Method, device, equipment and storage medium for evaluating the anchoring degree of vascular stent

Abstract

The present invention discloses a method, device, equipment and storage medium for evaluating the anchoring degree of a vascular stent. By establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent, an anchoring force index for evaluating the anchoring degree of the vascular stent is defined. Then, vascular imaging data is processed to generate a three-dimensional vascular model. In combination with the anchoring force index, a virtual implantation process of the vascular stent is performed on a computer. The anchoring force index of the vascular stent at different positions within the blood vessel is calculated and updated in real time according to the actual deployment degree of the vascular stent. Finally, clinical follow-up data after the vascular stent implantation is collected, and key characteristic parameters are extracted in combination with the anchoring force index. The trained vascular stent anchoring degree evaluation model is obtained using a machine learning model for training to evaluate the anchoring degree of the vascular stent. The anchoring force index proposed by the present invention from a mechanical perspective can more accurately evaluate the anchoring degree of the vascular stent.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method, device, equipment and storage medium for evaluating the anchoring degree of a vascular stent. Background Art

[0002] Intracranial aneurysms are abnormal bulges in the walls of intracranial arteries. Rupture of an intracranial aneurysm can cause subarachnoid hemorrhage, potentially leading to death. Currently, interventional treatment for small and medium-sized aneurysms, especially ruptured aneurysms, primarily involves embolization of the aneurysm cavity with metal coils. However, for wide-necked, large, or fusiform aneurysms, vascular stents offer better treatment options.

[0003] Existing technologies for stent selection have proposed calculation methods for visual parameters such as wall adhesion, metal coverage, and pore density to assist doctors in evaluating the surgical effect of virtual stents. However, these parameters are mainly based on a geometric perspective. Although they can reflect the fit between the stent and the blood vessel wall to a certain extent, they cannot fully reflect the degree of anchoring of the stent after implantation into the blood vessel. The degree of anchoring of the stent plays a vital role in the success of the operation: if the stent is too thin, the anchoring degree is low, the radial support force is insufficient, and it is easy to shift under the impact of blood flow; if it is too thick, it may encounter difficulties in opening during implantation. Although wall adhesion reflects the contact between the stent and the blood vessel wall to some extent, it cannot completely replace the evaluation of mechanical properties. For example, two stents with the same wall adhesion may have significantly different radial support forces due to different designs and material properties.

[0004] Therefore, while existing parameters such as wall adhesion, metal coverage, and pore density can reflect the fit of vascular stents to the vessel wall to a certain extent, these geometric parameters cannot fully assess the degree of stent anchoring after implantation. This leads to the risk of surgical failure, such as stent migration or difficulty in opening, being overlooked when selecting vascular stents. Therefore, existing technologies for assessing the degree of vascular stent anchoring have obvious limitations. Summary of the Invention

[0005] Based on this, the present invention addresses the above technical problems and provides a method, device, equipment and storage medium for evaluating the anchoring degree of a vascular stent.

[0006] In one aspect, the present invention provides a method for evaluating the anchoring degree of a vascular stent, the method comprising:

[0007] Establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent;

[0008] An anchoring force index for evaluating the anchoring degree of the vascular stent is defined based on the mapping relationship;

[0009] Processing vascular imaging data to generate a three-dimensional vascular model, combined with the anchoring force index, allows for a virtual stent implantation process on a computer. The anchoring force index of the stent at different locations within the vessel is calculated and updated in real time based on the actual extent of the stent deployment.

[0010] Clinical follow-up data after vascular stent implantation was collected and combined with the anchoring force index to extract key characteristic parameters. This data was then trained using a machine learning model to obtain a trained vascular stent anchoring degree assessment model.

[0011] Based on the trained evaluation model and the key characteristic parameters to be measured, the anchoring degree of the vascular stent is evaluated.

[0012] In one embodiment, the mapping relationship is established by at least one of the following methods:

[0013] Simulation method: Computer simulation technology is used to build a physical model of the vascular stent, simulate the radial compression process of the vascular stent, and output the relationship curve between the radial support force and the vascular stent radius;

[0014] Experimental method: The vascular stent was radially compressed using a radial loading device, and a force sensor was used to obtain a curve showing the relationship between the radial support force and the vascular stent radius.

[0015] Theoretical calculation: Based on the principles of materials science and mechanics, each stent wire of the vascular stent is regarded as a spring, and its stress conditions are analyzed to derive the functional relationship of the radial support force with respect to the radius of the vascular stent.

