System and method for finite element modeling and machine learning of eyes

Abstract

Hardware and software system solutions provide real-time, interactive predictive simulation of an eye (e.g., a human eye or animal eye). A predictive simulation of an individual eye may be created using finite element models (FEM) of ocular structures involved in optical biomechanics, including ocular adjustments. Unlike previous ocular models, the present FEM described herein is a three-dimensional FEM of an adjustment mechanism, including but not limited to the anterior of the human eye that simulates ciliary muscle fiber contraction actions, to predict the dynamic deformation of the crystalline lens required to adjust visual power variations, referred to herein as' Central Visual Power '(COP). The present FEM further describes aqueous / vitreous outflow and fluid flow mechanisms referred to herein as "hydrodynamics". The virtual FEM further describes posterior ocular biomechanics that simulates the actions of the Bruch membrane, retina, vitreous and sieve plates during pre-stretching, non-regulatory and regulatory actions of the eye.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application No. 63 / 334,107, filed on April 23, 2022, entitled “SYSTEMS AND METHODS FOR OCULAR FINITE ELEMENT MODELING AND MACHINE LEARNING,” the contents of which are hereby incorporated herein in their entirety by reference. Technical Field

[0003] The subject matter described herein relates to ocular finite element modeling (FEM) and machine learning, and more specifically, to a virtual ecosystem configured to provide real-time, dynamic, interactive virtual ocular image reconstructions in virtual reality of a living eye in multiple condition states using several related predictive simulation algorithms. The interactive virtual ocular images are capable of representing individual specific mechanical, material, and physical properties, and displaying and / or mimicking the behavior of a living eye (regardless of species) under various conditions and imposed demands - i.e., surgical interventions, healing interventions, representative progression of disease processes. Furthermore, the present invention is capable of delivering numerical simulations and results of these various interventions, including reversal of interventions and / or disease processes. Furthermore, the present invention is configured to apply numerical methods and / or computations on any data source(s) to predict multiple expected and / or unexpected conditions and events in a living eye. Background Art

[0004] The eye is a biomechanical structure that contains complex muscle, drainage, and fluid mechanisms responsible for visual function and ocular biological transport. The accommodation system is the major motor system in the eye organ, facilitating many physiological and visual functions of the eye. The physiological role of the accommodation system is to move aqueous humor, vitreous, blood, nutrients, oxygen, carbon dioxide, large and small molecule drugs, and other cells around the eye organ.

[0005] Overall, the loss of accommodation in presbyopia has many lenticular, as well as extralenticular and physiological factors that are affected by aging. Accumulation of Advanced Glycation End Products (AGE) = AGE creates harmful chemical bonds that lead to increased biomechanical stiffness of the body's connective tissues, including the eye.

[0006] Increased ocular stiffness with aging creates stress and strain on these ocular structures and can affect accommodation, which can affect the eye in the form of biomechanical impairments in physiological processes including visual accommodation, aqueous fluid dynamics, vitreous fluid dynamics, and ocular pulsatile blood flow, to name a few. Current surgeries manipulate the optical system only through artificial means, such as through refractive laser surgery, adaptive optics, or corneal or ocular implants, which exchange power within one optical system of the eye, while ignoring the importance of the other optical systems and maintaining the physiological function of the accommodation mechanism.

[0007] Furthermore, current implant devices in the sclera achieve mechanical effects when adjusted. They do not take into account the effects of holes (e.g., micropores) or create a matrix array of holes in 3D tissue. Therefore, current surgeries and devices cannot restore normal ocular physiological function.

[0008] Therefore, a need exists for improved systems and methods for restoring normal ocular physiological function. Summary of the invention

[0009] In general, hardware and software system solutions are presented herein that provide real-time, interactive predictive simulations of an eye (e.g., an individual eye or an animal eye). As described in more detail below, predictive simulations of an individual's eye can be created using a finite element model (FEM) of eye structures involved in optical biomechanics (including eye accommodation).

[0010] In one aspect, a system for reconstructing, manipulating, and intervening in virtual reality of an individual eye is disclosed, comprising: a 3D modeling component, including at least one data processor; an imaging system, a biothermal system, an interactive user interface; an input program for receiving and manipulating physical, biometric, biomechanical, material, and mechanical information and data; and at least one memory for storing instructions that, when executed by the at least one data processor, result in operations including: generating a first specific simulation in which a Bruch's membrane choroid is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of energy required for lens deformation; generating a second individual specific simulation in which a portion of a ciliary muscle fiber portion of a ciliary muscle ... The invention relates to a method for treating an eye with a plurality of ciliary muscle fibers, wherein the first contribution of the first ciliary muscle fiber portion to the deformation of the lens of the eye is determined; generating a second individual-specific simulation, wherein the second ciliary muscle fiber portion of the individual eye is activated to determine the second contribution of the second ciliary muscle fiber portion to the deformation of the lens of the eye; generating a third individual-specific simulation, wherein the third ciliary muscle fiber portion of the individual eye is activated to determine the third contribution of the second ciliary muscle fiber portion to the deformation of the lens of the eye; and determining one or more parameters of the individual treatment based on at least the first Bruch's membrane choroidal complex (BMCC) device, the first contribution of the first ciliary muscle fiber portion, the second contribution of the second ciliary muscle fiber portion, and the third contribution of the third ciliary muscle fiber portion. All simulations can be performed sequentially, individually and / or in parallel with other parameter manipulations. The system can also generate a fluid-structure interaction of the aqueous humor flow of the eye, wherein three outflow pathways are activated. The system includes one or more parameters of the Bruch's choroid structure, the first ciliary muscle fiber portion and / or the second ciliary muscle fiber portion and / or the third ciliary muscle fiber portion of the individual eye. In one embodiment, one or more micropores are generated in the eye scleral tissue above the Bruch's choroid structure, the first ciliary muscle fiber portion and / or the second ciliary muscle fiber portion and / or the third ciliary muscle fiber portion of the eye of the individual by laser. One or more parameters of the treatment may include: the number of matrices including one or more holes, the location of the matrix, the overall shape of each matrix, the overall size of each matrix, the number of holes in each matrix, the distribution of holes in each matrix and / or the depth of holes in each matrix. In one embodiment, one or more treatment parameters of the individual are determined by applying a machine learning model (e.g., a regression model and algorithm or an age progression model and algorithm). The machine learning model can be trained based on various combinations of treatment parameters and treatment results. The machine learning model is further trained based on individual demographic data related to various combinations of treatment parameters and treatment results. A user interface can be generated to display one or more parameters of treatment for the individual. The system may also include receiving at least one user input related to one or more parameters of treatment for the individual through the user interface; and updating the machine learning model based at least on the user input.

[0011] The operations also include systems and methods for evaluating biomechanical properties of tissue, a simulation generator incorporating various optical, biomechanical, physical properties of a given individual eye into a simulated VR phantom of a live eye, and / or a parameter calculation component calculating values ​​of biomechanical properties at multiple subsets (annular stress maps) of multiple locations in the eye (where the virtual eye is interactive), and / or physical calculations of the eye. The evaluation toolbox component is configured to calculate at least one parameter associated with various possible outcomes of a given therapeutic intervention in the individual eye from the multiple features extracted. The system output is configured to provide the calculated at least one parameter to one of the treatment system and the user. One or more optical, biometric, biomechanical, physical and geometric parameters are incorporated into the computational prediction. VESA's calculator toolbox is capable of reconciling various equations that produce the best solution(s) for the user for the therapeutic or surgical intervention.

[0012] On the other hand, a system for evaluating a patient's eye is disclosed, comprising: a modeling component configured to determine a representation of the entire eye based on a three-dimensional structural image of the entire eye and at least one biomechanical property of the eye; a feature extractor configured to extract multiple features from the model of the entire eye; a user interface configured to accept input from a clinician, defining an objective function as a function of at least one parameter of the eye after a treatment procedure; a condition or disease assessment component configured to calculate at least one parameter associated with the risk of eye disease based on the extracted multiple features and the objective function, the calculated at least one parameter including variables during a treatment procedure, therapeutic intervention and / or surgery, representing a surgical parameter that the clinician can change during the treatment procedure; and a system output configured to provide the calculated at least one parameter to one of a treatment system and a user.

[0013] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1A VESA, 3D-ID AI, MPTool and regression equation databases and their relationships are shown.

[0015] Figure 1B An exemplary process flow diagram is shown.

[0016] Figure 2A The VESA ecosystem is shown with various hardware / software systems located in the operating room, in the cloud, and / or in the database.

[0017] Figure 2B The various technologies and tools that make up the VESA ecosystem backend services are shown.

[0018] Figure 2C Multiple subsystems of the VESA ecosystem are shown, including standalone diagnostic tools, clinical trial tools, lasers, imaging devices, AI engines, etc.

[0019] Figure 2D Two of many disease application modules are shown.

[0020] Figure 3 VESA's contribution at various stages of critical medical procedures is shown.

[0021] Figure 4 The structure, various measurable variables, and boundary conditions that can be used to generate a finite element model of the eye are shown.

[0022] Figure 5 An exemplary region definition that can be used to generate a finite element model is shown. The region definition can be applied to any part of the eye, including the posterior region, the anterior region, or any other region.

[0023] Figure 6 Examples of each region description or anatomical location are shown for one or more regions of the eye.

[0024] Figure 7 An example 3D model generated by VESA is shown.

[0025] Figure 8 Shown is a FEM created using more fibers and elements.

