A non-penetrating trabecular surgery monitoring system for glaucoma treatment

Abstract

This invention relates to the field of data processing technology, specifically to a non-penetrating trabeculae surgery monitoring system for glaucoma treatment. It solves the technical problem in existing technologies where the reliance on discrete time-point examination data for lagging assessment of the continuous dynamic process of fibrosis leads to untimely warnings and delayed intervention windows during postoperative risk monitoring of non-penetrating trabeculae surgery. The system includes: a data acquisition module for collecting ocular examination data at multiple postoperative stages; the ocular examination data includes trabecular membrane thickness data, trabecular membrane adhesion data, implant angiography data, and intraocular pressure data; a fibrosis analysis module for determining the degree of trabecular membrane hyperplasia and implant absorption abnormality coefficient based on the ocular examination data for each postoperative stage, and for determining the postoperative degree of ocular fibrosis; and a monitoring and prompting module for adjusting the monitoring frequency based on the postoperative degree of ocular fibrosis for each postoperative stage.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment. Background Technology

[0002] Glaucoma is an irreversible blinding eye disease characterized by optic nerve damage and visual field defects, with pathologically elevated intraocular pressure being its most significant risk factor. Non-penetrating trabeculectomy is an important surgical procedure for treating glaucoma. It promotes aqueous humor outflow by creating an internal decompression chamber (filtration channel), thereby lowering intraocular pressure and protecting the optic nerve.

[0003] Maintaining long-term patency of the filtration tract post-surgery is crucial for surgical success. This process is accompanied by the wound healing response of ocular tissues, the core of which is the proliferation and migration of fibroblasts, followed by fibrosis. This fibrosis is a dynamic, continuous, and significantly individual-variable pathophysiological process that gradually leads to scarring, narrowing, or even closure of the filtration tract, directly causing intraocular pressure to become uncontrolled again and resulting in surgical failure.

[0004] Currently, clinical monitoring of the critical postoperative fibrosis process mainly relies on intraocular pressure measurements and ultrasound biomicroscopy at discrete, pre-defined time points (such as 1 week, 1 month, and 3 months postoperatively). This discrete sampling monitoring model cannot reflect the rate and extent of fibrosis and its true impact on intraocular pressure in real time and comprehensively, resulting in a significant lag in postoperative risk warning. Summary of the Invention

[0005] To address the technical problems in existing technologies where the reliance on discrete time-point examination data for lagging assessment of the continuous dynamic process of fibrous hyperplasia leads to untimely early warnings and delayed intervention windows in postoperative risk monitoring of non-penetrating trabeculectomy, the present invention aims to provide a monitoring system for non-penetrating trabeculectomy in glaucoma treatment. The specific technical solution adopted is as follows: Firstly, a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment is provided, comprising: a data acquisition module for acquiring ocular detection data at multiple postoperative stages; the ocular detection data includes trabecular membrane thickness data, trabecular membrane adhesion data, implant angiography data, and intraocular pressure data; a fibrosis analysis module for obtaining the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient based on the ocular detection data for each postoperative stage, and determining the postoperative ocular fibrosis degree; the trabecular membrane hyperplasia degree is used to characterize the degree of fibrosis growth of the trabecular membrane; the implant absorption abnormality coefficient is used to characterize the degree of deviation of implant degradation and absorption from a preset standard; and a monitoring prompting module for adjusting the monitoring frequency based on the postoperative ocular fibrosis degree for each postoperative stage.

[0006] Based on the above technical solution, the non-penetrating trabecular bone surgery monitoring system for glaucoma treatment provided by this invention accurately acquires four core data types—trabecular membrane state, implant absorption state, and intraocular pressure—through a data acquisition module. This provides comprehensive and crucial foundational support for subsequent analysis, avoiding the one-sidedness of relying solely on intraocular pressure data. The fibrosis analysis module then integrates and quantifies the data to obtain the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient, thereby determining the postoperative ocular fibrosis degree, which comprehensively reflects the impact of ocular fibrosis on surgical outcomes. This achieves an objective and accurate assessment of key postoperative recovery states. Finally, the monitoring and prompting module dynamically adjusts the monitoring frequency at each postoperative stage based on these core assessment indicators. This ensures that postoperative abnormalities are captured promptly while avoiding unnecessary high-frequency examinations, effectively solving the problems of delayed treatment effect understanding and low monitoring efficiency in postoperative risk monitoring.

[0007] In conjunction with the first aspect above, in one possible implementation, the aforementioned fibrosis analysis module is specifically used to: determine the degree of trabecular membrane hyperplasia based on trabecular membrane thickness data and trabecular membrane adhesion data; determine the implant absorption abnormality coefficient based on implant angiography data; correct the degree of trabecular membrane hyperplasia based on the implant absorption abnormality coefficient to obtain the effective degree of trabecular membrane hyperplasia; and determine the postoperative ocular fibrosis degree based on the effective degree of trabecular membrane hyperplasia, trabecular membrane adhesion data, and intraocular pressure data.

[0008] In conjunction with the first aspect above, in one possible implementation, the aforementioned fibrosis analysis module is specifically used for: determining the difference in trabecular membrane thickness based on adjacent trabecular membrane thickness data during the early postoperative critical period; determining the onset time of rapid fibrosis based on the difference in trabecular membrane thickness; determining the initial critical degree of trabecular membrane hyperplasia based on the time length from the onset time of rapid fibrosis to the end of the early postoperative critical period, the total time length of the early postoperative critical period, and the trabecular membrane thickness data within the early postoperative critical period; and determining the degree of trabecular membrane hyperplasia based on the trabecular membrane adhesion data, trabecular membrane thickness data, and the initial critical degree of trabecular membrane hyperplasia at each postoperative period.

