Personalized cerebral aneurysm rupture risk assessment and operation planning system

Abstract

The invention discloses a personalized cerebral aneurysm rupture risk assessment and operation planning system, and relates to the technical field of cerebral aneurysm treatment. The three-dimensional reconstruction module assists a doctor in performing surgical path planning and simulation, assists the doctor in performing surgical path planning, simulates different intervention schemes and visualizes adjacent key structures, so that surgical decisions are optimized, operation risks are reduced, and the treatment success rate is improved; the risk assessment module integrates the rupture probability data and the key morphological parameters, generates a personalized risk assessment report, and provides operation intervention priority suggestions in combination with clinical guidelines so as to enhance the personalized accuracy of assessment; the operation planning simulator simulates an operation process, provides an interactive virtual environment, provides a multi-scheme comparison function, allows a doctor to test various intervention strategies in the virtual environment, and automatically generates a structured operation scheme report for direct calling of the decision support module, thereby realizing whole-process closed-loop optimization.

Description

Technical Field

[0001] This invention relates to the field of cerebral aneurysm treatment technology, specifically to a personalized cerebral aneurysm rupture risk assessment and surgical planning system. Background Technology

[0002] Intracranial aneurysms are abnormal bulges that occur on the walls of intracranial arteries. They are the leading cause of subarachnoid hemorrhage and, among cerebrovascular accidents, the third leading cause after cerebral thrombosis and hypertensive intracerebral hemorrhage. They can occur at any age, but are most common in middle-aged and elderly women between 40 and 60 years old. The exact causes of intracranial aneurysms are not fully understood, but most scholars believe they arise from congenital defects in the walls of intracranial arteries and increased intraluminal pressure. Hypertension, cerebral arteriosclerosis, and vasculitis are associated with the occurrence and development of aneurysms. Intracranial aneurysms most commonly occur on the circle of Willis at the base of the brain, with 80% occurring in the anterior half of the circle.

[0003] To assist neurosurgeons, some researchers have proposed computer-aided surgical planning algorithms. For example, the removal of a brain tumor can be viewed as projecting the tumor onto the scalp along the surgical path, with the resulting scalp contour considered the craniotomy area along that path. However, in these methods, the determination of the surgical planning path still depends on the surgeon's subjective decision-making.

[0004] To address this issue, some studies have employed rule-based methods for risk quantification assessment of surgical pathways. This assessment primarily relies on the extent to which the pathway traverses normal brain tissue. Specifically, it first identifies normal brain tissue that may be damaged during surgery, assigns a risk weight to each tissue based on its importance, calculates the corresponding risk weight based on the extent to which different pathways traverse each structure, and presents the results as a risk map on the scalp, thus achieving both quantitative risk assessment and intuitive visual effects. However, setting these risk structure weights is highly dependent on the surgeon's expertise and subjectivity, and requires interactive manual adjustment based on individual patient conditions. This approach is semi-automatic and has poor applicability to a large number of patients. Furthermore, significant individual patient differences mean that weight adjustments for different patients incorporate the surgeon's extensive experience, which traditional rule-based algorithms cannot fully utilize. Therefore, we propose a personalized cerebral aneurysm rupture risk assessment and surgical planning system. Summary of the Invention

[0005] The purpose of this invention is to provide a personalized cerebral aneurysm rupture risk assessment and surgical planning system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0007] A personalized cerebral aneurysm rupture risk assessment and surgical planning system includes:

[0008] The medical image input module is used to receive CT or MRI image data and automatically preprocess it to eliminate noise;

[0009] The data processing unit is used to perform in-depth analysis on the preprocessed medical images, including segmentation of the aneurysm region and extraction of key morphological parameters, and to run a machine learning-based risk prediction algorithm to calculate the probability of rupture.

[0010] The 3D reconstruction module is used to transform the analysis results into a visualized 3D model to assist doctors in surgical path planning and simulation.

[0011] The risk assessment module integrates rupture probability data and key morphological parameters to generate personalized risk assessment reports and provides recommendations on surgical intervention priorities in conjunction with clinical guidelines.