[0016] In one embodiment, the anchoring force index is calculated based on the radial support force of the vascular stent or a derived parameter thereof.

[0017] In one embodiment, the key characteristic parameters include: parameters related to anchoring force, parameters related to individual patient differences in clinical follow-up data, and parameters related to vascular status.

[0018] In one embodiment, the anchoring force-related parameters include one or a combination of the following:

[0019] High anchoring area: the surface area of ​​the vascular stent with an anchoring force index higher than a certain threshold;

[0020] High anchoring area ratio: the ratio of the high anchoring area to the total stent surface area;

[0021] Low anchoring area: the surface area of ​​the vascular stent where the anchoring force index is lower than a certain threshold;

[0022] Low anchoring area ratio: the ratio of the low anchoring area to the total stent surface area;

[0023] Anchoring ratio: the ratio of the area of ​​high anchoring region to the area of ​​low anchoring region.

[0024] In one embodiment, the key characteristic parameters further include: derived characteristic parameters obtained through further analysis and calculation based on parameters related to the anchoring force.

[0025] In one embodiment, the derived characteristic parameters include one or a combination of the following:

[0026] Anchoring force extreme ratio: the ratio of the maximum anchoring force to the minimum anchoring force in the entire vascular stent;

[0027] Anchoring force spatial entropy: The area of ​​the vascular stent is divided into distal, mid-distal, lesion area, mid-proximal, and proximal ends according to the length. The anchoring force spatial entropy is calculated using the following formula:

[0028] ;

[0029] in, is the ratio of the maximum anchoring force to the minimum anchoring force in the i-th region.

[0030] Where, is the ratio of the maximum anchoring force to the minimum anchoring force in the i-th region.

[0031] In another aspect, the present invention provides a device for evaluating the anchoring degree of a vascular stent, the device comprising:

[0032] A mapping relationship establishment module, used to establish a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent;

[0033] An anchoring force index definition module, used to define an anchoring force index for evaluating the anchoring degree of the vascular stent based on a mapping relationship;

[0034] The virtual implantation and calculation module is used to process vascular imaging data, generate a three-dimensional vascular model, and implement the virtual implantation process of the vascular stent on the computer in combination with the anchoring force index. The anchoring force index of the vascular stent at different locations within the blood vessel is calculated and updated in real time based on the actual deployment degree of the vascular stent.

[0035] The training module is used to collect clinical follow-up data after vascular stent implantation, combine it with the anchoring force index, extract key characteristic parameters, and use machine learning model training to obtain a trained vascular stent anchoring degree assessment model;

[0036] Evaluation module, used to evaluate the anchoring degree of the vascular stent based on the trained evaluation model and key characteristic parameters to be measured

[0037] In another aspect, the present invention provides a computer device comprising a memory and a processor, wherein when the processor executes the computer program, the following steps are implemented:

[0038] Establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent;

[0039] An anchoring force index for evaluating the anchoring degree of the vascular stent is defined based on the mapping relationship;

[0040] Processing vascular imaging data to generate a three-dimensional vascular model, combined with the anchoring force index, allows for a virtual stent implantation process on a computer. The anchoring force index of the stent at different locations within the vessel is calculated and updated in real time based on the actual extent of the stent deployment.

[0041] Clinical follow-up data after vascular stent implantation was collected and combined with the anchoring force index to extract key characteristic parameters. This data was then trained using a machine learning model to obtain a trained vascular stent anchoring degree assessment model.

[0042] Based on the trained evaluation model and the key characteristic parameters to be measured, the anchoring degree of the vascular stent is evaluated.

[0043] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the following steps:

[0044] Establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent;

[0045] An anchoring force index for evaluating the anchoring degree of the vascular stent is defined based on the mapping relationship;

[0046] Processing vascular imaging data to generate a three-dimensional vascular model, combined with the anchoring force index, allows for a virtual stent implantation process on a computer. The anchoring force index of the stent at different locations within the vessel is calculated and updated in real time based on the actual extent of the stent deployment.

[0047] Clinical follow-up data after vascular stent implantation was collected and combined with the anchoring force index to extract key characteristic parameters. This data was then trained using a machine learning model to obtain a trained vascular stent anchoring degree assessment model.

[0048] Based on the trained evaluation model and the key characteristic parameters to be measured, the anchoring degree of the vascular stent is evaluated.

