Dynamic simulation model construction algorithm for implanting crystalline lens MPL in ophthalmology department

Abstract

The invention provides a dynamic simulation model construction algorithm for implanting a crystalline lens MPL in the ophthalmology department, and relates to the technical field of dynamic simulation models.The dynamic simulation model construction algorithm comprises the steps that microscopic dynamic data of the crystalline lens in the eye are collected in real time through a nanoscale microsensor array, and the crystalline lens micro-deformation degree (CTMD) and the interaction force (CTIS) of the crystalline lens and an intraocular structure are calculated; the collected microscopic data is transmitted to a central processing unit and is efficiently processed by a high-frequency data processing module; calculating the three-dimensional deformation and focal length change of the crystalline lens in real time based on a multi-physics field coupling algorithm in combination with a physics model; a highly dynamic simulation model is generated through the central processing unit to simulate the performance of the implanted MPL and optimize the surgical planning and design. According to the method, the precision and adaptability of the dynamic simulation model of the implanted crystalline lens MPL in the ophthalmology department can be effectively improved, and more accurate simulation and support are provided for ophthalmology operations.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic simulation models, and in particular to a dynamic simulation model construction algorithm for ophthalmic implant lens MPL. Background Art

[0002] Traditional MPL simulation models mostly rely on simplified physical assumptions, usually only considering basic factors such as the lens's geometry and optical properties. These models often ignore the complex mechanical interactions between the lens and other intraocular structures, such as the tiny mechanical changes between the lens and the eyeball wall, iris, and aqueous humor. Due to these simplified treatments, existing simulation models cannot fully and accurately reflect the dynamic interactions between the various intraocular structures and the performance of the lens in actual applications, resulting in insufficient accuracy of the model in predicting lens behavior and optimizing design.

[0003] Most existing simulation models are based on a single physical model, such as an eyeball mechanics model or an optical model, which usually cannot effectively couple the interactions of multiple physical fields. The intraocular environment involves multiple physical phenomena such as mechanics, optics, and fluid dynamics. The mutual influence and complex interweaving between these physical phenomena often cannot be accurately captured by traditional models. The limitations of a single physical model make it impossible for existing simulation technology to fully evaluate the dynamic changes after lens implantation, and it is difficult to reflect the real interaction between the lens and the intraocular environment, which in turn affects the accuracy of the design and the ability of personalized adjustment.

[0004] In addition, the existing technology is relatively weak in processing the dynamic feedback capability of the interaction between the lens and other structures in the eye. In particular, in the simulation of the long-term interaction and change between the lens and other structures, the existing simulation technology fails to provide sufficiently detailed and real-time feedback. This limits the optimization capability of the relevant design, and restricts the applicability and effect of the existing technology in practical applications.

[0005] In summary, the existing technology has obvious deficiencies in many aspects, especially in terms of accuracy, dynamic simulation and multi-physical field coupling. There is an urgent need for a more comprehensive and accurate simulation model that can overcome these deficiencies and provide more accurate dynamic prediction and optimization capabilities. Summary of the invention

[0006] In order to solve the technical problems in the prior art of insufficient accuracy of ophthalmic implant lens (MPL) simulation models, insufficient coupling of mechanical and physical phenomena, and weak dynamic feedback capability, the present invention provides a dynamic simulation model construction algorithm for ophthalmic implant lens MPL.

[0007] The technical solution provided by the present invention is as follows:

[0008] The present invention provides an algorithm for constructing a dynamic simulation model of an ophthalmic implant lens MPL, comprising:

[0009] S1. Using a nano-scale micro-sensor array to collect real-time microscopic dynamic data of the lens in the eye, including the crystal lens micro-deformation degree (CTMD) and the crystal lens interaction strength (CTIS). The crystal lens micro-deformation degree (CTMD) is used to quantify the subtle deformation of the lens in the eye, and the crystal lens interaction strength (CTIS) is used to quantify the interaction between the lens and the eyeball wall, iris and aqueous humor layer.

