VR-based immersive simulation system for refractive surgery procedures

Abstract

This invention belongs to the field of refractive surgery simulation technology and discloses a VR-based immersive simulation system for refractive surgery procedures. It supports patients inputting refractive parameters such as myopia and astigmatism via VR controllers or voice input, while also incorporating biomechanical data such as corneal elastic modulus and thickness distribution. Built-in parameter verification logic ensures accurate input. Utilizing a model that fuses B-spline curves with corneal biomechanical parameters, it can adjust key surgical data such as laser scanning path and microlens thickness in real time based on patient parameters. Through preset personalized plans, patients can switch between and compare the scanning effects of different plans, perceive surgical details adapted to their own eye conditions, and establish an understanding of the targeted nature and safety of the surgery. The VR headset's eye mask integrates a micro-vibration motor, air pump, and heating pad to replicate the pressure sensation during real surgery.

Description

Technical Field

[0001] This invention belongs to the field of refractive surgery simulation technology, specifically a VR-based immersive simulation system for refractive surgery procedures. Background Technology

[0002] Refractive surgery is the mainstream medical treatment for correcting refractive errors such as myopia and astigmatism. Preoperative patient education is a crucial part of clinical diagnosis and treatment. Through standardized information delivery, it helps patients build surgical knowledge, understand key points of cooperation, and reduce psychological burden. Currently, the mainstream forms of clinical patient education include verbal education, illustrated manuals, and basic visual demonstrations. However, the efficiency of information delivery and the user experience are no longer sufficient to meet patients' demands for understanding of individual surgical suitability and an immersive experience. Existing patient education tools all use standardized process templates, which only show general surgical steps. They cannot receive and apply individual patient refractive parameters (myopia degree, astigmatism degree, astigmatic axis) to adjust the simulation plan. As a result, the simulation content has a low correlation with the patient's actual surgical plan, making it difficult to support the patient's technical understanding of the targeted and safe nature of the surgery.

[0003] Existing VR patient education only provides visual demonstrations of surgical procedures and does not integrate tactile feedback components (negative pressure ring fit and pressure sensation, instrument contact sensation) synchronized with the surgical steps. The feedback dimension is singular, making it impossible for patients to adapt to the physical sensations during surgery in advance. At the same time, it lacks a postoperative vision recovery effect simulation module, which cannot quantify the degree of vision recovery at time points, resulting in patients being unable to predict the surgical outcome through technical means. Summary of the Invention

[0004] The purpose of this invention is to provide a VR-based immersive simulation system for refractive surgery procedures to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a VR-based immersive simulation system for refractive surgery procedures, comprising a scene modeling module, a simulation module, an interactive control module, an adaptation module, a feedback module, an effect preview module, and an intelligent patient education module; Preferably, the scene modeling module establishes a 3D model of the surgical environment, roles, and instruments, and uses 3DMax and Maya software to build a standard ophthalmic operating room scene, restoring key elements including the operating table, Zeiss VisuMax laser equipment, sterile operating table, and medical shadowless lamp; the doctor's image is modeled according to standard surgical attire, and motion capture is performed based on clinical surgical videos to ensure that the instrument angle error is ≤5° and the simulation accuracy of the operating force is ±0.1N; The core surgical instruments are modeled based on their physical structure, including a negative pressure ring, an eyelid speculum, a lenticule separator, and lenticule forceps; a transparent 3D layered model of the cornea is established to dynamically simulate the corneal bulging deformation under negative pressure suction and the microlens morphology after laser scanning.

[0006] Preferably, the simulation module is based on the basic model of the scene modeling module, and realizes a dynamic demonstration of the entire process according to the actual clinical bilateral refractive surgery procedure. It adopts a bilateral step-by-step simulation logic, first completing the steps of the first eye, and then repeating the same steps to complete the second eye. The specific implementation of each step is as follows: Step 1: Construct a virtual patient avatar lying flat model. The head fixation device automatically adjusts to a preset comfortable angle, and the laser head rotates out from the main body of the device and is aimed at the eye model. Step 2: Simulate a doctor holding a negative pressure ring model, bringing it close to the eye, and positioning it at a preset position on the corneal surface; Step 3: Demonstrate the action of the eyelid opener model in opening the eyelid, and simultaneously show the contact state between the eyelid opener fixing structure and the eyelid; Step 4: Generate a green positioning light dot model, move it along a spiral trajectory on the corneal surface, mark the positioning trajectory, and trigger a prompt sound after positioning is completed; Step 5: Use dynamic ripple effects to demonstrate the suction process and display the negative pressure value in real time; Step 6: Generate a blue beam model to simulate the scanning path, and demonstrate the microlens formation process through a corneal transparency model; Step 7: Simulate the shutdown action of the negative pressure system and remove the negative pressure ring model; Step 8: Magnify the eye area to several times its original size to demonstrate the actions of inserting the lens separator, separating the lens, and removing the microlens with lens tweezers; Set up a detail magnification function for key steps, support on-demand adjustment of magnification, and finally output step signals and dynamic parameters.

[0007] Preferably, the interactive control module receives step signals and screen data from the simulation module, supports patients to adjust their viewing angle by naturally rotating their heads, sets function buttons on the scene interface, triggers them by clicking with a VR controller, and includes playback of the current step and key steps, with adjustable playback speed. After each step is completed, a process Q&A question will automatically pop up. The patient selects the answer using a VR controller, and the system provides real-time feedback and outputs the analysis text, as well as user operation instructions.

[0008] Preferably, the adapter module receives patient parameters transmitted by the interactive control module, supports patients to input refractive parameters via VR handle or voice input, and simultaneously includes corneal elastic modulus, corneal thickness distribution, and dynamic pupil diameter; it has built-in parameter verification logic, and automatically pops up a prompt text when the input diopter exceeds the preset range; A model integrating B-spline curves and corneal biomechanical parameters is used to construct a real-time correlation algorithm between parameters and effects. For every 1.00D increase in myopia, the laser scanning path length and microlens thickness are adjusted, and the corneal deformation coefficient is corrected simultaneously. A virtual intraoperative monitoring submodule is added to simulate real-time corneal deformation data during surgery. When the deformation exceeds a preset threshold, the laser scanning compensation path is automatically adjusted. Doctors can preset adaptation schemes in the background, and patients can switch between different schemes to compare the scanning effects during simulation, and the adaptation data is output.