[0026] Fig. 9 An example mesh network of a 3D model of an eye is shown.

[0027] Figure 10-12 An example anatomical model is shown that may be used by VESA to generate the trabecular meshwork or any other aspect of the eye, including, for example, the ciliary zonules, the lens, or any other ocular anatomical structure.

[0028] Fig.13A The first step is shown, where the raw data is read and converted into a pseudo two-dimensional grid array.

[0029] Fig. 13B The before and after effects of the smoothing process are shown.

[0030] Fig.14 Micropore analysis tools including OCT (Optical Coherence Tomography) are shown.

[0031] Fig.15 A screenshot showing the pre-planning mode.

[0032] Fig.16 A microwell tool including registration features is shown.

[0033] Fig.17 and 18 Example steps and workflow for the prescan access process are shown.

[0034] Fig.19 A sample screenshot is shown.

[0035] Fig. 20 A block diagram showing an example of a computing system is depicted. DETAILED DESCRIPTION

[0036] The systems and methods described herein include many aspects that can be usefully used in combination or alone, and they can be advantageously used to treat a range of disease states, including diseases of the eye and diseases of other parts of the body. At least some particularly detailed examples focus on treating eye conditions, such as treating age-related glaucoma, cataract formation, and presbyopia, as well as other age-related eye diseases, such as age-related macular degeneration, etc.

[0037] In general, hardware and software system solutions are proposed herein, which provide real-time, interactive predictive simulations of eyes (e.g., human eyes or animal eyes). As described in more detail below, predictive simulations of individual eyes can be created using finite element models (FEMs) of eye structures involved in optical biomechanics (including eye accommodation). Different from previous eye models, this FEM (finite element model) described herein is a three-dimensional (3D) FEM of an accommodation mechanism, including but not limited to anterior human eyes, which simulates ciliary muscle fiber contraction to predict accommodative visual power changes-described herein as "central visual power" (COP)-required dynamic deformation of the lens. The FEM herein further describes the fluid flow mechanism of aqueous humor / vitreous outflow and described herein as "fluid dynamics". The virtual FEM further describes the posterior ocular biomechanics, which simulates the actions of Bruch's membrane, retina, vitreous body and lamina cribrosa during the pre-stretching, non-accommodative and accommodative actions of the eye.

[0038] More specifically, the disclosed systems and methods include a Virtual Eye Simulation Analyzer (VESA) that utilizes virtual eye modeling to create an accurate and realistic virtual interactive model of an individual's eye, and thereafter provides a 3D FEM based on the model to develop treatment plans, predict the future state of the eye based on the individual's age, material properties, optical properties, and other physical parameters, enabling it to predict the effects of various operations (e.g., surgery, healing interventions, and disease states, etc.). Thus, the virtual model of the individual's eye preserves biometric, mechanical, optical, and physical information in its construction as a unique 3D-ID for the individual's eye. Furthermore, the resulting virtual 3D FEM model, as well as the VESA, allows a user (e.g., user, user) to examine the biomechanical properties and various physical, optical, and physiological states (e.g., accommodation, non-accommodation, glaucoma, etc.) of the virtual 3D model, which in turn allows, for example, the user to more accurately simulate different treatments and determine the effects of the treatment relative to the individual's real eye. In other words, VESA provides a computer virtual method to perform a variety of treatments and operations on an individual's virtual eye model to determine output information (e.g., scleral notching, ciliary muscle fiber movement, and lens movement and shape) in real time that can inform the user about the current and future treatment effects, outcomes, and success probabilities for the individual's eye.

[0039] Figure 1A An example diagram showing VESA, 3D-ID AI, MP Tool, and Regression Equation Database and their interrelationships. Although the diagram shows VESA as separate and distinct from the 3D-ID AI, MP Tool, and Regression Equation Database components, these components are inherently part of the VESA ecosystem. Figure 1A As shown, VESA includes various elements, as explained in more detail below.

[0040] Regression equation database: A cloud database configured to store pre-run and manual individual specific finite element analysis (FEA) models, whether structural, fluidic, or from micro-aperture tool models (i.e., acquired from pre-operative and post-operative clinical trials). For example, the input(s) may include individual specific treatment or healing data results and specific eye geometry variables, and the data and regression equation outputs populate the database. The database allows VESA to reference and extract from a pre-built cloud library database of FEA runs to assign to each volume - which can then be used to suggest possible aperture matrix laser firing solutions(s) to achieve the desired outcome, rather than running computationally intensive FEA simulations in real time during surgery.

[0041] Laser Scleral Microsurgery (LSM)It is a treatment method applied to the human eye that helps to unravel the cross-linking of the underlying microfibrils in the human scleral tissue. This article describes a numerical analysis package based on ocular stiffness and Newtonian mechanics, called the Micropore Tool (MPTool). Using OCT images as the main input data, MPTool creates a data structure representing the internal and external ocular geometry, all individual pore geometry parameters, and the pore matrix geometry of each treatment area on both eyes for the characteristics of the eyes of a specific individual. The MP tool also allows medical technicians to experiment with pore depth, diameter, and spacing to achieve reduced biomechanical stiffness or increased compliance in virtual reality, so that VESA can perform a large number of simulations to recommend the most feasible pattern, depth, and location of the pores within the array. The MP tool receives mathematical and physical inputs from the Ligament Efficiency Tool (LET), which is able to calculate and predict the strength, flexibility, and tissue differences between each pore (i.e., ligament). A large number of simulations are performed to allow the LEC to propose improved or optimal pore configurations. The MP tool is capable of creating VESA's database of regression equations so that VESA's FEM(s) can be derived not only from literature images, but also from individual post-operative analyses, which can be used to train VESA's neural network. Hyperelastic image registration is image restoration within a 3D FEM, including registration of nonlinear deformations and shape changes (e.g., during clinical trials, etc.). The microporous tool includes various components. VESA is capable of accommodating multiple custom toolboxes for various surgical and / or healing VR interactions and operations. Some preferred embodiments currently under development are as follows:

[0042] Microwell Tools Artificial Intelligence and Machine Learning Module (AI_ML): AI_ML is a set of routines that preprocess OCT images and prepare the data for further analysis. The AI_ML routines perform the following tasks:

[0043] ○ Read proprietary binary OCT format,

[0044] ○ Create multiple data structures to pre-process the data and represent all quadrants, eyes, and patients in the treatment process. These data structures include but are not limited to:

[0045] Gridded microwell tool hole matrix grid based on OCT analysis.

[0046] Image color fragment

[0047] Tissue boundary segmentation,

[0048] Thickness from top to bottom,

[0049] ■Individual pore geometry, including but not limited to: three-dimensional coordinates of pore centroid, pore volume, pore depth, pore diameter, pore volume fraction, pore density.

[0050] ■Feature edge detection,

[0051] ■RGB real-time biofeedback color recognition depth control function3;

[0052] ○ Run multiple color smoothing and interpolation routines.

[0053] ○ Run feature recognition to distinguish the thickness of the sclera from top to bottom and the pore space;

[0054] o Running an automated tissue segmentation routine to differentiate between layers in the sclera-conjunctiva-choroidal geometric complex;

[0055] o Run an edge detection post-processor on the outer borders of the top and bottom layers of the sclera.

[0056] ○ Save the preprocessed results in a second set of data structures that can be read by various downstream tools (PVF, NSI, COP, IOP, etc.);

[0057] ○ Make data ready for use by Micropore Tool, PVF Calculator, NSI Calculator,

[0058] Micro-hole tool physics package

[0059] 1. Pore Volume Fraction Indicator Tool: A graphical user interface (GUI) evaluation and validation tool that measures the pore volume fraction of a given 1D, 2D, or 3D area. The pore volume fraction varies with scleral thickness based on area, pore shape, pore density, and pore size. The Pore Volume Fraction Indicator Tool provides information about the resected tissue, but does not include location-related information.

[0060] 2. IOP Predictor: The IOP calculation predictor tool is able to predict and measure changes in intraocular pressure within the eye as it relates to the predominant condition of the individual eye and / or as a present or future effect resulting from a selected intervention through release and alteration of fluid statics and fluid dynamics.

[0061] 3. Elastic Modulus Calculator: A processor of the biomechanical and physical data inputs of BioMeter, which takes into account the material properties, biomechanics, geometry and biometry of the ocular tissue. The application has 3 different uses: Pre-treatment planning, Pre- and Post-treatment evaluation, Verification and Post-treatment verification.

[0062] 4. Negative Stiffness Index (NSI) / Quotient Calculator (NSQ) / Predictor (NSP): Real-time Material Property & Biomechanical Property Calculator that indicates the Negative Stiffness Quotient of changes in biomechanical properties / physical outcomes of ocular tissues during and after treatment. A preferred embodiment is the properties and biomechanical characteristics of scleral tissue before & after LSM treatment. The NSI is dynamic and measured step-by-step in real-time as treatment progresses. In this example, there are four quadrants (Q1-Q4) of the eye treated in the LSM procedure until the required NSQ for the specified tissue is met. Inputs come from the BioMeter, Ligament Efficiency Calculator (LEC), and Elastic Modulus Calculator (EMC).

[0063] 5. Ligament Efficiency Tool (LET): A physical package following the Micropore Tool (MPTool) that determines things such as, but not limited to, pore location and good surgical judgment.