[0010] S2, transmitting the collected microscopic data to a central processing unit, wherein the central processing unit includes a high-frequency data processing module capable of efficiently processing the data from the microsensor;

[0011] S3, based on multi-physics field coupling algorithm, combined with physical model, real-time calculation of the three-dimensional deformation and focal length change of the lens;

[0012] S4. Generate a highly dynamic simulation model through the central processing unit to simulate the performance of MPL after implantation and optimize surgical planning and design.

[0013] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0014] (1) In the present invention, a nanoscale microsensor array is used to accurately capture the microscopic motion and mechanical change data in the eye in real time, thereby greatly improving the simulation accuracy of the interaction between the lens and other structures in the eye. The collection of such microscopic data provides a more realistic and detailed input for the dynamic simulation model, making the simulation results closer to the actual situation;

[0015] (2) In the present invention, by coupling multiple physical models such as eye mechanics, optics, and fluid dynamics, the limitation of a single physical model in traditional simulation technology is broken through, and a comprehensive simulation of the complex interaction of the intraocular environment is achieved. The multi-physics coupling algorithm can better reflect the dynamic behavior of the lens and other intraocular structures under different conditions, thereby providing more accurate optimization support for MPL design;

[0016] (3) In the present invention, through the real-time dynamic feedback optimization function, the simulation model can be continuously adjusted according to the microsensor data and the multi-physics field coupling results to provide accurate dynamic prediction. This real-time feedback mechanism significantly improves the adaptability of MPL after implantation and the accuracy of personalized design, improves the performance and effect after implantation, and reduces uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of a flow chart of an algorithm for constructing a dynamic simulation model of an ophthalmic implant lens MPL provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] Reference Manual Attached Figure 1 , shows a flow chart of an algorithm for constructing a dynamic simulation model of an ophthalmic implant lens MPL provided by an embodiment of the present invention.

[0025] The embodiment of the present invention provides a dynamic simulation model construction algorithm for an ophthalmic implant lens MPL, and the processing flow may include the following steps:

[0026] S1. Using a nano-scale micro-sensor array to collect real-time microscopic dynamic data of the lens in the eye, including the crystal lens micro-deformation degree (CTMD) and the crystal lens interaction strength (CTIS). The crystal lens micro-deformation degree (CTMD) is used to quantify the subtle deformation of the lens in the eye, and the crystal lens interaction strength (CTIS) is used to quantify the interaction between the lens and the eyeball wall, iris and aqueous humor layer.

[0027] S2, transmitting the collected microscopic data to a central processing unit, wherein the central processing unit includes a high-frequency data processing module capable of efficiently processing the data from the microsensor;

[0028] S3, based on multi-physics field coupling algorithm, combined with physical model, real-time calculation of the three-dimensional deformation and focal length change of the lens;

[0029] S4. Generate a highly dynamic simulation model through the central processing unit to simulate the performance of MPL after implantation and optimize surgical planning and design.

[0030] It should be noted that the lens micro deformation degree (CTMD) is calculated by the following formula:

[0031]

[0032] Among them, u i is the displacement of the ith sampling point, which is used to indicate the magnitude of lens deformation, and n is the total number of sampling points, which is used to calculate the average deformation degree.

[0033] It should be noted that the interaction force between the lens and the intraocular structure (CTIS) is calculated by the following formula:

[0034]

[0035] Among them, σ j is the stress at the jth contact point, used to represent the local interaction force, n j is the unit normal vector of the contact point, which is used to describe the direction of interaction, A j is the contact area of ​​the contact point, which is used to indicate the size of the interaction area, and m is the total number of contact points.

[0036] It should be noted that the central processing unit uses the following formula to calculate the three-dimensional deformation of the lens (LD, Lens Deformation):

[0037]

[0038] Among them, ε is the strain tensor of the lens, which is used to describe the local deformation of the lens, V is the volume of the lens, and t is the time, which is used to calculate the dynamic changes.