[0009] Preferably, the feedback module receives step signals from the simulation module, eye-tracking data from the interactive control module, and patient parameters from the adaptation module. The VR headset's eye mask integrates a micro-vibration motor, a micro-air pump, and a heating element, outputting multi-dimensional feedback step by step. During the negative pressure ring and suction phases, the air pressure linearly increases from 0 kPa to 5 kPa, the heating element maintains the contact temperature at 32-34°C, and the vibration motor outputs vibration intensities below 0.3G. During the laser scanning phase, the vibration motor outputs low-frequency vibrations of 20-50 Hz, with the vibration frequency varying with the scanning density. During the negative pressure ring removal phase, the vibration, air pressure, and temperature outputs stop synchronously. During the lens removal phase, the vibration motor outputs a weak pulse at a frequency of 1 Hz. The system integrates an eye-tracking module. When the patient's eye rotation angle exceeds a preset threshold during the simulated surgery, dual feedback is triggered: the vibration motor outputs a warning vibration, the speaker plays a prompt sound, the simulation is paused, and a pop-up window displays an explanation of the eye fixation during the surgery. It is equipped with a noise-reducing speaker and an operating room ambient sound library to play scene-specific sound effects synchronized with the steps. Patients can also adjust the feedback intensity using a VR controller.

[0010] Preferably, the effect preview module receives adaptation parameters from the adaptation module and node selection instructions from the interaction control module, and presets four time nodes: 1 day, 1 week, 1 month, and 3 months post-surgery. The patient selects the corresponding node through a VR controller. Each node generates a virtual standard vision chart model, dynamically adjusts the clarity of the optotypes, and marks the expected visible optotype lines and vision fluctuation range for the corresponding node. Simultaneously, a daily scene simulation model is generated, with scene clarity improving over time. Based on the patient's corneal parameters, an AI model for predicting recovery progress is generated. The system takes into account the patient's age, preoperative corneal thickness, and eye habits, and outputs a suitable recovery curve. It also generates a recovery risk heatmap and displays suggested care plans simultaneously. The system supports patients uploading photos of daily scenes, and generates preoperative and postoperative comparison images according to time points to simulate visual differences under different lighting conditions. At each point, a preset voice explanation is played simultaneously, and text prompts pop up. The output preview data is displayed to the patient through an interactive control module.

[0011] The AI ​​model for predicting recovery progress adopts a fusion architecture of gradient boosting regression and attention mechanism, including an input layer, a feature encoding layer, an attention weight allocation layer, a regression prediction layer, and an output layer. The feature encoding layer contains three fully connected sub-layers with 64, 32, and 16 neurons, respectively. The attention weight allocation layer sets dynamic weight factors based on preoperative corneal thickness, eye habits, and age, with initial weight ratios of 0.5, 0.3, and 0.2, respectively. The regression prediction layer uses the ReLU activation function, and the output layer maps the quantitative value of recovery progress through the Sigmoid function.

[0012] Input parameter preprocessing rules: Age: Classified and coded as follows: 18-25 years old = 1, 26-35 years old = 2, 36-45 years old = 3, 46 years old and above = 4; Preoperative corneal thickness: Original values ​​(in μm) were retained and normalized using Z-Score, with a mean of 520 μm and a standard deviation of 30 μm. Eye habits: Quantify the daily close-range eye use time as ≤4h=1, 4~8h=2, >8h=3, and construct a combined feature vector by combining whether you stay up late (0=no / 1=yes) and whether you wear contact lenses (0=no / 1=yes).

[0013] Input-output intrinsic relationship logic: Preoperative corneal thickness is positively correlated with recovery rate: for every 50μm increase in thickness, the rate of visual recovery one day after surgery increases by 8%~10%, and the area of ​​edema risk zone in the recovery risk heat map decreases by 15%; Age is positively correlated with recovery period: the percentage of patients over 36 years old who recovered their vision one month after surgery was 12% to 15% lower than that of patients under 25 years old, and the model automatically extended the recommended period for mid-term care. When the daily near-vision usage time is >8 hours, the range of visual fluctuations increases by ±0.1D 3 months after surgery. The dryness risk area is highlighted in the thermal map, and a targeted care plan to "reduce the continuous use of eyes" is output simultaneously.

[0014] Model training and validation parameters: Training dataset: Contains postoperative follow-up data of 10,000 clinical refractive surgery patients, including myopia of -0.50D to -12.00D, corneal thickness of 450 to 600μm, and age of 18 to 55 years, divided into training set, validation set and test set in a ratio of 7:2:1; Training parameters: number of iterations = 100 rounds, learning rate = 0.001, batch size = 32, loss function is mean squared error (MSE), regularization is L2 regularization, λ = 0.0001; Validation metrics: Vision recovery progress prediction error ≤ ±3%, risk heatmap accuracy ≥ 92%, R on the test set 2Rating ≥ 0.85.

[0015] System integration mechanism: The model receives standardized patient parameters output by the adaptation module (which pass parameter verification and logic validation), and links with the effect preview module in real time through the API interface. The prediction results are converted into a visual recovery curve by the interactive control module. The horizontal axis represents the postoperative time node, the vertical axis represents the visual recovery percentage, and a heat map is generated. The curve is mapped with three colors, red, yellow, and green, according to the risk level. The intelligent patient education module pushes related nursing questions simultaneously.

[0016] Preferably, the intelligent patient education module receives step identifiers, interactive data, and patient parameters transmitted from each module, and has a built-in question bank specifically for refractive surgery to achieve question-and-answer related to surgical steps. When a patient asks a question at a specific step, the system outputs voice and text answers and simultaneously plays the corresponding principle animation; it also supports follow-up question-and-answer, where the system automatically follows up on related questions after the patient asks a question and outputs targeted suggestions. Doctors can log in through the backend management system and perform 3D spatial annotation in a VR simulation scene. The annotations are displayed synchronously as the viewpoint rotates. The system supports launching an operation demonstration mode when doctors are annotating, and the annotation points dynamically follow the operation progress. The system supports multiple doctors annotating online at the same time, with different doctors' annotations distinguished by different colors, and the annotation content is merged and displayed in real time. It enables cross-modal linkage of parameters, feedback, preview, and Q&A data, records patient interaction data, and automatically prioritizes the display of detailed annotations and Q&A prompts for that step during the next simulation, improving patient education efficiency; the question bank supports doctors to update it regularly through the backend, and new questions take effect after approval, outputting collaborative instructions.