[0012] Surgical planning simulators are used to simulate surgical procedures, providing an interactive virtual environment to assess surgical feasibility and potential complication risks, and generating detailed surgical plan reports to assist doctors in decision-making.

[0013] The report generation module automatically integrates and analyzes data, risk scores, and 3D models to output structured reports, supporting personalized surgical decisions.

[0014] The user interface module provides an intuitive graphical user interface, enabling multi-user login authentication and permission management, supporting the uploading of medical image data and custom parameter settings, integrating interactive visualization tools for 3D models, allowing real-time operation of the surgical planning simulator to adjust the path and assess risks, embedding a report generation module for preview and export functions, and providing system help documentation and error feedback mechanisms to ensure efficient decision-making by doctors and optimization of personalized surgical plans.

[0015] Furthermore, the medical image input module also includes an adaptive image contrast enhancement function to optimize image quality under different lighting conditions.

[0016] Furthermore, the risk prediction algorithm is based on the following function formula:

[0017] P(rupture) = 1 / (1+e) Λ -(β0+β1×1+β2×2+...+βnXn)), (Formula 1)

[0018] Where P (rupture) represents the probability of aneurysm rupture;

[0019] e is the natural constant;

[0020] β0 is the model intercept term;

[0021] β1 to βn are weighting coefficients validated in clinical cohorts;

[0022] X1 to Xn are key predictive variables based on morphological and hemodynamic analysis, including but not limited to:

[0023] a) Aneurysm size ratio (maximum diameter / parent artery diameter); b) Non-sphericity index (NSI); c) Flow impact parameter (WSSmax); d) Daughter sac formation marker (binary variable); e) Parent artery curvature radius ratio; f) Aneurysm wall enhancement grade (0-3); g) Dynamic pulsatility index (DPI); h) Aneurysm top orientation correction factor (anterior / posterior circulation differential weighting).

[0024] Furthermore, the risk assessment module integrates patient age, gender, and medical history data, and combines machine learning algorithms to generate a more personalized risk score. The risk score includes low-risk, intermediate-risk, and high-risk levels. The low-risk level is when the probability of rupture is less than 10% and there are no related complications, and regular follow-up monitoring is recommended. The intermediate-risk level is when the probability of rupture is between 10% and 25%, and the necessity of surgery should be assessed within 3-6 months in accordance with clinical guidelines. The high-risk level is when the probability of rupture is greater than 25%, or when there are high-risk factors such as hypertension and smoking history, and immediate surgical intervention is strongly recommended to reduce the risk of rupture.

[0025] Furthermore, the risk assessment module dynamically adjusts the scoring threshold based on real-time updated medical data and highlights key risk indicators through visual reports to assist doctors in developing personalized treatment plans. The scoring threshold is based on the following function formula:

[0026] Scoring threshold = α * (age + β * (sex + γ * (medical history factor) + δ * (real-time data update rate + ε), (Formula 2)

[0027] Among them, α, β, γ, δ, and ε are adjustable weight coefficients, which are generated by training on historical datasets through machine learning algorithms and are periodically optimized online based on newly input patient image data, clinical records, and epidemiological statistics. The age factor is based on a nonlinear mapping of age segments; the gender factor is a binary variable; the medical history factor integrates the weighted sum of risk factors such as hypertension and smoking history; and the real-time data update rate reflects the refresh frequency of the medical database (δ = 0.05 when the update interval is <1 week, otherwise δ = 0).

[0028] Furthermore, the surgical planning simulator supports virtual reality headsets, enabling immersive surgical path simulation and complication assessment.

[0029] Furthermore, the report generation module provides customizable template options and supports exporting in multiple formats such as PDF and HTML.

[0030] Furthermore, the user interface module includes a voice control unit, allowing doctors to operate interface elements via voice commands.

[0031] Furthermore, the system also includes a data encryption module to protect the security of patient privacy information during transmission and storage.

[0032] Furthermore, the system also includes a cloud and remote access module for enabling multi-device synchronization and data sharing.