[0039] It should be noted that the multi-physics field coupling algorithm includes models of at least three physical fields, including an eye mechanics model, an optical model, and a fluid dynamics model. The multi-physics field coupling algorithm provides higher simulation accuracy than traditional simplified physical models through coupling calculations of multi-dimensional physical fields.

[0040] It should be noted that the central processing unit uses the following formula to calculate the interaction force F between the lens and other intraocular structures, where the other structures refer to structures other than the eyeball wall, iris and aqueous humor layer:

[0041] F = ∫ S (σ·n)dS

[0042] Among them, σ is the stress tensor, which is used to represent the local interaction force between the lens and the other structure, n is the unit normal vector, which is used to represent the normal direction of the contact surface, and S is the contact surface, which refers to the contact area between the lens and the other structure.

[0043] It should be noted that the multi-physics field coupling algorithm further includes:

[0044] The multi-physics field coupling algorithm is implemented using the finite element algorithm:

[0045]

[0046] The above finite element method uses the stiffness matrix K, displacement vector Δu and external force vector The coupling calculates the deformation of the lens and the interaction force.

[0047] In a possible implementation, the lens micro deformation degree (CTMD) includes not only the global deformation of the lens, but also takes into account the deformation differences in local areas. The micro deformation degree calculation adopts a regional weighted average method to improve the accuracy and adaptability of the simulation model.

[0048] In the specific implementation, the lens is first divided into multiple small areas, and the deformation of each area is independently calculated according to its geometric characteristics and physical properties. Then, the deformation results of each area are weighted by using the regional weighted average method. The weighting coefficient is determined based on the degree of deformation of each area and its contribution to the overall deformation. In this way, the calculation not only improves the accuracy of local deformation, but also better reflects the overall deformation trend, thereby significantly improving the accuracy and adaptability of the simulation model.

[0049] In one possible embodiment, the calculation of the interaction force between the lens and intraocular structures (CTIS) further takes into account the fluid interaction effects between the lens and the iris, eyeball wall and aqueous humor layer. The interaction effects are quantified by additional correction terms in the fluid dynamics model, thereby enhancing the model's ability to adapt to the complex intraocular environment.

[0050] Specifically, additional corrections in the fluid dynamics model are taken into account when calculating the interaction forces between the lens and the iris, the eyeball wall, and the aqueous humor layer. In order to quantify these fluid interaction effects, the flow characteristics of the intraocular fluid are first modeled, including factors such as flow velocity, pressure, and viscosity. Then, corrections for the fluid mechanics effects are added to the calculation formula for the interaction forces. These corrections can be dynamically adjusted according to the state of the fluid and the trajectory of the lens, thereby more accurately simulating the interaction between the lens and the various intraocular structures, and enhancing the adaptability of the simulation model in complex intraocular environments.

[0051] In one possible embodiment, the multi-physics field coupling algorithm further optimizes the calculation accuracy of the interaction force by introducing the friction coefficient between the lens surface and other intraocular structures. The calculation of the friction coefficient is based on real-time feedback data to better simulate the actual contact force between the lens and intraocular tissue during surgery.

[0052] In actual calculations, the friction coefficient between the contact surface of the lens and other intraocular structures (such as the eyeball wall, iris, etc.) is first determined. The calculation of the friction coefficient not only depends on the material and shape of the lens surface, but also needs to be adjusted according to real-time feedback data. These feedback data include the real-time contact force and motion state between the lens and the intraocular structure during surgery. When the contact force changes, the friction coefficient will be dynamically adjusted according to a predetermined algorithm to ensure a more realistic simulation of the actual contact between the lens and the intraocular tissue during surgery. Through this optimization strategy, the accuracy of the simulation model can be significantly improved, ensuring that the actual physical contact during surgery can be better reflected in practical applications.

