Simulation system and method for assisting kidney tumor operation approach

Abstract

The invention discloses a simulation system and method for assisting a kidney tumor operation approach, and relates to the technical field of computer medical auxiliary diagnosis and treatment, and the method comprises the steps: collecting kidney tumor related data through a data collection module, and then calculating a kidney tumor risk assessment coefficient through a data processing analysis module; the risk level of the kidney tumor is judged by setting a threshold value, a signal is sent to a server to remind a patient, then an image collection module collects a three-dimensional model image, and the number of optimal curve segments and the number of non-optimal curve segments are obtained through the processing technology of an image processing module; the method comprises the following steps of: determining a tumor risk degree, performing quantity comprehensive index calculation through a positioning analysis module to obtain a comprehensive quantity index, and then obtaining an optimal operation position evaluation model so as to determine an operation approach path and send the operation approach path to a server to prompt formula personnel, so that the functions of firstly determining the tumor risk degree and then planning the operation approach path can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided medical diagnosis and treatment, and specifically to a simulation system and method for assisting the surgical approach of renal tumors. Background Art

[0002] Renal tumors are one of the common tumors in the urinary system, with a relatively high incidence. Due to the deep location of renal tumors and the complex intra-abdominal anatomy, there are often difficulties during surgery, especially problems such as separating blood vessels, dealing with tumor sections, and avoiding intestinal organs, which increase the incidence of complications.

[0003] In the prior art, there is no effective auxiliary technology for renal tumor surgery, so surgeons can only rely on their experience to perform operations, which is difficult and has a relatively high risk. Summary of the Invention

[0004] To solve the deficiencies mentioned in the above background art, the purpose of the present invention is to provide a simulation system and method for assisting the surgical approach of renal tumors, which can first determine the tumor risk level and then plan the surgical approach path.

[0005] In a first aspect, the purpose of the present invention can be achieved through the following technical solutions: A simulation system for assisting the surgical approach of renal tumors, comprising:

[0006] A data acquisition module: used to collect renal tumor-related data and send the renal tumor-related data to the data processing and analysis module. Among them, the renal tumor-related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data, and alpha-fetoprotein data;

[0007] A data processing and analysis module: used to perform identification processing on the data tumor image data to obtain the identified renal tumor-related data, use the identified renal tumor-related data to calculate the tumor risk assessment of the kidney, obtain the renal tumor risk assessment coefficient, set the renal tumor risk assessment threshold, perform a ratio calculation and determination on the renal tumor risk assessment coefficient and the renal tumor risk assessment threshold, determine the renal tumor risk level according to the ratio size, and send risk signals of different levels to the server;

[0008] An image acquisition module: used to collect three-dimensional model images and send the three-dimensional model images to the image processing module. Among them, the three-dimensional model images include: kidney three-dimensional model images, tumor three-dimensional model images, and skin three-dimensional model images at the corresponding positions of the kidney;

[0009] An image processing module: used to input the kidney three-dimensional model image and the tumor three-dimensional model image into a pre-established three-dimensional Cartesian coordinate system, and use the optimal curve acquisition method to count the number of optimal curve segments and non-optimal curve segments, and send the number of optimal curve segments and non-optimal curve segments to the positioning and analysis module;

[0010] Positioning and analysis module: used to calculate the comprehensive quantity index by comprehensively calculating the quantity of the optimal curve segments and the non-optimal curve segments, obtain the comprehensive quantity index, acquire the optimal surgical position evaluation model stored in the server, input the comprehensive quantity index into the pre-established optimal surgical position evaluation model, output the reference point of the optimal surgical position, obtain the three-dimensional skin model image, intersect the reference point of the optimal surgical position with the three-dimensional skin model image to determine the two intersection points of the surgical approach, thereby determine the surgical approach path, and send the surgical approach path to the server;

[0011] Server: used to acquire and store the optimal surgical position evaluation model, and after receiving different levels of risk signals, remind the patient of the renal tumor risk level, and display a prompt for the working medical staff after receiving the surgical approach path.