[0049] Compared to existing technologies, this invention comprehensively considers the relationship between the radial support force of vascular stents and their radius to define an anchoring force index that quantifies the fixation strength of vascular stents. This new parameter, proposed from a mechanical perspective, can more accurately assess the degree of stent anchoring. Compared to relying solely on geometric parameters (such as wall adhesion, metal coverage, and pore density), this invention not only helps optimize vascular stent selection but also improves surgical success rates and long-term patient outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 FIG. 1 is a flow chart of a method for evaluating the anchoring degree of a vascular stent in one embodiment.

[0051] Figure 2 FIG. 1 is a curve showing the relationship between the radial supporting force of the vascular stent and the radius of the vascular stent in one embodiment.

[0052] Figure 3 Schematic diagram of the partitioning of a vascular stent in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] like Figure 1 As shown, a method for evaluating the anchoring degree of a vascular stent in this embodiment includes the following steps:

[0055] Step S100: establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent.

[0056] In step S100, the mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent needs to be established. This mapping relationship is the basis of the entire evaluation method because it directly reflects the intrinsic relationship between the mechanical properties of the vascular stent and its geometric shape. Specifically, the mapping relationship can be obtained through experiments, simulations or theoretical calculations, and is ultimately expressed as follows: Figure 2 The relationship curve between the radial supporting force of the vascular stent and the radius of the vascular stent is shown.

[0057] Using a simulation approach: In one embodiment, the present invention utilizes computer simulation technology to create a physical model of the stent and numerically simulate its radial compression process, thereby generating a curve showing the relationship between radial support force and stent radius. This approach has the advantage of rapidly generating large amounts of data, facilitating subsequent analysis and verification, while avoiding the high cost and complexity of actual experiments.

[0058] Experimental Method: In another embodiment, the present invention uses a radial loading device to physically compress the stent, and a force sensor records the relationship between the radial support force and the stent radius in real time. This method offers the advantage of reliable data, but due to experimental limitations, it may suffer from insufficient sample size or poor reproducibility.

[0059] Adopting theoretical calculation method: In the third embodiment, the present invention regards each stent wire of the vascular stent as a spring based on the principles of materials science and mechanics, and analyzes its force conditions. By deriving the functional relationship of the radial support force with respect to the radius of the vascular stent, theoretical support can be provided for subsequent evaluation. Specifically, each vascular stent wire can be regarded as a spring with equal pitch and equal diameter. By performing a detailed force analysis on each spring, a functional expression of the radial support force with respect to the spring radius is obtained. Subsequently, the radial support forces of all stent wires are superimposed to obtain the functional relationship between the radial support force and the radius of the entire vascular stent.

[0060] The mapping relationship curves obtained by the above three methods can not only intuitively reflect the mechanical behavior of the vascular stent, but also provide an important basis for further defining the anchoring force index.

[0061] Step S200 : defining an anchoring force index for evaluating the anchoring degree of the vascular stent based on the mapping relationship.

[0062] In step S200, the definition of the anchoring force index is one of the core links of the entire evaluation method. The anchoring force index is calculated based on the radial support force of the vascular stent or its derived parameters, and is intended to quantify the anchoring ability of the vascular stent at different positions. In order to ensure the comprehensiveness and accuracy of the evaluation, there are many possibilities for determining the anchoring force index. It is possible to directly select the magnitude of the radial support force as the anchoring force index, or to select a dimensionless parameter derived based on the radial support force (such as the normalized radial support force) as the anchoring force index. It is also possible to select the vascular stent radius corresponding to the radial support force as the anchoring force index. Any parameters related to the radial support force or the vascular stent radius used to evaluate the degree of anchoring of the vascular stent or to evaluate whether the diameter of the vascular stent is appropriate are within the scope of protection of the present invention.

[0063] In practical applications, the definition of the anchoring force index needs to be flexibly adjusted based on specific clinical needs and research objectives. For example, in some cases, doctors may be more concerned with the overall anchoring ability of the vascular stent and therefore choose to use the average radial support force as the anchoring force index. In other cases, if the performance of the vascular stent in a specific area needs to be evaluated, the local maximum radial support force or minimum radial support force can be selected as the anchoring force index. In addition, the anchoring force index can also be comprehensively evaluated in combination with the deployment radius of the vascular stent to better reflect the actual anchoring effect of the vascular stent in different locations.