[0009] In conjunction with the first aspect above, in one possible implementation, the aforementioned fibrosis analysis module is specifically used to: determine the implant absorption intensity coefficient based on implant imaging data; the implant absorption intensity coefficient is used to characterize the degradation and absorption rate of the implant; and determine the implant absorption abnormality coefficient for each postoperative period based on a preset absorption limit threshold and the implant absorption intensity coefficient.

[0010] In conjunction with the first aspect above, in one possible implementation, the aforementioned fibrosis analysis module is specifically used for: establishing an implant imaging data sequence in chronological order based on implant imaging data from multiple time points within the same postoperative period; performing trend analysis on the implant imaging data sequence to obtain degradation parameters characterizing the implant degradation trend; and determining the implant absorption intensity coefficient based on the degradation parameters.

[0011] In conjunction with the first aspect above, in one possible implementation, the monitoring and prompting module is specifically used to: preset the normal and abnormal ranges of postoperative ocular fibrosis in each postoperative period; if the postoperative ocular fibrosis in the target postoperative period is within the normal range, use the normal monitoring frequency corresponding to the target postoperative period; if the postoperative ocular fibrosis in the target postoperative period is within the abnormal range, use the abnormal monitoring frequency corresponding to the target postoperative period; the detection interval of the abnormal monitoring frequency is less than the normal monitoring frequency.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the monitoring and prompting module is further used to: statistically analyze the conventional distribution range of ocular fibrosis degree at different postoperative periods based on postoperative follow-up data from multiple similar non-penetrating trabeculectomies, and set it as the normal range for the corresponding period; and, based on the correlation analysis results between the risk of intraocular pressure loss of control and fibrosis degree in clinical practice, determine the critical value of fibrosis degree that may lead to abnormal increase in intraocular pressure at each period, and set the range that exceeds the normal range and is higher than the critical value as the abnormal range.

[0013] In conjunction with the first aspect above, in one possible implementation, the data acquisition module is specifically used to: divide the postoperative time into an early critical postoperative period, an early postoperative period, a mid-term postoperative period, and a late postoperative period; and collect eye detection data at a corresponding acquisition frequency within each postoperative period.

[0014] In conjunction with the first aspect above, in one possible implementation, the data acquisition module is specifically used for: acquiring eye image data using an ultrasonic biological microscope; and extracting eye detection data from the eye image data.

[0015] In conjunction with the first aspect above, in one possible implementation, the monitoring prompt module is further configured to: generate prompt information corresponding to the adjusted monitoring frequency, and output the prompt information to a display device or user terminal.

[0016] In a second aspect, a non-penetrating trabecular bone surgery monitoring device for glaucoma treatment is provided, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is configured to execute the instructions to perform the actions described in the first aspect and any possible implementation thereof. This non-penetrating trabecular bone surgery monitoring device for glaucoma treatment can be an electronic device or a chip within an electronic device.

[0017] Thirdly, a computer-readable storage medium is provided, in which instructions are stored, which, when executed on a non-penetrating trabecular surgical monitoring device for glaucoma treatment, cause the non-penetrating trabecular surgical monitoring device for glaucoma treatment to perform the actions described in the first aspect and any possible implementation thereof.

[0018] Fourthly, a computer program product containing instructions is provided that, when the computer program product is run on a non-penetrating trabecular surgical monitoring device for glaucoma treatment, causes the non-penetrating trabecular surgical monitoring device for glaucoma treatment to perform the actions described in the first aspect and any possible implementation thereof.

[0019] The present invention has the following beneficial effects: The data acquisition module precisely acquires four core data points characterizing the trabecular membrane state, implant absorption status, and intraocular pressure, providing comprehensive and crucial foundational support for subsequent analysis and avoiding the one-sidedness of relying solely on intraocular pressure data. The fibrosis analysis module then integrates and quantifies the data to obtain the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient, thereby determining the postoperative ocular fibrosis degree, which comprehensively reflects the impact of ocular fibrosis on surgical outcomes, achieving an objective and accurate assessment of key postoperative recovery status. Finally, the monitoring and prompting module dynamically adjusts the monitoring frequency at each postoperative stage based on these core assessment indicators, ensuring timely detection of postoperative abnormalities while avoiding unnecessary high-frequency examinations, effectively solving the problems of delayed understanding of treatment effects and low monitoring efficiency in postoperative risk monitoring. Attached Figure Description

[0020] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a system architecture diagram of a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment, provided as an embodiment of the present invention. Figure 2 This is a flowchart of a non-penetrating trabecular bone surgery monitoring method for glaucoma treatment, provided in one embodiment of the present invention. Figure 3 A schematic diagram of an early implant provided in one embodiment of the present invention; Figure 4 A schematic diagram of a late-stage implant provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of a non-penetrating trabecular bone surgery monitoring device for glaucoma treatment, provided as an embodiment of the present invention. Detailed Implementation

[0022] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] The following description, in conjunction with the accompanying drawings, details a specific solution for a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment provided by the present invention.

[0025] Please see Figure 1 The diagram illustrates a system architecture of a non-penetrating trabecular bone surgery monitoring system for glaucoma treatment, provided by an embodiment of the present invention. The non-penetrating trabecular bone surgery monitoring system for glaucoma treatment includes: a data acquisition module 1, a fibrosis analysis module 2, and a monitoring and prompting module 3.

[0026] Data acquisition module 1 is the core of the system's data input, primarily used to collect ocular examination data at multiple postoperative stages. This ocular examination data includes trabecular membrane thickness data, trabecular membrane adhesion data, implant angiography data, and intraocular pressure data. These data form the basis for accurate assessment by the subsequent fibrosis analysis module 2.