[0012] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The identification process of the data processing and analysis module includes:

[0013] Mark the carcinoembryonic antigen data as Ai, mark the first carbohydrate antigen data as V1i, mark the second carbohydrate antigen data as V2i, and mark the alpha-fetoprotein data as Di; where i is the label of the data acquisition times by the data acquisition module, and i = 1, 2, 3,..., n, and n is the total number of data acquisition times by the data acquisition module.

[0014] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The calculation process of the data processing and analysis module includes:

[0015] Using the formula Calculate the renal tumor risk assessment coefficient Fi, where log2() is the logarithmic function with base 2, A0 is the standard carcinoembryonic antigen coefficient, V10 is the standard first carbohydrate antigen coefficient, V20 is the standard second carbohydrate antigen coefficient, D0 is the standard alpha-fetoprotein coefficient, k1 is the preset carcinoembryonic antigen influence coefficient, k2 is the preset carbohydrate antigen influence coefficient, and k3 is the preset alpha-fetoprotein influence coefficient.

[0016] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The analysis process of the data processing and analysis module includes:

[0017] Set the renal tumor risk assessment threshold F0;

[0018] If Then determine that the renal tumor risk level is grade one, and send a grade one risk signal to the server;

[0019] If If the renal tumor risk level is determined to be level two, then send a level two risk signal to the server.

[0020] If then determine that the renal tumor risk level is level three, and send a level three risk signal to the server.

[0021] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The processing process of the image processing module includes:

[0022] According to the three-dimensional renal model image and the tumor three-dimensional model image in the three-dimensional Cartesian coordinate system, determine the intersection curve of the three-dimensional renal model image and the tumor three-dimensional model image;

[0023] Derive the intersection curve of the three-dimensional renal model image and the tumor three-dimensional model image to obtain a change rate curve graph, and obtain the change rate value of the change rate curve graph;

[0024] Set a change rate threshold, compare the change rate value with the change rate threshold, and determine whether the curve segment corresponding to the change rate value is the optimal curve segment according to the comparison result:

[0025] Mark the change rate value as St and mark the change rate threshold as S0;

[0026] If 0 < St ≤ S0; then determine that the curve segment corresponding to the change rate value of the change rate curve is the optimal curve segment;

[0027] If St > S0; then determine that the curve segment corresponding to the change rate of the change rate is a non-optimal curve segment;

[0028] Count the number of optimal curve segments, denoted as m1, and count the number of non-optimal curve segments, denoted as m2.

[0029] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The calculation process of the quantity comprehensive index of the positioning and analysis module:

[0030]

[0031] In the formula, m is the comprehensive quantity index, and p1 and p2 are both preset proportionality coefficients.

[0032] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The optimal surgical position evaluation model is trained based on an artificial intelligence model, and the training process:

[0033] Collect standard three-dimensional image data and standard renal tumor-related data through the data acquisition unit in the server, where the standard three-dimensional image data includes standard three-dimensional renal image data and standard three-dimensional tumor image data;

[0034] Combine the standard three-dimensional image data and the standard kidney tumor-related data to generate a standard surgical position evaluation data set, and input the standard surgical position evaluation data set into the artificial intelligence model for training to output an optimal surgical position evaluation model.

[0035] In a second aspect, to achieve the above object, the present invention discloses a method for simulating the surgical approach of kidney tumors, the method comprising the following steps:

[0036] Obtain kidney tumor-related data, perform identification processing on the kidney tumor-related data and perform a tumor risk assessment calculation of the kidney to obtain a renal tumor risk assessment coefficient, wherein the kidney tumor-related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data, and alpha-fetoprotein data;

[0037] Set a renal tumor risk assessment threshold, perform a ratio calculation determination on the renal tumor risk assessment coefficient and the renal tumor risk assessment threshold, and determine the renal tumor risk level according to the ratio size;