[0033] The beneficial effects of this invention are as follows:

[0034] 1. The 3D reconstruction module of this invention transforms the analysis results into a visualized 3D model, assisting doctors in surgical path planning and simulation. It also supports interactive operation functions, including multi-angle rotation, scaling, cutting simulation, and volume measurement. This helps doctors plan surgical paths, simulate different interventional procedures, and visualize adjacent key structures, thereby optimizing surgical decisions, reducing operational risks, and improving treatment success rates. At the same time, the output of the 3D reconstruction module can be directly integrated into a surgical navigation system or 3D printing platform, supporting personalized surgical planning and simulation training.

[0035] 2. The risk assessment module of this invention integrates rupture probability data and key morphological parameters to generate a personalized risk assessment report. It also provides suggestions on surgical intervention priorities based on clinical guidelines. The risk indicators can be dynamically updated based on real-time blood pressure fluctuations or new imaging data, and visualization charts can be automatically generated. It also supports seamless integration with hospital information systems to retrieve patients' past medical history and medication records, thereby enhancing the individualized accuracy of the assessment.

[0036] 3. The surgical planning simulator of this invention simulates the surgical operation process, provides an interactive virtual environment to assess the feasibility of the surgery and the risk of potential complications, and generates a detailed surgical plan report to assist doctors in decision-making. At the same time, the surgical planning simulator provides a multi-plan comparison function, allowing doctors to test various intervention strategies in the virtual environment and automatically generate a structured surgical plan report for the decision support module to directly call, realizing closed-loop optimization of the entire process. Attached Figure Description

[0037] Figure 1 This is a flowchart of the process of this invention;

[0038] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0040] Please see Figure 1 - Figure 2 This invention provides a personalized cerebral aneurysm rupture risk assessment and surgical planning system, comprising:

[0041] The medical image input module receives CT or MRI image data and automatically preprocesses it to eliminate noise. This includes applying advanced filtering algorithms such as Gaussian filtering or nonlocal mean denoising techniques to effectively reduce image artifacts. Simultaneously, the medical image input module performs image segmentation, automatically extracting cerebral vascular structures and aneurysm regions, optimizing segmentation accuracy through machine learning models, and providing high-quality standardized data input for the subsequent risk assessment module.

[0042] The data processing unit performs in-depth analysis of preprocessed medical images, including segmentation of the aneurysm region and extraction of key morphological parameters, and runs a machine learning-based risk prediction algorithm to calculate the probability of rupture. The unit integrates a hemodynamic simulation engine, enabling real-time calculation of hemodynamic parameters (such as wall shear stress and oscillatory shear index) based on patient-specific vascular geometry. It also includes a built-in finite element analysis module to model the biomechanical properties of the aneurysm wall, assessing stress distribution and deformation characteristics under different blood pressure conditions. Finally, it generates a comprehensive analysis report containing quantitative risk indicators and visualized parameter maps for use by the risk assessment module.

[0043] The 3D reconstruction module transforms analysis results into visualized 3D models to assist surgeons in surgical path planning and simulation. It supports interactive functions, including multi-angle rotation, scaling, cutting simulation, and volume measurement, aiding surgeons in surgical path planning, simulating different interventional procedures (such as coil embolization or stent implantation), and visualizing adjacent critical structures (such as nerve or vascular branches). This optimizes surgical decisions, reduces operational risks, and improves treatment success rates. Furthermore, the output of the 3D reconstruction module can be directly integrated into surgical navigation systems or 3D printing platforms, supporting personalized surgical planning and simulation training.

[0044] The risk assessment module integrates rupture probability data and key morphological parameters to generate personalized risk assessment reports and provides surgical intervention priority recommendations based on clinical guidelines. The module also incorporates intelligent algorithms that dynamically update risk indicators based on real-time blood pressure fluctuations or new imaging data, automatically generating visual charts (such as risk heatmaps and trend curves). It supports seamless integration with hospital information systems, retrieving patient medical history and medication records to enhance the individualized accuracy of the assessment. Furthermore, the risk assessment module provides an interactive interface, allowing physicians to adjust parameter weights (such as morphological characteristics or hemodynamic factors), simulate risk changes under different intervention scenarios, and output structured reports (including quantitative risk scores, priority surgical levels, and a list of recommended procedures) for direct use by the decision support module.