[0064] In summary, the definition of the anchoring force index not only reflects the mechanical properties of the vascular stent but also provides a key reference for subsequent virtual implantation and evaluation. By rationally selecting and defining the anchoring force index, the accuracy and reliability of the evaluation results can be effectively improved.

[0065] In step S300, the vascular image data is processed to generate a three-dimensional vascular model. Combined with the anchoring force index, a virtual stent implantation process is performed on a computer. The anchoring force index of the stent at different locations within the blood vessel is calculated and updated in real time based on the actual deployment degree of the stent.

[0066] In step S300, the vascular image data is read and a 3D vascular model is reconstructed based on the data. Then, a vascular stent is virtually implanted, and finally, the anchoring force index is calculated and visualized. The steps are as follows:

[0067] S310, Image Reading and Vascular Reconstruction. First, the patient's vascular imaging data must be obtained. This data is typically derived from DSA, CTA, or MRA. These imaging data not only contain information about the geometric structure of the blood vessels, but also reflect the state of the vessel wall and the location of the lesion, thus forming an important foundation for subsequent analysis.

[0068] To extract vascular geometric information from image data, image sequences are typically segmented using thresholding, level set methods, or artificial intelligence (AI) segmentation models. Each of these methods has its own advantages and disadvantages: the thresholding method is simple and easy to use, but is sensitive to noise; the level set method can effectively handle complex boundary conditions, but is computationally complex; and AI segmentation models, with their powerful learning capabilities, can significantly improve efficiency while maintaining accuracy. After segmentation, the marching cubes algorithm is used to reconstruct the surface of the segmented results to generate a three-dimensional vascular model. This model not only intuitively displays the morphological characteristics of the blood vessels but also provides the necessary geometric framework for subsequent virtual implantation.

[0069] S320, Virtual Vascular Stent Implantation. First, the coordinate sequence of the centerline points from the proximal to the distal opening of the vessel and the radius along the line (i.e., the maximum inscribed sphere radius) must be calculated. This information not only reflects the overall geometry of the vessel but also provides an important reference for subsequent virtual implantation.

[0070] Next, based on the coordinate sequence of the centerline points, the tangent unit vector, principal normal vector, and secondary normal vector are calculated at each point on the centerline. These vectors together form the local coordinate system of the vessel, which helps us better understand the vessel's bending and torsion. Furthermore, the radius of curvature, cross-sectional area, and cross-sectional perimeter at each point on the centerline must be calculated. These parameters are crucial for assessing the deployment state and anchoring ability of the stent.

[0071] After completing the above preparations, virtual stent implantation can begin. Currently, commonly used virtual implantation technologies include finite element simulation, active contour algorithm-based virtual implantation, and geometric algorithm-based virtual implantation. Each of these technologies has its own unique characteristics: finite element simulation can accurately simulate the mechanical behavior of vascular stents, but at a high computational cost; active contour algorithm-based virtual implantation is known for its flexibility and ability to adapt to complex vascular morphologies; and geometric algorithm-based virtual implantation, with its high efficiency, is ideal for rapid evaluation.

[0072] S330, Calculation and Visualization of the Anchoring Force Index. First, the anchoring force index at any location on the stent surface must be calculated based on the stent's deployment radius and the anchoring force index defined in step S200. This process not only involves numerous mathematical calculations but also takes into account the actual deployment state of the stent and the influence of the surrounding environment.

[0073] To facilitate user understanding and analysis, the anchoring force index is typically visualized in two ways: one is to directly represent it using a continuous gradient color bar based on the magnitude of the anchoring force index, which can intuitively demonstrate the spatial distribution of the anchoring force index; the other is to divide the anchoring force index into different ranges (such as "insufficient anchoring," "adequate anchoring," and "over-anchoring") and use different colors to represent each range. The latter method has the advantage of helping doctors quickly determine the anchoring effectiveness of the vascular stent, but it relies on experimental data provided by the vascular stent manufacturer to determine the critical values ​​for the ranges.

[0074] Furthermore, considering the impact of virtual pushing or pulling on the stent's anchoring force index, this step also implements a dynamic update function for the anchoring force index. When the stent's deployment radius increases after virtual pushing, its anchoring force index changes accordingly; similarly, when the stent's deployment radius decreases after virtual pulling, the anchoring force index also adjusts accordingly. This real-time update mechanism not only improves the accuracy of the assessment but also provides a more flexible operating experience for physicians.