[0064] MPTool – eye stiffness post-processing tool

[0065] ○ Hyperelastic Image Restoration: HIR: is a highly robust numerical simulation of hyperelasticity, as a regularizer, and introduces a new, stable numerical implementation of highly nonlinear deformations,

[0066] ○OCT tissue / color segmentation: Depth perception tool AI automatic segmentation software

[0067] ■Displayed by OCT scan;

[0068] ■It creates an augmented reality view of the eye’s tissue-layer biometrics;

[0069] ■ Overlays display key tissues such as, but not limited to, cornea, sclera, conjunctiva, zonules, ciliary muscle fibers, and bursa tendinous tissues while correlating RGB database colors with real-time interactive tissue identification.

[0070] Micropore Tool Simulation Package: This simulation package consists of a set of tools that allow users to use the same pore matrix analysis tools on simulated (not real) pores, pore matrices, and the human eye. The toolset includes:

[0071] ○ Multiple sets of FEA runs that can adequately characterize the individual patient’s eye and possible LSM findings

[0072] ○ Database to store FEA runs and run regression algorithms on those data

[0073] ○ Real-time finite element operation concurrent with LSM processing

[0074] ○Other numerical methods come from empirical derivation, such as moment of inertia.

[0075] FEM Geometry and Mesh:Commercial FEM biomechanical simulation software was used to generate the geometry and mesh of a complex human eye accommodation model. The input was geometric data obtained from, for example, published literature (e.g., literature images, hyperelastic image restoration (HIR)) and / or regression equation databases, ultrasound biomicroscopy (UBM), optical coherence tomography (OCT), scanning electron microscopy (SEM), and histology. The analysis of this FEM geometry and mesh involved geometry built in SpaceClaim and meshed in ANSYS. Therefore, boundary conditions, loads, elastic supports, neo-Hookean material properties, stresses, strains, forces, moments, displacements, etc. were applied in ANSYS.

[0076] area: As discussed in more detail below, the FEM geometry and mesh of the eye are constructed using regions that are constructed with different aperture matrix emission regions that are selected based on the geometric locations of specific anatomical landmarks found in the literature and / or regression equation databases.

[0077] VESA structure FEM : An eye accommodation model built in ANSYS. Inputs include but are not limited to FEM geometry and meshes, while outputs include but are not limited to simulations of multi-age eye accommodation models (one or more) that improve accommodation based on changes in diopter values ​​of individual vision captured from central optical power (COP) calculations.

[0078] ● Laser scleral microporation: See description above.

[0079] ● Center Optical Power Calculator: The calculator is related to the COP of the lens. Since deformation of the lens changes its optical power expressed in diopters (D), the calculator is intended to calculate the free body forces of the lens. The input may include data from, for example, LSM pre- and post-operative accommodation simulations. As an alternative to this calculation, an extended formula is provided by using the measured values ​​calculated by ray tracing as a direct input to the COP. Therefore, the present invention is novel.

[0080] ● An example embodiment is a Lens formula derived by ray tracing; The lens shape is determined by finite element analysis. The shape is then exported to a software process or other process that tracks the ray paths in both the accormodated and non-accormodated states. The COP is then determined using the following formula:

[0081]

[0082] VESA Fluid Simulation: Computational Fluid Dynamics (CFD) & Fluid / Solid Interaction (FSI):It is similar to the VESA structural FEM, but here, the eye accommodation conceptual model built in ANSYS also captures dynamic fluids, inflows and outflows. Therefore, the input includes but is not limited to the same geometry / mesh data from the FEM geometry and mesh, and additional geometric fluid paths are added to be assigned for the specified fluid motion based on, for example, clinical studies and literature. Similar to the VESA structural FEM, the output here includes but is not limited to simulations (one or more) of multi-age eye accommodation models, which are improved accommodations based on changes in individual vision diopter values ​​captured from the central optical power (COP) calculation. In addition, the incorporation of fluids in the FSI model allows VESA's FEM to more accurately reflect the real way how individual eyes work. This is because VESA's structural FEM includes assumptions made for any lack of dynamic fluids in the FEM. For example, an assumption can be made that the fluid exists and is incompressible. That is, the internal volume remains unchanged during accommodation.

[0083] 3D-ID : The input(s) may include individual specified VESA structural / fluid models (FSI) that are analyzed by accommodation simulations before and after the LSM procedure. The output is therefore an accommodation improvement based on changes in individual vision diopter values ​​captured from central optical power calculations. VESA is therefore able to be used as a virtual platform to import individual specific eye related data, thereby placing any individual's eye into their own VESA structural and / or fluid model. The 3D-ID also includes individual specific geometry, stored in a CAD file or some similar database. The 3D-ID includes all individual specific metadata collected during treatment, such as name, age, gender and race.

[0084] 3D-ID AI:3D-ID AI is a machine learning based analysis engine that is communicatively coupled to VESA, MP tools, and a regression equation database. 3D-ID is further derived from an “age progression” formula that accounts for the normative progressive changes in material properties and biometrics as they change with age. 3D-ID AI can apply one or more off-the-shelf machine learning or customized regression models deep learning or regression that are trained to determine one or more body-specific treatment parameters based on real-time FEA simulations performed by VESA and / or pre-existing individual-specific FEA models from a regression equation database. Machine learning models associated with 3D-ID AI can include regression models (and / or other types of machine learning models) that have been trained based on simulation and / or clinical data of various treatment parameters, conditions, and treatment outcomes (e.g., accommodation improvements based on changes in individual visual acuity diopter values ​​captured from central optical power calculations). Through such training, the machine learning models of 3D-ID AI can learn the relationships between different combinations of eye conditions, treatment parameters, and corresponding treatment outcomes, as well as progressive trends in response to treatment, such as age-related changes in material properties over time. In addition, in some cases, the 3D-ID AI machine learning model can be trained to take into account other individual data, such as demographic information. Therefore, the 3D-ID AI is able to determine the precise treatment parameters suitable for each individual through computer simulation before any in vivo procedure is performed. For example, in some example embodiments, the output of the 3D-ID AI can be a dose for a specific individual, which includes one or more hole matrix configurations, such as the number of matrices, the position of the matrix, the overall shape of each matrix, the overall size of each matrix, the number of holes in each matrix, the hole distribution in each matrix, the hole depth in each matrix, and the like. For example, another output of the 3D-ID can be a prediction of the treatment dose over a period of time and the number of treatments required over a period of time. For example, another output of the 3D-ID can be a recommendation of an intraocular lens that may be more suitable or more suitable for the specific results of the individual eye, taking into account the ability of the individual eye to achieve COP at each focus. For example, another output of the 3D-ID can be a recommendation of a minimally invasive glaucoma surgery (MIGS) implant that may be more suitable or more suitable for the specific results of the individual eye, taking into account the conditions of the individual disease state. Multiple virtual treatment options can be presented to the VESA through the 3D-ID insertion to solve the eye problems of a specific individual.

[0085] It should be recognized that the 3D-ID AI machine learning model may receive additional and in some cases ongoing training. For example, for a set of treatment parameters output by the 3D-ID AI, the 3D-ID AI may receive feedback from the user performing the treatment, including real-time adjustments to one or more treatment parameters. The 3D-ID AI machine learning model can be updated based on these additional data points, allowing the 3D-ID AI machine learning model to learn additional and / or more nuanced relationships between treatment parameters, treatment outcomes, and other individual-specific data (e.g., demographics).

[0086] VESA may include other elements such as, but not limited to:

[0087] Registration Tools: Micropore Registration Tool (MIRT): Provides information about the dynamic position and pattern of pores or dots using the Matrix Registration Tool (MART):

[0088] Micropore Biometer: Personalized biomechanical / physical inputs for the Elasticity Modulator Calculator (EMC) and 3D-ID VESA components: age-related MPa, depth, PVF, bulk density (aka areal stiffness), density, pore size, and pore shape (squareness).

[0089] ● Treatment Progress Meter: A progressive completion meter for treatment progress that is synchronized with the countdown clock for each quadrant. Complete by quadrant and add a countdown.

[0090] ●Treatment countdown clock: GUI treatment countdown clock.

[0091] ●Hyperelastic Image Registration (HIR) for UBM analysis: Converting inverse imaging of dynamic biological motion into 3DFEM.

[0092] ●Artificial Intelligence / Neural Networks: Statistical and / or heuristic methods to implement the decision engine for the VESA system.

[0093] ● Hoop stress coordinate diagram: Real-time physical calculation / deep calculation global positioning system (GPS). Hoop stress is the stress generated along the circular edge of the pipe when pressure is applied. Hoop stress is perpendicular to the axial direction. Hoop stress is a tensile stress that is generated to resist the inflation effect caused by the applied pressure. HSCM demonstrates a diagram of various rings existing on a sphere that conforms to the ring structure theory. It is used to construct the hoop strain constitutive formula. HSCM is further used to develop countless processing modes based on nodes and node arrays that can be aligned for ideal processing using LSM, for example, to create holes with specific purposes.

[0094] ●4D Hyperspherical Coordinate Scanning and Holographic Mapping (3D ID: is the Global Positioning System (GPS) post-processing tool of VESA FEM): Virtual OCT post-processing scanner. 4D HCSPM can be used as a training database for VESA.

[0095] ●Resultant Force Calculator: Calculates free body forces and moments on the lens capsule of the eye. Used to determine the reaction speed and direction of the lens during accommodation.

[0096] ●Spherical stiffness index: Stiffness calculated based on a 3D shell attached to a spherical coordinate system. Emphasis on hoop stress and strain.

[0097] ●Ocular stiffness coefficient: mathematical formula that predicts a constant that is a macroscopic parameter characterizing the relationship between pressure and volume changes in the human eye.