[0017] The beneficial effects of this invention are as follows: 1. This invention supports patients to input refractive parameters such as myopia and astigmatism via VR controllers or voice input, while incorporating biomechanical data such as corneal elastic modulus and thickness distribution. Built-in parameter verification logic ensures accurate input. Utilizing a model that integrates B-spline curves with corneal biomechanical parameters, it can adjust key surgical data such as laser scanning path and microlens thickness in real time based on patient parameters. For example, for every 1.00D increase in myopia, the relevant parameters are corrected accordingly. Doctors can also preset personalized plans, and patients can switch between and compare the scanning effects of different plans, intuitively perceive the surgical details adapted to their own eye conditions, clearly establish an understanding of the surgical's targetedness and safety, and reduce concerns caused by information asymmetry.

[0018] 2. The VR headset of this invention integrates a micro vibration motor, an air pump, and a heating element on the inner side of the eye mask. During the negative pressure suction phase, the air pressure linearly increases from 0 kPa to 5 kPa, and the heating element maintains the contact temperature at 32-34°C. At the same time, the vibration motor outputs low-intensity vibrations to recreate the pressure sensation in real surgery. During laser scanning, the vibration motor outputs low-frequency vibrations of 20-50 Hz according to the scanning density, which, combined with operating room-style sound effects, further enhances the realism. In terms of interaction, patients can adjust their viewing angle by rotating their head and can also replay the surgical steps at multiple speeds. Each step is followed by a Q&A session to help consolidate memory, allowing patients to become familiar with the surgical procedure through interaction, adapt to the physical sensations in the surgical environment in advance, and improve the efficiency of patient education information transmission.

[0019] 3. The effect preview module of this invention presets four key nodes: 1 day, 1 week, 1 month, and 3 months post-operation. After the patient selects a node, the system generates a virtual vision chart, dynamically adjusts the clarity of the optotypes, and displays the simulation effect of daily scenarios. It can also generate personalized recovery curves based on the patient's age, preoperative corneal thickness, and other data, mark recovery risk areas, and provide nursing suggestions. It supports uploading personal scene photos to generate pre- and post-operative comparison images. The intelligent patient education module integrates real-time Q&A and doctor 3D annotation functions. When patients have questions about specific steps, the system can provide answers with principle animations. Doctors can also provide online 3D annotation explanations. Multiple doctors' annotations are distinguished by different colors to ensure professional answers. At the same time, it records patient interaction data and prioritizes pushing key content in the next simulation, forming a complete patient education loop of pre-operative simulation, effect prediction, and professional Q&A, helping patients fully grasp the key points of surgery and nursing care. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the overall workflow of the system of the present invention; Figure 2 This is a flowchart illustrating the personalized adaptation and surgical simulation process of the present invention. Figure 3 This is a flowchart of the postoperative effect preview and intelligent patient education process of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1 to 3As shown, this embodiment of the invention provides a VR-based immersive simulation system for refractive surgery procedures, including a scene modeling module, a simulation module, an interactive control module, an adaptation module, a feedback module, an effect preview module, and an intelligent patient education module. The specific implementation of each module is as follows: The scene modeling module establishes a 1:1 3D model of the ophthalmic surgical environment, characters, and instruments. A standard ophthalmic operating room scene is built using 3DMax and Maya software, recreating key elements including the operating table (texture accuracy 0.1mm, simulating the texture of fabric coverage), the Zeiss VisuMax laser equipment (1:1 scale model, white matte material drawn using Substance Painter, recreating the surface reflectivity parameters of the equipment), the sterile operating table (marked with instrument zoning and positioning labels), and the medical shadowless lamp (simulating the spectrum of cool white light to avoid direct strong light during modeling). The doctor's image is modeled according to standard surgical attire, and motion capture is performed based on clinical surgical videos to ensure that the angle error of instrument movements is ≤5° and the simulation accuracy of operational force is ±0.1N. The core surgical instruments are modeled based on the physical structure, including a negative pressure ring (with annular adsorption port diameter error ≤0.5mm), an eyelid speculum (elastic opening structure simulating deformation coefficient ≥0.9), a lens separator, and lens forceps (clamps simulating the friction coefficient of silicone anti-slip coating); a transparent 3D layered model of the cornea is established, including the epithelium, stroma, and endothelium, which can dynamically simulate the corneal bulging deformation under negative pressure suction and the microlens morphology after laser scanning. It is a thin, transparent model with a thickness and refractive parameter correlation degree ≥98%.

[0023] Specific parameters of the corneal layering model: Epidermal layer: thickness 50~55μm, material properties set as elastic modulus 2.0~2.5MPa, Poisson's ratio 0.45, modeled using transparent polygonal mesh with mesh accuracy 0.01mm; Matrix layer: thickness 400~500μm, dynamically adjusted according to the patient's actual parameters, elastic modulus 0.8~1.2MPa, Poisson's ratio 0.40, with collagen fiber texture added inside, the fiber direction is consistent with the corneal meridian; Endodermis: thickness 5~7μm, elastic modulus 1.5~1.8MPa, Poisson's ratio 0.42. Cell arrangement texture is added during modeling, with hexagons tightly arranged.

[0024] Deformation calculation logic under negative pressure attraction: Using the finite element analysis algorithm, the corneal model was divided into 10,000+ tetrahedral elements, and a negative pressure load was applied to the anterior surface of the cornea in a linear increment of 0→5 kPa (incremental growth rate of 0.5 kPa / s). Deformation calculation formula: H = (P × R) 2 ) / (2×E×t) Where H is the corneal elevation height in mm; P is the negative pressure value in kPa; R is the corneal radius of curvature in mm; E is the corneal elastic modulus in MPa; and t is the corneal thickness in mm. Microlens morphology simulation after laser scanning: Based on the microlens thickness (50~150μm) and diameter (6~8mm) parameters output by the adapter module, Boolean operation is used to cut the corneal stroma model, and a 0.1mm rounded corner transition is set at the cutting edge to simulate the real surgical effect.

