A method for constructing a prediction model for vascular exposure during hepatectomy

Abstract

The present invention relates to a method for constructing a prediction model for vascular exposure during liver resection based on anatomy, including constructing the geometric structure of the liver and blood vessels based on CT and MRI data, and introducing biomechanical property modeling; combining the principles of biomechanics, the model simulates the stress and strain behavior of blood vessels under different physiological conditions and their response when surgical instruments apply pressure through finite element analysis (FEA); providing a static anatomical structure, and the prediction model provides basic data on dynamic factors of vascular elasticity and toughness; introducing a hierarchical branch complexity model, classifying the vascular branch network according to the number of branches, angles, and diameter change characteristics, and analyzing the difficulty of vascular exposure at different levels; establishing vascular exposure scenarios under different intraoperative operation paths through surgical dynamic simulation based on a physics engine; including predicting deformation or micro-displacement caused when the instrument contacts the tissue around the blood vessel during surgery.

Description

Technical Field

[0001] The present invention relates to a method for constructing a vascular exposure prediction model, and in particular to a method for constructing a vascular exposure prediction model based on anatomy during liver resection. Background Art

[0002] Currently, the technologies used to construct a prediction model for vascular exposure during hepatectomy dissection, particularly those described in Chinese invention patent 2023115266473, have several drawbacks, primarily in terms of dynamic prediction capabilities, spatiotemporal data fusion, real-time feedback, and adaptability. These are specifically discussed below:

[0003] First, the patent utilizes static 3D modeling technology. While this 3D modeling module can model vascular information in the liver region and segment the anatomical structures of the liver and blood vessels, such as the left and right branches of the proper hepatic artery, this static model can only provide preoperative planning and lacks the ability to predict real-time changes in vascular exposure during surgery. During surgery, the patient's liver and blood vessels may dynamically deform with the manipulation of surgical instruments. This static modeling technology cannot capture these changes, resulting in the surgeon being unable to timely predict the risk of vascular exposure during surgery. Existing technologies employ semantic segmentation and instance-based segmentation models to segment anatomical regions such as the liver, gallbladder, and round ligament of the liver, but these segmentation results do not reflect the dynamic stress and strain responses of liver tissue and blood vessels during surgery. Because surgical instrument manipulation exerts varying degrees of compression and stretch on blood vessels, vascular exposure is not fixed but subject to constant change. Existing solutions lack the ability to predict and respond to these dynamic changes. Relying solely on static preoperative planning, they are prone to errors during actual surgery. Secondly, while existing technologies can capture the target vessel area in surgical images and distinguish individual vascular branches through instance segmentation models, these operations are based on static image or surgical image segmentation and lack the dynamic fusion analysis of spatiotemporal data. Critical data during surgery includes not only spatial information (such as vessel location and structure), but also temporal information (such as instrument movement and surgical progress). During liver resection, the movement trajectory and pressure distribution of surgical instruments can affect vessel deformation and exposure risk, making real-time fusion analysis of this spatiotemporal data crucial. However, the analysis module, semantic segmentation module, and instance segmentation module in this patent do not fully consider the dynamic interaction between surgical instruments and blood vessels. While a Transformer encoder-based vascular branch recognition model is introduced to identify vessel types and branch structures, these technologies focus primarily on image segmentation and static classification, lacking the ability to describe and process the dynamic stress-strain relationships generated by the interaction between instruments and blood vessels. Thirdly, existing technologies lack real-time feedback and adaptability during surgery. While this patent provides an acquisition module for real-time acquisition of surgical images, it does not make adaptive adjustments based on this real-time data. During surgery, the risk of vascular exposure may change with the movement of surgical instruments, changes in pressure, and fluctuations in the patient's physiological state. Therefore, real-time feedback mechanisms and adaptive models are extremely important. The prediction model can be updated based on real-time data to ensure a more accurate prediction of vascular exposure risk during surgery. However, the analysis module in this patent can only divide the area of ​​vascular exposure based on the predetermined surgical plan and lacks adaptive adjustment of vascular exposure during surgery.For example, during surgery, when the pressure applied by the instrument on the blood vessels exceeds a certain threshold, the blood vessels may undergo significant deformation or exposure, but existing technologies are unable to update the model or adjust the surgical path in a timely manner based on these dynamic changes. There is a certain lag, which increases the risk of blood vessel rupture or damage during surgery.

[0004] Finally, existing technologies have limitations in the application of multimodal data fusion and deep learning techniques. Although the patent introduces a multi-layer feature extraction network and classification head model, which can perform image segmentation and vascular identification through certain machine learning techniques, this segmentation and identification cannot fully address the complex dynamic issues encountered during liver resection surgery. The data collected during modern surgery is complex and diverse, involving multiple information sources such as imaging data (such as CT and MRI), the real-time position of surgical instruments, and pressure sensor feedback data. However, existing technologies do not utilize this multimodal data for comprehensive analysis. Especially in dynamic surgical scenarios, the movement trajectory and time series changes of instruments are closely related to the risk of vascular exposure. If deep learning models can combine this spatiotemporal data and capture the complex interactions between instruments and blood vessels during surgery, they can better predict the risk of vascular exposure in the future. However, the patent does not fully utilize these deep learning technologies to process and analyze multimodal and time series data, resulting in limitations in handling complex dynamic surgical scenarios. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for constructing a prediction model of vascular exposure during liver resection anatomy, thereby solving some of the drawbacks pointed out in the background art.

[0006] The present invention solves the above-mentioned technical problems by adopting the following technical solution, including the steps of:

[0007] S1. Deep anatomical modeling of liver vascular distribution:

[0008] S1.1. The geometric structure of the liver and blood vessels is constructed based on CT and MRI data, and biomechanical property modeling is introduced. The model combines biomechanical principles and uses finite element analysis (FEA) to simulate the stress and strain behavior of blood vessels under different physiological conditions, as well as their response to pressure applied by surgical instruments. The model provides static anatomical structures, and the predictive model provides basic data on dynamic factors of vascular elasticity and toughness.

[0009] S1.2. Introducing a hierarchical branch complexity model, the vascular branch network is classified according to the number, angle, and diameter variation characteristics of the branches, and the difficulty of vascular exposure is analyzed at different levels;

[0010] S2. Dynamic surgical simulation and vascular exposure scenario construction:

[0011] S2.1. Establish vascular exposure scenarios under different surgical paths through dynamic surgical simulation based on a physics engine. This includes predicting deformation or micro-displacement caused by instruments contacting surrounding tissue during surgery.

