are established to form an SL finite element model database; the material parameter of the SL bending resistance performance is the bending modulus. (2) Establish a personalized whole-eye finite element model First, based on the data of the classical Gullstrand eye model, the mesh is divided by C3D15H elements to establish an idealized whole-eye model; then, on the basis of the idealized model, the three-dimensional coordinates of the cornea and sclera of each human eye are obtained according to the anterior segment OCT images, and the node coordinates of the cornea and sclera parts in the model are modified to obtain a morphologically personalized whole-eye finite element model. Secondly, set the boundary conditions of the model to restrict the displacement of the scleral equator in the Z direction and the displacement of the whole-eye center node in the XY plane; simulate the intraocular pressure by controlling the pressure degrees of freedom of the fluid cavity enclosed by the inner surface of the model. Each personalized model uses the biomechanical corrected intraocular pressure measurement value provided by Corvis ST, and the Ogden material model is used to simulate the material properties of the cornea and sclera. The equation expression of the Ogden material model is shown in the following formula: ， wherein U is the strain potential energy, is the principal stretch, μ and α represent the material parameters of the tissue, and are set according to the biomechanical parameters measured for each subject in Step Three; μ and α ​ (3) Establish the contact simulation between the scleral lens and the human eye Set the mechanical contact between the SL model and the whole-eye model. Set the friction contact mode between the SL and the human eye. The friction coefficient FRICTION is 0.05, and the damping coefficient STABILIZE of the contact between the SL and the human eye is 1.0e-5. Set the gravity according to the material and density of the SL, use the non-uniform pressure method to simulate the tear tension between the SL and the anterior surface of the cornea, and use the non-uniform pressure on the anterior surface of the SL to simulate the pressure of the eyelid on the SL. (4) Overall simulation of the wearing process of the scleral lens and the human eye The three-dimensional coordinates of the anterior segment collected clinically are the shape of the eyeball under the action of intraocular pressure. The geometric shape of the eyeball under the condition of no intraocular pressure, that is, no stress, is obtained through reverse iteration. The solution is as follows: first, the three-dimensional coordinates, that is, the shape under the action of intraocular pressure IOP, are obtained based on the anterior segment image measured by OCT, a finite element model is established, and the shape is recorded as X0. IOP is applied to the finite element model, and the node displacement vector u1=X0–x0 caused by IOP is calculated. Then, the shape of the stress-free form estimated for the first time can be expressed as X1=X0–u1; then in the second step, IOP is reloaded on the stress-free form X1, and the node displacement vector u2=X0–x1 between the loaded shape x1 and the target shape X0 is calculated, and then the shape X2=X1–u2 for subsequent iterative analysis is calculated, and this process is repeated, while monitoring the node displacement vector and distribution u relative to the target shape. k =X0–x k-1 , X k =X k-1 –u k , until the error is less than 10 -5 , it is considered to have converged to a stress-free state; In the stress-free state of the whole eye, take the load and contact settings during the contact process between the SL and the human eye as input conditions, and finally output the SL fitting result obtained by simulation; the load includes: the positive loading of the intraocular pressure simulated by the fluid cavity on the whole eye, the tear tension between the SL and the cornea, and the pressure of the eyelid on the SL; the contact settings include: the friction, damping, and adhesive contact between the SL and the eyeball.

7. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, characterized in that, The SL parameters include: diameter, base curve, and elevation of each zone; the AI optimization algorithm includes: genetic algorithm GA or Bayesian optimization BO.

8. The personalized scleral lens intelligent fitting system based on biomechanics according to claim 1, wherein It also includes: Step Six, clinically verify the prediction scheme.

9. Application method of a personalized scleral lens intelligent fitting system based on biomechanics, characterized in that, A computer-readable storage medium applied to the personalized scleral lens intelligent fitting system as claimed in claim 1, and a device provided with the computer-readable storage medium.

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