[0027] In some implementations, data acquisition module 1 first divides the postoperative period into four phases: early critical postoperative period, early postoperative period, mid-postoperative period, and late postoperative period. Within each phase, ocular examination data is collected at the corresponding acquisition frequency. Simultaneously, ocular image data is acquired using ultrasound biomicroscopy (UBM), and the aforementioned four types of ocular examination data are extracted from these image data to ensure the comprehensiveness and accuracy of the collected data. After data acquisition module 1 completes the acquisition, the standardized ocular examination data is transmitted to fibrosis analysis module 2, providing data support for subsequent calculations of trabecular membrane hyperplasia, implant absorption abnormality coefficient, and postoperative ocular fibrosis.

[0028] The fibrosis analysis module 2 is the core analysis unit of the system. It is used to obtain the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient based on the ocular detection data transmitted by the data acquisition module 1 for each postoperative period, and finally determine the postoperative ocular fibrosis degree. This core indicator directly provides the basis for the frequency adjustment of the monitoring prompt module 3.

[0029] In some implementations, the fibrosis analysis module 2 further includes four sub-modules: trabecular membrane hyperplasia calculation sub-module 21, implant absorption abnormality coefficient calculation sub-module 22, trabecular membrane effective hyperplasia correction sub-module 23, and postoperative ocular fibrosis determination sub-module 24.

[0030] The trabecular membrane hyperplasia calculation submodule 21 is specifically used to determine the difference in trabecular membrane thickness based on adjacent trabecular membrane thickness data during the early postoperative critical period. Then, it determines the onset time of rapid fibroblast growth based on this difference value. Furthermore, it combines the time length from the onset time of rapid fibroblast growth to the end of the early postoperative critical period, the total time length of the early postoperative critical period, and the trabecular membrane thickness data during this period to determine the initial critical hyperplasia degree of the trabecular membrane. Finally, based on the trabecular membrane adhesion data, trabecular membrane thickness data, and the initial critical hyperplasia degree of the trabecular membrane at each postoperative period, it determines the trabecular membrane hyperplasia degree (this index is used to characterize the degree of fibrotic growth of the trabecular membrane).

[0031] The implant absorption anomaly coefficient calculation submodule 22 is specifically used to determine the implant absorption intensity coefficient (which is used to characterize the degradation and absorption rate of the implant) based on the implant imaging data. Then, combined with the preset absorption limit threshold and the implant absorption intensity coefficient, the implant absorption anomaly coefficient (which is used to characterize the degree of deviation of the implant degradation and absorption from the preset standard) is determined for each postoperative period. When determining the implant absorption intensity coefficient, it is necessary to establish an implant imaging data sequence in chronological order for implant imaging data at multiple time points within the same postoperative period, perform trend analysis on the sequence to obtain degradation parameters characterizing the degradation trend of the implant, and then determine the implant absorption intensity coefficient based on the degradation parameters.

[0032] The trabecular membrane effective hyperplasia correction submodule 23 is specifically used to correct the trabecular membrane hyperplasia obtained by the trabecular membrane hyperplasia calculation submodule 21 based on the implant absorption abnormality coefficient obtained by the implant absorption abnormality coefficient calculation submodule 22, so as to obtain the trabecular membrane effective hyperplasia, thereby eliminating the interference of implant absorption abnormality on the trabecular membrane hyperplasia assessment and ensuring the accuracy of the assessment results.

[0033] The postoperative ocular fibrosis determination submodule 24 is specifically used to determine the postoperative ocular fibrosis degree based on the effective trabecular membrane proliferation degree obtained from the trabecular membrane effective proliferation degree correction submodule 23, the trabecular membrane adhesion data and intraocular pressure data collected by the data acquisition module 1. This indicator comprehensively reflects the degree of influence of ocular fibrosis on the surgical outcome.

[0034] The fibrosis analysis module 2 transforms the raw test data into clinically significant core assessment indicators through the collaborative calculation of the above sub-modules, and transmits these indicators to the monitoring and prompting module 3.

[0035] The monitoring prompt module 3 is the system's output execution unit, used to adjust the monitoring frequency based on the degree of postoperative ocular fibrosis transmitted by the fibrosis analysis module 2 for each postoperative period, and to push monitoring prompt information to the user.

[0036] In some implementations, the monitoring prompt module 3 further includes two sub-modules: a monitoring frequency adjustment sub-module 31 and a prompt information generation and output sub-module 32.

[0037] The monitoring frequency adjustment submodule 31 is specifically used to preset the normal and abnormal ranges of postoperative ocular fibrosis in each postoperative period. If the postoperative ocular fibrosis in the target postoperative period is within the normal range, the normal monitoring frequency corresponding to that period is used; if it is within the abnormal range, the abnormal monitoring frequency corresponding to that period is used, and the detection interval of the abnormal monitoring frequency is less than that of the normal monitoring frequency, so as to achieve timely monitoring of abnormal situations. The preset of the normal and abnormal ranges needs to be based on the postoperative follow-up data of multiple similar non-penetrating trabeculectomies, to statistically analyze the conventional distribution range of ocular fibrosis in different postoperative periods as the normal range, and then combine the correlation analysis results between the risk of intraocular pressure loss of control and fibrosis in clinical practice to determine the critical value of fibrosis that may lead to abnormal increase in intraocular pressure in each period, and set the range that exceeds the normal range and is higher than the critical value as the abnormal range.

[0038] The prompt information generation and output submodule 32 is specifically used to generate corresponding prompt information based on the monitoring frequency adjusted by the monitoring frequency adjustment submodule 31, and output the prompt information to the display device or user terminal to remind the user to review at the adjusted frequency to ensure the timeliness and effectiveness of monitoring.

[0039] In this invention, "trabecular membrane" refers to trabecular mesh or surgically related membrane structures.