[0012] S2.2. Build a spatiotemporal vascular exposure prediction model to predict the predictability of vascular exposure at different time points. Based on the spatiotemporal data stream, a long short-term memory (LSTM) network or a temporal convolutional network (TCN) is used to capture the temporal relationship between intraoperative instrument movement and vascular exposure, predicting the probability of vascular exposure at a specific time point.

[0013] S3. Vascular exposure prediction model based on the fusion of global and local factors:

[0014] S3.1. Design a fusion model that combines local geometric features (including vessel diameter and branching angle) with global features (including vessel distribution pattern and density of adjacent tissues) for analysis.

[0015] S3.2. Construct a multimodal data fusion model by integrating multimodal physiological data; perform weighted fusion of data from different information sources using ensemble learning methods;

[0016] S4. Intraoperative real-time feedback and model adaptive optimization:

[0017] S4.1. Introduce ultrasound and endoscopic image data collected in real time during surgery and combine them with a deep learning model for real-time correction. The model adaptively adjusts the prediction results based on the new input information; through convolution operations on real-time images, the regional distribution of expected exposed blood vessels is recalculated.

[0018] Furthermore, the deep anatomical modeling of the liver vascular distribution includes:

[0019] Based on the patient's CT and MRI images, the liver and blood vessel structures are first reconstructed in three dimensions; the morphology of the blood vessels is captured, and the deformation behavior of the blood vessels under different surgical pressure conditions is perceived; the geometric model is constructed through the function Describe, where is a function that defines the three-dimensional geometry of the liver and blood vessels, where x, y, and z represent the three-dimensional coordinates in space. To capture the complex geometry and distribution of blood vessels, the equation combines local biomechanical properties, including the local deformation velocity field v(x, y, z), and is described by the following formula:

[0020]

[0021] Among them, γ i is the weight coefficient in the geometric model, which is used to control the influence of different anatomical regions; is the Laplace operator It is used to describe the degree of change between points in the geometric structure; v(x, y, z) is the local deformation velocity field, which represents the velocity distribution of blood vessels and surrounding tissues when subjected to force and reflects the dynamic response of blood vessels; κ is the coupling coefficient, which indicates the degree of coupling between the velocity field and the geometric shape and is used to control the degree of deformation of blood vessels under different conditions.

[0022] Furthermore, the deep anatomical modeling of the liver vascular distribution includes:

[0023] Finite element analysis (FEA) simulates the dynamic behavior of blood vessels through stress-strain relationships and predicts the risk of rupture or exposure during surgery. The core formula used in dynamic stress-strain analysis is:

[0024]

[0025] in, is the local pressure that varies with time, representing the force acting on the blood vessel at a certain time t and position x. It is used to simulate the pressure exerted on the blood vessel by surgical instruments during operation. λ1 and λ2 are the stress and strain influencing factors of the system, respectively. λ1 mainly controls the effect of stress on the system, while λ2 controls the effect of changes in contact force on the blood vessel. These two parameters affect the response of the blood vessel under different physiological conditions by regulating the relationship between pressure and strain. It is the rate of change of strain with time, describing the strain rate of the blood vessel at time t, that is, the deformation speed of the blood vessel at a certain moment; it represents the instantaneous deformation behavior of the blood vessel and captures the rapid deformation during surgical operation; Δ(x, t) is the spatial distribution function of the contact force between the surgical instrument and the blood vessel, which represents the force exerted by the surgical instrument on the blood vessel at spatial position x and time t.

[0026] Furthermore, the deep anatomical modeling of the liver vascular distribution includes:

[0027] By integrating static geometric information with dynamic stress-strain relationships, a spatiotemporal coupling model is created to predict the dynamic deformation and exposure risk of blood vessels during surgery. The spatiotemporal coupling model is described by the following formula:

[0028]

[0029] Among them, R(x, t) is the output of the fusion model, which represents the comprehensive result of static geometric information and dynamic stress response at position x and time t, and is ultimately used to assess the risk of vascular exposure during surgery;

[0030] η(x) is the weight coefficient that controls the influence of static geometric features on the overall response; represents the gradient of the geometric morphology, reflecting the shape change in the static geometric features; μ(t) is the time-dependent dynamic deformation function, describing the dynamic response characteristics of the blood vessel at different times; ξ(x, t) is the spatiotemporal coupling factor, representing the comprehensive effect of the spatial and temporal changes of stress on the blood vessel deformation; This second-order derivative captures the rate of change of pressure in space and reflects the impact of local pressure on the entire vessel.

[0031] Furthermore, the dynamic surgical simulation and vascular exposure scenario construction includes:

[0032] Various data are collected in real time during surgery, including real-time ultrasound and endoscope images, instrument position information, and pressure sensor feedback. This multidimensional data forms a dynamic spatiotemporal data stream that describes the interaction between instrument movement, applied pressure, and blood vessels. The spatiotemporal data stream includes the spatial dimensions x, y, and z, and also combines the time dimension t to create a dynamic scene containing spatiotemporal information. This scene records the trajectory of the surgical instrument in real time, capturing the changes in the pressure applied by the instrument on the blood vessels at different time points, and then establishes a spatiotemporal model:

[0033]

[0034] in Represents the risk of vascular exposure at spatial coordinates x, y, z and time t; the function describes the predictability of vascular exposure at a specific time and space point during surgery; represents the pressure gradient applied by the surgical instrument, describing the pressure change applied by the surgical instrument on the blood vessel at position x, y, z and time t′; Represents the instrument motion velocity at position x, y, z and time t′; this velocity term is used to capture the motion trajectory of the surgical instrument, t′: is the integral time variable, integrated from the initial time to the current time t, describing the comprehensive impact of the instrument on the blood vessel over the past period of time.

[0035] Furthermore, the dynamic surgical simulation and vascular exposure scenario construction includes:

[0036] By introducing the long short-term memory (LSTM) network and the temporal convolutional network (TCN), the long-term and short-term dependencies in time series are learned and predicted. Based on the action sequences of surgical instruments in different time periods, the vascular exposure at future moments is predicted. The TCN uses convolution operations to efficiently process short-term patterns in time series and capture rapid changes within a short period of time during surgery.