[0025] The simulation module, based on the scene modeling module, dynamically demonstrates the entire process of clinical bilateral refractive surgery. It employs a step-by-step simulation logic, completing eight steps for the first eye and then repeating the same steps for the second eye. The specific implementation of each step is as follows: Step 1: Construct a virtual patient avatar lying flat model. The head fixation device automatically adjusts to a preset comfortable angle, and the laser head rotates out from the main body of the device at a rate of 0.5° / s and is aimed at the eye model. Step 2: Simulate a doctor holding a negative pressure ring model and bringing it close to the eye at a speed of 3mm / s to accurately position it at a preset location on the corneal surface; Step 3: Demonstrate the action of the eyelid opener model in opening the eyelid, and simultaneously show the contact state between the eyelid opener fixing structure and the eyelid; Step 4: Generate a green positioning light spot model, move it along a spiral trajectory on the corneal surface at a speed of 1mm / s, mark the positioning trajectory with bright lines, and trigger a 1kHz, 0.5s prompt sound after positioning is completed; Step 5: Use dynamic ripple effects to display the suction process, display the negative pressure value in real time, and indicate the normal range of 60~80mmHg; Step 6: Generate a blue beam model to simulate the scanning path, and demonstrate the microlens formation process through a corneal transparency model; Step 7: Simulate the closing action of the negative pressure system, and gently remove the negative pressure ring model at a speed of 2mm / s; Step 8: Enlarge the eye area to 3 times its original size to demonstrate the actions of inserting the lens separator, separating the lens, and removing the microlens with lens tweezers; For key steps (laser scanning, lens removal), a detail magnification function is set up, with the magnification factor adjustable from 2 to 5 times as needed. Finally, the step signals and dynamic parameters are output to the interactive control module and the feedback module.

[0026] Key steps and precision control parameters: Step 1: Laser head rotation: The rotation rate is fixed at 0.5° / s, and the deviation from the eye model is ≤0.1°. The laser head is aligned with the eye model in real time using the baseline (red virtual line) in the VR scene. Step 2: Negative pressure ring positioning: Positioning deviation ≤ 0.2mm. Precise alignment is achieved through 3 preset positioning markers (green virtual dots, 0.5mm in diameter) on the corneal surface. If the deviation exceeds the limit, a positioning failure prompt sound (1.5kHz, lasting 0.3s) is triggered. Step 5: Negative pressure value display: The real-time display accuracy is ±1mmHg, and the negative pressure rise rate is synchronized with the frame rate of the ripple effect animation (30fps) to ensure visual and data consistency. Step 8: Detail magnification: The magnified area uses center clipping and edge blur rendering. The screen resolution remains at 1920×1080 after magnification, and the rendering frame rate is ≥60fps to avoid screen stuttering after magnification. The magnification is triggered by pressing and holding the magnification button on the VR controller for 1 second. After releasing, the original view will be automatically restored.

[0027] Step signal output format: The output signal includes the step ID (1~8), the current step progress (0%~100%), and key parameter values ​​(such as negative pressure value and scan density). The data transmission format is JSON, and the transmission frequency is 10Hz to ensure that the interactive control module and the feedback module receive the data in real time.

[0028] The interactive control module receives step signals and screen data from the simulation module, allowing patients to adjust their viewing angle by naturally rotating their heads. The horizontal rotation range is ±120°, and the vertical rotation range is ±60°. The viewing angle switching frame rate is ≥90fps to ensure no lag. The lower right corner of the scene interface has pause, continue, and replay function buttons, which are triggered by clicking the VR controller. The replay function includes replaying the current step and replaying key steps. The replay speed can be adjusted between 0.5x and 1.5x, with a step size of 0.25x. After each step, a process question will automatically pop up with 3 options. The patient selects the answer using the VR controller. The system will provide real-time feedback on the correct or incorrect result and output the analysis text. If the error is incorrect, the correct device name and function will be displayed. At the same time, user operation commands such as viewing angle adjustment and parameter input will be output to the adaptation module and preview module.

[0029] Viewpoint adjustment technical parameters: Rotational angular velocity: The horizontal / vertical rotational angular velocity is 60° / s, which can be adjusted by the patient via the VR hand controller joystick. The adjustment range is 30° / s to 90° / s, with a step size of 10° / s. Viewpoint delay: Viewpoint adjustment command response delay ≤20ms, screen rendering delay ≤30ms, to avoid screen ghosting when the viewpoint is rotated; Viewpoint boundary limitation: When the horizontal rotation exceeds ±120° or the vertical rotation exceeds ±60°, boundary vibration feedback (0.1G, lasting 0.2s) is triggered, and a semi-transparent gray mask is displayed at the edge of the screen to indicate the viewpoint boundary.

[0030] Playback function control logic: Playback progress bar: A linear progress bar is displayed at the bottom of the scene interface, which allows VR controllers to click on any part of the progress bar to jump to the corresponding time point; Speed ​​switching: At 0.5x speed, the frame rate drops to 30fps, at 1.0x speed it is 60fps, and at 1.5x speed it is 90fps. There is no screen stuttering or frame skipping when switching speeds. Key steps marked: The system automatically adds red markers for steps 5 (negative pressure suction), 6 (laser scanning), and 8 (lens removal). Clicking on the marker during playback will directly jump to the starting position of the corresponding step.

[0031] Question and answer interaction data records: The recorded information includes patient ID, step ID, answer duration, answer selection, and whether the answer is correct. The data is stored in a local database (SQLite) and simultaneously synchronized to the doctor's backend to assess the patient's understanding of the surgical procedure.