[0040] Based on the above technical solution, the data acquisition module accurately acquires four core data types characterizing the trabecular membrane state, implant absorption status, and intraocular pressure, providing comprehensive and crucial foundational support for subsequent analysis and avoiding the one-sidedness of relying solely on intraocular pressure data. The fibrosis analysis module then integrates and quantifies the data to obtain the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient, thereby determining the postoperative ocular fibrosis degree, which comprehensively reflects the impact of ocular fibrosis on surgical outcomes, achieving an objective and accurate assessment of key postoperative recovery status. Finally, the monitoring and prompting module dynamically adjusts the monitoring frequency at each postoperative stage based on these core assessment indicators, ensuring that postoperative abnormalities are captured promptly while avoiding unnecessary high-frequency examinations, effectively solving the problems of delayed understanding of treatment effects and low monitoring efficiency in postoperative risk monitoring.

[0041] The following combination Figure 2 The following details the steps of the non-penetrating trabecular bone surgery monitoring system for glaucoma treatment in this invention for implementing the non-penetrating trabecular bone surgery monitoring method for glaucoma treatment: S1. Collect eye examination data at multiple postoperative periods.

[0042] The ocular examination data includes trabecular membrane thickness data, trabecular membrane adhesion data, implant angiography data, and intraocular pressure data. Trabecular membrane thickness data characterizes the thickness of the trabecular membrane and is a core parameter for analyzing its proliferative state. Trabecular membrane adhesion data reflects the adhesion between the trabecular membrane and surrounding ocular tissues, directly correlated with the migration and diffusion of fibroblasts. Implant angiography data captures the imaging characteristics of the implant in the eye, specifically including the area of ​​the imaging region and signal intensity, providing a basis for assessing implant degradation and absorption. Intraocular pressure data characterizes the actual level of ocular pressure and is a key indicator for judging the effectiveness of surgical treatment.

[0043] In some implementation methods, the postoperative time is first divided into early critical postoperative period, early postoperative period, mid-postoperative period and late postoperative period. Then, eye detection data is collected at the corresponding acquisition frequency in each postoperative period. Specifically, eye image data is collected by ultrasound biomicroscopy, and then eye detection data is extracted from the eye image data.

[0044] This invention employs a hybrid collection strategy that combines multiple detection methods to balance monitoring needs with clinical safety.

[0045] The critical period after surgery is the first 6 days, during which the ocular tissues are fragile. To minimize interference with the surgical area, high-frequency monitoring primarily relies on non-contact methods, including: high-frequency (e.g., every 4-6 hours) intraocular pressure measurements using a non-contact tonometer, which has no impact on the wound; and slit-lamp microscopy for daily assessment of anterior segment inflammation and filtering bleb morphology, which can indirectly indicate risks. Furthermore, as the gold standard for obtaining precise anatomical data such as trabecular membrane thickness, adhesions, and implant angiography, UBM examinations are performed only when necessary and at a lower frequency during this critical period, for example, once each on postoperative days 1, 3, and 6, carefully performed by clinicians under aseptic conditions to avoid increasing the risk of wound complications.

[0046] The early postoperative period is 6-180 days post-surgery, the mid-postoperative period is 180-360 days post-surgery, and the late postoperative period is after 360 days post-surgery. During the postoperative period, the ocular wound heals, tissue stability increases, and the standard examination procedure centered on UBM can be resumed. All four types of ocular examination data are collected at preset frequencies such as once a month, once every 1.5 months, and once every 3 months. During the intervals between UBM examinations, the system utilizes high-frequency acquired non-contact intraocular pressure data and appearance characteristics, combined with the patient's historical UBM data trends, to construct a continuous risk assessment curve through interpolation algorithms. This achieves a near-continuous monitoring effect, and when the algorithm identifies abnormal trends, it prompts for additional UBM examinations to confirm the diagnosis.

[0047] Postoperative data acquisition was performed using an ultrasonic biomicroscope, a device with high-resolution imaging capabilities that can clearly capture image information of deep ocular structures, including the morphology of the trabecular membrane, implant location, and imaging status. The acquisition process is as follows: first, ultrasonic signals are emitted into the eye through the ultrasonic biomicroscope, and standardized ocular image data is generated after receiving the reflected signals; then, the four types of ocular detection data mentioned above are precisely extracted from the ocular image data. Specifically, the trabecular membrane thickness data is obtained by measuring the pixel dimension of the trabecular membrane in the image; the trabecular membrane adhesion data is obtained by identifying the proportion of the adhesion area between the trabecular membrane and tissues such as the sclera; implant imaging data is obtained by analyzing the imaging area and signal intensity of the implant in the image; and intraocular pressure data is obtained by combining the pressure feedback of the ultrasonic signal.

[0048] Specifically, the method for acquiring trabecular membrane thickness data is as follows: First, the spatial resolution of the ultrasonic biological microscope is calibrated to clarify the correspondence between pixels in the image and actual physical dimensions; then, the region of interest of the trabecular membrane is located and segmented using edge detection or semantic segmentation techniques, the pixel thickness of the region along the vertical surface direction is measured, and the actual trabecular membrane thickness data is obtained by combining the spatial resolution.

[0049] The method for collecting trabecular membrane adhesion data is as follows: using a semantic segmentation model, the regions of the trabecular membrane and the sclera are simultaneously segmented in the ultrasound biological microscope image. The pixel-level intersection region (i.e., the adhesion region) of the two is statistically analyzed, and the proportion of the area of ​​this region to the total area of ​​the trabecular membrane is calculated as the trabecular membrane adhesion value, thereby obtaining the trabecular membrane adhesion data.

[0050] The method for acquiring implant imaging data is as follows: the region of interest of the implant is located in the ultrasound biological microscope image by target detection or template matching technology, the number of pixels in the region is counted and converted into the actual imaging area by combining spatial resolution, and statistical features such as gray mean and variance in the region are calculated to characterize the signal intensity. Finally, these features are combined by normalized weighted summation to obtain implant imaging data. That is, these features are normalized (such as maximum and minimum value normalization), and the normalized features are weighted and summed to obtain implant imaging data. The weights can be optimized by combining empirical values ​​with multiple sets of clinical data.