[0037] ε t =γ1·σ(W x ·x t +W h ·h t-1 +b)

[0038] where ε t represents the probability of vascular exposure at the current time t; is the output of the system prediction, reflecting the vascular exposure risk caused by the surgical instrument operation prediction at the current moment; x t W represents the input data at time t, including the position, speed, and contact information of the device with the blood vessel; x Represents the weight matrix of the input data, which is used to adjust the influence weight of the input features; h t-1 Represents the implicit state of the previous moment, retaining the historical information of the operation at the previous moment. Through this state, LSTM captures the long-term dependency of the operation; W h The weight matrix representing the implicit state controls the influence of the state at the previous moment on the current prediction; σ is the activation function, which is a nonlinear function used to map the network output to a probability value range; b is the bias term used to balance the output results.

[0039] Furthermore, the dynamic surgical simulation and vascular exposure scenario construction includes:

[0040] By dynamically combining the spatial interaction and time series of surgical instruments and blood vessels, a comprehensive vascular exposure probability prediction model is generated, which outputs the probability value of vascular exposure to guide surgical operation optimization:

[0041]

[0042] in, represents the probability of vascular exposure at the future time t+Δt; the final result predicted by the model is used to reflect the vascular exposure risk at a specific future moment; α is the adjustment factor used to control the confidence level of the model output; Indicates the exposure risk level of the blood vessel at the current time t, based on the spatiotemporal movement and pressure of the surgical instrument;

[0043] It represents the derivative of the instrument movement speed with respect to time, capturing the acceleration and deceleration of the surgical instrument movement; it is used to analyze the impact of rapid instrument movement on the risk of vascular exposure. Ω is the integration area, representing the three-dimensional space of the entire surgical operation scene. The integration process is to calculate all spatial points in the surgical scene to generate a comprehensive vascular exposure probability.

[0044] The present invention has the following beneficial effects:

[0045] By introducing dynamic surgical simulation and spatiotemporal data flow analysis, the system can monitor the interaction between surgical instruments and blood vessels in real time, and combine data such as pressure and speed to accurately predict the risk of blood vessels being exposed during surgery. This real-time prediction function helps doctors predict high-risk areas in advance, thereby optimizing the surgical path and reducing the possibility of blood vessel exposure and damage. By combining the spatial operation of surgical instruments with time series, the present invention can provide a comprehensive analysis of blood vessel deformation and exposure during surgery. During critical operations, the system can automatically adjust the prediction results and provide real-time feedback to the doctor, reminding the doctor to take necessary measures to avoid blood vessel rupture or serious exposure, significantly improving the safety of the operation.

[0046] This invention combines CT and MRI imaging data, real-time ultrasound and endoscope data, and dynamic information about surgical instruments to construct a multimodal data fusion model. By integrating this multidimensional data, the system can analyze the dynamic behavior of blood vessels from multiple perspectives, resulting in more accurate and comprehensive predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the method for constructing a vascular exposure prediction model during liver resection anatomy based on the present invention.

[0048] Figure 2 This is a flowchart of the deep anatomical modeling of the liver vascular distribution of the present invention.

[0049] Figure 3 This is a surgical diagram of a 45-year-old patient undergoing right hepatic lobectomy in Example 1 and Example 2 of the present invention. DETAILED DESCRIPTION

[0050] The following is a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.

[0051] Combine Figure 1One of the core steps in constructing a model to predict vascular exposure during liver resection is deep anatomical modeling of the liver's vascular distribution. This step uses medical imaging data such as CT and MRI to obtain the patient's three-dimensional liver and vascular geometry and perform a detailed reconstruction. To simulate the pressure exerted by instruments on the vessels during surgery and the resulting deformation, biomechanical modeling is necessary. This step combines the mechanical properties of liver tissue and vessels, such as elastic modulus, toughness, stress and strain. Finite element analysis (FEA) is used to simulate the stress (pressure) and strain (deformation) behavior of the vessels under different physiological conditions. Finite element analysis is a numerical method that divides complex geometric structures into smaller units to calculate the stress and strain distribution of the vessels under different pressure conditions, specifically how they respond to pressure applied by surgical instruments. This process provides surgeons with detailed data on the static anatomical structure and predicts the dynamic changes that occur in the vessels during surgery, such as biomechanical properties such as elastic deformation and tensile strength. This data provides crucial insights for surgical planning, helping surgeons predict potential vascular exposure risks, optimize surgical routes, and prevent vascular injury.

[0052] In order to better capture the complex distribution characteristics of blood vessels, a hierarchical branch complexity model was further introduced. This model analyzes the complexity of the vascular network in a hierarchical manner, mainly considering geometric characteristics such as the number of branches, branch angles, and diameter changes of the blood vessels. Vascular regions with a large number of branches, steep angles, and significant diameter changes usually have a higher risk of exposure, because the blood vessels in these areas are more likely to be inadvertently exposed or damaged during surgical operations. Through this hierarchical branch complexity analysis, the difficulty of vascular exposure during surgery can be accurately predicted at different levels, helping doctors to make more detailed surgical plans for high-risk areas.

[0053] The second step is dynamic surgical simulation and vascular exposure scenario construction. First, the system uses a physics engine to dynamically simulate the surgical process, simulating the interaction between surgical instruments and blood vessels and surrounding tissues under different operation paths, especially when the instruments contact the tissues around the blood vessels, which may cause tissue deformation and micro-displacement. The physics engine can accurately simulate the physical interaction between instruments and soft tissues during surgery, helping to predict the force, deformation response and displacement changes of blood vessels when the instruments apply pressure or move. This simulation not only provides a visual surgical scene, but also provides quantitative reference data for the risks of vascular exposure that may occur during surgery.

[0054] Based on this, the system constructed a spatiotemporal vascular exposure prediction model. This model combines the dynamic movement trajectories of surgical instruments with time series data during surgery to predict the probability of vascular exposure at different time points. The core of this model is to capture the continuity of surgical operations and the causal relationships between different steps through spatiotemporal data streams. The spatiotemporal data stream records the position of surgical instruments, the speed of operation, and their impact on blood vessels and surrounding tissues. To enhance the model's predictive capabilities, the system introduced two deep learning algorithms: long short-term memory (LSTM) and temporal convolutional network (TCN). The LSTM network is capable of capturing the long-term dependencies of instrument operations during surgery, particularly the delayed impact of a particular operation on subsequent vascular exposure. Its memory cell structure preserves the key operation history during surgery and predicts vascular exposure risks at future time points. Furthermore, the TCN, by processing time series data through a convolutional network, can effectively capture rapid changes and local patterns of surgical instruments within a short time window. This makes it particularly suitable for analyzing the impact of continuous instrument movement within a short period of time on vascular exposure during surgery. By combining the advantages of LSTM and TCN, the system can capture the temporal relationship between surgical instrument movement and vascular exposure in real time during surgery, thereby accurately predicting the probability of vascular exposure at a specific time point.