[0053] In the embodiment provided by the present invention, the microscopic dynamic data of the lens is first collected in real time. Specifically, a nanometer-scale microsensor array is installed in the eye, and these sensors are used to obtain the micro deformation of the lens and the interaction force with the eyeball wall, iris and aqueous humor layer. After collecting the data, these sensors transmit it to the central processing unit. The central processing unit is equipped with a high-frequency data processing module, which can efficiently process the raw data from the microsensors.

[0054] After the data is transmitted to the central processing unit, a multi-physics field coupling algorithm is used to combine multiple physical models such as eye mechanics, optics, and fluid dynamics for simulation calculations. Through this algorithm, the three-dimensional deformation and focal length change of the lens can be calculated in real time. The calculations in the algorithm involve multiple important physical quantities, such as the micro-deformation degree of the lens (CTMD) and the interaction force between the lens and the intraocular structure (CTIS). Among them, CTMD not only considers the global deformation of the lens, but also takes into account the deformation differences in local areas, and uses a weighted average method for calculation by region to improve the simulation accuracy. When calculating the interaction force between the lens and other intraocular structures, CTIS further considers the fluid interaction effect, especially the fluid interaction between the lens and the iris, the wall of the eyeball, and the aqueous humor layer. This correction is quantified by introducing an additional correction term of the fluid dynamics model.

[0055] When calculating the interaction force between the lens and the intraocular structure, the friction coefficient between the lens surface and other intraocular structures is introduced to improve accuracy. The calculation of the friction coefficient is not static, but is dynamically adjusted based on real-time feedback data to better simulate the contact force between the lens and intraocular tissue. During this process, the algorithm needs to update the feedback data in real time and perform corresponding calculations to ensure the accuracy of the simulation results.

[0056] One of the main problems that may be encountered during actual operation is the complexity of high-dimensional physical models during calculations, especially when dealing with the coupling of multiple physical fields, which requires huge amounts of calculations and high computing resources. To this end, efficient data processing strategies, such as parallel computing and multi-threaded processing, can be used to ensure rapid data processing and real-time simulation. In addition, due to the variability in the physical properties of intraocular tissues, the model may need to be adjusted accordingly based on individual differences. At this time, more individual data can be collected for model calibration, thereby improving the universality and accuracy of the simulation model.

[0057] Through the above implementation steps, the dynamic simulation model finally generated can not only simulate the performance of MPL after implantation, but also provide accurate dynamic prediction and optimize surgical planning. Based on the optimization of real-time feedback data, personalized customization can be performed during the surgical design stage, thereby improving the success rate of the operation and the adaptation effect after implantation.

[0058] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0059] (1) In the present invention, a nanoscale microsensor array is used to accurately capture the microscopic motion and mechanical change data in the eye in real time, thereby greatly improving the simulation accuracy of the interaction between the lens and other structures in the eye. The collection of such microscopic data provides a more realistic and detailed input for the dynamic simulation model, making the simulation results closer to the actual situation;

[0060] (2) In the present invention, by coupling multiple physical models such as eye mechanics, optics, and fluid dynamics, the limitation of a single physical model in traditional simulation technology is broken through, and a comprehensive simulation of the complex interaction of the intraocular environment is achieved. The multi-physics coupling algorithm can better reflect the dynamic behavior of the lens and other intraocular structures under different conditions, thereby providing more accurate optimization support for MPL design;

[0061] (3) In the present invention, through the real-time dynamic feedback optimization function, the simulation model can be continuously adjusted according to the microsensor data and the multi-physics field coupling results to provide accurate dynamic prediction. This real-time feedback mechanism significantly improves the adaptability of MPL after implantation and the accuracy of personalized design, improves the performance and effect after implantation, and reduces uncertainty.