[0098] ●Dose Regulator: Dose Regulator is able to control, predict, manage, calculate and report the dose of surgical, chemical, biochemical, therapeutic, physical, fluid, geometric, biological and / or biomechanical interventions in virtual reality.

[0099] Additionally, more information regarding certain elements of VESA may be found in U.S. Patent No. 11,071,450 and U.S. Patent Publication No. 2020 / 0185106, which are incorporated herein by reference in their entireties.

[0100] VESA is configured to enable applications that are not possible with conventional eye modeling systems and methods. For example, such applications may include confirmation and visualization of cause-effect relationships of accommodation function that are intuitively derived from relevant observations of eye geometry, material properties, physical forces, and dynamic shape changes. VESA can achieve this confirmation by FEM prediction of the displacement of ciliary muscle fiber motions and forces (dynamic 3D shape changes) during all stages of accommodation from far to near and from near to far focus or from pre-stretched to non-accommodated to accommodated and from maximum accommodation to non-accommodated to pre-stretched. These stages, motions, and forces are the result of a combination of elastic stored energy in the BMCC, specified ciliary muscle fiber structure\specified band geometry and structure and their corresponding prescribed activation. The deformation of the lens from pre-stretched, accommodated to non-accommodated states (dynamic 3D shape changes) is caused by a combination of specified band geometry, capsular strain and prescribed tension and / or lens deformation caused by ciliary muscle fiber displacement during accommodation. The modeling component is configured to determine a three-dimensional finite element model of the entire eye based on a three-dimensional structural image of the eye and at least one biomechanical property. The modeling component is configured to provide a three-dimensional finite element model representing the entire eye after a treatment procedure, the system further comprising a user interface configured to accept input from a clinician, the input defining at least the type and location of the treatment procedure. The feature extractor is configured to extract at least one of a plurality of features from the three-dimensional finite element model representing the entire eye after the treatment procedure, the dilation assessment component is configured to calculate a parameter representing the expected risk of dilation of the patient given a treatment procedure of the associated type and location. The dilation assessment component is configured to perform a sensitivity analysis on at least one feature, so that the magnitude of the effect of the value of the at least one feature on the at least one parameter can be determined. The at least one extracted feature represents one of a geometric or biomechanical feature of the eye. The modeling component is configured to provide a three-dimensional finite element model representing the entire eye, wherein a load is applied to the eye, the system further comprising a user interface configured to accept input from a clinician, the input defining at least the magnitude and location of the load. The modeling component is configured to determine a finite element model of the entire eye based on the three-dimensional structural image of the eye and at least one biomechanical property. The objective function may be a function of at least one of a strain value of the eye and a stress value of the eye. The objective function may be a function of at least one metric of the refractive outcome. The modeling component may determine a representation of the entire eye as a virtual model comprising a set of parameters extracted from evaluating a statistical model based on a three-dimensional structural image of the eye and at least one biomechanical property.

[0101] Another application may include the estimation of aspects of accommodation function that are currently not experimentally measurable. For example, such estimation may be performed via VESA's FEM via analysis of the biomechanical effects of ciliary muscle fiber segments, performing simulations where each ciliary muscle fiber segment is individually activated to quantify its unique contribution to lens deformation, and / or performing simulations where each band segment is individually tensioned to quantify its unique contribution to lens deformation. Any anatomical component of the accommodation system may be run individually to determine its unique contribution to the lens shape change output in virtual reality, which cannot be studied in vivo or in vitro. Using VESA, an infinite number of manipulations of geometry, activation, physical properties, material properties, and biomechanical relationships and interactions are possible that would not be possible in vivo.

[0102] In addition, as described above, VESA provides the capability for in silico experimentation, which allows a user (e.g., a physician or surgeon) to understand the pathophysiology of accommodative dysfunction and identify effective treatments by performing various simulations and "what-if" scenarios. For example, simulations in which ciliary muscle fiber fraction activation is altered to reflect changes in elderly presbyopic patients (age-related accommodative dysfunction) due to measured atrophy or connective tissue infiltration to compare predicted accommodative lens responses, and / or simulations in which material properties of passive structures (e.g., lens components, scleral regions) are individually altered to reflect measured changes in stiffness in elderly presbyopic patients to compare predicted accommodative lens responses / COP, and / or measured changes in stiffness due to a healing intervention (e.g., intraocular lens replacement, scleral microporation) to compare predicted accommodative lens responses.

[0103] In an exemplary VESA embodiment, the FEM identifies for each individual the exact ciliary muscle fiber portion where one or more holes need to be placed in different parts of the eye (e.g., the scleral region). The VESA FEM allows the separation of individual ciliary muscle fiber portions in a computer. For example, each simulation can separate at least one ciliary muscle fiber portion (or a combination of some muscle fiber portions) so that at the end of x simulations, the user can determine where and how many holes need to be added in the eye. In other words, VESA is configured not only to model the individual eye as a whole, but also to help determine the ideal location (one or more) of one or more holes to be created in the eye in discrete parts, such as by laser scleral microporation (LSM). In addition, the VESA FEM is capable of separating individual ciliary muscle fiber portions by computer. VESA is capable of performing these isolated analyses on any component of the anatomical system of the eye (e.g., the elastic motion of the BMCC device).

[0104] Figure 1BAn exemplary process flow chart 9300 is shown, which generally includes generating a first individual-specific simulation 9302, generating a second individual-specific simulation 9304, and determining one or more treatment parameters 9306 based on at least the first individual-specific simulation and the second individual-specific simulation. In an exemplary embodiment, the first individual-specific simulation 9302 may include activating a first ciliary muscle fiber portion of the individual's eye to determine a first contribution of the first ciliary muscle fiber portion to a lens deformation of the individual's eye, the second individual-specific simulation 9304 may include activating a second ciliary muscle fiber portion of the individual's eye to determine a second contribution of the second ciliary muscle fiber portion to the lens deformation, and the one or more treatment parameters 9306 may include the location of one or more holes in the first ciliary muscle fiber portion and / or the second ciliary muscle fiber portion. In some exemplary embodiments, the one or more parameters of the treatment 9306 may be determined by applying a 3D-ID AI analysis based on machine learning. Examples of treatment parameters that are precisely determined to be optimal for each individual may include the number of matrices, the location of the matrices, the overall shape of each matrix, the overall size of each matrix, the number of holes in each matrix, the distribution of holes in each matrix, the depth of holes in each matrix, etc. Other anatomical structures besides ciliary muscle fibers are also within the scope of the present disclosure. For example, a specific stimulus (e.g., a first, second, and / or third specific stimulus) may include activating one or more ciliary bands or any of the fiber bundle loops that form a band connecting the ciliary body to the lens of the eye. These fibers are sometimes collectively referred to as the zonular ligaments of the lens. The system may take into account, model, or otherwise take into account zonular forces and anatomical responses. In this response, the bands may be divided into separate regions or groups based on anatomical location, the force applied by the bands, or any other factor.

[0105] More specifically, the FEM identification of the desired location(s) of the one or more apertures is based on VESA components and functionality, such as, but not limited to, a detailed representation of the ciliary muscle fiber structure and contractile behavior, taking into account the pre-tension applied to the lens via the band structure, and a discrete 3D geometric representation and independent material specifications of the passive structures of the eye that mechanically interact during accommodation function. These passive structures may include, but are not limited to, lens structures (e.g., lens capsule, lens cortex, lens nucleus), scleral zones (e.g., zone 0, zone 1, zone 2, zone 3, zone 4, and others), posterior sclera, and / or extra-lens structures (e.g., cornea, scleral spurs, bands, lamellae, choroid, Bruch's membrane vitreous membrane).

[0106] For example, a detailed representation of the ciliary muscle fiber structure and contractile behavior is generated based on, but not limited to, a discretized 3D geometry of three-part muscle fibers, fiber directions assigned to the muscle geometry to represent the structure of each part, constitutive laws defining material behavior (where stresses in the fiber directions vary with activation level), and time-varying conditions of activation level in each part that result in dynamic contraction. Similarly, the pre-tension applied to the lens by the band structure is generated based on a discretized 3D geometry of a seven-segment band, represented as 3D sheets extending between connection points, fiber directions assigned to the band geometry to represent the fibers within each sheet, constitutive laws defining material behavior (where stresses in the fiber directions scale with tension level), and time-varying conditions of tension level in each segment that result in dynamic tension.

[0107] Figure 2A The VESA ecosystem is shown with various hardware / software systems located in the operating room, cloud, and / or database. Figure 2B The various technologies and tools that make up the backend services of the VESA ecosystem are shown. The backend services can be located on one or more computing systems (such as AVG PC units) or in the cloud. Figure 2B As shown, VESA includes applications of AI, neural networks, machine learning, signal processing, mathematics, neurobiology, physics, imaging, and robotics.

[0108] Figure 2C Multiple subsystems of the VESA ecosystem are shown, including standalone diagnostic tools, clinical trial tools, lasers, imaging devices, AI engines, etc. Figure 2D Two of the many disease application modules are shown. Module 1 is used to treat presbyopia and module 2 is used to treat glaucoma. For module 1, modeling of lens deformation, band tension, choroidal elastic properties, scleral movement and ciliary forces is important. For module 2, tools such as FEA and / or computational fluid dynamics (CFD) can be used to model at least the following properties of the eye: aqueous humor outflow pathway, trabecular meshwork fluidics, flow rate changes and fluid dynamics. In some embodiments, VESA can use CFD and / or constitutive calculations to measure the intraocular pressure (IOP) of the eye. VESA can use FEA to calculate or simulate the deflected shape of the IRIS.

