Method for precisely customizing simulation organ for clinic and teaching

Abstract

The invention relates to a method for precisely customizing a simulated organ for clinical and teaching. The method comprises the following steps: S1, establishing a deep learning model of the simulated organ; s2, acquiring three-dimensional data of the simulation organ model; s3, 3D printing data processing of the simulated organ model is carried out, a part A printing model and a part B printing model are established, the part A printing model corresponds to the blood vessel, bile duct or focus structure of the simulated organ and can be externally connected with a circulating pump, and the part B printing model corresponds to the outline of the simulated organ; and S4, assembling the simulated organ model. Compared with the prior art, the manufacturing method has the advantages of being short in manufacturing period and low in cost, the blood vessel, bile duct or focus structure of the manufactured simulated organ is accurately positioned, blood circulation and bile flow of the organ are simulated through a real blood vessel and biliary tract system, and a real operation state can be provided in teaching and operation training.

Description

Technical Field

[0001] The invention relates to the fields of biomaterials and biomedical engineering, and in particular to a method for accurately customizing simulated organs for clinical and teaching purposes. Background Art

[0002] As the largest internal organ of the human body, the liver undertakes key physiological functions such as metabolism, detoxification, protein synthesis, bile production and storage. The liver has widely distributed blood vessels as a nutrient transmission medium. Blood vessels in the liver include multiple functions such as substance transfer, selective permeability, and toxic reactions. Therefore, they are of great significance to the physiological function of liver tissue. As an auxiliary organ of the liver, the gallbladder is located below the liver and is mainly responsible for storing and concentrating bile.

[0003] In the field of medicine and related medical education, surgical navigation and simulation teaching are key links in cultivating the technical capabilities of medical students and surgeons. Especially for high-risk liver surgery, the importance of simulation navigation and practical training is self-evident. At present, the means of making simulated organs mainly include traditional manual production, three-axis 3D printing technology, five-degree-of-freedom 3D printing devices, organ-on-a-Chip technology, virtual simulation experimental teaching, and high-simulation whole-organ surgery training systems. Although traditional hand-made production can simulate the appearance and texture of organs, it has limitations in terms of the authenticity and details of anatomical structures; although three-axis 3D printing technology can build bionic organ models, it is limited by printing freedom and path planning when creating complex structures, and cannot accurately reproduce structures such as blood vessels inside the organs; five-degree-of-freedom 3D printing devices improve the surface quality of models through more free printing path planning, but they are still limited in simulating subtle physiological structures; organ chip technology has advantages in simulating physiological environments, but may be limited in tactile feedback and actual operation experience; virtual simulation experimental teaching is convenient, but it may cause visual fatigue and lack of real texture; although the high-simulation whole-organ surgery training system provides a close to real surgical experience, it requires high costs and technical support. Therefore, future research needs to focus on improving simulation, reducing costs, and enhancing operability in order to more effectively cultivate the technical capabilities of medical students and surgeons.

[0004] Chinese patent CN117464990A provides a bionic liver and a preparation method, wherein the bionic liver preparation method includes using 3D printing to make a positive blood vessel model of a bionic liver; using 3D printing to make a negative liver body model of a bionic liver; placing the positive blood vessel model in the negative liver body model, and injecting a solvent for generating the liver body into the negative liver body model; dissolving the positive blood vessel model in the liver body to obtain a bionic liver. However, due to the limitations of construction technology and cost considerations, existing 3D printing technology can only replicate external features or specific internal structures, and there are challenges in replicating the feel of organs and simulating complex blood vessels, bile ducts and other structures inside organs, and it cannot meet the needs of medical students for learning and surgical simulation. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for accurately customizing a simulated organ for clinical and teaching purposes, which not only has an anatomically correct external shape, but also includes an internal structure of blood vessels or bile ducts that can be connected to an external circulating pump, thereby improving the authenticity of the inside and outside of the simulated organ and providing a platform that is closer to real surgical simulation.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a method for accurately customizing a simulated organ for clinical and teaching use, comprising:

[0008] S1. Establishment of deep learning model of simulated organs

[0009] Based on the multimodal imaging data of the organ and the corresponding patient medical record information, a training data set is constructed, and a convolutional neural network model with the medical record information as input and the three-dimensional imaging data as output is trained using the training data set;

[0010] S2. Acquisition of three-dimensional data of simulated organ models

[0011] Based on the acquired medical record information of the target patient, the trained convolutional neural network model is used to output the three-dimensional model data corresponding to the pathological state of the target patient;

[0012] S3. 3D printing data processing of simulated organ models

[0013] Based on the three-dimensional model data obtained in S2, a part A printing model and a part B printing model are established. The part A printing model corresponds to the blood vessels, bile ducts or lesion structures of the simulated organ and can be connected to an external circulation pump. The part B printing model corresponds to the outline of the simulated organ.

[0014] S4. Assembly of simulated organ models.

[0015] From S1-S4, we can get: 1. A large amount of clinical data is analyzed by deep learning technology to train a convolutional neural network model for different organ lesions. By analyzing historical case data and imaging data, an algorithm is used to generate three-dimensional model data consistent with the patient's pathological state, so that clinical cases can be customized and the patient's pathological state can be fully reproduced; 2. By 3D printing the simulated organ model at a ratio of 1:1, the consistency between the simulated organ model and the real organ can be better guaranteed, so it can play a better clinical guidance role in preoperative planning and intraoperative navigation; 3. By setting up the A part printing model and the B part printing model, this split can effectively simplify the subsequent 3D printing and filling process. The design focus of the A part printing model is to simulate the blood circulation and bile flow of the organ through the real blood vessel and biliary system, and design a system that can simulate different inflow and outflow states according to the needs of different lesions; 4. Since the organ model is assembled by two parts of the model, the overall position of the blood vessel, bile duct or lesion in the organ is more accurate, so it has better clinical guidance significance.

[0016] Furthermore, S1 specifically includes the following steps:

[0017] S11. Obtain detailed imaging data of the patient's organs from medical imaging equipment through the PACS system;

[0018] S12. Use the trained convolutional neural network model to identify and optimize low-quality medical images and perform data fusion on multimodal imaging data.

[0019] Furthermore, S2 specifically includes the following steps:

[0020] S21, using the convolutional neural network model trained in S1, inputting the target patient's medical record information, and obtaining the medical imaging data of the organ model in DICOM format;

[0021] S22, using multimodal diffeomorphic flexible registration to fuse the reconstruction results of multiple modalities into a unified voxel space;

[0022] S23. In the process of converting voxels to a three-dimensional mesh model, the model is accurately calibrated in combination with DICOM internal references, and compared with the organ appearance reconstruction results in multiple dimensions, and finally the three-dimensional model data corresponding to the pathological state of the target patient is output.

[0023] Furthermore, constructing the printing model of part A in S3 specifically includes the following steps:

[0024] S31. Extract the data of the printed model of part A from the three-dimensional model data obtained in S2, and then construct a structural model of blood vessels, bile ducts or lesions, and use 3D printing to make a positive model of blood vessels, bile ducts or lesions.

[0025] Preferably, the 3D printing material of the printed model of part A is a flexible photosensitive resin, and a coating of the vascular wall or bile duct wall is generated by brushing on the surface of the blood vessel, bile duct or lesion positive model to improve the authenticity of the simulated organ, and the coating includes at least one layer of release agent and three layers of silicone solvent, and the thickness of each silicone layer does not exceed 1 mm.

[0026] Furthermore, constructing the printing model of part B in S3 specifically includes the following steps:

[0027] S32, extracting the data of the printed model of part B from the three-dimensional model data obtained in S2, and reserving fixed holes and inlets and outlets matching the blood vessels, bile ducts or lesion structures on the outline of the organ, thereby establishing an outline model of the organ;

[0028] S33, segmenting the organ contour model, and using 3D printing to make an upper mold and a lower mold of the organ contour.