[0075] Finally, this step also includes post-processing of the anchoring force index distribution. For example, based on certain thresholds, the high anchoring region area (i.e., the stent surface area with an anchoring force index above the predetermined threshold), the high anchoring region ratio (the ratio of the high anchoring region area to the total stent area), the low anchoring region area (the stent surface area with an anchoring force index below the predetermined threshold), the low anchoring region ratio (the ratio of the low anchoring region area to the total stent area), and the anchoring ratio (the ratio of the high anchoring region area to the low anchoring region area) can be calculated. These indicators not only quantify the anchoring effectiveness of the stent but also provide important references for subsequent risk assessment.

[0076] Step S400: collecting clinical follow-up data after vascular stent implantation, combining it with the anchoring force index, extracting key characteristic parameters, and using a machine learning model for training to obtain a trained vascular stent anchoring degree assessment model.

[0077] In step S400, in order to further improve the accuracy of the assessment, it is necessary to collect clinical follow-up data after vascular stent implantation, and extract key feature parameters in combination with the anchoring force index, and then use the machine learning model for training to finally obtain a trained vascular stent anchoring degree assessment model.

[0078] Among them, the extraction of key feature parameters is the key link in building the evaluation model, which mainly includes the following two categories:

[0079] Anchoring force-related parameters: These directly reflect the stent's anchoring ability and include the area of ​​high anchoring regions, the proportion of high anchoring regions, the area of ​​low anchoring regions, the proportion of low anchoring regions, and the anchoring ratio. These metrics not only quantify the overall anchoring effect of the stent but also reveal its specific performance in different regions.

[0080] Patient-specific and vascular status-related parameters from clinical follow-up data: To ensure the universal applicability of the evaluation model, imaging follow-up data from at least 1,000 patients 6-24 months after stent implantation is required. This data should include patient age, vascular calcification level (such as the Agatston score), stent type (e.g., cobalt-chromium alloy wire, nickel-titanium alloy wire), stent deployment length, and vascular curvature. These parameters not only reflect the impact of individual patient differences on anchoring effectiveness but also help identify potential risk factors for stent migration.

[0081] Based on the extraction of the above key characteristic parameters, in order to further improve the performance of the evaluation model, it is necessary to introduce derived characteristic parameters derived through further analysis and calculation based on parameters related to anchoring force. These derived parameters can reveal the anchoring ability of the vascular stent from different perspectives, thereby providing richer input information for the evaluation model. For example:

[0082] Anchoring force extreme value ratio: By calculating the ratio of the maximum anchoring force to the minimum anchoring force throughout the stent, the uniformity of the stent's anchoring ability can be quantified. If the extreme value is relatively large, it indicates that the anchoring ability of the stent varies significantly in different areas, and there may be a risk of insufficient local anchoring.

[0083] Anchoring force spatial entropy: The area of ​​the vascular stent is divided into distal, mid-distal, lesion area, mid-proximal and proximal (e.g. Figure 3 The anchoring force space entropy is calculated using the following formula:

[0084] ;

[0085] here, is the ratio of the maximum anchoring force to the minimum anchoring force in the i-th region. The spatial entropy of the anchoring force reflects the spatial distribution of the stent's anchoring capacity. A higher entropy indicates a more uneven distribution of the anchoring capacity. By introducing these derived characteristic parameters, a more comprehensive description of the stent's anchoring capacity can be achieved, providing richer data support for subsequent model training.

[0086] After extracting key feature parameters, the next step is to select an appropriate machine learning model for training. Common models include random forests and support vector machines. These models each have their own advantages and can be flexibly selected based on specific needs. Furthermore, to optimize model performance, it is necessary to select an appropriate loss function (such as Focal Loss) and fine-tune the model through methods such as cross-validation.

[0087] During training, the dataset is typically split into a training set and a validation set in a 4:1 ratio. The training set is used to fit the model parameters, while the validation set is used to evaluate the model's generalization ability. This ensures that the trained model not only performs well on the training data but also maintains high prediction accuracy on new data.

[0088] Step S500 : evaluating the anchoring degree of the vascular stent based on the trained evaluation model and the key characteristic parameters to be measured.

[0089] In step S500, the anchoring degree of the vascular stent can be evaluated based on the trained evaluation model and the key characteristic parameters to be measured. Specifically, the trained evaluation model is used with the characteristic parameters to be extracted as input to evaluate the anchoring degree of the vascular stent and predict the probability of vascular stent migration. Based on the prediction results, the migration risk can be divided into three levels:

[0090] High risk: The migration probability is greater than 30%, indicating that the anchoring ability of the vascular stent is insufficient and there is a high risk of migration, and appropriate intervention measures need to be taken.

