A method and system for predicting corneal ectasia

Abstract

This invention discloses a method for predicting corneal bulging. The key technical points include: S1, establishing a personalized CVS finite element corneal model; S2, establishing a personalized corneal model in terms of morphology and materials; S3, performing CVS jet simulation based on the corneal model; S41, extracting important corneal biomechanical parameters along different meridians of the entire cornea based on the model coordinates; S42, proposing parameters representing corneal asymmetry based on the line connecting the corneal apex and the maximum corneal curvature, and the proportion of proximity between different meridians; S43, determining the optimal combination of corneal biomechanical parameters using Logistic regression and Wald-Forward stepwise regression methods; and S44, obtaining comprehensive corneal biomechanical parameters representing the entire cornea, and indicating the risk of corneal bulging based on numerical risk classification. This method for predicting corneal bulging can provide early prediction and alleviate patient suffering.

Description

Technical Field

[0001] The present invention relates to a prediction system, and more specifically, to a method and system for predicting corneal bulging. Background Technology

[0002] Corneal bulging is a clinically challenging ophthalmic problem. Due to its irreversibility and serious consequences, early identification of subclinical and suspected keratoconus, as well as pre-operative assessment of post-operative corneal ectasia risk, are of great clinical significance in controlling its progression and reducing the risk of blindness. According to incomplete statistics, there are approximately 40,000 to 6 million patients with corneal bulging after refractive surgery in China. Among them, keratoconus (KC), a prevalent and irreversible corneal bulging disease in young adults (15-25 years old), is one of the most common and reversible corneal bulging diseases, often causing high irregular astigmatism, significant vision loss in later stages, and even blindness. The incidence of keratoconus in the general population is approximately 1 in 2300, meaning there are approximately 1 million keratoconus patients in China, making it one of the leading causes of blindness in the country.

[0003] While current clinical corneal biomechanical measuring instruments can identify some corneal bulging, they still have limitations in predicting subclinical keratoconus, assessing regionally variability in corneal bulging, and evaluating the risk of corneal bulging before and after refractive surgery.

[0004] Currently, there are two main problems that cannot be solved in clinical practice regarding corneal bulging diseases such as keratoconus:

[0005] First, there is a lack of clear diagnostic criteria for subclinical keratoconus. Due to atypical clinical presentations and corneal morphological parameters, diagnosing subclinical keratoconus is difficult and often requires a comprehensive assessment of several examinations. Currently, the main diagnostic criteria for subclinical keratoconus are: anterior and posterior corneal surface parameters, corneal thickness distribution, corneal epithelial layer distribution morphology, and corneal biomechanical parameters. For some suspected cases where a definitive diagnosis is not possible, long-term follow-up observation is often necessary. If these patients are not diagnosed early and corneal refractive surgery is performed prematurely, the risk of postoperative bulging increases dramatically.

[0006] Secondly, current preoperative screening and postoperative follow-up for refractive surgery cannot completely rule out the possibility of postoperative corneal bulging. For individuals undergoing laser eye surgery for myopia, early postoperative bulging risk assessment is crucial. If high-risk individuals undergo myopia surgery, the likelihood of developing corneal bulging and resulting in vision loss increases significantly. Postoperative follow-up after refractive surgery also needs to monitor for the possibility of secondary corneal bulging. Monitoring corneal morphology and biomechanical properties at each follow-up visit is essential. Due to the imperfections in detection methods, postoperative corneal bulging cannot be completely prevented; research data from various regions indicates its incidence rate is approximately 0.04%-0.6%.

[0007] However, there are currently two main types of corneal biomechanical measurement instruments commonly used in clinical practice:

[0008] (1) The corneal hysteresis (CH) and corneal resistance factor (CRF) measured by the ocular response analyzer (ORA) cannot directly reflect the biomechanical performance of the cornea;

[0009] (2) The results of the corneal biomechanical performance measured by the Corvis ST (CVS) visual corneal biomechanical analyzer cannot represent the entire cornea with regional differences.

[0010] Both instruments can provide clinicians with a reference for the biomechanical properties of the patient's cornea, but the measurement results are not very specific and cannot fully reflect regional changes in corneal biomechanics. Therefore, it is difficult to identify and diagnose corneal bulging in the subclinical stage.

[0011] Therefore, it is necessary to design and develop analysis and prediction tools to address the limitations of existing instruments and help doctors predict the risk of corneal bulging in advance, thereby reducing patient suffering. Summary of the Invention

[0012] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for predicting corneal bulging. This system can predict corneal bulging in advance, thereby reducing patient suffering.

[0013] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting corneal bulging, comprising:

[0014] S1, Establish a personalized CVS finite element corneal model;

[0015] S2, establish personalized corneal models in terms of morphology and materials;

[0016] S3, CVS jet simulation based on corneal model;

[0017] S4, Result Extraction.