[0051] The method for acquiring intraocular pressure (IOP) data is as follows: morphological parameters (such as radius of curvature and thickness variation) of the eyeball wall (e.g., cornea and sclera) in ultrasound biomicroscopy images are analyzed. A mathematical model of IOP relationship between eyeball wall morphological parameters and clinically calibrated parameters is then established. The extracted morphological parameters are substituted into the model for calculation, thereby obtaining real-time IOP data. Alternatively, IOP data can also be obtained directly using a tonometer.

[0052] S2. For each postoperative period, the degree of trabecular membrane hyperplasia and the implant absorption abnormality coefficient are obtained based on ocular examination data, and the degree of postoperative ocular fibrosis is determined.

[0053] Among them, the trabecular membrane proliferation degree is used to characterize the degree of fibrosis growth of the trabecular membrane, and the implant absorption anomaly coefficient is used to characterize the degree of deviation between implant degradation and absorption and the preset standard.

[0054] In some implementation methods, the degree of trabecular membrane hyperplasia is first determined based on trabecular membrane thickness and adhesion data, and the implant absorption abnormality coefficient is determined based on implant angiography data. Then, the degree of trabecular membrane hyperplasia is corrected based on the implant absorption abnormality coefficient to obtain the effective degree of trabecular membrane hyperplasia. Finally, the postoperative ocular fibrosis degree is determined based on the effective degree of trabecular membrane hyperplasia, trabecular membrane adhesion data, and intraocular pressure data.

[0055] Specifically, the focus is first on the critical early postoperative period (the first 6 days after surgery). This period is crucial for surgical success, as ocular tissues are prone to inflammation and fibrin deposition, necessitating the establishment of a baseline for basal proliferation. During this critical early postoperative period, the trabecular membrane thickness difference (the absolute value of the difference between adjacent time points) is determined based on data from adjacent trabecular membrane thickness measurements. This captures the dynamic changes in trabecular membrane thickness, and the onset time of rapid fibroblast proliferation is then determined based on this thickness difference. Specifically, this is achieved by analyzing all thickness differences... The maximum thickness difference value was selected from the samples. The subsequent acquisition time is defined as the fibroblast proliferation initiation time, which marks the point at which cellulose deposition reaches a threshold, stimulating the proliferation of fibroblasts. The time from the fibroblast proliferation initiation time to the end of the early postoperative critical period is then considered. The total length of time in the critical period after early surgery And the mean value of trabecular membrane thickness during the critical early postoperative period. Determine the critical degree of early proliferation of the trabecular membrane. , represented as: In the formula, Reflecting the time span of the proliferation process, Provides a benchmark reference in the time dimension. Used to indicate the proportion of proliferation time; This reflects the average thickness of the trabecular membrane during the critical early postoperative period. Finally, by combining the average thickness and the proportion of proliferation time, and normalizing the core proliferation degree during the critical early postoperative period using a normalization function (e.g., the maximum value (max)) to the (0,1] interval, the initial critical proliferation degree of the trabecular membrane is obtained. .

[0056] Next, for each postoperative period, the mean value of the trabecular membrane adhesion data at the i-th postoperative period is extracted. Mean of trabecular membrane thickness data Based on the established critical proliferation rate of the trabecular membrane in its early stages The degree of trabecular membrane hyperplasia at the i-th postoperative period was calculated. , represented as: In the formula, the thickness data of the trabecular membrane is... Data on trabecular membrane adhesion directly reflects the physical basis of hyperplasia. This reflects the tissue adhesion state after fibroblast migration and diffusion, and both are related to the initial key proliferation degree of the trabecular membrane. By combining these methods and normalizing them using a function like norm (e.g., max) to the (0, 1] interval, the fibrosis growth intensity of the trabecular membrane at different stages can be fully quantified.

[0057] Furthermore, during non-penetrating trabecular meshwork surgery, a certain amount of transparent gel (cross-linked sodium hyaluronate gel) is typically implanted into the eye to promote the formation of the filtration channels and maintain their stability. After the surgery, these implants, such as the transparent gel, will be gradually degraded and absorbed over time (e.g., ...). Figure 3 and Figure 4 (As shown). However, due to individual differences and environmental factors affecting ocular recovery at different postoperative periods, the absorption of the implant varies, and abnormal absorption may occur, thus affecting the accuracy of intraocular pressure data at those periods. Therefore, it is necessary to analyze the implant absorption at different postoperative periods to determine the degree of implant absorption abnormalities at each stage.

[0058] First, the implant absorption intensity coefficient is determined based on the implant imaging data. Specifically, an implant imaging data sequence is established chronologically based on imaging data from multiple time points within the same postoperative period. This sequence visually reflects the changes in the implant's imaging characteristics during that period. Trend analysis is then performed on the implant imaging data sequence to capture the changing patterns of the implant imaging data over time, obtaining degradation parameters characterizing the implant's degradation trend (such as the slope and amplitude of the data sequence). The implant absorption intensity coefficient is then determined based on these degradation parameters. The implant absorption intensity coefficient characterizes the rate of implant degradation and absorption. Taking the physical slope of the data sequence (i.e., the rate of change per unit time) as an example, the physical slope between adjacent data points in the sequence is first calculated. The number of slopes with negative values ​​at the i-th postoperative period is then counted. And calculate the mean of the absolute values ​​of the slopes that are negative. To eliminate the impact of changes in sampling point density caused by adjustments in monitoring frequency... The influence of this makes it comparable across different monitoring frequencies, and the number of negative slopes is [not specified]. Normalized to the frequency of negative slope occurrence per unit time (i.e., negative slope density). The calculation formula is: in, This represents the actual time span (in days) of the i-th postoperative period, i.e., the time difference between the last sampling point and the first sampling point in that period. Then, the implant absorption intensity coefficient for the i-th postoperative period is calculated. , represented as: In the formula, Quantify the frequency of negative changes (absorption and degradation) in implant imaging data; the larger the value, the more frequent the absorption. The magnitude of each negative change is quantified; a larger absolute mean value indicates a more significant degree of single-phase absorption and degradation. A normalization function, such as max normalization, transforms the product of the number of absorptions and the absorption magnitude into a dimensionless coefficient within the [0, 1] interval, comprehensively quantifying the implant's absorption intensity at that period. Furthermore, the physical slope calculation based on timestamps ensures that the slopes at different acquisition frequencies have the same physical meaning (both are rates of change), thus making the implant absorption intensity coefficient at different postoperative periods consistent. comparable.