[0038] Obtain a three-dimensional model image, the three-dimensional model image includes a kidney three-dimensional model image, a tumor three-dimensional model image, and a skin three-dimensional model image at the corresponding position of the kidney. Obtain the optimal curve by establishing a three-dimensional coordinate system for the kidney three-dimensional model image and the tumor three-dimensional model image, and obtain the number of optimal curve segments and the number of non-optimal curve segments;

[0039] Perform a quantitative comprehensive index calculation on the number of optimal curve segments and the number of non-optimal curve segments to obtain a comprehensive quantitative index. Input the comprehensive quantitative index into the optimal surgical position evaluation model, output a reference point for the optimal surgical position, and intersect the reference point for the optimal surgical position with the skin three-dimensional model image to determine two intersection points of the surgical approach, thereby determining the surgical approach path.

[0040] Advantages of the present invention:

[0041] The present invention collects kidney tumor-related data through a data acquisition module, then calculates a renal tumor risk assessment coefficient through a data processing and analysis module, determines the renal tumor risk level by setting a threshold, and sends a signal to the server to remind the patient. Then, the image acquisition module collects a three-dimensional model image, obtains the number of optimal curve segments and the number of non-optimal curve segments through the image processing technology of the image processing module, then performs a quantitative comprehensive index calculation through the positioning and analysis module to obtain a comprehensive quantitative index, and then obtains an optimal surgical position evaluation model, thereby combining to determine the surgical approach path and sending it to the server to prompt the formula personnel, which can realize the function of first determining the tumor risk degree and then planning the surgical approach path. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings;

[0043] Figure 1 is a schematic structural diagram of the system of the present invention;

[0044] Figure 2 is a schematic flowchart of the method of the present invention. Specific embodiments

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0046] Embodiment 1:

[0047] The following introduces the relevant terms involved in the embodiments of the present application:

[0048] A 3D model is a polygonal representation of an object, usually displayed using a computer or other video device. The object displayed can be a real-world entity or a fictional object. Anything that exists in the physical nature can be represented by a 3D model.

[0049] 3D models are often generated using specialized software such as 3D modeling tools, but can also be generated by other methods. As a set of data including points and other information, 3D models can be generated manually or according to certain algorithms. Although they usually exist virtually in a computer or a computer file, similar models described on paper can also be considered 3D models. 3D models are widely used wherever 3D graphics are used. In fact, their application predates the popularity of 3D graphics on personal computers. Many computer games use pre-rendered 3D model images as sprites for real-time computer rendering.

[0050] As Figure 1 shown, the simulation system for assisting the surgical approach of renal tumors includes:

[0051] a data acquisition module, a data processing and analysis module, an image acquisition module, an image processing module, a positioning and analysis module, and a server;

[0052] The data acquisition module is used to collect kidney tumor-related data and send the collected kidney tumor-related data to the data processing and analysis module for processing and analysis;

[0053] It should be further noted that the kidney tumor-related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data, and alpha-fetoprotein data;

[0054] In the specific implementation process, the normal value of carcinoembryonic antigen is 0-5 ng / mL, which mainly exists on the surface of cancer cells differentiated from endodermal cells and is a broad-spectrum tumor marker; the normal value of the first carbohydrate antigen data (carbohydrate antigen 19-9) index is ≤25 μg / L, which mainly exists in the bodies of patients with pancreatic cancer, colon cancer, gastric cancer, cholangiocarcinoma, lung cancer, etc., and can assist in the judgment of kidney cancer; carbohydrate antigen 12-5: the normal value ≤35 μg / mL. If the value of this index is found to be >35 μg / mL during subsequent monitoring, the possibility of cancer should be alerted; alpha-fetoprotein: mainly used to detect the presence of primary liver cancer, which is often related to liver diseases such as hepatitis and cirrhosis. However, in a few benign diseases, such as hepatitis, cirrhosis, liver cancer, etc., the alpha-fetoprotein will also increase, which can assist in the judgment of kidney cancer;