[0018] A system for predicting corneal bulging includes an input module, a model building module, a simulation module, and an output module;

[0019] The input module is used to input patient information and examination results.

[0020] The model building module is used to build a corneal model;

[0021] The simulation module is used to perform jet simulation;

[0022] The output module is used to output simulation results.

[0023] In summary, the present invention has the following beneficial effects: it is the first time that finite element analysis technology has been used to predict the risk of corneal bulging.

[0024] 1. By applying Logistic regression and Wald-Forward stepwise regression methods to different parameters obtained from the model, new parameters, Zonal DCRs, are proposed to assess the risk of corneal bulging.

[0025] 2. By using finite element analysis to obtain biomechanical parameters along different meridians of the cornea, the risk of corneal bulging can be predicted, thus extending the biomechanical parameters of the cornea beyond a single meridian.

[0026] 3. Using finite element analysis, DCR parameters considering the full ocular displacement response are obtained, thereby enabling the prediction and analysis of ocular biomechanical parameters;

[0027] 4. Accurate fitting of CVS jet simulation and corneal topography data is achieved using MATLAB programs. Attached Figure Description

[0028] Figure 1 A logic diagram of a system for predicting corneal bulging;

[0029] Figure 2 A schematic diagram of the dynamic biomechanical response of the eyeball under CVS pressure;

[0030] Figure 3 This is a diagram showing the vertex indentation h and the position vector r of any point P relative to the corneal vertex.

[0031] Figure labels: 1. Input module; 2. Model building module; 3. Simulation module; 4. Output module. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0033] Reference Figures 1 to 3 As shown, to achieve the above objectives, the present invention provides the following technical solution: a method for predicting corneal bulging, comprising:

[0034] S1, Establish a personalized CVS finite element corneal model;

[0035] In this invention, a personalized CVS finite element corneal model is established using finite element modeling software independently developed in the MATLAB programming environment. This includes basic model settings such as element configuration, mesh generation, and setting appropriate boundary conditions. Specifically, in this ideal model, the cornea is configured with 24 rings and a thickness divided into two layers, forming 17304 elements. The shape of the cornea depends on its anterior surface, central corneal thickness (CCT), and peripheral corneal thickness (PCT). The anterior corneal topography is represented by ellipses, which are flatter at the periphery, and the corneal thickness at any location is linearly interpolated based on CCT and PCT. The model is meshed using 15-node solid elements (C3D15H), arranged in rings distributed across the entire ocular surface and in layers of the entire thickness. C3D15H elements are second-order triangular prism elements with nodes at the center of each edge of the cornea. The boundary conditions of the model are set as follows: the nodes at the limbus restrict the displacement in the y and z directions, so that the entire corneal motion is constrained by the position of the limbus.

[0036] S2, establish personalized corneal models in terms of morphology and materials;

[0037] Morphological personalization: Based on an idealized model mesh structure and using the same coordinate system and boundary conditions, patient-specific models are created by changing the coordinates of the nodes. This step involves deriving the subject's anterior and posterior corneal elevation data matrix using the Pentacam corneal topography system (OCULUS Optikgerate GmbH, Wetzlar, Germany). A 10th-order Zernike polynomial is used to fit the anterior corneal elevation and thickness maps for each eye to calculate the node coordinates and corresponding thicknesses at arbitrary locations. Once the nodes on the anterior corneal surface and their associated thickness values ​​are determined, the posterior nodes can be obtained by applying the thickness along the surface normal direction, which can be determined using a Zernike expression or the finite difference method.

[0038] Material personalization: This step primarily aims to endow the model with specific material properties. This invention employs the Ogden equation to describe material properties because it effectively expresses the nonlinearity (hyperelasticity) and compressibility of materials, and is widely used in describing soft tissues such as the cornea. The expression of the Ogden equation is as follows:

[0039]

[0040] Where U is the strain potential, J is the total volumetric strain, and λ is the principal strain. For elastic volumetric strain, μi, αi, and Di are material parameters representing the hyperelasticity and compressibility of the structure.

[0041] The table below shows the first-order material parameters (μ1 and α1) used for all regions of the eyeball in this invention.

[0042]

[0043] In order to achieve personalized input of corneal material properties, a core algorithm for DCR parameters that considers the whole eyeball effect during CVS measurement was developed. The algorithm uses the principle of inverse analysis to measure the biomechanical material properties of the cornea in vivo, solves for personalized corneal material constitutive parameters that are independent of corneal morphology and intraocular pressure, and establishes a personalized material model for mechanical properties.