[0062] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0063] There are a few points to note:

[0064] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0065] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0066] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0067] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An algorithm for constructing a dynamic simulation model of an ophthalmic implant lens MPL, characterized in that: include: S1. Using a nano-scale micro-sensor array to collect real-time microscopic dynamic data of the lens in the eye, including the crystal lens micro-deformation degree (CTMD) and the crystal lens interaction strength (CTIS). The crystal lens micro-deformation degree (CTMD) is used to quantify the subtle deformation of the lens in the eye, and the crystal lens interaction strength (CTIS) is used to quantify the interaction between the lens and the eyeball wall, iris and aqueous humor layer. S2, transmitting the collected microscopic data to a central processing unit, wherein the central processing unit includes a high-frequency data processing module capable of efficiently processing the data from the microsensor; S3, based on multi-physics field coupling algorithm, combined with physical model, real-time calculation of the three-dimensional deformation and focal length change of the lens; S4. Generate a highly dynamic simulation model through the central processing unit to simulate the performance of MPL after implantation and optimize surgical planning and design.

2. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 1, characterized in that: The CTMD specifically includes: The lens micro deformation degree (CTMD) is calculated by the following formula: Among them, u i is the displacement of the ith sampling point, which is used to indicate the magnitude of lens deformation, and n is the total number of sampling points, which is used to calculate the average deformation degree.

3. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 1, characterized in that: The CTIS specifically includes: The lens-intraocular structure interaction force (CTIS) is calculated by the following formula: Among them, σ j is the stress at the jth contact point, used to represent the local interaction force, n j is the unit normal vector of the contact point, which is used to describe the direction of interaction, A j is the contact area of ​​the contact point, which is used to indicate the size of the interaction area, and m is the total number of contact points.

4. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 1, characterized in that: include: The central processing unit calculates the three-dimensional deformation of the lens (LD) using the following formula: Among them, ε is the strain tensor of the lens, which is used to describe the local deformation of the lens, V is the volume of the lens, and t is the time, which is used to calculate the dynamic changes.

5. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 1, characterized in that: The S3 further comprises: The multi-physics field coupling algorithm includes models of at least three physical fields, including an eye mechanics model, an optical model, and a fluid dynamics model. The multi-physics field coupling algorithm provides higher simulation accuracy than traditional simplified physical models through coupling calculations of multi-dimensional physical fields.

6. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 1, characterized in that: include: The central processing unit uses the following formula to calculate the interaction force F between the lens and other structures in the eye, where the other structures refer to structures other than the eyeball wall, iris and aqueous humor layer: F=∫ S (s·n)ds Among them, σ is the stress tensor, which is used to represent the local interaction force between the lens and the other structure, n is the unit normal vector, which is used to represent the normal direction of the contact surface, and S is the contact surface, which refers to the contact area between the lens and the other structure.

7. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 5, characterized in that: The multi-physics coupling algorithm further includes: The multi-physics field coupling algorithm is implemented using the finite element algorithm: The above finite element method uses the stiffness matrix K, displacement vector Δu and external force vector The coupling calculates the deformation of the lens and the interaction force.

8. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 2, characterized in that: include: The lens micro deformation degree (CTMD) not only includes the global deformation of the lens, but also takes into account the deformation differences in local areas. The micro deformation degree calculation adopts a regional weighted average method to improve the accuracy and adaptability of the simulation model.

9. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 3, characterized in that: include: The calculation of the interaction force between the lens and intraocular structures (CTIS) further takes into account the fluid interaction effects between the lens and the iris, eyeball wall and aqueous humor layer. The interaction effects are quantified by additional correction terms in the fluid dynamics model, thereby enhancing the model's adaptability to the complex intraocular environment.

10. The dynamic simulation model construction algorithm for ophthalmic implant lens MPL according to claim 7, characterized in that: include: The multi-physics coupling algorithm further optimizes the calculation accuracy of the interaction force by introducing the friction coefficient between the lens surface and other intraocular structures. The calculation of the friction coefficient is based on real-time feedback data to better simulate the actual contact force between the lens and intraocular tissue during surgery.

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