[0055] After receiving the kidney tumor-related data sent by the data acquisition module, the data processing and analysis module performs data processing and analysis. Specifically, the processing and analysis process of the data processing and analysis module includes the following steps:

[0056] Perform identification processing on the data tumor image data to obtain the identified kidney tumor-related data. Among them, the identification process:

[0057] Mark the carcinoembryonic antigen data as Ai, mark the first carbohydrate antigen data as V1i, mark the second carbohydrate antigen data as V2i, and mark the alpha-fetoprotein data as Di; where i is the serial number of the data acquisition times of the data acquisition module, and i = 1, 2, 3,..., n, and n is the total number of data acquisition times of the data acquisition module;

[0058] Use the identified kidney tumor-related data to perform kidney tumor risk assessment calculation to obtain the kidney tumor risk assessment coefficient. Among them, the calculation process of the kidney tumor risk assessment calculation is as follows:

[0059] Use the formula Calculate the kidney tumor risk assessment coefficient Fi. In the formula, log2() is the logarithmic function with base 2, A0 is the standard carcinoembryonic antigen coefficient, V10 is the standard first carbohydrate antigen coefficient, V20 is the standard second carbohydrate antigen coefficient, D0 is the standard alpha-fetoprotein coefficient, k1 is the preset carcinoembryonic antigen influence coefficient, k2 is the preset carbohydrate antigen influence coefficient, and k3 is the preset alpha-fetoprotein influence coefficient;

[0060] Set the renal tumor risk assessment threshold F0;

[0061] Calculate the ratio of the renal tumor risk assessment coefficient Fi to the renal tumor risk assessment threshold F0 for determination, and determine the renal tumor risk level according to the ratio size, and send risk signals of different levels to the server. Specifically, the process of calculating and determining the risk level by the ratio of the image processing and analysis module is as follows:

[0062] If Then determine that the renal tumor risk level is grade one, and send a grade one risk signal to the server;

[0063] If Then judge that the renal tumor risk level is grade two, and send a grade two risk signal to the server.

[0064] If Then judge that the renal tumor risk level is grade three, and send a grade three risk signal to the server;

[0065] Among them, in this embodiment, the higher the renal tumor risk level, the greater the risk of surgery. Therefore, it is necessary to remind the patient to be prepared for the risk of surgery;

[0066] The image acquisition module is used to acquire three-dimensional model images, and send the acquired three-dimensional model images to the image processing module for processing. Among them, the three-dimensional model images include: kidney three-dimensional model images, tumor three-dimensional model images, and skin three-dimensional model images at the corresponding positions of the kidney; and the tumor three-dimensional model images are subjected to dilation processing;

[0067] Among them, the acquisition process of the three-dimensional model images includes the following steps:

[0068] First, perform MRI and CT scans: The technology uses a strong magnetic field and X-rays to generate detailed images of the internal structures of the body. MRI provides high-resolution images of soft tissues, while CT scans can provide high-contrast anatomical structure images.

[0069] Data conversion: The acquired MRI or CT scan data needs to be converted through professional medical image processing software to convert these original two-dimensional image data into three-dimensional model images. This step is crucial because it converts two-dimensional medical image data into three-dimensional visual models, and finally generates three-dimensional model images;

[0070] After receiving the three-dimensional model images sent by the image acquisition module, the image processing module performs image processing. Specifically, the processing process of the image processing module includes the following steps:

[0071] Input the three-dimensional model image of the kidney and the three-dimensional model image of the tumor in the three-dimensional model image into the pre-established three-dimensional Cartesian coordinate system;

[0072] Determine the intersection curve of the three-dimensional model image of the kidney and the three-dimensional model image of the tumor according to the three-dimensional model image of the kidney and the three-dimensional model image of the tumor in the three-dimensional Cartesian coordinate system;

[0073] Derive the intersection curve of the three-dimensional model image of the kidney and the three-dimensional model image of the tumor to obtain a change rate curve graph, and obtain the change rate value of the change rate curve graph;