[0044] DCR parameters are jointly determined by eye morphology (such as corneal and scleral shape and thickness) and mechanical properties (such as material properties, loads, and boundary conditions), and their mathematical expression is shown in Equation 1. The combination of DCR parameters in the temporal and spatial domains of an individual eye is uniquely related to the personalized attributes of that eye in terms of morphology and mechanical properties. Given the measured values ​​of DCR parameters and eye morphology parameters, mechanical property parameters can be obtained through inverse analysis.

[0045]

[0046] In the formula, {DCRk|k=1,2,…} represents the DCR parameter group determined by the correlation analysis in the previous step; ConMati represents the constitutive material parameters of the cornea and sclera, which in this study are µ and α, used to characterize material properties; GeoParaj represents the geometry parameters of the entire eyeball, such as axial length, corneal and scleral thickness, and curvature. The formula derivation actually utilizes the DCR parameter database obtained in the previous step, and through fitting, derives a formula for calculating the in vivo corneal biomechanical properties, as shown in Formula 2.

[0047]

[0048] In the formula, f-1(...) is the functional form of the in vivo corneal biomechanical performance calculation formula to be determined, the superscript '-1' means that it is theoretically the inverse function of formula 2, and testCMati(i=1,2) are the corneal biomechanical performance parameters (test corneal material parameters) output by the formula, namely constitutive material parameters µ and α.

[0049] S3, CVS jet simulation based on corneal model;

[0050] According to previous studies, under CVS air pressurization, the entire eyeball undergoes a complex dynamic deformation process, including the effects of gas on corneal FSI, intraocular fluid on corneal and scleral walls and lens FSI, and the damping effect of periocular tissues on the movement of the entire eyeball (such as...). Figure 2 )

[0051] Based on the results of fluid dynamics (CFD) simulation, the air pressure distribution data of the anterior surface of the cornea at different morphologies and time points obtained from CFD simulation were obtained, and the air pressure distribution equation in the form of Equation (3) was obtained:

[0052] p=f(t,h,r) Formula 3

[0053] In Formula 3, p represents the air pressure at any point P on the corneal surface, which is determined by time t, corneal apex indentation h, and the position vector r of point P relative to the corneal apex (e.g., ...). Figure 3 (As shown). A user subroutine written in the Fortran programming language and designed for the finite element analysis software Abaqus, used for accurate simulation of dynamic pressurization in the CVS model.

[0054] S4, Result Extraction.

[0055] Based on the model coordinates, important corneal biomechanical parameters along different meridians of the entire cornea are extracted, such as the inverse concave radius, integrated radius, maximum deflection amplitude, DA ratio 1mm at 1mm, DA ratio 2mm at 2mm, and corneal hardness parameter SP-A1. Based on the line connecting the corneal apex and the maximum corneal curvature and the proportion of the degree of proximity of different meridians, parameters representing corneal asymmetry are proposed (e.g., formula (1)). Logistic regression and Wald-Forward stepwise regression methods are used to determine the optimal combination of corneal biomechanical parameters (e.g., formula (2)). Further, comprehensive corneal biomechanical parameters representing the entire cornea are obtained, and the numerical risk classification indicates whether there is a risk of corneal bulging.

[0056]

[0057]

[0058] The jet simulation includes data on the air pressure distribution of the anterior surface of the cornea at different morphologies and time points, obtained from fluid dynamics simulations and CFD simulations. The air pressure distribution equation is given in equation 3.

[0059] p=f(t,h,r)

[0060] In the formula, p is the air pressure value at any point P on the corneal surface, which is determined by time t, corneal vertex indentation h, and the position vector r of point P relative to the corneal vertex.

[0061] The results extraction includes:

[0062] S41, extract important corneal biomechanical parameters along different meridians of the entire cornea based on model coordinates;

[0063] S42, based on the line connecting the corneal apex and the maximum corneal curvature and the proportion of the degree of proximity of different meridians, a parameter representing corneal asymmetry is proposed;

[0064] S43, and the optimal combination of corneal biomechanical parameters was determined using Logistic regression and Wald-Forward stepwise regression methods;

[0065] S44 yields comprehensive corneal biomechanical parameters representing the entire cornea, and the numerical risk classification indicates the risk of corneal bulging.

[0066] A system for predicting corneal bulging includes an input module, a model building module, a simulation module, and an output module;

[0067] The input module is used to input patient information and examination results.

[0068] The model building module is used to build corneal models;

[0069] The simulation module is used for jet simulation;

[0070] The output module is used to output simulation results.

[0071] This invention establishes a method and system for early identification of subclinical stage and suspected keratoconus, as well as for assessing the risk of corneal ectasia after refractive surgery.