[0045] The surgical planning simulator simulates surgical procedures, providing an interactive virtual environment to assess surgical feasibility and potential complication risks, and generates detailed surgical plan reports to assist physicians in decision-making. The simulator also seamlessly integrates with a 3D reconstruction module, using patient-specific vascular models for high-fidelity surgical scenario reproduction. It supports real-time interactive training (such as catheter navigation or instrument placement) and dynamically loads data from the risk assessment module (such as rupture probability or hemodynamic parameters) during simulation to quantify the complication risks of different procedures. Furthermore, the simulator offers multi-option comparison capabilities, allowing physicians to test various interventional strategies in a virtual environment (such as adjusting coil density or stent placement) and automatically generate structured surgical plan reports (including feasibility scores, risk heatmap comparisons, and optimization suggestions) for direct use by the decision support module, achieving closed-loop optimization throughout the entire process.

[0046] The report generation module automatically integrates and analyzes data, risk scores, and 3D models to output structured reports, supporting personalized surgical decisions. The reports generated by this module include key indicators such as feasibility scores, risk heatmap comparisons, and optimization suggestions. They visually present the potential complication risks and benefits of different surgical options, facilitating rapid assessment and selection of the optimal strategy by physicians. Simultaneously, the report generation module works closely with the decision support module to enable real-time access and feedback of report data, ensuring an efficient and accurate personalized decision-making process. The reports also support custom output formats, such as PDF or interactive views, allowing physicians to dynamically adjust content based on the patient's specific model, enhancing the flexibility and clinical applicability of surgical planning.

[0047] The user interface module provides an intuitive graphical user interface, enabling multi-user login authentication and permission management (supporting role definitions such as doctors, administrators, and audit log functions), supporting medical image data upload (compatible with standard formats such as DICOM) and parameter customization settings (including hemodynamic parameters, rupture probability threshold adjustment, and personalized model configuration), integrating interactive 3D model visualization tools (supporting rotation, zoom, slice views, and multi-planar reconstruction for precise observation of aneurysm structures), allowing real-time operation of the surgical planning simulator to adjust pathways and assess risks (and synchronously displaying changes in risk scores and updates to complication heatmaps), embedding a report generation module for preview and export functions (supporting PDF, HTML, and interactive view formats, with customizable report content and layout), and providing system help documentation and error feedback mechanisms (including context-sensitive help, online tutorials, and real-time error log reporting) to ensure efficient decision-making by physicians and optimization of personalized surgical plans.

[0048] In this embodiment, preferably, the medical image input module further includes an adaptive image contrast enhancement function to optimize image quality under different lighting conditions. Employing an intelligent algorithm based on histogram equalization, it can dynamically adjust the brightness and contrast parameters of the image, performing local adaptive processing on medical images captured by different devices (such as CT or MRI), thereby significantly improving the clarity and detail visibility of blood vessel boundaries in low-light, high-noise, or shadowed areas.

[0049] In this embodiment, preferably, the risk prediction algorithm is based on the following function formula:

[0050] P(rupture) = 1 / (1+e) Λ -(β0+β1×1+β2×2+...+βnXn)), (Formula 1)

[0051] Where P (rupture) represents the probability of aneurysm rupture;

[0052] e is the natural constant;

[0053] β0 is the model intercept term;

[0054] β1 to βn are weighting coefficients validated in clinical cohorts;

[0055] X1 to Xn are key predictive variables based on morphological and hemodynamic analysis, including but not limited to:

[0056] a) Aneurysm size ratio (maximum diameter / parent artery diameter); b) Non-sphericity index (NSI); c) Flow impact parameter (WSSmax); d) Daughter sac formation marker (binary variable); e) Parent artery curvature radius ratio; f) Aneurysm wall enhancement grade (0-3); g) Dynamic pulsatility index (DPI); h) Aneurysm top orientation correction factor (anterior / posterior circulation differential weighting).