[0074] Set a change rate threshold, compare the change rate value with the change rate threshold, and determine whether the curve segment corresponding to the change rate value is the optimal curve segment according to the comparison result; specific process:

[0075] Mark the change rate value as St and the change rate threshold as S0;

[0076] Among them, the acquisition process of the change rate threshold S0 includes the following steps:

[0077] By obtaining the maximum change rate Smax and the minimum change rate Smin, take Smax - Smin as the numerator of the formula for calculating the change rate threshold, and take Smax + Smin as the denominator of the formula for calculating the change rate threshold. The formula is as follows:

[0078]

[0079] If 0 < St ≤ S0; then determine that the curve segment corresponding to the change rate value of the change rate curve is the optimal curve segment;

[0080] If St > S0; then determine that the curve segment corresponding to the change rate value of the change rate is the non-optimal curve segment;

[0081] Count the number of optimal curve segments, denoted as m1, and count the number of non-optimal curve segments, denoted as m2;

[0082] Send the number of optimal curve segments m1 and the number of non-optimal curve segments m2 to the positioning analysis module for positioning analysis. Specifically, the positioning analysis process of the positioning analysis module includes the following steps:

[0083] Obtain the three-dimensional model image of the skin at the corresponding position of the kidney stored in the image acquisition module;

[0084] Calculate the comprehensive quantity index by calculating the comprehensive quantity index of the number of optimal curve segments m1 and the number of non-optimal curve segments m2. The specific calculation process is as follows:

[0085]

[0086] Wherein, m is the comprehensive quantity index, and both p1 and p2 are preset proportionality coefficients;

[0087] Obtain the optimal surgical position evaluation model stored in the server, input the comprehensive quantity index m into the pre-established optimal surgical position evaluation model, and output the reference point of the optimal surgical position;

[0088] Intersect the reference point of the optimal surgical position with the three-dimensional skin model image, determine the two intersection points of the surgical approach, connect the two intersection points of the surgical approach, thereby determine the surgical approach path, and send the surgical approach path to the server;

[0089] The server is used to obtain and store the optimal surgical position evaluation model, and after receiving different-level risk signals sent by the data processing and analysis module, remind the patient of the renal tumor risk level, and after receiving the surgical approach path, display and prompt it for the working medical staff;

[0090] Specifically, the solution of the present invention will be further elaborated through the following embodiments:

[0091] The optimal surgical position evaluation model is trained based on an artificial intelligence model. Specifically, the process of training the optimal surgical position evaluation model based on the artificial intelligence model is as follows:

[0092] Collect standard three-dimensional image data and standard renal tumor-related data through the data collection unit in the server. Among them, the standard three-dimensional image data includes standard renal three-dimensional image data and standard tumor three-dimensional image data;

[0093] Combine the standard three-dimensional image data and the standard renal tumor-related data to generate a standard surgical position evaluation data set, input the standard surgical position evaluation data set into the artificial intelligence model for training, and output the optimal surgical position evaluation model; wherein, the artificial intelligence model includes a deep convolutional neural network model and an RBF neural network model, and the optimal surgical position evaluation model combines the human bone structure to avoid obtaining positions through bones.

[0094] Embodiment 2: A method for simulating the surgical approach of renal tumors, the method includes the following steps:

[0095] S101: Obtain renal tumor-related data, perform identification processing on the renal tumor-related data and perform renal tumor risk assessment calculations to obtain a renal tumor risk assessment coefficient. Among them, the renal tumor-related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data, and alpha-fetoprotein data;

[0096] S102: Set the renal tumor risk assessment threshold, calculate the ratio of the renal tumor risk assessment coefficient to the renal tumor risk assessment threshold for determination, and determine the renal tumor risk level according to the ratio size;

[0097] S103: Obtain three-dimensional model images, which include the three-dimensional model image of the kidney, the three-dimensional model image of the tumor, and the three-dimensional model image of the skin at the corresponding position of the kidney. Use the method of establishing a three-dimensional coordinate system to obtain the optimal curve for the three-dimensional model image of the kidney and the three-dimensional model image of the tumor, and obtain the number of optimal curve segments and the number of non-optimal curve segments;