[0109] VESA can also simulate conduit flow using CFD to predict flow in the eye. In some embodiments, the CFD model can simulate / model pump efficiency, constrained outflow, and ambient pressure. The CFD can also show the fluid structure interaction or FSI of the aqueous humor outflow system and particles and liquids in the outflow path under both normal and disturbed conditions in VESA. Geometric modeling from SIMSCALE Forum can be used. A flow rate of 3.6mL / day of aqueous humor can be used as input to the CFD model. Porous media can be used in the model. ANSYS Fluent is used to develop the model. Other modeling software can also be used. When modeling the flow, one or more of the following can be assumed:

[0110] The modified Goldmann equation summarizes the relationship between many of these factors and the intraocular pressure (IOP) of the undisturbed eye:

[0111] ●P0=(FU) / C+Pv

[0112] ●Where P0 is the IOP in mmHg,

[0113] F is the rate of aqueous humor formation in microliters per minute (μL / min).

[0114] U is the aqueous humor drainage rate through the pressure-insensitive uveo_x0002_scleral pathway in microliters per minute (μL / min),

[0115] C is the patency of outflow through the pressure-sensitive trabecular pathways, expressed in microliters per minute per millimeter of mercury (μL / min / mmHg).

[0116] ●Pv is the episcleral venous pressure, in mmHg. Outflow resistance (R) is the reciprocal of patency (C).

[0117] Figure 3 Figure 1 shows how VESA can contribute at various stages of key medical procedures. As shown, VESA can bring many benefits at all stages of the medical procedure by providing powerful AI-based predictive analytics (e.g., predictive software) as well as improving processes and enhancing knowledge.

[0118] As described above, VESA includes one or more finite element models of individual eyes. One or more assumptions can be made for the overall eye model. For example, a portion of the sclera can be split into 5 different regions for modeling. Each region can be additionally defined by its own material and biomechanical properties. Figure 4 The structure, various measurable variables and boundary conditions that can be used to generate a finite element model of the eye are shown.As previously mentioned, various zones of the eye are within the scope of the present disclosure.

[0119] Figure 5 An exemplary region definition that can be used to generate a finite element model is shown. In some embodiments, the region is assumed to be:

[0120] ●The different parts of the ciliary muscle fibers are intermingled, so there is no reference that can provide a specific size.

[0121] • These areas are perpendicular to the outer surface of the sclera. Thus, area 0 is the area between a line starting at the inner point of the spur and another line starting at the inner point on the limbus.

[0122] The model consists of different regions and the boundaries were chosen so as to reproduce the description of each part in the literature. The boundaries of each region are based on the geometrical position of specific anatomical landmarks in the 30-year-old eye model.

[0123] • The description of the boundaries relative to specific anatomical landmarks will be slightly different for each subject.

[0124] Figure 6 Examples of each region description or anatomical location are shown for one or more regions of the eye.

[0125] VESA may consider properties of at least one or more of the following non-limiting elements:

[0126] Corneoscleral junction

[0127] Cornea (license)

[0128] Lens details

[0129] Lens capsule details

[0130] Weigert's ligament

[0131] ●Rear angle structure

[0132] Retina

[0133] Choroid

[0134] ●Bruch membrane

[0135] Vitreous membrane

[0136] Water-based

[0137] Glass

[0138] Eye shape

[0139] ●Axial length

[0140] ●Ciliary body, all muscle fiber directions

[0141] ●Ciliary tip

[0142] Optometry

[0143] IRIS (Pupil)

[0144] ● Biomechanical and elastic properties of sclera

[0145] ●Sclera-Zinn ring junction

[0146] ●Muscle attachments of all ciliary muscle fibers

[0147] ●Sieve plate

[0148] ●Details of all strap attachments

[0149] Lens capsule

[0150] Scleral puncture

[0151] ●Schlemm tube

[0152] Subchoroidal space

[0153] Subchoroidal layer

[0154] ●Trabecular meshwork

[0155] In some embodiments, the fluid-solid interaction solver can use software such as, but not limited to, Ansys Fluent to model the trabecular meshwork. The trabecular meshwork can include the following anatomical features:

[0156] ● Corneosclera - contains a large amount of elastin arranged as a series of thin, flat, perforated sheets that are arranged in layers; considered to be the tendons of the ciliary muscle fibers.

[0157] ● Juxtacanalicular tissue - Juxtacanalicular tissue (also called cribriform reticular formation) - is located immediately adjacent to Schlemm's canal and is composed of a connective tissue matrix filled with glycosaminoglycans and glycoproteins. This thin strip of tissue is covered by a single layer of endothelial cells.

[0158] ●Schlemm's canal is a circular lymphatic vessel in the eye that collects aqueous humor from the anterior chamber and transports it to the suprascleral vessels via the aqueous veins.

[0159] The tube is essentially an endothelial-lined tube, similar to a lymphatic vessel. Inside the tube, closest to the aqueous humor, it is covered by the trabecular meshwork; this area contributes most to the outflow resistance of the aqueous humor.

[0160] ● Traditionally, the vessels were thought to be blood vessels, but research published in 2014 showed that the molecular signature of Schlemm's vessels is very similar to that of lymphatic vessels.

[0161] ●Iridoartic ring - a ring of blood vessels located in the anterior part of the ciliary body near the root of the iris. It is formed by the union of two longer posterior ciliary arteries and seven anterior ciliary arteries. It supplies the iris, ciliary processes, and anterior choroid.

[0162] ●Ciliary capillaries- These capillaries supply blood to the surrounding tissues.

[0163] ● Tributaries of the eye include, but are not limited to: The anterior and posterior ciliary veins. The two superior vortex veins (vortex veins) from the upper half of the eye. The suprascleral vein from the eyelid and conjunctiva. The central retinal vein. The lacrimal vein.

[0164] ●Collection channel

[0165] In some embodiments, the grid can be divided into several parts, each part characterized by a different structure. They may include but are not limited to:

[0166] ●Inner uveal mesh - closest to the anterior chamber angle, contains thin cords of trabeculae that are primarily radially oriented and enclose larger trabecular spaces than the corneoscleral mesh.

[0167] ● Corneoscleral lattice - contains a large amount of elastin arranged as a series of thin, flat, perforated sheets arranged in layers; considered to be the tendons of the ciliary muscle fibers.

[0168] ● Juxtacanalicular tissue (also called cribriform lattice) - Immediately adjacent to Schlemm's canal, it is composed of a connective tissue matrix filled with glycosaminoglycans and glycoproteins. This thin strip of tissue is covered by a single layer of endothelial cells.

[0169] ■ The trabecular meshwork is aided somewhat in the drainage of aqueous humor by a secondary outflow pathway, the uveal-scleral pathway (5-10% of outflow occurs this way). Glaucoma medications (eg, prostaglandins) are used to enlarge the uveal-scleral pathway.

[0170] ■ It was previously thought that the trabecular meshwork originated from the point (apex) corresponding to where the DM (Schwalbe's line) terminates, but it is now thought to extend into the cornea, forming the Dua layer.

[0171] The Schwalbe line is an anatomical line present on the inner surface of the cornea and delineates the outer limit of the corneal endothelium. Specifically, it represents the termination of the Descemet membrane. In many cases, it can be seen with gonioscopy.

[0172] ■Some evidence suggests that the corneal endothelium actually possesses stem cells that are capable of generating endothelial cells, especially after injury, albeit on a limited scale.

[0173] ■ Collecting channels are endothelial-lined openings that originate in the outer wall of Schlemm's canal and serve as conduits for the delivery of aqueous humor to the intrascleral and episcleral vessels that lead to the venous system. The number of collecting channels varies in humans but generally ranges from 24 to 35 per eye, with 8-10 distributed in a random pattern around the outer wall of Schlemm's canal.

[0174] ●Scleral veins - Aqueous humor flows out of the eye through the trabecular meshwork and into Schlemm's canal (a channel at the limbus, the point where the cornea and the sclera that surrounds the cornea join). It flows through 25-30 collecting ducts into the scleral veins.

[0175] ●Iris-aortic ring

[0176] ■A vascular ring located in the anterior part of the ciliary body, near the root of the iris. It is formed by the union of two longer posterior ciliary arteries and seven anterior ciliary arteries. It supplies the iris, ciliary processes, and anterior choroid.

[0177] ●The conjunctiva is the tissue that lines the inner wall of the eyelid and covers the sclera (white of the eye). It is composed of non-keratinized stratified squamous epithelium (containing goblet cells) and stratified columnar epithelium.

[0178] Figure 7 An example 3D model generated by VESA is shown. This 3D model is a FEM consisting of 703 fibers and 42,000 elements. It should be noted that more or fewer fibers and mesh elements can be used. For example, Figure 8 Shown is a FEM created with more fibers and elements, which increases the calculation time.

[0179] Fig. 9 An example mesh network of a 3D model of an eye is shown. Fig. 9 The simulated accommodation of the bands and muscles within the eye is shown, and further illustrates that for the purpose of generating the FEM, the bands are approximated as springs. After the mesh model is generated, the model can be imported into FEBio.

[0180] Figure 10-12 An example anatomical model of VESA used to generate the trabecular meshwork is shown. For example, material properties for each feature (e.g., Schlemm's Canal, Scleral Vein, Conjunctiva) can be assigned to one or more mesh elements associated with the corresponding feature.