[0032] The adaptation module receives patient parameters from the interactive control module and dynamically adjusts the simulation plan based on the patient's individual refractive parameters to construct a differentiated adaptation model. Upon system startup, a parameter input interface pops up, allowing patients to input refractive parameters via VR controllers or voice input. The ranges are: myopia -0.50D to -12.00D, astigmatism 0 to -6.00D, and astigmatism axis 0° to 180°. The system also incorporates corneal elastic modulus (50-100 kPa), corneal thickness distribution (data collected at 1mm intervals from 20 nodes), and pupil dynamic diameter (difference between low and bright light environments). Built-in parameter verification logic automatically displays a prompt when the input refractive power exceeds the preset range. A model integrating B-spline curves and corneal biomechanical parameters is used to construct a real-time correlation algorithm between parameters and effects. For every 1.00D increase in myopia, the laser scanning path length (5%~8%) and microlens thickness (10%~12%) are adjusted, simultaneously correcting the corneal deformation coefficient (reducing it by 0.02~0.03, dimensionless). A virtual intraoperative monitoring submodule is added to simulate real-time corneal deformation data during surgery, such as a corneal bulge height deviation of ±0.05mm during negative pressure suction. When the deformation is >0.1mm, the laser scanning compensation path is automatically adjusted, with a compensation angle of 0.5°~1°. Doctors can preset adaptation schemes in the background, such as a high astigmatism-specific scheme and a thin corneal protection scheme. Patients can switch between different schemes during simulation to compare the scanning effects, and the adaptation data is output to the simulation module and feedback module.

[0033] Algorithm for fusing B-spline curves and biomechanical parameters: B-spline curve parameters: A 3rd-order B-spline curve is used, with a basis function degree of n=3, and 8 control points evenly distributed within a corneal diameter of 8mm. The node vector is set to [0, 0, 0, 0, 0.25, 0.5, 0.75, 1, 1, 1, 1]. Parameter fusion weighting: Refractive parameters (myopia / astigmatism) account for 60% of the weighting, and corneal biomechanical parameters (elastic modulus / thickness) account for 40% of the weighting. The fusion formula is as follows: W = 0.6 × D + 0.4 × (E / t) Where W is the fusion weight value; D is the standardized value of the refractive parameter, -12.00D to -0.50D is mapped to 0 to 1; E is the elastic modulus in MPa; and t is the corneal thickness in mm. Laser scanning path adjustment rules: Based on the fusion weight W, when W increases by 0.1, the helical spacing of the scanning path decreases by 0.05mm and the scanning depth increases by 5μm to ensure that the scanning accuracy matches the patient parameters.

[0034] Virtual intraoperative monitoring submodule deformation compensation logic: Deformation threshold setting: Compensation is triggered when the corneal elevation height deviation is >0.1mm or the horizontal offset is >0.08mm; Compensation path calculation: Using the inverse iterative method, the coordinates of the control points of the scanning path are corrected based on real-time deformation data (ΔH, ΔX, ΔY). P'=P+(ΔH×k1, ΔX×k2, ΔY×k2) Where P is the original control point coordinates; P' is the corrected coordinates; k1=0.8 is the height compensation coefficient; k2=1.2 is the horizontal compensation coefficient; Compensation frequency: Deformation data is collected once every 10ms, and the compensation response delay is ≤50ms to ensure that the scanning path matches the corneal morphology in real time.

[0035] Parameter validation exception handling mechanism: Abnormal myopia degree: When entering <-12.00D or >-0.50D, a prompt text will pop up saying "Myopia degree must be within the range of -0.50D to -12.00D, please re-enter", and the VR controller will trigger a vibration prompt at the same time; if the input is abnormal 3 times in a row, the system will automatically pop up a pop-up window "Contact doctor to confirm parameters" and display the doctor's back-end contact information; Astigmatism axis error: When inputting <0° or >180°, the system will automatically correct the value to 0° or 180° and pop up a correction prompt, such as "Astigmatism axis out of range, has been automatically corrected to XX°"; Missing corneal parameters: If corneal elastic modulus or thickness data is not entered, the system will forcibly jump to the parameter entry interface before the simulation starts. The entry interface will display required parameters marked with a red asterisk to ensure that the core parameters are complete.

[0036] Doctor's preset treatment plan switching logic: The storage format of the treatment plan is as follows: Each treatment plan contains a "treatment plan ID", "suitable population tag", "laser scanning parameter set" and "microlens parameter set", which are stored as XML files and support batch import / export by doctors in the backend. Switching Response: The patient clicks the "Switch Plan" button with the VR controller. The switching delay is ≤1 second. After switching, the system automatically recalculates the scanning path and refreshes the corneal model display. At the same time, a text prompt "Plan has been switched, the current plan is XX" pops up. The scheme comparison function supports a dual-screen comparison mode. The scene interface is divided into two columns: the left column displays the current scheme and the right column displays the comparison scheme. The two columns play the scanning process simultaneously. Key parameters (such as scan length and microlens thickness) are displayed in numerical form below the corresponding column, making it easy for patients to intuitively compare differences.

[0037] The feedback module receives step signals from the simulation module, eye-tracking data from the interactive control module, and patient parameters from the adaptation module, achieving synchronized coordination between surgical steps and multi-sensory feedback, focusing on tactile simulation specific to refractive surgery. The VR headset integrates a micro-vibration motor (amplitude range 0.1mm~0.3mm), a micro-air pump (pressure adjustment range 0~5kPa), and a heating element on the inner side of the eye mask. It outputs multi-dimensional feedback step by step. During the negative pressure ring and negative pressure suction stages, the air pressure linearly increases from 0kPa to 5kPa, the heating element maintains the contact temperature at 32~34℃, and the vibration motor outputs vibration intensities below 0.3G (G is the unit of gravitational acceleration, 1G=9.8m / s²). 2 During the laser scanning stage, the vibration motor outputs low-frequency vibrations of 20~50Hz with an amplitude of 0.05~0.1mm. The vibration frequency varies with the scanning density, with 50Hz in the high-density scanning area and 20Hz in the low-density scanning area. During the stage of removing the negative pressure ring, the vibration, air pressure, and temperature outputs stop synchronously. During the stage of removing the lens, the vibration motor outputs a weak pulse at a frequency of 1Hz. Integrated with an eye-tracking module and a sampling rate of 120Hz, the device triggers dual feedback when the patient's simulated eye movement exceeds 3° during surgery. The vibration motor outputs a 0.2G warning vibration, and the speaker plays a 1.2kHz prompt tone. Simultaneously, the simulation is paused, and a pop-up window displays an explanation of the eye fixation during surgery. It is equipped with a noise-reducing speaker and an operating room ambient sound library to play scene-specific sound effects synchronized with the steps, including a ≤30dB mechanical operation sound from the laser head rotation, a 1kHz / 0.5s prompt tone for successful positioning, a ≤25dB low-pitched equipment sound from negative pressure suction, doctor's instructions, and ≤25dB sounds from sterilizing instruments. The device allows patients to adjust the feedback intensity via a VR controller, with three levels: mild, moderate, and severe. The default setting is mild, enabling multi-sensory feedback output.