[0091] Medium risk: The migration probability is between 10% and 30%, indicating that the anchoring ability of the vascular stent is acceptable, but still requires close monitoring.

[0092] Low risk: The migration probability is less than 10%, indicating that the vascular stent has a strong anchoring ability and a low migration risk.

[0093] In this way, it can not only provide doctors with intuitive risk assessment results, but also provide important reference for patients to formulate personalized treatment plans.

[0094] It should be understood that although Figure 1 The steps in the flowchart are shown in the order indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0095] In one embodiment, the present invention provides a device for evaluating the anchoring degree of a vascular stent, comprising: a mapping relationship establishment module, an anchoring force index definition module, a virtual implantation and calculation module, a training module, and an evaluation module, wherein:

[0096] The mapping relationship establishment module is used to establish a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent.

[0097] The anchoring force index definition module is used to define an anchoring force index for evaluating the anchoring degree of the vascular stent based on a mapping relationship.

[0098] The virtual implantation and calculation module is used to process vascular imaging data, generate a three-dimensional vascular model, and implement the virtual implantation process of the vascular stent on the computer in combination with the anchoring force index. The anchoring force index of the vascular stent at different positions in the blood vessel is calculated and updated in real time according to the actual deployment degree of the vascular stent.

[0099] The training module is used to collect clinical follow-up data after vascular stent implantation, combine it with the anchoring force index, extract key feature parameters, and use machine learning model training to obtain a trained vascular stent anchoring degree assessment model.

[0100] The evaluation module is used to evaluate the anchoring degree of the vascular stent based on the trained evaluation model and the key characteristic parameters to be measured.

[0101] The specific limitations of the stent anchoring assessment device can be found in the limitations of the stent anchoring assessment method described above and will not be further elaborated here. Each module in the stent anchoring assessment device can be implemented in whole or in part via software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0102] In one embodiment, a computer device is provided, which may be a terminal and includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for evaluating the anchoring degree of a vascular stent is implemented. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a key, trackball, or touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0103] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0104] Step S100: establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent.

[0105] Step S200 : defining an anchoring force index for evaluating the anchoring degree of the vascular stent based on the mapping relationship.

[0106] In step S300, the vascular image data is processed to generate a three-dimensional vascular model. Combined with the anchoring force index, a virtual stent implantation process is performed on a computer. The anchoring force index of the stent at different locations within the blood vessel is calculated and updated in real time based on the actual deployment degree of the stent.

[0107] Step S400: collecting clinical follow-up data after vascular stent implantation, combining it with the anchoring force index, extracting key characteristic parameters, and using a machine learning model for training to obtain a trained vascular stent anchoring degree assessment model.

[0108] Step S500 : evaluating the anchoring degree of the vascular stent based on the trained evaluation model and the key characteristic parameters to be measured.

[0109] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0110] Step S100: establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent.

[0111] Step S200 : defining an anchoring force index for evaluating the anchoring degree of the vascular stent based on the mapping relationship.

[0112] In step S300, the vascular image data is processed to generate a three-dimensional vascular model. Combined with the anchoring force index, a virtual stent implantation process is performed on a computer. The anchoring force index of the stent at different locations within the blood vessel is calculated and updated in real time based on the actual deployment degree of the stent.

[0113] Step S400: collecting clinical follow-up data after vascular stent implantation, combining it with the anchoring force index, extracting key characteristic parameters, and using a machine learning model for training to obtain a trained vascular stent anchoring degree assessment model.

[0114] Step S500 : evaluating the anchoring degree of the vascular stent based on the trained evaluation model and the key characteristic parameters to be measured.