[0181] In some embodiments, the VESA may include a micropore analysis tool that is configured to analyze and determine the characteristics of the micropore array on the edge of an individual's eye. Some features of the micropore analysis tool are listed below. This list is not exhaustive.

[0182] The Micropore Tool (MPTool) can arrange xyz data in a grid or mesh configuration and display the data in a color-coded manner according to the Z value. The Z value is a height coordinate and is converted from the grayscale value in the image.

[0183] The tool may employ a cell smoothing routine. The tool may implement an edge detection algorithm to find the edges of each hole. Examples and criteria for finding edges can be found in this document. Using the edge detection algorithm, a second copy of the mesh may be created where values ​​that are not found to be within a hole are discarded. A box may be drawn around each hole and those dimensions reported. This box may be drawn using the edges found in the previous step. The height values ​​within this box may be used to report 1 characteristic height for each hole.

[0184] A method is now described for determining the characteristics, quantitative and qualitative properties (such as but not limited to ligament efficiency, hole thickness and hole height) of a micropore array using a micropore analysis tool. Using the characteristic height data of the hole grid, the tool can employ the ASME ligament efficiency principle to evaluate the change in the volume pore value of the overall patch imparted by the hole patch.

[0185] Fig.13A The first step is shown, where the raw data is read and converted into a pseudo 2D grid array. In step 2 (not shown), the tool is configured to determine the width of the well block. This can be manually input by the user. The width of the well block can be the average width of the holes in the array. Alternatively, the width of the well block can be determined programmatically using various algorithms and / or computer vision analysis tools.

[0186] Next, determine the color grid for each hole. This will ultimately create a color grid that represents a matrix with multiple holes. For example, a 5x5 matrix.

[0187] Next, the grid is smoothed to remove outlier color values ​​that are most likely erroneous, such as the color value pointed by the arrow, which should have a green shade instead of an orange / red shade. Removing outliers will help the edge detection process. Fig. 13B Shown is the effect before and after smoothing.

[0188] Next, the tool cleans up the blanks. This step can include determining the linear mean (non-zero values) for the entire well block; zeroing all cells that are less than 105% of the well block linear mean (note: the 105% qualifier is an engineering judgment. It may need to be adjusted based on available data); and recoloring the data.

[0189] Next, the following actions will occur:

[0190] ●Assume that the remaining non-zero cells are continuous and roughly square in shape (engineering judgment).

[0191] ●Determine the linear average of the remaining non-zero cells. The reported depth of the hole cylinder will be 80%-90% of this value. This is the characteristic height of the hole.

[0192] Finally, the characteristics of each hole are tabulated, recorded and registered.

[0193] The micropore analysis tool can be an OCT (optical coherence tomography) viewer configured to generate images of the micropores at various views (e.g., X slices, Y slices, Z slices) to measure certain characteristics of the micropores, such as but not limited to depth, width, edges, size, shape, coordinates, diameter, distance to adjacent pores, such as Fig.14 The micropore analysis tool may include multiple AI (e.g., neural network) engines that are trained to perform edge detection, measure diameter, measure depth, layers (e.g., tissue segmentation), etc.

[0194] Once the FEM or virtual eye model is created, it can be used to perform additional modeling (e.g., sub-modeling, predictive modeling). The purpose of sub-modeling is to determine the new biomechanical properties of the sclera after surgery. Some assumptions of the model are as follows:

[0195] ●The initial sclera is either homogenous or heterogeneous material.

[0196] The properties of the sclera can be obtained from experimental and / or adjusted FEM data from previous modeling work. (e.g. Young's modulus = 1.62 MPa, Poisson's ratio = 0.48)

[0197] • Holes have a stiffness ranging from zero to negative.

[0198] For the geometric modeling of the hole, the following variables or conditions are observed:

[0199] Each hole is modeled individually. Each hole cross-section is a square with a variable side length, in this case 433 μm.

[0200] • The distance between the center of each hole can vary, in this case it is 722 μm.

[0201] ●Well depth was obtained from microwell OCT review.

[0202] ●The thickness of the sclera is the average thickness measured in the same reference.

[0203] Micro-hole tools

[0204] In a non-limiting example, a micro-via tool may have 3 different modes (although there may be more). The three modes are:

[0205] ● Pre-planning mode: Pre-processing step - PVF is calculated and accepted as input. Image recognition is performed on the operating area to find any obstacles (blood vessels, foreign bodies, etc.) that would block the hole placement

[0206] ●Processing Mode: Surrounding processing steps - real-time update of PVF and EMC, and display to the user in the form of color graph

[0207] Post-Processing Mode: The post-processing step - Micropore Tool (which has an embedded EMC calculator) calculates relevant quantities and generates reports on the current pore matrix, each of the 4 pore matrices individually, and the 4 pore matrices as a system. Micropore matrices can be independent, continuous, and arranged in a variety of patterns. Micropore size can also be reduced to have the same nanopores in a variety of configurations, shapes, sizes, and densities.

[0208] Fig.15 Screen shots showing pre-planning mode include but are not limited to:

[0209] ●A snapshot of an individual's anatomical features (eg, an eyeball).

[0210] ●Red outline of the matrix.

[0211] ●With anatomical avoidance button.

[0212] ●Annotation button.

[0213] ●Edit / Mark / Font / Arrow / Drawing toolbar.

[0214] ●EMC tells you the biomechanical properties of the tissue.

[0215] PVFI tells you about the tissue that was removed, but not anything about its location.

[0216] ●Bulk density reading (BDR) tells you the hardness of the area.

[0217] The Microwell Registration Tool (MIRT) tells you the location of the wells, not the pattern.

[0218] ●The Matrix Registration Tool (MART) tells you information about the pattern.

[0219] The GUI may include one or more of the following tools:

[0220] ● Micropore Tool: A 3D touchable GUI simulation tool that contains parameters that accept user input to define the Ligament Efficiency Tool (LET) ASME. Tool that generates intermediate results in the simulation solution

[0221] ● Ligament Efficiency Tool (LET) (ASME): The physics package behind the micro-hole tool.

[0222] ●Registration tools: (MPR) - (MAR) Position tracker that communicates and registers the position and orientation of microwells and the matrix.

[0223] ● Elastic Modulus Calculator: Processor for the following biomechanical and physical data inputs from BioMeter:

[0224] ● The application will be used for 3 different purposes. Pre-operative planning, intra-operative evaluation and verification, and post-operative verification. Real-time GUI solver.

[0225] Micropore Biometer (MBM): Personalized biomechanical / physical inputs for EMC and 3D-ID VESA. The following inputs can be entered and / or measured:

[0226] ○Ingredients:

[0227] ○ Age-related MPA

[0228] Depth

[0229] ○PVF

[0230] ○Volume density (also known as regional stiffness)

[0231] ○ Density

[0232] ○Hole size

[0233] ○Hole shape (square) [coding language: diameter]

[0234] ○Hole pattern

[0235] ○Hole array

[0236] The Microwell Tool GUI may also display one or more of the following features / values:

[0237] Negative Stiffness Index (NSI): A progression bar of the negative stiffness quotient indicating changes in the biomechanical properties / physical outcomes of the scleral tissue during and after LSM (Laser Scleral Microporation). See more descriptions below and Figures 65-70 for more details on the LSM process.

[0238] o Progress measurements by quadrant Q1-Q4 until the total NSQ is met for the entire eye.

[0239] ○ Input from BioMeter+EMC

[0240] Treatment progress meter:

[0241] o A progressive completion meter for treatment progress, which is also connected to the clock, as the time clocks counting back for each quadrant are synchronized.

[0242] ○It needs to be completed by merging by pressing the countdown of quadrant w.

[0243] ●Treatment countdown clock.

[0244] ●OCT tools: Integration of AI and OCT automatic segmentation

[0245] ○ Depth sensing tools to differentiate tissue layers

[0246] ○3D image processing and image rendering.

[0247] ○3D in-depth analysis

[0248] ●Customized micro-hole tools:

[0249] ○ A tray of microwell patterns to customize the treatment platform by selection or through AI recommendation.

[0250] ●COP (Center Optical Power) Calculator:

[0251] o The COP of the lens is calculated using ray tracing using standard formulas and proprietary methods.

[0252] ○Calculate the free body forces of the lens.

[0253] ○COP formula and chart, see Figures 54-57

[0254] ●Depth sensor tool: Innominds

[0255] In some embodiments, the microporous tool is configured to determine the material properties of the tissue between the pores. Once the pores, depth, spacing, and diameter are determined, the entire structure of the eye can be simulated with the effects of the microporous procedure. By determining the accommodation response, the velocity can be obtained. In addition, with the new microporous matrix in place, the new post-treatment intraocular dynamic pressure can be obtained. This pressure can be compared to the pre-operative dynamic pressure.

[0256] The microvia tool is configured to determine the following:

[0257] ●Coefficient of eye rigidity (COR): Mathematical formula for predicting constant

[0258] • Pore Volume Fraction Index (PVFI): A mathematical formula that predicts the pore volume density and the fraction of tissue removed versus the percentage of tissue remaining.

[0259] GUI evaluation and validation tool for measuring PVF across the treatment area

[0260] ○PVF is related to the scleral thickness by region, scleral material properties, micropore shape, micropore density and micropore size

[0261] ○Tell you what tissue was removed, but not where it was removed

[0262] ● Elastic Modulus Calculator (EMC): Related to the viscoelastic and elastic properties of tissues. Tells you the output of the physical meter in terms of negative stiffness transducers and is a 3D analog of the BioMeter.