[0057] In this embodiment, preferably, the risk assessment module integrates patient age, gender, and medical history data, and combines machine learning algorithms to generate a more personalized risk score. The risk score includes low-risk, intermediate-risk, and high-risk levels. The low-risk level is when the probability of rupture is less than 10% and there are no related complications, and regular follow-up monitoring is recommended. The intermediate-risk level is when the probability of rupture is between 10% and 25%, and the necessity of surgery needs to be assessed within 3-6 months in accordance with clinical guidelines. The high-risk level is when the probability of rupture is greater than 25%, or when there are high-risk factors such as hypertension and smoking history, and immediate surgical intervention is strongly recommended to reduce the risk of rupture.

[0058] In this embodiment, preferably, the risk assessment module also dynamically adjusts the scoring threshold based on real-time updated medical data, and highlights key risk indicators through a visual report to assist doctors in developing personalized treatment plans. The scoring threshold is based on the following function formula:

[0059] The scoring threshold is calculated as follows: α*(age factor) + β*(gender factor) + γ*(medical history factor) + δ*(real-time data update rate) + ε, (Formula 2)

[0060] Among them, α, β, γ, δ, and ε are adjustable weight coefficients, which are generated by training on historical datasets through machine learning algorithms and are periodically optimized online based on newly input patient image data, clinical records, and epidemiological statistics. The age factor is based on a nonlinear mapping of age segments; the gender factor is a binary variable; the medical history factor integrates the weighted sum of risk factors such as hypertension and smoking history; and the real-time data update rate reflects the refresh frequency of the medical database (δ = 0.05 when the update interval is <1 week, otherwise δ = 0).

[0061] In this embodiment, preferably, the surgical planning simulator supports a virtual reality headset to achieve immersive surgical path simulation and complication assessment. The surgical planning simulator imports a patient-specific vascular model through high-precision 3D reconstruction technology and combines real-time hemodynamic parameters to simulate the deployment process and effects of different interventional devices (such as coils, stents, and flow diverters) within the blood vessel. Users can operate catheters and guidewires, release and adjust devices via a virtual handle. The system simultaneously calculates and visualizes the impact of the simulated operations on the degree of aneurysm neck coverage, branch vessel patency, and changes in blood flow patterns.

[0062] In this embodiment, preferably, the report generation module provides customizable template options and supports exporting in multiple formats such as PDF and HTML. Users can flexibly select or design templates according to clinical needs, covering core parts such as risk assessment results, surgical planning recommendations, and hemodynamic analysis summaries. The system automatically integrates patient imaging data, weighted coefficient optimization records, and simulation operation results to generate a structured report, supporting one-click export for easy sharing, printing, or electronic archiving by the medical team. In addition, the module has an embedded preview interface, allowing users to review the report content in real time before exporting to ensure data accuracy and completeness, and supports multi-language version adaptation to meet the usage standards of different regions.

[0063] In this embodiment, preferably, the user interface module includes a voice control unit, allowing doctors to operate interface elements via voice commands. For example, doctors can switch image views, adjust hemodynamic parameters, or initiate simulation processes via voice. This unit integrates a real-time voice recognition engine, supports custom command sets such as "zoom in on the aneurysm neck area" or "export report," ensuring efficient response in the surgical environment while reducing manual operation interference and improving overall workflow efficiency. Furthermore, the voice control unit can seamlessly collaborate with other modules, such as combining with the report generation module to enable voice-triggered report preview or export, and has background noise filtering functionality to maintain recognition accuracy.

[0064] In this embodiment, preferably, the system further includes a data encryption module to protect the security of patient privacy information during transmission and storage. The data encryption module adopts the AES-256 advanced encryption standard and implements end-to-end protection via SSL / TLS protocol during data transmission to prevent unauthorized access. During storage, it automatically encrypts sensitive information such as patient image data and risk assessment results, and integrates a key management system to ensure that only authorized medical personnel can decrypt and access the data. Furthermore, the data encryption module supports audit log functionality, recording all data access and operation events for easy post-event tracking and compliance review, while also complying with international privacy regulations such as HIPAA and GDPR to ensure the security and reliability of the system in various medical scenarios.