[0055] The third step is to develop a vascular exposure prediction model based on the fusion of global and local factors. The core of the model's design lies in the joint analysis of the fusion of local and global features to achieve more accurate vascular exposure prediction. Local features mainly include geometric parameters directly related to the blood vessels themselves, such as the diameter and branching angle of the blood vessels. These features can directly reflect the specific morphological structure of the blood vessels and the difficulty of exposure during surgery. Global features focus more on the distribution pattern of blood vessels in the entire liver and their relationship with surrounding tissues, such as the density of adjacent tissues and the distribution pattern of blood vessels in the liver. This global information is crucial for vascular exposure prediction, because the liver has a complex tissue structure and the risk of vascular exposure in different parts will be affected by the characteristics of adjacent tissues. For example, higher-density tissue may provide more support for blood vessels and reduce the risk of exposure.

[0056] On this basis, a multimodal data fusion model is constructed by integrating multimodal physiological data. Multimodal data includes information from various sources, including imaging data from CT and MRI, physiological parameters collected in real time during surgery (such as blood flow and blood pressure), and historical surgical data. These data come from different sources and in different forms, but each can reflect the possibility of vascular exposure from different perspectives. By integrating these data, a more comprehensive predictive model can be formed.

[0057] To optimize data fusion from different information sources, the model utilizes ensemble learning. Ensemble learning is a method that improves predictive performance by combining multiple models. Here, local and global features, as well as multimodal data, are jointly analyzed through a weighted fusion approach. Data from each information source is assigned a different weight within the overall model based on its impact on vascular exposure risk. These weights are determined through data training, ensuring that the contribution of each data source in the model is appropriately assessed and utilized.

[0058] The fourth step is real-time feedback and model adaptive optimization during surgery. The core of this step is to use the data collected in real time during surgery, especially ultrasound and endoscopic imaging data, combined with deep learning models to dynamically correct the vascular exposure prediction. During the operation, with the movement of instruments and the deformation of tissues, the pre-established static model may no longer be accurate. Therefore, the introduction of real-time data enables the model to be adaptively adjusted based on the latest anatomical conditions. By inputting real-time imaging data into the deep learning network, the model can analyze this new data and correct the prediction results according to the changes that occur during the operation. By learning a large amount of historical surgical data and real-time image patterns, the deep learning model can accurately identify the blood vessel and tissue characteristics in the image, ensuring the accuracy and flexibility of the prediction.

[0059] To perform this real-time correction, the model uses a convolutional neural network (CNN) to perform convolution operations on real-time image data. The convolution layer can extract spatial features in the image, such as the edges and shapes of blood vessels and changes in the density of surrounding tissues. This operation can dynamically update the spatial distribution information of blood vessels in a real-time environment, helping the model recalculate the regional distribution of expected exposed blood vessels. If instrument contact in a certain area during surgery causes local tissue displacement or pressure changes, the convolution operation can quickly identify these changes and feed them back to the model for adjustment. After receiving new data, the model adaptively recalculates the risk of vascular exposure in different areas and presents these results to the surgeon in real time, helping the surgeon make more accurate operational decisions at critical moments.

[0060] Example 1:

[0061] Combine Figure 2 、 Figure 3 A 45-year-old patient was undergoing right hepatic lobectomy. Preoperative CT and MRI data were collected of the patient's liver and vascular imaging. The doctor hoped to use this model to predict intraoperative vascular exposure, ensuring the safety of the surgical path and reducing the risk of possible vascular injury.

[0062] First, based on the patient's CT and MRI images, the system performs a three-dimensional reconstruction of the patient's liver and its vascular structure. To describe the complex geometric shape of the liver and blood vessels, x, y, and z represent the three-dimensional coordinates of space. This geometric function can capture the morphology of the liver and blood vessels, including geometric features such as the length, diameter, and branching angle of the blood vessels. In order to ensure that the vascular exposure model can reflect the actual situation during surgery, the deformation behavior of the blood vessels under different surgical pressure conditions also needs to be considered. Therefore, the model combines local biomechanical properties to capture the local deformation velocity field v(x, y, z), which is used to represent the velocity distribution and deformation response of the blood vessels and their surrounding tissues when subjected to instrument pressure.

[0063] The geometric model is described by the following formula:

[0064]

[0065] In this formula, γ i is the weight coefficient in the geometric model, which is used to control the influence of different anatomical regions. Due to the different tissue characteristics and vascular density in different regions of the liver, γ i The value of may be different in different areas. It can be set in the main blood vessel area, i The value range of γ is 0.8-1.0, which means that the geometric characteristics of the area have a greater impact on the surgical process. In the smaller branch vessel area, γ i The value range of is 0.4-0.6, which means that the geometric features of these areas have little influence on the overall deformation.

[0066] In the equation is the Laplace operator, which is used to describe the degree of change between points in a geometric structure. This operator can reflect the deformation trend of perivascular tissue under different surgical pressures, especially those areas with more severe deformation. Doctors can identify vascular areas that may cause significant deformation during surgery and predict risks in advance.

[0067] The local deformation velocity field v(x, y, z) represents the velocity distribution of blood vessels and surrounding tissues when they are under stress. It can reflect the dynamic response of blood vessels and surrounding tissues when surgical instruments apply pressure to blood vessels. Through this velocity field, doctors can foresee whether the blood vessels will be displaced or deformed at the moment the surgical instrument applies pressure, thereby affecting the surgical path. In the right lobe area of ​​the patient's liver, the maximum value of the velocity field v(x, y, z) is 5mm / s, indicating that when the instrument applies pressure, the maximum deformation speed of the blood vessels and surrounding tissues is 5 millimeters per second. In smaller branched blood vessel areas, the value of the velocity field may be lower, about 2-3mm / s, indicating that the deformation of these areas is small and relatively stable.

[0068] Finally, κ is the coupling coefficient, which indicates the degree of coupling between the velocity field and the geometry. By adjusting the value of K, the system can control the degree of vessel deformation under different pressure conditions. In the trunk region, κ may range from 0.7 to 0.9, indicating that the device will induce a more significant deformation response to pressure in this area, while in the branch region, K ranges from 0.3 to 0.5, indicating that these areas are less deformed and less sensitive to force.