[0059] Then, the implant absorption anomaly coefficient for each postoperative period is determined based on the preset absorption limitation threshold and the implant absorption intensity coefficient. Specifically, the preset absorption limitation threshold K for each postoperative period (e.g., 0.1 for the early critical postoperative period, 0.45 for the early postoperative period, 0.7 for the mid-postoperative period, and 0.9 for the late postoperative period) is based on the functional requirements of the implant at different recovery stages (a stable filtration channel is required in the early stage, and the degradation rate needs to be strictly controlled; in the mid-to-late stages, the filtration channel tends to be stable, and a higher degradation rate is allowed). The implant absorption anomaly coefficient is used to quantify the deviation of the actual absorption rate from the safety threshold, and its value is positively correlated with the abnormal risk. The calculated implant absorption intensity coefficient for the i-th postoperative period is then... Preset absorption limit threshold for the corresponding period Comparative analysis was conducted to determine the implant absorption abnormality coefficient at the i-th postoperative period. , represented as: In the formula, Directly reflects the actual absorption rate Relative to the allowable threshold The ratio. When ≤ hour, ≤1 indicates normal or slow absorption, with no amplifying effect on the risk of proliferation or showing an inhibitory trend (but may indicate insufficient support); when > hour, A value greater than 1 indicates rapid absorption and a higher degree of abnormality. The larger the value, the more the effect is preserved after normalization. The coefficients are normalized to the interval [0, 1] using a normalization function, such as `norm` or `max`.

[0060] Since the supporting effect of implants (such as sodium hyaluronate gel) is a crucial factor in inhibiting fibrous hyperplasia, rapid absorption of these implants weakens this support, significantly increasing the risk of trabecular membrane collapse and fibrous adhesion. Therefore, the calculated degree of trabecular membrane hyperplasia needs to be corrected using an implant absorption anomaly coefficient. Specifically, the implant absorption anomaly coefficient at the i-th postoperative period... Corresponding trabecular membrane proliferation degree By combining these methods, the effective proliferation rate of the trabecular membrane can be obtained. , represented as: In the formula, As a risk weighting factor: when When the value is greater than 1 (absorption is too fast), it will be amplified. To obtain higher To accurately reflect the increase in risk; when When ≤1, Maintain or slightly below This aligns with physiological logic. Based on this, assessment biases caused by implant absorption abnormalities can be comprehensively considered, resulting in a more accurate reflection of the actual proliferative state of the trabecular membrane.

[0061] Finally, considering the overall effective proliferation rate of the trabecular membrane (Reflecting the degree of tissue hyperplasia), mean value of trabecular membrane adhesion data (Reflecting the degree of obstruction to tissue adhesion) and mean intraocular pressure data (Reflecting the final pressure feedback indicating obstructed aqueous humor drainage), the degree of postoperative ocular fibrosis is determined by weighted summation. , represented as: In the formula, , , The preset weighting coefficients, and + + =1. Weights can be set based on the analysis results of the association between each factor and the risk of surgical failure in clinical studies (e.g., =0.4, =0.3, =0.3). This weighted summation model ensures the independent contribution of risk information from intraocular pressure, tissue hyperplasia, and adhesions. Even if intraocular pressure remains temporarily normal postoperatively, significant tissue hyperplasia or adhesions (caused by...) and (Reflection) can still generate sufficient risk value This triggers the system to strengthen monitoring, thereby achieving true early warning.

[0062] This is a risk transformation function for intraocular pressure (IOP) values. Its purpose is to convert IOP values ​​into a non-negative risk contribution value, providing a basic risk base even when IOP is normal, and significantly increasing the risk contribution when IOP is elevated. A simple implementation is as follows: Use a baseline value for intraocular pressure risk (e.g., set at 18 mmHg, below the clinical upper limit of 21 mmHg, to provide early risk sensitivity). A scaling factor (e.g., set to 3 mmHg) is used to adjust the contribution of intraocular pressure changes. This function ensures that when... ≤ At that time, the risk contribution of the intraocular pressure item was 0, but the contributions of the other two items (proliferation and adhesion) were still retained; when the intraocular pressure increased, their contributions increased linearly.

[0063] Finally, the degree of postoperative ocular fibrosis was quantified by normalizing the result to the (0, 1] interval using a normalization function, such as max normalization. This can fully demonstrate the comprehensive impact of ocular fibrosis on surgical outcomes.

[0064] In this invention, the normalization function `norm` refers to linear normalization based on a preset clinical limit constant. The clinical limit constant includes, but is not limited to, the maximum theoretical thickness of the trabecular membrane (e.g., 500 μm) and the upper limit of clinical intraocular pressure risk (e.g., 60 mmHg). The original value to be normalized is divided by the corresponding clinical limit constant, and the result is restricted to a reasonable range. In this way, all calculated intermediate indicators and the final postoperative ocular fibrosis degree are determined. All are dimensionless scales based on objective medical consensus.

[0065] S3. For each postoperative period, adjust the monitoring frequency according to the degree of postoperative ocular fibrosis.