[0029] Preferably, the 3D printing material of the printed model of part B is photosensitive resin.

[0030] Furthermore, S4 specifically includes the following steps:

[0031] S41, making a solid negative mold of the organ outside the upper mold and the lower mold of the outer contour of the organ;

[0032] S42, taking out the organ solid positive mold with blood vessels, bile ducts or lesion structures from the organ solid negative mold.

[0033] Furthermore, S41 specifically includes the following steps:

[0034] S411, providing a mold for preparing the organ solid negative mold and a removable mold bottom;

[0035] S412, applying a release agent to the outer surface of the upper mold of the organ outline, and placing the mold into the mold;

[0036] S413, pouring gypsum into the mold;

[0037] S414, waiting for the plaster to solidify, and removing the bottom of the mold to prepare a first parting surface of the organ solid negative mold;

[0038] S415, applying a release agent on the outer surface of the lower mold of the organ outline and placing it in the mold, and then repeating steps S413 and S414 to prepare a second parting surface of the organ solid negative mold;

[0039] S416, placing blood vessels, bile ducts or lesion structures in the organ entity negative mold and positioning them, combining the first parting surface and the second parting surface of the organ entity negative mold, applying hot melt glue to fill the seams and reserving a glue inlet, and then injecting a solution for generating the organ entity into the glue inlet;

[0040] S417. After the solution is completely solidified, take out the organ solid positive mold with blood vessels, bile ducts or lesion structures, and finally use silicone glue to bond the ligaments and the simulated organ model.

[0041] Preferably, the solution for generating the organ entity comprises silica gel, silicone oil, a curing agent, and a colorant, wherein the ratio of the silica gel to the silicone oil is 1:5-10.

[0042] Preferably, the solution for generating the organ entity needs to be vacuum extracted to remove air bubbles.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) The present invention analyzes historical case data and imaging data through a convolutional neural network model, outputs three-dimensional model data corresponding to the pathological state of the target patient, realizes the customization of the clinical pathology of the simulated organ, and completely reproduces the pathological state of the patient. Through the artificial intelligence algorithm, the error in the modeling process can be greatly reduced, the accuracy of obtaining the three-dimensional model can be improved, and the model can be updated in real time to adapt to new data and ensure that it can always keep pace with the latest medical research and technology. By 3D printing the simulated organ model in a 1:1 ratio, the consistency between the simulated organ model and the real organ can be better guaranteed, so it can play a better clinical guidance role in preoperative planning and intraoperative navigation.

[0045] (2) The present invention constructs blood vessels, bile ducts and lesion positive models and organ entity negative models through 3D printing, and then places the blood vessels, bile ducts and lesion positive models in the organ entity negative model, and injects a solution for generating the organ entity. Finally, a simulated organ with blood vessels, bile ducts or lesion structures is obtained. The blood vessels are accurately positioned, and the blood circulation and bile flow of the organ are simulated through the real blood vessels and biliary system, which is closer to the real surgical state.

[0046] (3) The 3D printing material of the simulated organ in the present invention has an adjustable softness, and can directly construct key surgical structures such as blood vessels, bile ducts, and tumor pathogens, and is coated in the organ substance that can be directly cut with an ultrasonic knife; the manufactured simulated organ has a high degree of visualization and can display its internal structure. It also simulates the texture, elasticity and cutting feeling of the organ, and feels close to the liver, gallbladder, and stomach in a physiological state, greatly enhancing the authenticity of surgical training.