[0069] By calculating the above formula, the system can predict in real time the deformation behavior and exposure risk of different vascular regions under the pressure of surgical instruments during surgery. For example, during the surgical simulation of this patient, the system calculated the main vessels of the right lobe of the liver. The function value changes rapidly after pressure is applied. Combined with the local biomechanical properties and velocity field distribution, it predicts that the area may be exposed during surgery and reminds the doctor to avoid the area or reduce pressure during operation. In addition, in the area of ​​small blood vessel branches, the system calculates The smaller changes in function values ​​indicate that these areas are relatively safe during surgery and have a lower risk of vascular exposure.

[0070] The 45-year-old patient was undergoing a right hepatic lobectomy. Doctors pre-operatively reconstructed a 3D structural model of the patient's liver and blood vessels using CT and MRI imaging data. To further predict the risk of vascular exposure and rupture during surgery, the system used finite element analysis (FEA) to simulate the stress-strain relationship of the blood vessels, thereby predicting the potential dynamic behavior of the vessels when pressure is applied by surgical instruments.

[0071] Finite element analysis uses stress-strain analysis to capture the deformation behavior of blood vessels under varying pressures. Stress refers to the force applied by the device to the vessel, while strain describes the deformation of the vessel due to the external force. To dynamically simulate these phenomena, the system incorporates the following stress-strain analysis formula:

[0072]

[0073] In this formula, is the local pressure, which represents the force applied by the surgical instrument to the blood vessel at time t and position x. Set in the main blood vessel area of ​​the right lobe of the liver, the pressure applied by the doctor when operating the instrument is about 1000-1200 Pa (Pascal). After this pressure value is input into the formula, The temporal trend of local pressure can be reflected through the integration process.

[0074] The stress-strain influencing factors λ1 and λ2 control the effects of stress and contact force on blood vessels, respectively. In this patient's right liver lobe, λ1 was set to a value range of 0.8-1.2, primarily to control the effects of stress, as the vessels in this area have larger diameters and are subjected to relatively uniform external forces. λ2, on the other hand, controls the effects of changes in contact force on blood vessels. In the branch vessel region, λ2 was set to a value of 0.5-0.7, indicating that blood vessels in this branch region are more sensitive to changes in external force, especially when small displacements during instrument contact can cause the strain behavior of the vessels to become more pronounced.

[0075] Rate of change of strain with time represents the strain rate of the vessel at time t. In actual calculations, assuming the initial stage of surgery, the applied pressure causes the main vessel to deform at an instantaneous rate of 0.02 mm / s, while the branch vessels deform at a higher rate of approximately 0.05 mm / s. These rates reflect the instantaneous deformation behavior of different regions of the vessel under the pressure of the surgical instrument, particularly deformation that may be caused by excessive pressure or improper instrument operation during the operation.

[0076] Δ(x, t) is the spatial distribution function of the contact force between the surgical instrument and the blood vessel. It is assumed that during surgery, the contact force between the instrument and the main blood vessels of the right lobe of the liver is relatively uniform, with a contact force of 100 Pa per unit area. For branch vessels, however, the spatial distribution of the contact force is less uniform and may be concentrated in certain specific areas, such as a local area where the contact force reaches 200 Pa. By introducing this contact force function, the system can accurately capture the force applied by the instrument to different locations of the blood vessels during surgery, thereby better predicting the deformation and exposure risk of blood vessels in different areas.

[0077] Assuming the operation lasted for 5 seconds, the system calculated each time segment using a formula. For example, in the first second, the doctor applied a low pressure, and the system calculated the local pressure of the main trunk blood vessels of the right lobe of the liver. The strain rate was 1000 Pa and 0.02 mm / s, indicating minimal deformation and a low risk of vascular exposure. However, at the third second, due to the rapid movement of the instrument and the increase in pressure, the system calculated that the local pressure had increased to 1200 Pa, and the strain rate of the branch vessels reached 0.05 mm / s, indicating significant local deformation. The system alerted the doctor to the increased risk of vascular exposure and recommended slowing down the procedure or adjusting the pressure point.

[0078] From the above examples, we can see that the system can accurately predict the exposure and rupture risks of blood vessels in different areas during surgery by combining stress-strain formulas with actual data for dynamic calculations. For example, by reasonably setting the values ​​of λ1 and λ2, the system can perform personalized control of the stress and contact force of the main and branch blood vessels. By analyzing the dynamic response of Δ(x, t) and Δ(x, t), the system can not only predict the instantaneous deformation behavior of blood vessels when surgical instruments apply pressure, but also help doctors adjust operations based on real-time feedback to avoid potential risks of vascular damage.

[0079] The 45-year-old patient's right hepatectomy surgery had entered a critical phase. To ensure the safety of surgical instrument handling, the doctors decided to further utilize a spatiotemporal coupling model to accurately predict the risk of vascular exposure during surgery. By integrating static geometric information with dynamic stress-strain relationships, this model captures the potential impact of surgical procedures on blood vessels in both time and space, specifically predicting the dynamic deformation and exposure risk of blood vessels under pressure changes.

[0080] First, the core formula of the space-time coupling model is:

[0081]

[0082] in, is the output of the spatiotemporal coupling model, representing the combined static geometric information and dynamic stress response of the vessel at position x and time t. This value helps physicians assess the exposure risk of vessels during surgery. To better explain this formula and its practical application, a specific example will be used.

[0083] At the current stage of surgery, the surgeon needs to assess the exposure of the main and branch vessels of the right lobe of the liver. First, η(x) is a weight coefficient that controls the impact of static geometric features on the overall model response. In the region of the main vessels of the right lobe of the liver, the value of η(x) ranges from 0.9 to 1.0, because the static geometric features of the main vessels have a very important impact on the overall surgical exposure risk. In the region of the branch vessels, the value of η(x) is smaller, perhaps 0.4 to 0.6, reflecting the weaker influence of these areas in the static state. This weight coefficient ensures that the model makes accurate assessments of local static geometric features by reflecting the importance of vessels in different regions.

[0084] then, The gradient represents geometric morphology and reflects shape changes in static geometric features. In the main vessels of the right lobe of the liver, the gradient is small and the shape changes little, indicating a relatively regular and stable vascular structure. In the branch vessels, however, the gradient varies significantly, and the shape changes are more complex. The gradient range is set to 0.01-0.05 in the main region, while the gradient in the branch region can be as high as 0.08-0.12. This variation indicates that deformation is more likely to occur in the branch vessels, necessitating special attention to these areas during surgery.