[0066] In some implementation methods, the normal and abnormal ranges of postoperative ocular fibrosis are preset for each postoperative period. If the postoperative ocular fibrosis at the target postoperative period is within the normal range, it indicates that the ocular recovery is stable, and the normal monitoring frequency corresponding to the target postoperative period is used (consistent with the default acquisition frequency for each period during data acquisition). If the postoperative ocular fibrosis at the target postoperative period is within the abnormal range, it indicates that there is an abnormal risk of ocular fibrosis growth, and the detection interval needs to be shortened. The abnormal monitoring frequency corresponding to the target postoperative period is used, and the detection interval of the abnormal monitoring frequency is shorter than the normal monitoring frequency (e.g., 2 hours / time in the early critical postoperative period, 7 days / time in the early postoperative period, 15 days / time in the mid-postoperative period, and 1 month / time in the late postoperative period) in order to capture changes in intraocular pressure and fibrosis progression in a timely manner, providing timely basis for adjusting the subsequent treatment plan.

[0067] In some implementation methods, a large amount of postoperative follow-up data from similar non-penetrating trabeculectomy surgeries is first collected, covering core information such as postoperative ocular fibrosis, intraocular pressure changes, and complication occurrences at different postoperative periods. The sample size must meet the statistical significance requirement to ensure data representativeness. The normal range for ocular fibrosis at different postoperative periods (e.g., the range corresponding to the mean ± standard deviation) is defined as the normal range for the corresponding period. Within this range, it indicates that the ocular fibrosis growth is consistent with postoperative recovery patterns and poses a low risk of obstructing aqueous humor drainage. Furthermore, based on the correlation analysis results between the risk of uncontrolled intraocular pressure and fibrosis in clinical practice, regression analysis and risk threshold modeling are used to determine the critical value of fibrosis that may lead to abnormally high intraocular pressure (exceeding the conventional limit of 21 mmHg) at each period. Ranges exceeding the normal range and above the critical value are defined as abnormal ranges. This range indicates excessively rapid fibrosis growth, which may lead to filtration tract stenosis and obstruction of aqueous humor drainage, requiring enhanced monitoring to avoid the risk of uncontrolled intraocular pressure.

[0068] In some implementation methods, determining the critical value by combining the risk of intraocular pressure loss of control with the degree of fibrosis in clinical practice may include: first, standardizing the collection of covariates such as effective trabecular membrane proliferation (or postoperative ocular fibrosis), intraocular pressure data, and baseline intraocular pressure at each postoperative period; then, clarifying the association between fibrosis and intraocular pressure loss of control through univariate correlation analysis, multivariate logistic regression, or risk models; after stratified and refined analysis by postoperative period, plotting the receiver operating characteristic (ROC) curve with intraocular pressure loss of control as the outcome, false positive rate (the proportion of those who do not experience intraocular pressure loss of control but are mistakenly judged as high-risk, reflecting the risk of misdiagnosis) as the x-axis and true positive rate (the proportion of those who have experienced intraocular pressure loss of control but are correctly judged as high-risk, reflecting the risk of missed diagnosis) as the y-axis; and finding the proliferation threshold at which the risk of intraocular pressure loss of control is significantly increased, including: traversing all possible values ​​of fibrosis, using each value as a temporary threshold, calculating the Youden index for each temporary threshold, and the temporary threshold corresponding to the maximum value of this index is the optimal critical value. The threshold value corresponds to the strongest ability to correctly identify high risk of intraocular pressure loss of control, while balancing the risks of misdiagnosis and missed diagnosis. Fibrous hyperplasia exceeding this threshold means that the risk of intraocular pressure loss of control is significantly increased.

[0069] Finally, through internal cross-validation and external independent cohort validation and optimization, critical values ​​for fibrosis at each postoperative stage were determined.

[0070] For example, as shown in Table 1, the monitoring frequency can be set as follows: Table 1. Preset monitoring frequencies at different postoperative stages The “Preset monitoring frequency” in the table refers to the overall assessment frequency recommended by the system. In the early critical postoperative period, high-frequency monitoring (e.g., 2-4 hours / time) is mainly achieved through non-contact methods (e.g., intraocular pressure); the precise UBM examination frequency should be significantly reduced according to the actual clinical situation (e.g., on postoperative days 1, 3, and 6).

[0071] In some implementations, to ensure users can complete monitoring at the adjusted frequency, a prompt message corresponding to the adjusted monitoring frequency can be generated. This message includes the target monitoring period, the adjusted monitoring frequency, the reason for the frequency adjustment (e.g., "Postoperative ocular fibrosis is within an abnormal range, requiring enhanced monitoring"), and suggested monitoring time points. The generated prompt message is output through multiple channels: firstly, it is output to display devices for real-time monitoring and reminders; secondly, it is output to user terminals via pop-ups, SMS messages, etc., to push monitoring reminders and avoid missed checks.

[0072] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0074] In this embodiment of the invention, the non-penetrating trabecular bone surgery monitoring device for glaucoma treatment can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0075] This invention also provides a schematic diagram of the hardware structure of a non-penetrating trabecular bone surgery monitoring device for glaucoma treatment, see [link / reference]. Figure 5 The non-penetrating trabecular surgery monitoring device 500 for glaucoma treatment includes a processor 501, and optionally, a memory 502 connected to the processor 501.

[0076] In the first possible implementation, see Figure 5The non-penetrating trabecular bone surgery monitoring device 500 for glaucoma treatment also includes a transceiver 503. The processor 501, memory 502, and transceiver 503 are connected via a bus. The transceiver 503 is used to communicate with other devices or communication networks. Optionally, the transceiver 503 may include a transmitter and a receiver. The device in the transceiver 503 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 503 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0077] Based on the first possible implementation method Figure 5 The schematic diagram shown can be used to illustrate the structure of the non-penetrating trabecular surgical monitoring device for glaucoma treatment involved in the above embodiments.