[0098] S104: Calculate the comprehensive quantity index by calculating the comprehensive quantity index of the number of optimal curve segments and the number of non-optimal curve segments. Input the comprehensive quantity index into the optimal surgical position evaluation model, output the reference point of the optimal surgical position, and intersect the reference point of the optimal surgical position with the three-dimensional model image of the skin to determine the two intersection points of the surgical approach, thereby determining the surgical approach path.

[0099] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions. Specifically, it is used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0100] It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the above-mentioned method. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electro-magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0101] The above formulas are all calculated by removing the dimension and taking their numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0102] In the description of this specification, the descriptions referring to terms such as "an embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0103] The above shows and describes the basic principles, main features, and advantages of the present disclosure. Those skilled in the art of this industry should understand that the present disclosure is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes and improvements all fall within the scope of the present disclosure claimed.

Claims

1. A simulation system for assisting surgical approach to kidney tumors, characterized in that: include: Data acquisition module: used for collecting kidney tumor related data and sending the kidney tumor related data to the data processing and analysis module, wherein the kidney tumor related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data and alpha-fetoprotein data; Data processing and analysis module: used to label tumor image data, obtain labeled kidney tumor related data, use the labeled kidney tumor related data to perform kidney tumor risk assessment calculation, obtain kidney tumor risk assessment coefficient, set kidney tumor risk assessment threshold, calculate and determine the ratio of the kidney tumor risk assessment coefficient to the kidney tumor risk assessment threshold, determine the kidney tumor risk level according to the ratio, and send risk signals of different levels to the server; Image acquisition module: used to acquire 3D model images and send the 3D model images to the image processing module, wherein the 3D model images include: kidney 3D model images, tumor 3D model images and skin 3D model images at the corresponding position of the kidney; Image processing module: used for inputting the kidney three-dimensional model image and the tumor three-dimensional model image into a pre-established three-dimensional Cartesian coordinate system, and performing an optimal curve acquisition method to obtain the number of optimal curve segments and the number of non-optimal curve segments, and sending the number of optimal curve segments and the number of non-optimal curve segments to the positioning analysis module; Positioning analysis module: used to calculate the quantity comprehensive index of the number of optimal curve segments and the number of non-optimal curve segments, obtain the comprehensive quantity index, obtain the optimal surgical position evaluation model stored in the server, input the comprehensive quantity index into the pre-established optimal surgical position evaluation model, output the optimal surgical position reference point, obtain the skin three-dimensional model image, intersect the optimal surgical position reference point with the skin three-dimensional model image, determine the two intersection points of the surgical approach, thereby determining the surgical approach path, and send the surgical approach path to the server; Server: used to obtain and store the optimal surgical position evaluation model, and after receiving different levels of risk signals, remind patients of their renal tumor risk levels, and after receiving the surgical approach path, display prompts to the working medical staff.

2. The simulation system for assisting renal tumor surgical approach according to claim 1, characterized in that: The identification process of the data processing and analysis module includes: The carcinoembryonic antigen data is marked as Ai, the first carbohydrate antigen data is marked as V1i, the second carbohydrate antigen data is marked as V2i, and the alpha-fetoprotein data is marked as Di; wherein, i is the number of collection times of the data collection module, and i=1, 2, 3, ..., n, and n is the total number of collection times of the data collection module.

3. The simulation system for assisting renal tumor surgical approach according to claim 2, characterized in that: The calculation process of the data processing and analysis module includes: Using the formula The renal tumor risk assessment coefficient Fi was calculated, where log2() is a logarithmic function with base 2, A0 is the standard carcinoembryonic antigen coefficient, V10 is the standard first carbohydrate antigen coefficient, V20 is the standard second carbohydrate antigen coefficient, D0 is the standard alpha-fetoprotein coefficient, k1 is the preset carcinoembryonic antigen influence coefficient, k2 is the preset carbohydrate antigen influence coefficient, and k3 is the preset alpha-fetoprotein influence coefficient.