[0065] In this embodiment, preferably, the system also includes a cloud and remote access module for enabling multi-device synchronization and data sharing. The cloud module, based on a distributed cloud storage architecture, provides high availability and elastic scalability, ensuring that surgical planning data, risk assessment results, and patient image information are synchronized to authorized devices in real time, reducing data silos. The remote access module, through secure HTTPS protocol and OAuth 2.0 authentication mechanism, supports seamless access to the system by medical teams globally, enabling remote consultations and collaborative editing. It also integrates a data encryption module to automatically apply end-to-end encryption to transmitted data, preventing man-in-the-middle attacks. Furthermore, the cloud and remote access modules have access control policies, restricting data sharing operations to authorized roles (such as attending physicians or surgical assistants) and generating detailed audit logs to record all synchronization events, facilitating compliance review and traceability, and complying with privacy regulations such as HIPAA and GDPR, thereby improving cross-device collaboration efficiency and ensuring the security and reliability of the system in a distributed medical environment.

[0066] Working principle and usage process of this invention:

[0067] After logging into the system through the user interface module, the doctor first uploads the patient's raw CT or MRI data through the medical image input module. The system automatically executes the preprocessing process, including noise reduction (using a nonlocal mean denoising algorithm), image enhancement (optimizing vascular contrast based on histogram equalization), and vascular structure segmentation (using a convolutional neural network model to accurately extract the aneurysm region), generating a standardized three-dimensional vascular mask.

[0068] The data processing unit then receives the preprocessed data and performs three core analyses in parallel: automatically measuring key parameters such as aneurysm size ratio and non-sphericity index using morphological algorithms; calculating the wall shear stress distribution and oscillatory shear index using a hemodynamic simulation engine; and constructing a biomechanical model of the aneurysm wall using a finite element analysis module to simulate stress deformation characteristics under different blood pressure conditions. After comprehensive calculation of the above analysis results, a personalized rupture probability score is generated using formula (1).

[0069] The three-dimensional reconstruction module synchronously receives vascular geometric data and blood flow parameters output by the data processing unit to construct an interactive dynamic three-dimensional model. Doctors can rotate / cut the model to observe the spatial relationship between the aneurysm and the carrier artery, measure key surgical dimensions such as the aneurysm neck width, and call the surgical planning simulator to perform virtual interventional operations: when the coil embolization scheme is selected, the system simulates the catheter path and coil deployment effect in real time, dynamically displays the aneurysm cavity filling rate and changes in branch vessel patency; if stent implantation is selected, the stent wall adhesion status and blood flow pattern changes are visualized, and the wall shear stress cloud map is automatically generated. All operation data are synchronized to the risk assessment module in real time. This module integrates clinical factors such as patient age, gender, and history of hypertension, dynamically adjusts the risk grading threshold through formula (2), and highlights high-risk characteristics (such as cyst formation or WSSmax>7Pa) in the interface.

[0070] After the surgeon completes the surgical plan simulation, the report generation module automatically integrates the following elements: a quantitative parameter table (including rupture probability and morphological indicators) provided by the data processing unit, a comparison chart of interventional effects from the 3D reconstruction module (heatmap of aneurysm neck coverage for multiple options), and structured recommendations from the risk assessment module (surgical priority and follow-up period). The final customizable report is exported as an interactive PDF with a single click via the user interface module, while all encrypted data is synchronized to the operating room navigation system via the cloud module. The system records operation logs throughout the process, and critical data transmissions are protected by AES-256 encryption and SSL / TLS protocols to ensure compliance with medical privacy regulations.