[0038] Multi-sensory feedback collaborative synchronization mechanism: Time synchronization: Based on the timestamp of the step signal output by the analog module, the accuracy is 1ms, the trigger delay of the vibration motor, air pump and heating plate is ≤10ms, and the time difference between the scene sound effect and the step action is ≤50ms. Parameter synchronization rules: In the negative pressure suction stage (step 5), the air pressure increment rate is synchronized with the negative pressure value display progress; 0→5kPa corresponds to a negative pressure value of 0→80mmHg. The vibration motor is triggered when the heating element temperature reaches 32℃. In the laser scanning stage (step 6), the vibration frequency is correlated with the scanning path density in real time; the scanning point density increases by 10 points / mm. 2 The vibration frequency increased by 5Hz.

[0039] Eye-tracking module calibration logic: Preoperative calibration steps: After the patient puts on the VR headset, the system displays 5 calibration points, with the four corners of the screen plus the center. The patient looks at each calibration point in turn, and stays at each point for 2 seconds. The system records the eye movement coordinate deviation. Error correction formula: Δθ = θ measured -θ target Where Δθ is the eye movement angle deviation, θ measured θ is the measured angle. target For the target angle, the error after calibration is ≤0.5°; Intraoperative dynamic calibration: After each surgical step is completed (such as after the eyelid speculum action in step 3), the center calibration point verification is automatically triggered once. If the deviation is >1°, the local calibration is re-performed, and only the center and two edge points are verified.

[0040] Feedback intensity quantification standard: Light setting: vibration intensity 0.1~0.15G, air pressure 1~2kPa, heating element temperature 32℃; Medium setting: vibration intensity 0.2~0.25G, air pressure 2~3kPa, heating element temperature 33℃; Heavy setting: vibration intensity 0.25~0.3G, air pressure 3~5kPa, heating element temperature 34℃; Adjustment step size: Each adjustment involves a vibration intensity change of 0.05G, an air pressure change of 1kPa, and a temperature change of 0.5℃, ensuring a smooth and abrupt adjustment.

[0041] The effect preview module receives adaptation parameters from the adaptation module and node selection instructions from the interaction control module, visually presenting the simulated visual recovery effects at different postoperative time points, achieving multi-dimensional quantification and interactive functions. It presets four time points: 1 day, 1 week, 1 month, and 3 months post-surgery, allowing patients to select the corresponding point using a VR controller. Each point generates a virtual standard visual acuity chart model, dynamically adjusting the optotype clarity. The optotype clarity on 1 day post-surgery is 60%±5% of that at 1 month post-surgery, and the clarity at 1 week post-surgery is 85%±3% of that at 1 month post-surgery. It also marks the expected visual target line and visual fluctuation range for each point, such as ±0.2D on 1 day post-surgery. Simultaneously, it generates simulated models of daily scenes, such as reading, using a computer, and outdoor road signs, with scene clarity improving over time. The clarity of daily scenes on 1 day post-surgery is 55%±5% of that at 1 month post-surgery, and the clarity of daily scenes at 1 week post-surgery is 80%±3%. Based on the patient's corneal parameters, an AI model for predicting recovery progress is generated. The system takes into account the patient's age, preoperative corneal thickness, and eye habits, and outputs a personalized recovery curve. It also generates a recovery risk heatmap, for example, marking the corneal edema risk area on the first day after surgery in red for patients with thin corneas (thickness <480μm), and simultaneously displays suggested care plans, such as increasing the frequency of artificial tears. The system supports patients uploading photos of daily scenes, such as home environment and work scene, and generates preoperative and postoperative comparison images according to time nodes, simulating the difference in vision under different lighting conditions (strong light / weak light). Each node simultaneously plays a preset voice explanation and pops up text prompts. The output preview data is displayed to the patient through the interactive control module.

[0042] Technical details of comparison image generation: Photo upload requirements: Support JPG / PNG format, resolution ≥1920×1080, file size ≤10MB. After uploading, the system will automatically compress the image to 1920×1080 using JPEG compression algorithm and 70% compression quality to avoid consuming too much storage resources. Preoperative / postoperative outcome management: Preoperative results: Based on the patient's preoperative refractive parameters, visual blur was simulated using Gaussian blur and contrast reduction algorithms. For every 1.00D increase in myopia, the blur radius increased by 0.5px, and the contrast decreased by 5%. Postoperative results: Based on the percentage of visual recovery at corresponding time points, such as 60% on day 1 post-surgery, the blur radius and contrast are adjusted in reverse. When the recovery percentage reaches 60%, the blur radius is 40% of the preoperative value, and the contrast is 160% of the preoperative value. Lighting simulation logic: Under strong light conditions (simulating midday outdoor light), the photo brightness is increased by 20%, and a slight glare effect is added (center brightness 120%, edge gradient); under weak light conditions (simulating nighttime indoor light), the photo brightness is reduced by 30%, and noise reduction processing is added (noise reduction intensity 10%) to ensure that the visual differences under different lighting conditions conform to the real physiological laws.