[0078] in, Figure 5 The diagram also illustrates the system chip in a non-penetrating trabecular bone surgery monitoring device for glaucoma treatment. In this case, the actions performed by the aforementioned non-penetrating trabecular bone surgery monitoring device for glaucoma treatment can be implemented by this system chip; the specific actions performed are described above and will not be repeated here.

[0079] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in this embodiment can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0080] The processor in this invention may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a System-on-a-Chip (SoC), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations.

[0081] The memory in the embodiments of the present invention may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0082] This invention also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0083] This invention also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0084] This invention also provides a chip, which includes a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0085] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0086] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0087] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A non-penetrating trabecular bone surgery monitoring system for glaucoma treatment, characterized in that, include: The data acquisition module is used to collect eye examination data at multiple postoperative stages; The ocular detection data includes trabecular membrane thickness data, trabecular membrane adhesion data, implant angiography data, and intraocular pressure data; The fibrosis analysis module is used to obtain the trabecular membrane hyperplasia degree and implant absorption abnormality coefficient based on the ocular detection data for each postoperative period, and to determine the postoperative ocular fibrosis degree; the trabecular membrane hyperplasia degree is used to characterize the degree of fibrosis growth of the trabecular membrane; the implant absorption abnormality coefficient is used to characterize the degree of deviation of implant degradation and absorption from the preset standard; The monitoring and prompting module is used to adjust the monitoring frequency according to the degree of postoperative ocular fibrosis for each postoperative period.

2. The non-penetrating trabecular surgical monitoring system according to claim 1, characterized in that, The fiber proliferation analysis module is specifically used for: The degree of trabecular membrane proliferation is determined based on the trabecular membrane thickness data and trabecular membrane adhesion data. The implant absorption abnormality coefficient is determined based on the implant imaging data. The trabecular membrane hyperplasia degree is corrected based on the implant absorption abnormality coefficient to obtain the effective trabecular membrane hyperplasia degree. The postoperative ocular fibrosis was determined based on the effective proliferation of the trabecular membrane, the trabecular membrane adhesion data, and the intraocular pressure data.

3. The non-penetrating trabecular bone surgery monitoring system according to claim 2, characterized in that, The fiber proliferation analysis module is specifically used for: In the critical early postoperative period, the difference in trabecular membrane thickness was determined based on the trabecular membrane thickness data at adjacent time points. The fiber formation rate increase initiation time is determined based on the difference in trabecular membrane thickness. The initial critical proliferation degree of the trabecular membrane is determined based on the time length from the start of the rapid fibroblast proliferation to the end of the critical period after early surgery, the total time length of the critical period after early surgery, and the trabecular membrane thickness data during the critical period after early surgery. The degree of trabecular membrane hyperplasia is determined based on the data on trabecular membrane adhesion, trabecular membrane thickness, and initial critical hyperplasia of the trabecular membrane at each postoperative period.

4. The non-penetrating trabecular bone surgery monitoring system according to claim 2, characterized in that, The fiber proliferation analysis module is specifically used for: The implant absorption intensity coefficient is determined based on the implant imaging data; the implant absorption intensity coefficient is used to characterize the degradation and absorption rate of the implant. The implant absorption abnormality coefficient for each postoperative period is determined based on the preset absorption limit threshold and the implant absorption intensity coefficient.

5. The non-penetrating trabecular bone surgery monitoring system according to claim 4, characterized in that, The fiber proliferation analysis module is specifically used for: Based on the imaging data of the implant at multiple time points within the same postoperative period, an implant imaging data sequence was established in chronological order; Trend analysis was performed on the imaging data sequence of the implant to obtain degradation parameters characterizing the degradation trend of the implant; The absorption strength coefficient of the implant is determined based on the degradation parameters.

6. The non-penetrating trabecular surgical monitoring system according to claim 1, characterized in that, The monitoring and alerting module is specifically used for: Predetermine the normal and abnormal ranges of postoperative ocular fibrosis in each postoperative period; If the postoperative ocular fibrosis at the target postoperative period is within the normal range, the normal monitoring frequency corresponding to the target postoperative period shall be adopted. If the degree of postoperative ocular fibrosis is within an abnormal range at the target postoperative period, the abnormal monitoring frequency corresponding to the target postoperative period shall be used; the detection interval of the abnormal monitoring frequency shall be less than that of the normal monitoring frequency.

7. The non-penetrating trabecular surgery monitoring system according to claim 6, characterized in that, The monitoring and alerting module is also used for: Based on postoperative follow-up data from multiple similar non-penetrating trabeculectomies, the normal distribution range of ocular fibrosis at different postoperative periods was statistically analyzed and set as the normal range for the corresponding periods. Based on the correlation analysis results between the risk of uncontrolled intraocular pressure and the degree of fibrosis in clinical practice, the critical value of fibrosis that may lead to abnormal increase in intraocular pressure in each period was determined, and the range that exceeds the normal range and is higher than the critical value was set as the abnormal range.

8. The non-penetrating trabecular surgery monitoring system according to claim 1, characterized in that, The data acquisition module is specifically used for: According to the postoperative time, it is divided into the early critical postoperative period, the early postoperative period, the middle postoperative period, and the late postoperative period. The eye examination data were collected at the corresponding acquisition frequency during each postoperative period.

9. The non-penetrating trabecular surgery monitoring system according to claim 1, characterized in that, The data acquisition module is specifically used for: Eye image data were acquired using an ultrasonic biological microscope; The eye detection data is extracted from the eye image data.

10. The non-penetrating trabecular surgical monitoring system according to claim 1, characterized in that, The monitoring and alerting module is also used for: Generate a prompt message corresponding to the adjusted monitoring frequency, and output the prompt message to a display device or user terminal.

Images