[0115] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0116] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for evaluating the anchoring degree of a vascular stent, used for an intracranial aneurysm vascular stent, characterized in that: The method comprises: Establishing a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent; An anchoring force index for evaluating the anchoring degree of the vascular stent is defined based on the mapping relationship, wherein the anchoring force index is calculated based on the radial supporting force of the vascular stent or its derivative parameters; Processing vascular imaging data to generate a three-dimensional vascular model, the computer performs a virtual stent implantation process. After the virtual stent implantation, the anchoring force index of the stent at different locations within the vessel is calculated and updated in real time based on the actual deployment degree of the stent, in combination with the definition of the anchoring force index. Clinical follow-up data after vascular stent implantation are collected, combined with the anchoring force index, key feature parameters are extracted, and a machine learning model is used for training to obtain a trained vascular stent anchoring degree assessment model; wherein, the key feature parameters include: anchoring force-related parameters and patient individual difference-related parameters and vascular status-related parameters in the clinical follow-up data, and anchoring force-related parameters include one or a combination of the following: high anchoring region area: the surface area of ​​the vascular stent with an anchoring force index higher than a certain threshold; high anchoring region ratio: the ratio of the high anchoring region area to the total vascular stent surface area; low anchoring region area: the surface area of ​​the vascular stent with an anchoring force index lower than a certain threshold; low anchoring region ratio: the ratio of the low anchoring region area to the total vascular stent surface area; anchoring ratio: the ratio of the high anchoring region area to the low anchoring region area; Based on the trained evaluation model and the key characteristic parameters to be measured, the anchoring degree of the vascular stent is evaluated and the probability of migration of the vascular stent is predicted.

2. The method for evaluating the anchoring degree of a vascular stent according to claim 1, characterized in that: The mapping relationship is established in at least one of the following ways: Simulation method: Computer simulation technology is used to build a physical model of the vascular stent, simulate the radial compression process of the vascular stent, and output the relationship curve between the radial support force and the vascular stent radius; Experimental method: The vascular stent was radially compressed using a radial loading device, and a force sensor was used to obtain a curve showing the relationship between the radial support force and the vascular stent radius. Theoretical calculation: Based on the principles of materials science and mechanics, each stent wire of the vascular stent is regarded as a spring, and its stress conditions are analyzed to derive the functional relationship of the radial support force with the vascular stent radius.

3. The method for evaluating the anchoring degree of a vascular stent according to claim 1, wherein: The key characteristic parameters also include: derived characteristic parameters obtained through further analysis and calculation based on parameters related to anchoring force.

4. The method for evaluating the anchoring degree of a vascular stent according to claim 3, characterized in that: The derived characteristic parameters include one or a combination of the following: Anchoring force extreme ratio: the ratio of the maximum anchoring force to the minimum anchoring force in the entire vascular stent; Anchoring force spatial entropy: The area of ​​the vascular stent is divided into the distal end, mid-distal end, lesion area, mid-proximal end, and proximal end according to the length. The anchoring force spatial entropy is calculated using the following formula: ； in, is the ratio of the maximum anchoring force to the minimum anchoring force in the i-th region.

5. A device for evaluating the anchoring degree of a vascular stent, used for an intracranial aneurysm vascular stent, characterized in that: The device comprises: A mapping relationship establishment module, used to establish a mapping relationship between the radial support force of the vascular stent and the radius of the vascular stent; An anchoring force index definition module, used to define an anchoring force index for evaluating the anchoring degree of the vascular stent based on the mapping relationship, wherein the anchoring force index is calculated based on the radial support force of the vascular stent or its derivative parameters; The virtual implantation and calculation module is used to process vascular imaging data, generate a three-dimensional vascular model, and perform the virtual implantation of the vascular stent on a computer. After the virtual implantation of the vascular stent, the anchoring force index of the vascular stent at different locations within the blood vessel is calculated and updated in real time based on the definition of the anchoring force index and the actual deployment degree of the vascular stent; A training module is used to collect clinical follow-up data after vascular stent implantation, extract key feature parameters in combination with the anchoring force index, and use a machine learning model for training to obtain a trained vascular stent anchoring degree assessment model; wherein the key feature parameters include: anchoring force-related parameters and patient individual difference-related parameters and vascular status-related parameters in the clinical follow-up data, and anchoring force-related parameters include one or a combination of the following: high anchoring region area: the surface area of ​​the vascular stent with an anchoring force index higher than a certain threshold; high anchoring region ratio: the ratio of the high anchoring region area to the total vascular stent surface area; low anchoring region area: the surface area of ​​the vascular stent with an anchoring force index lower than a certain threshold; low anchoring region ratio: the ratio of the low anchoring region area to the total vascular stent surface area; anchoring ratio: the ratio of the high anchoring region area to the low anchoring region area; The evaluation module is used to evaluate the anchoring degree of the vascular stent based on the trained evaluation model and the key characteristic parameters to be measured.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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