[0263] ●The value of the physical meter is NSI. NSI is related to the negative Poisson equation in the tissue.

[0264] ●NSI is the trigger for the recommendation.

[0265] The MPTool can also include registration features such as Fig.16 The Micropore Registration Tool (MIRT) is configured to determine the dynamic location of the pores, but not the pattern. The Matrix Registration Tool (MART) tells you about the pattern of the micropores. MART and MIRT provide continuous biofeedback to the data stack about the center of gravity of the ocular ecosystem as it relates to the NSI and biomechanical strain in the hoop stress coordinate map. The hoop stress coordinate map (HSCM) provides location, density, and registration information to the database and provides continuous biofeedback to the data stack about this and other specified information.

[0266] The micro-port tool can also display one or more raw OCT images simultaneously, such as but not limited to depth sensing images, y slices, z slices, and x slices, such as in 1D, 2D, 3D, and 4D viewer capacities. The MPTool also has zoom capabilities for macro and micro viewing and evaluation. The GUI of the micro-port tool is also configured to overlay key tissues on the image. Key tissues that can be overlaid include but are not limited to the cornea, sclera, conjunctiva, ciliary muscle fibers, and orbital capsule. This makes diagnosis easy.

[0267] The Micro-Via tool has 5 main operating modes:

[0268] ●Pre-treatment planning (snapshot)

[0269] ●Treatment (dynamic and real-time)

[0270] Immediately after treatment (simulation)

[0271] After treatment and processing:

[0272] Hole matrix evaluation and reporting (simulation)

[0273] During all modes, a simulation of the matrix with respect to (PVF / BD / EM / COR) is required. This will help medical professionals:

[0274] ●Plan the matrix in advance.

[0275] ●Evaluate each of the 4 hole matrices while performing laser treatment. This will allow for re-lasering of areas that do not meet guidelines and inform the user of the recommended correction.

[0276] ●View the real-time dynamic changes of ocular tissues in LSM treatment results.

[0277] ●Report the results of the intervention or laser procedure to the responsible party to determine the success of the procedure.

[0278] This needs to be done in real time during the treatment step, which makes FEA simulation inappropriate. Therefore, preoperative VESA simulation uses various 3D-ID TM - DOE methodology (Design of Experiments) completed. Further iterations of VESA FEA are possible through the cloud application.

[0279] The micropore tool may include an EMC calculator based on first principles based stress calculations (ASME ligament efficiency) that is able to solve stress calculations in real time.

[0280] Pre-scan access process

[0281] The pre-scan access process may include: Fig.17 The steps and processes are shown in the figure, and both the pre-planning and post-OCT scanning workflow are Fig.18 Shown in. Fig.19 An application is shown having multiple GUIs and features configured to facilitate an individual's medical visit process.

[0282] from Fig.19 The screen shown in , allows the user to:

[0283] ●Use an eye tracker for position alignment.

[0284] ●Calibrate the laser.

[0285] ●Step 1. Set the location.

[0286] • Step 2. Run the simulation on the estimated NSI (Negative Stiffness Index) processed matrix Q1.

[0287] ●Step 3. Confirm the location.

[0288] ●Step 4. Go to the edit screen or process screen or various sub screens.

[0289] With microwell arrays, users can:

[0290] ●Select / deselect / add holes.

[0291] ●Run the simulation.

[0292] ●Perform real-time PVF calculations.

[0293] ●Execute review and assessment.

[0294] ●Perform real-time LET, NSI and EMC calculations.

[0295] ●View real-time elastic hole placement assumptions.

[0296] ●Receive real-time AI recommendations based on BioMeter / EMC / NSI

[0297] Fig.19 The screen may also include a physical dynamic progress meter that measures:

[0298] ●PVF.

[0299] ●Bulk density.

[0300] Negative Stiffness Index (NSI)

[0301] ●Elastic modulus.

[0302] ●Depth.

[0303] The micropore tool may include a treatment mode screen that may include a real-time OCT signal graph.

[0304] The Microvia Tool can perform simulations and include subscreens. VESA simulations can be used to predict results and set GO NOGO parameters. VESA simulations in MPT will run simulations when:

[0305] ● Pre-planning - If an edit is performed, VESA will control to stop editing / as long as the embedded EMC code will achieve the required NSI, VESA will allow to enter the next quadrant with holes subtracted to create a successful individual matrix.

[0306] • Progressive matrices will run in the background and allow the surgeon to bypass Go to MPT if the series of matrices still meets the overall requirements of VESA predicted NSI values ​​when treatment is completed.

[0307] ●When the OPF ratio of each quadrant is not satisfied, a forced retreat is performed, using MRT to return to the registered hole.

[0308] If editing of the matrix requires microwell replacement arrays, force ADD ON well arrays when the threshold is met.

[0309] ●Serve as a medical execution control center to prevent unnecessary treatment parameters or interventions.

[0310] ●Serve as an informing agent of biomechanical, healing or surgical intervention parameters and conditions.

[0311] ●Acts as a stop system mechanism or abort mechanism.

[0312] The microwell tool may include post-processing screening (Figures 53A-53B). For post-processing, the NSI needs to have a threshold of 1) GO / 2) Consider Retreat 3) NO GO and should change color accordingly.

[0313] Hyperelastic Image Registration

[0314] Hyperelastic modeling in Ansys is able to track critical points in the numerical model as the simulation adjusts. These critical points can also be tracked in life imaging, thus allowing the accuracy of our model to be confirmed using multiple data points.

[0315] In hyperelastic modeling, the inverse imaging of dynamic biological motion can be transformed into a 3D FEM. In addition, the stress determination within the deforming body can be calculated.

[0316] Eye Tracking Augmented Reality

[0317] VESA may include systems for eye tracking with augmented reality.

[0318] Fig. 20 A block diagram illustrating an example of a computing system 500 consistent with implementations of the current subject matter is depicted. The computing system 500 can implement VESA, 3D-ID AI, MP Tool, and / or any components thereof. The system can include or be coupled to an imaging system, a biothermal system, an interactive user interface, and / or an input program to receive and manipulate physical, biometric, biomechanical, material, and mechanical information and data.

[0319] like Fig. 20 As shown, computing system 500 may include processor 510, memory 520, storage device 530 and input / output device 540. Processor 510, memory 520, storage device 530 and input / output device 540 may be interconnected via system bus 550. Processor 510 is capable of processing instructions for execution within computing system 500. These executed instructions may implement one or more components of, for example, VESA, 3D-ID AI and / or MP Tool. In some implementations of the current theme, processor 510 may be a single-threaded processor. Alternatively, processor 510 may be a multi-threaded processor. Processor 510 is capable of processing instructions stored in memory 520 and / or storage device 530 to display graphical information of a user interface provided by input / output device 540.

[0320] Memory 520 is a computer readable medium, such as a volatile or non-volatile medium, which stores information within the computing system 500. Memory 520 can, for example, store a data structure representing a configuration object database. Storage device 530 can provide persistent storage for computing system 500. Storage device 530 can be a floppy disk device, a digital cloud, a hard disk device, an optical disk device or a tape device, or other suitable persistent storage device. Input / output device 540 provides input / output operations for computing system 500. In some implementations of the current theme, input / output device 540 includes a keyboard and / or a pointing device. In various implementations, input / output device 540 includes a display unit for displaying a graphical user interface.

[0321] According to some implementations of the current subject matter, the input / output device 540 can provide input / output operations for network devices. For example, the input / output device 540 can include an Ethernet port or other network port to communicate with one or more wired and / or wireless networks, Bluetooth, or digital cloud systems (e.g., local area network (LAN), wide area network (WAN), the Internet).

[0322] In some implementations of the current subject matter, computing system 500 may be used to execute various interactive computer software applications that may be used to present information in various (e.g., tabular) formats (e.g., Microsoft and / or any other type of software) for organizing, analyzing and / or storing data. Alternatively, the computing system 500 can be used to execute any type of software application. These applications can be used to perform various functions, such as planning functions (e.g., generating, managing, editing spreadsheet documents, word processing documents, and / or any other objects, etc.), computing functions, communication functions, etc. The application can include various additional functions, plug-ins, or can be independent computing products and / or functions. After activation in the application, these functions can be used to generate a user interface provided by the input / output device 540. The user interface can be generated by the computing system 500 and presented to the user (e.g., on a computer screen monitor, etc.). The user interface can be integrated with other devices or virtual ecosystems.

[0323] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuits, integrated circuits, specially designed ASICs, field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features may include implementations in one or more computer programs that may be executed and / or interpreted on a programmable system that includes at least one programmable processor that may be dedicated or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions thereto. A programmable system or computing system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship between a client and a server is generated by computer programs that run on respective computers and have a client-server relationship with each other.

[0324] These computer programs—which may also be referred to as programs, software, software applications, applications, components, or codes—comprise machine instructions for programmable processors and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the term "machine-readable medium" refers to any computer program product, device, and / or apparatus for providing machine instructions and / or data to a programmable processor, such as, for example, a disk, an optical disk, a memory, and a programmable logic device (PLD), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor. A machine-readable medium may store such machine instructions non-temporarily, such as, for example, as a non-temporary solid-state memory or a magnetic hard disk or any equivalent storage medium. A machine-readable medium may alternatively or additionally store such machine instructions in a transient manner, such as, for example, as a processor cache or other random access memory associated with one or more physical processor cores.