[0071] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A personalized cerebral aneurysm rupture risk assessment and surgical planning system, characterized in that, include: The medical image input module is used to receive CT or MRI image data and automatically preprocess it to eliminate noise; The data processing unit is used to perform in-depth analysis on the preprocessed medical images, including segmentation of the aneurysm region and extraction of key morphological parameters, and to run a machine learning-based risk prediction algorithm to calculate the probability of rupture. The 3D reconstruction module is used to transform the analysis results into a visualized 3D model to assist doctors in surgical path planning and simulation. The risk assessment module integrates rupture probability data and key morphological parameters to generate personalized risk assessment reports and provides recommendations on surgical intervention priorities in conjunction with clinical guidelines. Surgical planning simulators are used to simulate surgical procedures, providing an interactive virtual environment to assess surgical feasibility and potential complication risks, and generating detailed surgical plan reports to assist doctors in decision-making. The report generation module automatically integrates and analyzes data, risk scores, and 3D models to output structured reports, supporting personalized surgical decisions. The user interface module provides an intuitive graphical user interface, enabling multi-user login authentication and permission management, supporting the uploading of medical image data and custom parameter settings, integrating interactive visualization tools for 3D models, allowing real-time operation of the surgical planning simulator to adjust the path and assess risks, embedding a report generation module for preview and export functions, and providing system help documentation and error feedback mechanisms to ensure efficient decision-making by doctors and optimization of personalized surgical plans.

2. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The medical image input module also includes an adaptive image contrast enhancement function to optimize image quality under different lighting conditions.

3. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The risk prediction algorithm is based on the following function formula: P(rupture) = 1 / (1 + e Λ -(β0 + β1×1 + β2×2 +... + βnXn)), (Equation 1) Where P (rupture) represents the probability of aneurysm rupture; e is the natural constant; β0 is the model intercept term; β1 to βn are weighting coefficients validated in clinical cohorts; X1 to Xn are key predictive variables based on morphological and hemodynamic analysis, including but not limited to: a) Aneurysm size ratio (maximum diameter / parent artery diameter); b) Non-sphericity index (NSI); c) Flow impact parameter (WSSmax); d) Daughter sac formation marker (binary variable); e) Parent artery curvature radius ratio; f) Aneurysm wall enhancement grade (0-3); g) Dynamic pulsatility index (DPI); h) Aneurysm top orientation correction factor (anterior / posterior circulation differential weighting).

4. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The risk assessment module integrates patient age, gender, and medical history data, and combines machine learning algorithms to generate a more personalized risk score. The risk score includes low-risk, intermediate-risk, and high-risk levels. The low-risk level is when the probability of rupture is less than 10% and there are no related complications, and regular follow-up monitoring is recommended. The intermediate-risk level is when the probability of rupture is between 10% and 25%, and the necessity of surgery should be assessed within 3-6 months in accordance with clinical guidelines. The high-risk level is when the probability of rupture is greater than 25%, or when there are high-risk factors such as hypertension and smoking history, and immediate surgical intervention is strongly recommended to reduce the risk of rupture.

5. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The risk assessment module also dynamically adjusts the scoring thresholds based on real-time updated medical data and highlights key risk indicators through visual reports to assist doctors in developing personalized treatment plans. The scoring thresholds are based on the following function formula: The scoring threshold is calculated as follows: α*(age factor) + β*(gender factor) + γ*(medical history factor) + δ*(real-time data update rate) + ε, (Formula 2) Among them, α, β, γ, δ, and ε are adjustable weight coefficients, which are generated by training on historical datasets through machine learning algorithms and are periodically optimized online based on newly input patient image data, clinical records, and epidemiological statistics. The age factor is based on a nonlinear mapping of age segments; the gender factor is a binary variable; the medical history factor integrates the weighted sum of risk factors such as hypertension and smoking history; and the real-time data update rate reflects the refresh frequency of the medical database (δ = 0.05 when the update interval is <1 week, otherwise δ = 0).

6. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The surgical planning simulator supports virtual reality headsets, enabling immersive surgical path simulation and complication assessment.

7. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The report generation module offers customizable template options and supports exporting in multiple formats such as PDF and HTML.

8. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The user interface module includes a voice control unit, which allows doctors to operate interface elements via voice commands.

9. The personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The system also includes a data encryption module to protect the security of patient privacy information during transmission and storage.

10. A personalized cerebral aneurysm rupture risk assessment and surgical planning system according to claim 1, characterized in that, The system also includes a cloud and remote access module for enabling multi-device synchronization and data sharing.

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