[0085] μ(t) is a time-dependent dynamic deformation function that describes the dynamic response characteristics of blood vessels at different times. At the first second of surgery, due to the low pressure applied by the instrument, the dynamic deformation function μ(t) value of the main blood vessels is 0.02. At the third second, as the pressure applied by the instrument increases, the value of μ(t) increases to 0.05, indicating that the deformation of the blood vessels is gradually increasing. In the branch vessel area, the dynamic response is more significant. Within the same time period, the value of μ(t) increases from 0.05 to 0.1, indicating that these blood vessels are more likely to undergo larger deformations when subjected to pressure, increasing the risk of exposure.

[0086] Finally, ξ(x, t) is a spatiotemporal coupling factor that represents the combined effect of spatial and temporal stress variations on vascular deformation. This factor allows the system to capture pressure variations in the vessel at different spatial points and over different time periods. During surgery, the pressure variation of the instrument in the main vessels of the right lobe of the liver is relatively stable, with ξ(x, t) ranging from 0.6 to 0.8. However, in the branch vessel region, due to the complexity of instrument manipulation and the unevenness of contact force, ξ(x, t) may range from 0.3 to 0.5, reflecting more significant stress variations in these regions.

[0087] It is the second-order derivative of pressure in space and can capture the rate of change of pressure applied by surgical instruments to blood vessels at different locations. In the main vessel region, the set pressure changes relatively uniformly, and the second-order derivative value is small, approximately 0.02, which means that the pressure applied by the instrument in this area is relatively stable. In the branch vessel region, however, the pressure changes more dramatically, and the second-order derivative value may be 0.1, indicating that the pressure changes in these areas are more complex, and there may be increased deformation of the blood vessels due to local pressure, increasing the risk of exposure.

[0088] By substituting these data into the formula, the exposure risk of blood vessels at a specific time point and spatial position can be calculated. For example, at the 3rd second of the operation, the system calculates that at a certain position x of the main trunk blood vessels of the right lobe of the liver, The value of is 0.7, indicating that the risk of vascular exposure in this area is relatively low. In a high-risk area of ​​a branch vessel, the system calculates The value is 1.2, which indicates that there is a high risk of vascular exposure here. It is recommended to reduce pressure or adjust the surgical path during the operation.

[0089] This spatiotemporal coupled model, combining static geometric features with dynamic stress-strain analysis, comprehensively assesses the risk of vascular exposure during surgery and helps surgeons adjust procedures in real time to minimize vascular exposure. Computational results demonstrate the feasibility of this model, which not only provides real-time risk assessment but also effectively reduces uncertainty during surgery by dynamically adjusting predictions.

[0090] Example 2:

[0091] Combine Figure 3 A 45-year-old patient is undergoing right hepatectomy. The doctor wants to assess the risk of surgical instrument exposure to the main and branch vessels of the right hepatic lobe at a specific moment. The system uses real-time ultrasound and endoscopic imaging data, instrument position information, and pressure sensor feedback to form a spatiotemporal data stream. The system uses the following spatiotemporal model formula:

[0092]

[0093] Step 1: Get real-time data

[0094] Set the pressure exerted by the surgical instrument on the main blood vessels of the right lobe of the liver at a certain moment to 800 Pa , while the pressure applied in the branch vessel area is 1000 Pa. Pressure gradient The velocity of the device is 0.05 Pa / mm in the trunk vessel area and 0.08 Pa / mm in the branch vessel area. It is 2 mm / s in the main vessel area and 1 mm / s in the branch vessel area.

[0095] Step 2: Substitute into the formula

[0096] Substituting these data into the formula, the exposure risk of the main vessels and branch vessels was calculated separately.

[0097] Calculating exposure risk in major vascular territories

[0098] In the main vascular area, the pressure gradient 0.05Pa / mm, instrument speed The speed is 2mm / s. If the operation starts at 0 seconds and the doctor continues to operate until 3 seconds, the time period from 0 to 3 seconds needs to be integrated:

[0099]

[0100] Compute this integral:

[0101]

[0102] Therefore, at 3 seconds, the vascular exposure risk level in the main vessel area is 0.3, which indicates that the exposure risk in this area is low and the doctor can continue to operate according to the current path.

[0103] Calculating exposure risk in branch vessel territories

[0104] In the branch vessel area, the pressure gradient 0.08Pa / mm, instrument speed is 1 mm / s. Similarly, calculate the time period from 0 seconds to 3 seconds:

[0105]

[0106] Compute this integral:

[0107]

[0108] Therefore, at 3 seconds, the risk of vascular exposure in the branch vessel region is 0.24. Although slightly higher than that in the main vessel region, it is still within an acceptable range. However, if the pressure or speed increases, this value may rise rapidly, prompting the physician to adjust the operation appropriately.

[0109] Step 3: Interpret the calculation results:

[0110] The above calculations show that at 3 seconds, the exposure risk of the main vessels in the right hepatic lobe was a low 0.3, while the exposure risk of the branch vessels was 0.24, which, while relatively high, was still within a controllable range. Based on these calculations, the system can provide real-time feedback to the physician, indicating whether the current operation is safe or whether it is necessary to reduce instrument pressure or adjust the surgical path.

[0111] A 45-year-old patient was undergoing a right hepatectomy. The surgeon sought to use advanced dynamic surgical simulation tools to predict vascular exposure during future surgeries. To this end, the system incorporated a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to process time series data, helping the surgeon better understand the impact of surgical instruments on blood vessels during surgery. By combining LSTM and TCN, the system was able to learn from the instrument's operational history and predict future vascular exposure risks, thereby improving surgical safety and accuracy.

[0112] During surgery, LSTM is responsible for capturing long-term dependencies, namely the delayed impact of a surgical procedure on vascular exposure over a period of time. TCN, on the other hand, excels at processing short-term operational variations, capturing rapid changes within a short period of time during surgery. By combining these two networks, the system can effectively handle the complexity of long-term and short-term dependencies during surgery. The system's vascular exposure prediction formula is as follows:

[0113] ε t =γ1·σ(W x ·x t +W h ·h t-1 +b)

[0114] In this formula, ε tThe probability of vascular exposure at the current time t is the final output of the system. This value reflects the risk of vascular exposure caused by surgical instrument operation at the current moment, and the doctor can adjust the surgical strategy based on this prediction result.