4. The simulation system for assisting renal tumor surgical approach according to claim 3, characterized in that: The analysis process of the data processing and analysis module includes: Set the renal tumor risk assessment threshold F0; like The renal tumor risk level is determined to be level one, and a level one risk signal is sent to the server; like If the risk level of kidney tumor is judged to be level 2, a level 2 risk signal is sent to the server. like If the risk level of kidney tumor is judged to be level three, a level three risk signal is sent to the server.

5. The simulation system for assisting renal tumor surgical approach according to claim 1, characterized in that: The processing process of the image processing module includes: Determine an intersection curve of the kidney three-dimensional model image and the tumor three-dimensional model image according to the kidney three-dimensional model image and the tumor three-dimensional model image in the three-dimensional Cartesian coordinate system; Deriving the intersection curve of the kidney three-dimensional model image and the tumor three-dimensional model image to obtain a change rate curve graph, and obtaining a change rate value of the change rate curve graph; Set the change rate threshold, compare the change rate value with the change rate threshold, and determine whether the curve segment corresponding to the change rate value is the optimal curve segment based on the comparison result: The change rate value is marked as St, and the change rate threshold is marked as S0; If 0<St≤S0; then the curve segment corresponding to the change rate value of the change rate curve is determined to be the optimal curve segment; If St>S0; then the curve segment corresponding to the change rate value of the change rate is determined to be a non-optimal curve segment; The number of optimal curve segments is counted, recorded as m1, and the number of non-optimal curve segments is counted, recorded as m2.

6. The simulation system for assisting renal tumor surgical approach according to claim 1, characterized in that: The calculation process of the quantitative comprehensive index of the positioning analysis module: Where m is the comprehensive quantity index, and p1 and p2 are both preset proportional coefficients.

7. The simulation system for assisting renal tumor surgical approach according to claim 1, characterized in that: The optimal surgical position evaluation model is based on artificial intelligence model training, and the training process is: Collecting standard three-dimensional image data and standard kidney tumor related data through a data acquisition unit in the server, wherein the standard three-dimensional image data includes standard kidney three-dimensional image data and standard tumor three-dimensional image data; The standard three-dimensional image data and standard kidney tumor-related data are combined to generate a standard surgical position evaluation data set, which is then input into an artificial intelligence model for training, and the optimal surgical position evaluation model is output.

8. A simulation method for assisting renal tumor surgical approach, characterized in that: The method comprises the following steps: Acquire kidney tumor related data, perform labeling processing on the kidney tumor related data and perform kidney tumor risk assessment calculation to obtain a kidney tumor risk assessment coefficient, wherein the kidney tumor related data includes carcinoembryonic antigen data, first carbohydrate antigen data, second carbohydrate antigen data and alpha-fetoprotein data; A renal tumor risk assessment threshold is set, and the renal tumor risk assessment coefficient is calculated and determined by the ratio of the renal tumor risk assessment coefficient to the renal tumor risk assessment threshold, and the renal tumor risk level is determined according to the ratio; Acquire a three-dimensional model image, the three-dimensional model image includes a kidney three-dimensional model image, a tumor three-dimensional model image, and a skin three-dimensional model image at a position corresponding to the kidney, and acquire an optimal curve for the kidney three-dimensional model image and the tumor three-dimensional model image by establishing a three-dimensional coordinate system, and obtain the number of optimal curve segments and the number of non-optimal curve segments; The number of optimal curve segments and the number of non-optimal curve segments are calculated to obtain a comprehensive quantitative index, which is input into the optimal surgical position evaluation model to output the optimal surgical position reference point. The optimal surgical position reference point is intersected with the skin three-dimensional model image to determine the two intersection points of the surgical approach, thereby determining the surgical approach path.

Images