[0043] Basis for calculating the range of visual acuity fluctuation: Calculation formula: Fluctuation range = ±(0.1D + 0.02 × preoperative myopia), for example, when the preoperative myopia is -5.00D, the fluctuation range = ±(0.1 + 0.02 × 5) = ±0.2D; Correction for influencing factors: When corneal thickness is <480μm, the fluctuation range is increased by 10% (e.g., ±0.2D becomes ±0.22D); when age is >40 years, the fluctuation range is increased by 5%. The corrected value is displayed next to the visual acuity chart model in the form of "Expected visual acuity: 1.0±0.22D". Data source for recovery curves: Based on clinical follow-up data of 10,000 cases, the same training dataset for the AI ​​model of recovery progress prediction. The visual recovery value at each time point is the average value of the corresponding population, and the fluctuation range is the standard deviation of the data to ensure that the prediction results have clinical reference significance.

[0044] Voice narration and text prompt configuration: Voice narration: Recorded by real people, with each narration session lasting ≤60 seconds. The narration content includes the current vision recovery status, precautions, and expected recovery. Text prompt: The font is Microsoft YaHei, the font size is 24px, the color is #333333, the background is semi-transparent white, the display position is at the bottom center of the scene interface, the display duration is 5 seconds, and it should avoid obscuring the comparison image or eye chart.

[0045] The intelligent patient education module receives step identifiers, interactive data, and patient parameters from various modules. It has a built-in question bank specifically for refractive surgery, including questions about instrument function, surgical parameters, and postoperative care. The answers are reviewed and confirmed by ophthalmologists, enabling step-by-step Q&A. When a patient asks a question at a specific step, the system outputs voice and text answers and simultaneously plays the corresponding principle animation, such as the 15-second animation on the principle of painlessness during the scanning stage. It also supports follow-up questions, where the system automatically follows up on related issues after a patient asks a question. For example, if a patient asks about postoperative driving time, the system will further inquire about the driving scenario and provide targeted suggestions. Doctors can log in through the backend management system and perform 3D spatial annotations in VR simulation scenes. They can choose between red and blue colors, with line widths of 2mm to 3mm. For example, they can use 3D lines to mark key areas for astigmatism correction on a corneal model. The annotations are displayed synchronously as the viewpoint rotates. Real-time operation demonstration annotation is supported. Doctors can start an operation demonstration mode while annotating, and the annotation points will dynamically follow the operation progress. Multiple doctors can annotate online simultaneously, with different colors used to distinguish the annotations of different doctors. For example, the chief surgeon uses red and the assistant doctor uses blue. The annotation content is merged and displayed in real time. The system enables cross-modal linkage of parameters, feedback, preview, and Q&A data. For example, after a patient inputs the parameter of high myopia (-10.00D), the system automatically activates the thin corneal protection mode (reducing negative pressure suction by 10%) in the multi-sensory feedback, displays the high myopia recovery curve by default in the postoperative preview, and prioritizes pushing postoperative care-related questions for high myopia in the Q&A. It records patient interaction data, such as repeatedly replaying the "lens removal" step more than 3 times, and automatically prioritizing the display of detailed annotations and Q&A prompts for that step in the next simulation, improving the efficiency of personalized patient education. The question bank supports doctors to update it monthly through the backend. New questions take effect after approval and output collaborative instructions to the feedback module and the effect preview module. For example, the feedback module synchronously adds tactile feedback trigger logic corresponding to the new question, and the effect preview module associates the timing of prompts for new postoperative care questions.

[0046] Multi-doctor 3D annotation collaborative logic: Annotation data format: Stored in GLB format, including annotation lines (line width 2~3mm), color information (RGB values, such as chief surgeon #FF0000, assistant surgeon #0000FF), and annotation text (font size 24px, position offset from annotation point 5mm). Real-time collaboration mechanism: Based on the WebSocket protocol, data transmission between multiple doctors is realized with a transmission rate of ≥1Mbps and a label update delay of ≤200ms. When multiple doctors label the same area at the same time, the system retains the latest label according to the latest label timestamp, and stores historical label versions to support retrospective viewing. Labeling permission control: The chief surgeon has the permission to edit / delete labels, while the assistant surgeon only has the permission to add labels. Permission configuration is assigned based on the doctor ID through the backend management system.

[0047] Cross-modal data linkage rules: Parameter and feedback linkage: When the patient inputs parameters for high myopia (≤-8.00D) or thin cornea (≤480μm), the system automatically sets the feedback intensity to mild by default and extends the air pressure increase time during the negative pressure suction phase from 10s to 15s; Parameter and preview linkage: When the patient's age is >40 years old, the effect preview module will prioritize displaying the 3-month postoperative node by default, and add a presbyopia risk warning label to the recovery curve; Interactive and Q&A linkage: If the patient replays the lens removal step ≥ 3 times, the intelligent patient education module will automatically push related questions such as the corneal healing principle after lens removal and the reasons for avoiding rubbing the eyes after surgery. The push will be made within 10 seconds after step 8 ends.

[0048] Issue database update and review process: Added question format: It must include 4 fields: question text, answer text, associated step ID, and principle animation path. For example: Question text "Why is there no pain during laser scanning?", associated step ID "6"; Review process: After a doctor submits a new question, it will be reviewed by two ophthalmologists at the deputy director level or above. Once the review is approved, the system will automatically update the question database and synchronize it to all VR devices. The update method is incremental update to avoid duplicate downloads.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A VR-based immersive simulation system for refractive surgery procedures, characterized in that, It includes a scene modeling module, a simulation module, an interactive control module, an adaptation module, a feedback module, an effect preview module, and an intelligent patient education module; Scene modeling module: Build a 3D model, construct the operating room scene, and complete doctor motion capture, core instrument modeling, and corneal layer model construction; Simulation module: Based on the refractive surgery process, it uses a binocular step-by-step logic to complete the monocular operation steps sequentially, including key step detail magnification function, and outputs step signals and dynamic parameters; Interactive control module: Enables patients to operate and manage the process of the simulated scene independently, including view adjustment, playback control and step-by-step question and answer interactive functions; Adaptation module: Based on individual patient refractive and biomechanical parameters, it constructs a differentiated adaptation model to achieve dynamic adjustment of the simulation plan, supports doctors in pre-setting adaptation plans, and outputs adaptation data; Feedback module: Enables synchronized surgical procedures with multi-sensory feedback, including vibration, air pressure, temperature, and contextual sound effects feedback, and triggers warning feedback when eye movement is abnormal; Effect Preview Module: Presents simulated visual recovery effects at different time points after surgery, including a visual acuity chart, scene simulation, and recovery curve, and supports patient interaction. Intelligent Patient Education Module: Integrates real-time intelligent Q&A, remote doctor annotation, and cross-module data collaboration functions to establish a closed-loop patient education system, realize data linkage and suggestion prompts, and support multi-doctor collaborative annotation.