[0115] Assuming that the surgery is in the 5th second, the system needs to predict the risk of blood vessel exposure in the next 3 seconds (6th to 8th seconds). Current input data x t Including the position, speed, and contact information of the surgical instrument with the blood vessels. At the 5th second, the instrument is located at the main blood vessel of the right lobe of the liver, the applied pressure is 900Pa, the instrument speed is 2.5mm / s, and the contact area with the main blood vessel is 20mm. 2 This data is fed into the network and processed by LSTM and TCN to generate predictions.

[0116] First, x t Represents the input data at the current time point t=5, which includes pressure, velocity and contact area. Set the weight matrix W of the input data x The value range is 0.5-0.7, which is used to control the contribution of each input feature to the model prediction. The current weight is 0.6. The hidden state h at the previous moment t-1 The operation history information at the 4th second is retained. It is set that at the 4th second, the pressure applied by the instrument is 850Pa, the instrument speed is 2.2mm / s, and the contact area is 18mm 2 These historical information are passed to the current moment through the hidden state and are transmitted through the weight matrix W h Adjust its influence, W h The value range is 0.6-0.8, and the value is 0.7 at this time.

[0117] Next, the activation function σ acts as a nonlinear function to map the model's output to a range of probabilities. Setting σ to a sigmoid function maps the output to a range between 0 and 1. Finally, the bias term b balances the model's output. The value of b is set between 0.1 and 0.3, and is currently set to 0.2.

[0118] Substitute these data into the formula to calculate:

[0119] ε t =0.6·σ((0.6·900+0.7·850)+0.2)

[0120] First calculate the weighted sum of the input data and the hidden state:

[0121] 0.6·900=540.0.7·850=595

[0122] After summing:

[0123] 540+595+0.2=1135.2

[0124] Next, calculate through the sigmoid activation function:

[0125] σ(1135.2)≈1

[0126] Therefore, the vascular exposure risk probability ε at the current time t = 5 seconds is t If the value is close to 1, it means that the risk of vascular exposure during the current surgical procedure is very high. Based on this result, the system will prompt the doctor to reduce pressure or adjust the position of the instrument in real time to reduce the risk of exposure.

[0127] By combining LSTM and TCN, the system not only calculates the risk of vascular exposure in real time based on the current operation but also predicts future risks based on historical data. Assuming the doctor continues to operate at the same speed and pressure, the system can predict the risk from the sixth to the eighth second. Based on the data from the previous few seconds, the system predicts that the risk of vascular exposure remains high at approximately 0.95 at the sixth second. However, at the seventh and eighth seconds, assuming the doctor reduces pressure, the system predicts that the risk drops to 0.7 and 0.5, respectively. Based on these predicted values, the doctor can adjust his operation to ensure surgical safety.

[0128] By incorporating LSTM and TCN, the system can accurately predict the risk of vascular exposure based on the surgical instrument's operational history and current input data. The data and computational process demonstrated that this approach can capture the complexity of long- and short-term dependencies during surgery and provide real-time decision support for physicians.

[0129] A 45-year-old patient's right hepatectomy surgery had reached a critical stage, and the doctor sought a more accurate prediction tool to assess the risk of vascular exposure over the next period of time. To this end, the system introduced a comprehensive vascular exposure probability prediction model. This model combines the spatial interaction of surgical instruments and the time series dynamics to generate a probability value for vascular exposure, helping doctors optimize surgical procedures and mitigate potential risks.

[0130] The core formula of this model is:

[0131]

[0132] The output of this formula represents the probability of vascular exposure at the future time t+Δt, predicting future risk based on the current surgical instrument operation. To understand this formula, the application of the model will be detailed using specific data and calculations, and the role of each variable in the surgery will be explained.

[0133] Assume that at the current time t=5 seconds, the doctor is operating the instrument to apply pressure to the main blood vessels of the right lobe of the patient's liver. The speed is 2.5 mm / s, the pressure is 900 Pa, and the contact area between the device and the blood vessel is 25 mm 2 These data are input into the system in real time, and the system calculates the exposure risk level of the blood vessels at the current time t = 5 seconds. Set up the system to calculate the current exposure risk This indicates that there are certain risks in the current surgical operation.

[0134] α is a factor used to adjust the confidence level of the model output, indicating the physician's confidence in the prediction. Its value is typically adjusted based on the complexity of the surgical scenario and the physician's experience. In this case, the physician selected α = 0.9, indicating a high level of confidence in the model's prediction. This value plays a weighting role in the final predicted probability of vascular exposure.

[0135] Changes in machine speed

[0136] During surgery, the speed change of surgical instruments has a significant impact on the risk of vascular exposure. Capture the rate of change of the moving speed of the device (i.e. acceleration or deceleration). Assume that the speed of the current device increases to 3mm / s from t=5 seconds to t=6 seconds, so the acceleration is This means that the device is accelerated during this time period, which may lead to an increased risk of vascular exposure, and the system will calculate this.

[0137] The integration area Ω represents the three-dimensional space in the surgical scene. The system needs to calculate each spatial point in the entire surgical operation scene. The integration area is set to the main blood vessels and nearby branch vessels of the right lobe of the liver. The volume of the main blood vessels is about 500mm 3 , and the volume of the branch vessels is 150mm 3 The three-dimensional integration area Ω of the entire surgical scene includes these two parts.

[0138] Substitute these data into the formula for calculation:

[0139]

[0140] First calculate the product part of the integral term:

[0141] 0.7.0.5=0.35

[0142] Next, perform spatial integration on the entire integration area Ω. Set the integration result of the main vessels to 350mm 3 , while the integral result of branch vessels is 105mm 3, then the total integral result is:

[0143] ∫ Ω 0.35dx dy dz=0.35·(350+105)=0.35·455=159.25

[0144] Finally, multiply the integral result by the adjustment factor α = 0.9:

[0145]

[0146] The system calculated that the predicted probability of vascular exposure at a future time of t + Δt = 6 seconds is 143.325. Although this value is high in absolute terms, it represents a relative risk, suggesting that the doctor's current procedure may result in a high risk of vascular exposure. Based on this result, the system recommends that the doctor slow down the procedure or reduce the pressure applied to reduce the risk of vascular exposure in the future.

Claims

1. A method for constructing a vascular exposure prediction model during liver resection anatomy, characterized by: S1. Deep anatomical modeling of liver vascular distribution: S1.