[0072] It utilizes the computational fluid dynamics (CFD) simulation research results of CVS and independently developed a comprehensive analysis software based on MATLAB, which has functions such as fine parameter setting, reading and fitting corneal topography maps, stress-free state solution, and calling the finite element analysis software ABAQUS. Based on the core algorithm of DCR parameters that considers the whole-eye effect in the CVS measurement process, the established finite element model accurately reproduces the clinical CVS measurement process. By building a corneal bulging risk prediction software platform, based on a personalized CVS eye model, it provides corneal biomechanical performance parameters for different meridians that cannot be directly measured clinically, achieving the goal of more reliable and accurate identification of suspected keratoconus and assessment of the risk of corneal bulging after refractive surgery.

[0073] Under the premise of achieving accurate identification, the obtained corneal biomechanical performance parameters serve as a reliable basis, providing a reference for keratoconus screening and treatment, accurate prediction of refractive surgery outcomes, and personalized surgical customization in ophthalmology clinics.

[0074] The present invention has the following beneficial effects: for the first time, finite element analysis technology is used to predict the risk of corneal bulging.

[0075] 1. By applying Logistic regression and Wald-Forward stepwise regression methods to different parameters obtained from the model, new parameters, Zonal DCRs, are proposed to assess the risk of corneal bulging.

[0076] 2. By using finite element analysis to obtain biomechanical parameters along different meridians of the cornea, the risk of corneal bulging can be predicted, thus extending the biomechanical parameters of the cornea beyond a single meridian.

[0077] 3. Using finite element analysis, DCR parameters considering the full ocular displacement response are obtained, thereby enabling the prediction and analysis of ocular biomechanical parameters;

[0078] 4. Accurate fitting of CVS jet simulation and corneal topography data is achieved using MATLAB programs.

[0079] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting corneal bulging, characterized in that: Including: S1, Establish a personalized CVS finite element corneal model; S2, establish personalized corneal models in terms of morphology and materials; S3, CVS jet simulation based on corneal model; S4, Result Extraction; Material personalization includes assigning specific material properties to the model, using the Ogden equation to describe the material properties. The expression of the Ogden equation is as follows: Where U is the strain potential, J is the total volumetric strain, and λ is the principal strain. For elastic volumetric strain, μi, αi, and Di are material parameters representing the hyperelasticity and compressibility of the microstructure. It also includes a DCR parameter algorithm that considers the whole-eye effect, and its calculation formula 1 is: In the formula, {DCRk|k=1,2,…} is the DCR parameter group determined by the correlation analysis in the previous step, ConMati is the constitutive material parameter of cornea and sclera, and GeoParaj is the geometric morphology parameter of the whole eyeball; The formula derivation actually utilizes the DCR parameter database obtained in the previous step, and through fitting, derives Formula 2 for calculating in vivo corneal biomechanical properties: In the formula, f-1(...) is the functional form of the in vivo corneal biomechanical performance calculation formula to be determined, the superscript '-1' means that its theoretical cause is the inverse function of formula 2, testCMati(i=1,2) are the corneal biomechanical performance parameters output by the formula, and μ and α are the constitutive material parameters.

2. The method for predicting corneal bulging according to claim 1, characterized in that: The basic settings of the finite element corneal model include setting elements, dividing the mesh, and setting reasonable boundary conditions.

3. The method for predicting corneal bulging according to claim 1, characterized in that: The morphological personalization includes exporting the anterior and posterior corneal surface elevation data matrix of the subject through the Pentacam corneal topography system, fitting the anterior corneal surface elevation map and corneal thickness map of each eye with a 10th-order Zernike polynomial to calculate the node coordinates and corresponding thickness at any location.

4. The method for predicting corneal bulging according to claim 1, characterized in that: The jet simulation includes air pressure distribution data of the anterior surface of the cornea at different morphologies and time points obtained from fluid dynamics simulations and CFD simulations. The air pressure distribution equation is shown in Formula 3. p=f(t,h,r) In the formula, p is the air pressure value at any point P on the corneal surface, which is determined by time t, corneal vertex indentation h, and the position vector r of point P relative to the corneal vertex.

5. The method for predicting corneal bulging according to claim 1, characterized in that: The result extraction includes: S41, extract important corneal biomechanical parameters along different meridians of the entire cornea based on model coordinates; S42, based on the line connecting the corneal apex and the maximum corneal curvature and the proportion of the degree of proximity of different meridians, a parameter representing corneal asymmetry is proposed; S43, and the optimal combination of corneal biomechanical parameters was determined using Logistic regression and Wald-Forward stepwise regression methods; S44 yields comprehensive corneal biomechanical parameters representing the entire cornea, and the numerical risk classification indicates the risk of corneal bulging.

6. A system employing the method for predicting corneal bulging according to any one of claims 1-5, characterized in that: It includes an input module, a model building module, a simulation module, and an output module; The input module is used to input patient information and examination results. The model building module is used to build a corneal model; The simulation module is used to perform jet simulation; The output module is used to output simulation results.

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