2. The VR-based immersive simulation system for refractive surgery procedures according to claim 1, characterized in that, The scene modeling module establishes a 3D model of the surgical environment, roles, and instruments, builds a standard ophthalmic operating room scene, and restores key medical equipment and facilities; it models the doctor's image according to surgical standards and performs motion capture to ensure the accuracy of instrument operation; The core surgical instruments are modeled based on their physical structure, and a 3D layered model of the cornea is established to dynamically simulate the corneal bulging deformation under negative pressure suction and the microlens morphology after laser scanning.

3. The VR-based immersive simulation system for refractive surgery procedures according to claim 2, characterized in that, The simulation module is based on the basic model of the scene modeling module and performs a dynamic demonstration of the entire process of clinical bilateral refractive surgery. It adopts a step-by-step simulation logic for both eyes, completing the steps for the first eye first, and then repeating the same steps to complete the second eye. The specific implementation of each step is as follows: Step 1: Construct a virtual patient avatar lying flat model, the head fixation device automatically adjusts to the preset angle, and the laser head rotates out and aligns with the eye model; Step 2: Simulate a doctor holding a negative pressure ring model close to the eye and positioning it at a preset position on the corneal surface; Step 3: Demonstrate the eyelid opening action of the eyelid opener model, simultaneously showing the contact state; Step 4: Generate a positioning light spot model, move it on the corneal surface according to a preset trajectory and mark the positioning trajectory. Trigger a prompt sound after positioning is completed; Step 5: Demonstrate the negative pressure suction process and display the negative pressure value in real time; Step 6: Generate a beam model to simulate the scanning path, and use a corneal model to demonstrate the microlens formation process; Step 7: Simulate the shutdown action of the negative pressure system and remove the negative pressure ring model; Step 8: Zoom in on the eye area to demonstrate the actions of inserting the lens separator, separating the lens, and removing the microlens with lens tweezers; The key steps support amplification factor adjustment, and the final output steps include step signals and dynamic parameters.

4. The VR-based immersive simulation system for refractive surgery procedures according to claim 3, characterized in that, The interactive control module receives step signals and screen data from the simulation module, supports patients in adjusting their viewing angle and triggering function operations, and includes playback of the current step and key steps, with adjustable playback speed. After each step is completed, a process Q&A question will automatically pop up. After the patient selects an answer, the system will immediately provide feedback on the result and output the analysis text, as well as the user's operation instructions.

5. The VR-based immersive simulation system for refractive surgery procedures according to claim 4, characterized in that, The adapter module receives patient parameters from the interactive control module, supports patient input or voice input of refractive parameters, and incorporates corneal-related biomechanical parameters; it has built-in parameter verification logic, and automatically pops up a prompt text when the input diopter exceeds the preset range; A parameter fusion model is used to construct a real-time correlation algorithm between parameters and effects. The laser scanning path and microlens parameters are dynamically adjusted according to the refractive power, and the corneal deformation coefficient is corrected simultaneously. A virtual intraoperative monitoring submodule is added to simulate real-time corneal deformation data. When the deformation exceeds the preset threshold, the laser scanning compensation path is automatically adjusted. Doctors can preset adaptation schemes in the background, and patients can switch between different schemes to compare the differences in scanning effects and output adaptation data.

6. The VR-based immersive simulation system for refractive surgery procedures according to claim 5, characterized in that, The feedback module receives step signals from the simulation module, eye movement data from the interactive control module, and patient parameters from the adaptation module. The feedback component integrated inside the eye mask of the VR headset outputs multi-dimensional feedback step by step, with different vibration, air pressure, and temperature outputs corresponding to different surgical stages. It integrates an eye-tracking module, triggering an alert and pausing the simulation when the patient's eye rotation angle exceeds a preset threshold during the simulated surgery, while simultaneously displaying a prompt explanation window; it is equipped with a noise-canceling speaker and an operating room ambient sound library to play contextual sound effects synchronized with the steps; and it supports patients in adjusting the intensity of feedback.

7. The VR-based immersive simulation system for refractive surgery procedures according to claim 6, characterized in that, The effect preview module receives the adaptation parameters from the adaptation module and the node selection instructions from the interaction control module. It presets multiple key time nodes after the operation and supports patients in selecting the corresponding nodes. Each node generates a virtual standard vision chart model and dynamically adjusts the sharpness of the optotypes, marking the expected visual acuity and fluctuation range of the corresponding node. Simultaneously, it generates a simulation model of daily scenes. Based on individual patient parameters, a recovery progress prediction model is generated, and an adaptive recovery curve and suggested nursing plan are output. Patients can upload photos of daily scenes, and the system generates pre- and post-operative comparison images according to time nodes to simulate visual differences under different lighting conditions. At each node, a preset voice narration is played and a text prompt pops up. The output preview data is displayed to the patient through the interactive control module.

8. The VR-based immersive simulation system for refractive surgery procedures according to claim 7, characterized in that, The intelligent patient education module receives step identifiers, interactive data, and patient parameters from each module. It has a built-in question bank specifically for refractive surgery, enabling Q&A related to surgical steps. When a patient asks a question, the system outputs voice and text answers along with corresponding principle animations; it also supports follow-up questions. Doctors can log in through the backend management system and perform 3D spatial annotations in a VR simulation scene. The annotations are displayed synchronously with the viewpoint. It supports launching an operation demonstration mode while annotating. It also supports multiple doctors annotating online at the same time, and the annotations of different doctors can be displayed separately. The annotation content is merged in real time. It enables cross-modal linkage of cross-module data, records patient interaction data, and automatically prioritizes the display of detailed annotations and Q&A prompts for relevant steps during the next simulation; it supports doctors to regularly update the question database through the backend, and new questions take effect after approval, ultimately outputting collaborative instructions.

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