1. The geometric structure of the liver and blood vessels is constructed based on CT and MRI data, and biomechanical property modeling is introduced. The model combines biomechanical principles and uses finite element analysis (FEA) to simulate the stress and strain behavior of blood vessels under different physiological conditions, as well as their response to pressure applied by surgical instruments. The model provides static anatomical structures, and the predictive model provides basic data on dynamic factors of vascular elasticity and toughness. S1.

2. Introducing a hierarchical branch complexity model, the vascular branch network is classified according to the number, angle, and diameter variation characteristics of the branches, and the difficulty of vascular exposure is analyzed at different levels; S2. Dynamic surgical simulation and vascular exposure scenario construction: S2.

1. Establish vascular exposure scenarios under different surgical paths through dynamic surgical simulation based on a physics engine. This includes predicting deformation or micro-displacement caused by instruments contacting surrounding tissue during surgery. S2.

2. Build a spatiotemporal vascular exposure prediction model to predict the predictability of vascular exposure at different time points. Based on the spatiotemporal data stream, a long short-term memory (LSTM) network or a temporal convolutional network (TCN) is used to capture the temporal relationship between intraoperative instrument movement and vascular exposure, predicting the probability of vascular exposure at a specific time point. S3. Vascular exposure prediction model based on the fusion of global and local factors: S3.

1. Design a fusion model that combines local geometric features (including vessel diameter and branching angle) with global features (including vessel distribution pattern and density of adjacent tissues) for analysis. S3.

2. Construct a multimodal data fusion model by integrating multimodal physiological data; perform weighted fusion of data from different information sources using ensemble learning methods; S4. Intraoperative real-time feedback and model adaptive optimization: S4.

1. Introduce ultrasound and endoscopic image data collected in real time during surgery and combine them with a deep learning model for real-time correction. The model adaptively adjusts the prediction results based on the new input information; through convolution operations on real-time images, the regional distribution of expected exposed blood vessels is recalculated.

2. The method for constructing a prediction model for vascular exposure during liver resection according to claim 1, characterized in that The in-depth anatomical modeling of liver vascularity includes: Based on the patient's CT and MRI images, the liver and blood vessel structures are first reconstructed in three dimensions; the morphology of the blood vessels is captured, and the deformation behavior of the blood vessels under different surgical pressure conditions is perceived; the geometric model is constructed through the function Describe, where is a function that defines the three-dimensional geometry of the liver and blood vessels, where x, y, and z represent the three-dimensional coordinates in space. To capture the complex geometry and distribution of blood vessels, the equation combines local biomechanical properties, including the local deformation velocity field v(x, y, z), and is described by the following formula: Among them, γ i is the weight coefficient in the geometric model, which is used to control the influence of different anatomical regions; is the Laplace operator It is used to describe the degree of change between points in the geometric structure; v(x, y, z) is the local deformation velocity field, which represents the velocity distribution of blood vessels and surrounding tissues when subjected to force and reflects the dynamic response of blood vessels; κ is the coupling coefficient, which indicates the degree of coupling between the velocity field and the geometric shape and is used to control the degree of deformation of blood vessels under different conditions.

3. The method for constructing a prediction model for vascular exposure during liver resection according to claim 2, characterized in that The in-depth anatomical modeling of the liver vascular distribution includes: finite element analysis (FEA), simulating the dynamic behavior of blood vessels through stress-strain relationships, and predicting the risk of rupture or exposure during surgery.

4. The method for constructing a prediction model for vascular exposure during liver resection according to claim 3, characterized in that The deep anatomical modeling of liver vascular distribution includes: creating a spatiotemporal coupling model by fusing static geometric information with dynamic stress-strain relationships to predict the dynamic deformation and exposure risk of blood vessels during surgery.

5. The method for constructing a prediction model for vascular exposure during liver resection anatomy according to claim 1, characterized in that The dynamic surgical simulation and vascular exposure scenario construction include: Various data are collected in real time during surgery, including real-time ultrasound and endoscope images, instrument position information, and pressure sensor feedback data. This multidimensional data forms a dynamic spatiotemporal data stream to describe the interaction between instrument movement, applied pressure, and blood vessels. The spatiotemporal data stream contains the spatial dimensions x, y, and z, combined with the time dimension t, to create a dynamic scene containing spatiotemporal information. By recording the trajectory of the surgical instrument in real time, the changes in the pressure applied by the instrument on the blood vessels at different time points are captured, and a spatiotemporal model is then established. in Represents the risk level of vascular exposure at spatial coordinates x, y, z and time t; the function describes the predictability of vascular exposure at a specific time and spatial point during surgery; Represents the pressure gradient applied by the surgical instrument, describing the surgical instrument at position x, y, z and time t ′ Changes in pressure exerted on blood vessels; Represents the position x, y, z and time t ′ The instrument motion velocity at t; this velocity term is used to capture the motion trajectory of the surgical instrument, t ′ : It is the integral time variable, which is integrated from the initial time to the current time t and describes the comprehensive impact of the device on the blood vessel in the past period of time.

6. The method for constructing a prediction model for vascular exposure during liver resection according to claim 5, characterized in that The dynamic surgical simulation and vascular exposure scenario construction include: By introducing the long short-term memory network (LSTM) and the temporal convolutional network (TCN), the long-term and short-term dependencies in time series are learned and predicted; based on the action sequence of surgical instruments in different time periods, the vascular exposure at future moments is predicted; TCN uses convolution operations to efficiently process short-term patterns in time series and capture rapid changes within a short period of time during surgery.

7. The method for constructing a prediction model for vascular exposure during liver resection according to claim 6, characterized in that The dynamic surgical simulation and vascular exposure scenario construction include: By dynamically combining the spatial interaction and time series of surgical instruments and blood vessels, a comprehensive vascular exposure probability prediction model is generated, which outputs the probability value of vascular exposure to guide surgical operation optimization: in, represents the probability of vascular exposure at the future time t+Δt; the final result predicted by the model is used to reflect the vascular exposure risk at a specific future moment; α is the adjustment factor used to control the confidence level of the model output; Indicates the exposure risk level of the blood vessel at the current time t, based on the spatiotemporal movement and pressure of the surgical instrument; It represents the derivative of the instrument movement speed with respect to time, capturing the acceleration and deceleration of the surgical instrument movement; it is used to analyze the impact of rapid instrument movement on the risk of vascular exposure. Ω is the integration area, representing the three-dimensional space of the entire surgical operation scene. The integration process is to calculate all spatial points in the surgical scene to generate a comprehensive vascular exposure probability.

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