[0325] To provide interaction with a user, one or more aspects or features of the subject matter described herein may be implemented on a computer having a display device, such as a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor, for displaying information to the user, and a keyboard and a pointing device, such as, for example, a mouse or trackball, through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user. For example, the feedback provided to the user may be any form of sensory feedback, such as, for example, visual feedback, auditory feedback, or tactile feedback; and the input from the user may be received in any form, including acoustic, voice, or tactile input. Other possible input devices include a touch screen or other touch-sensitive device, such as a single-point or multi-point resistive or capacitive touch pad, a joystick, voice recognition hardware and software, an optical scanner, an optical pointer, a digital image capture device and related interpretation software, and the like.

[0326] In the above description and in the claims, phrases such as "at least one" or "one or more" may appear before the connected list of elements or features. The term "and / or" may also appear in a list of two or more elements or features. Unless there is an implicit or explicit contradiction with the context in which it is used, such a phrase is intended to represent any element or feature listed separately or any combination of the listed elements or features with any other listed elements or features. For example, the phrases "at least one of A and B", "one or more of A and B" and "A and / or B" are each intended to represent "alone A, alone B, or A and B together". A similar interpretation is also intended for a list including three or more items. For example, the phrases "at least one of A, B and C", "one or more of A, B and C" and "A, B and / or C" are each intended to represent "alone A, alone B, alone C, A and B together, A and C together, B and C together, or A and B and C together". The term "based on" used above and in the claims is intended to represent "based at least in part", so that unlisted features or elements are also allowed.

[0327] The subject matter described herein may be embodied in systems, devices, methods and / or articles according to the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the subject matter. Although some changes are described in detail above, other modifications or additions are also possible. Specifically, in addition to the features and / or changes described herein, further features and / or changes may also be provided. For example, the implementations described above may be for various combinations and sub-combinations of the disclosed features and / or combinations and sub-combinations of several further features disclosed above. In addition, the logical flows depicted in the accompanying drawings and / or described herein do not necessarily require the specific order or sequential order shown to achieve the desired results. Other implementations may be within the scope of the following claims.

Claims

1. A system for reconstructing, manipulating and intervening in an individual's eyes in virtual reality, comprising: 3D modeling components, including at least one data processor; Imaging system, Biothermal Systems, Interactive user interface; input programs for receiving and manipulating physical, biometric, biomechanical, material, and mechanical information and data; as well as at least one memory storing instructions that, when executed by the at least one data processor, result in operations including: generating a first specific simulation in which a Bruch's membrane choroidal structure is activated to determine a first contribution of energy required for lens deformation; generating a second individual-specific simulation in which a starting portion of a ciliary muscle fiber portion of an eye of the individual is activated to determine a first contribution of the first ciliary muscle fiber portion to a deformation of a lens of the eye; generating a second individual-specific simulation in which a second ciliary muscle fiber portion of an eye of the individual is activated to determine a second contribution of the second ciliary muscle fiber portion to deformation of a lens of the eye; generating a third individual-specific simulation in which a third ciliary muscle fiber portion of an eye of the individual is activated to determine a third contribution of the second ciliary muscle fiber portion to deformation of a lens of the eye; as well as One or more parameters of treatment for the individual are determined based at least on the first Bruch's choroid structure, the first contribution of a first ciliary muscle fiber portion, the second contribution of a second ciliary muscle fiber portion, and the third contribution of a third ciliary muscle fiber portion.

2. A system according to claim 1, wherein all simulations can be performed sequentially, individually and / or in parallel with other parameter manipulations.

3. The system according to claim 1, wherein: The results of the system include: The fluid structures that generate the aqueous humor flow of the eye interact with each other, with three outflow pathways being activated.

4. The system according to claim 1, wherein: The system includes one or more parameters of a Bruch's choroid structure, a first ciliary muscle fiber portion, a second ciliary muscle fiber portion, and a third ciliary muscle fiber portion of an eye of the individual.

5. The system of claim 4, wherein one or more micropores are created by laser in the scleral tissue of the eye above the Bruch's choroidal structure, the first ciliary muscle fiber portion, and / or the second ciliary muscle fiber portion, and / or the third ciliary muscle fiber portion of the eye of the individual.

6. The system according to claim 4, wherein: One or more parameters of the treatment include the number of matrices containing one or more holes, the location of the matrices, the overall shape of each matrix, the overall size of each matrix, the number of holes in each matrix, the distribution of holes in each matrix, and / or the depth of holes in each matrix.

7. The system of claim 1, wherein one or more treatment parameters for the individual are determined by applying a machine learning model.

8. The system of claim 7, wherein the machine learning model comprises a regression model and an algorithm.

9. The system of claim 7, wherein the machine learning model comprises an age progression model and algorithm.

10. The system of claim 7, wherein the machine learning model is trained based on various combinations of treatment parameters and treatment outcomes.

11. The system of claim 9, wherein the machine learning model is further trained based on individual demographic data associated with various combinations of the treatment parameters and treatment outcomes.

12. The system of claim 7, wherein the operations further comprise: A user interface is generated that displays one or more parameters of the treatment of the individual.

13. The system of claim 11, wherein the operations further comprise: receiving, via the user interface, at least one user input related to the one or more parameters of treatment of the individual; as well as The machine learning model is updated based at least on the user input.

14. The system of claim 1, wherein the operations further comprise systems and methods configured for assessing biomechanical properties of tissue.

15. The system of claim 1 , wherein the evaluation toolbox component is configured to compute at least one parameter associated with various possible outcomes of a given therapeutic intervention in an individual eye from the plurality of extracted features; the system output is configured to provide the computed at least one parameter to one of the treatment system and a user; and one or more optical, biometric, biomechanical, physical, and geometric parameters are incorporated into the computational prediction.

16. The system and method of claim 1, wherein: VESA's calculator toolbox is able to coordinate equations that produce the optimal healing or surgical intervention solution for the user.

17. A system for evaluating an eye of a patient, comprising: a modeling component configured to determine a representation of the whole eye based on a three-dimensional structural image of the whole eye and at least one biomechanical property of the eye; a feature extractor configured to extract a plurality of features from the model of the entire eye; a user interface configured to accept input from a clinician defining a target function as a function of at least one parameter of the eye after a healing procedure; a condition or disease assessment component configured to calculate at least one parameter associated with a risk of disease in the eye based on the plurality of extracted features and the objective function, the at least one parameter calculated comprising a variable in a treatment procedure representing a surgical parameter that a clinician can vary in the treatment procedure; as well as A system output is configured to provide the calculated at least one parameter to one of a treatment system and a user.

18. The system of claim 16, wherein the modeling component is configured to determine a three-dimensional finite element model of the entire eye based on the three-dimensional structural image of the eye and at least one biomechanical property.

19. A system according to claim 17, wherein the modeling component is configured to provide a three-dimensional finite element model representing the entire eye after a treatment procedure, and the system also includes a user interface configured to accept input from a clinician, the input defining at least the type and location of the treatment procedure.

20. The system of claim 18, wherein the feature extractor is configured to extract at least one of the plurality of features from a three-dimensional finite element model representing the entire eye after a treatment procedure, and the dilation assessment component is configured to calculate parameters representing an expected risk of dilation for a patient in the event of a treatment procedure of a relevant type and location.

21. The system of claim 19, the dilation assessment component configured to perform a sensitivity analysis on at least one feature so that a magnitude of an effect of a value of the at least one feature on the at least one parameter can be determined.

22. The system of claim 19, wherein at least one extracted feature represents one of a geometric property or a biomechanical property of the eye.

23. A system according to claim 17, wherein the modeling component is configured to provide a three-dimensional finite element model representing the entire eye when a load is applied to the eye, and the system also includes a user interface configured to accept input from a clinician, the input at least defining the magnitude and location of the load.

24. The system of claim 16, the modeling component being configured to determine a finite element model of the entire eye based on the three-dimensional structural image of the eye and at least one biomechanical property.

25. The system of claim 16, wherein the objective function is a function of at least one of a strain value of the eye and a stress value of the eye.

26. The system of claim 16, wherein the objective function is a function of at least one metric of refractive outcome.

27. The system of claim 16, wherein the modeling component determines a representation of the entire eye as a virtual model comprising a set of parameters extracted by evaluating a statistical model based on a three-dimensional structural image of the eye and at least one biomechanical property.

28. A system for reconstructing, manipulating and intervening in an individual's eyes in virtual reality, comprising: 3D modeling components, including at least one data processor; Imaging systems, biothermal systems, interactive user interfaces; input programs for receiving and manipulating physical, biometric, biomechanical, material, and mechanical information and data; as well as at least one memory storing instructions that, when executed by the at least one data processor, result in operations including: generating a first specific simulation in which the Bruch's membrane choroidal complex device is activated to determine a first contribution of energy required for lens deformation; generating a second individual-specific simulation in which a starting portion of a zonular portion of the eye of the individual is activated to determine a first contribution of said first zonular portion to a deformation of a lens of the eye; generating a second individual-specific simulation in which a second zone portion of the individual's eye is activated to determine a second contribution of the second zone portion to deformation of the lens of the eye; generating a third individual-specific simulation in which a third zone portion of the individual's eye is activated to determine a third contribution of the second zone portion to deformation of the lens of the eye; as well as Based at least on the first Bruch's choroid structure, the first contribution of the first band portion, the second contribution of the second band portion, and the third contribution of the third band portion, one or more parameters of a treatment for the individual are determined.

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