[0047] (4) The manufacturing cycle of the simulated organ in the present invention is short, the degree of digitization is high, and the cost is low. The use of this model can not only enable medical students and surgeons to practice repeatedly in a risk-free environment, familiarize themselves with the surgical steps, and improve their techniques, thereby improving the quality and safety of operations in actual operations and reducing complications and errors, but also help patients achieve the best surgical results, shorten hospital stays, and save hospitalization costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic structural diagram of the vascular positive model of the simulated liver described in Example 1 of the present invention;

[0049] Figure 2 This is a schematic structural diagram of a blood vessel positive mold for coating a blood vessel wall in Example 1 of the present invention;

[0050] Figure 3 This is a schematic diagram of the structure of the simulated liver with built-in blood vessels described in Example 1 of the present invention;

[0051] Figure 4 This is a schematic diagram of the structure of the simulated gallbladder outline model prepared by 3D printing in Example 2 of the present invention;

[0052] Figure 5 This is a schematic diagram of the structure of the simulated gallbladder with built-in bile duct described in Example 2 of the present invention;

[0053] Figure 6 This is a schematic diagram of the structure of the simulated stomach outline model prepared by 3D printing in Example 3 of the present invention;

[0054] Figure 7 This is a schematic diagram of the structure of the simulated stomach with built-in blood vessels described in Example 3 of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. Any preparation means, materials, structures, composition ratios, algorithms, software and other features not clearly described in this technical solution are deemed to be common technical features disclosed in the prior art.

[0056] Example 1

[0057] The present invention provides a method for accurately customizing a simulated liver for clinical and teaching purposes, comprising:

[0058] S1. Establishment of deep learning model of simulated liver

[0059] Based on the multimodal imaging data of the liver and the corresponding patient medical record information, a training data set is constructed, and a convolutional neural network model with the medical record information as input and the three-dimensional imaging data as output is trained using the training data set;

[0060] S2. Acquisition of three-dimensional data of the simulated liver model

[0061] Based on the acquired medical record information of the target patient, the trained convolutional neural network model is used to output the three-dimensional model data corresponding to the pathological state of the target patient;

[0062] S3. 3D printing data processing of simulated liver model

[0063] Based on the three-dimensional model data obtained in S2, a part A printing model and a part B printing model are established. The part A printing model corresponds to the vascular structure of the simulated liver and can be connected to an external circulation pump. The part B printing model corresponds to the outline of the simulated liver.

[0064] S4. Assembly of the simulated liver model.

[0065] S1 of this embodiment specifically includes the following steps:

[0066] S11, obtaining detailed imaging data of the patient's liver from a medical imaging device through a PACS system;

[0067] S12. Use the trained convolutional neural network model to identify and optimize low-quality medical images and perform data fusion on multimodal imaging data.

[0068] It should be noted that the "trained convolutional neural network model" mentioned in S12 refers to the use of labeled high-quality or low-quality medical imaging data sets to train the convolutional neural network, so that the trained convolutional neural network model can automatically evaluate the imaging effect, and according to the evaluation effect, apply image processing technology to optimize the image quality.

[0069] S2 of this embodiment specifically includes the following steps:

[0070] S21, using the convolutional neural network model trained in S1, inputting the target patient's medical record information, and obtaining the medical imaging data of the liver model in DICOM format;

[0071] S22, using multimodal diffeomorphic flexible registration to fuse the reconstruction results of multiple modalities into a unified voxel space;

[0072] S23. In the process of converting voxels to a three-dimensional mesh model, the model is accurately calibrated in combination with DICOM internal references, and compared with the liver appearance reconstruction results in multiple dimensions, and finally the three-dimensional model data corresponding to the pathological state of the target patient is output.

[0073] See also Figure 1 and 2 In S3 of this embodiment, constructing the printing model of part A specifically includes the following steps:

[0074] S31. Extract the data of the printed model of part A from the three-dimensional model data obtained in S2, and then construct a blood vessel, bile duct or lesion structure model, and use 3D printing to make a blood vessel positive mold.

[0075] The 3D printing material of the printed model of part A of this embodiment is a flexible photosensitive resin, which is brushed on the surface of the positive model of the blood vessel to generate a coating of the blood vessel wall to improve the authenticity.

[0076] The coating comprises a layer of release agent and three layers of silicone solvent, wherein the silicone solvent comprises a single group silicone and a masterbatch, 1g of the masterbatch is added to every 100g of the single group silicone until fully stirred, the thickness of each silicone layer is 0.2-0.3mm, and the drying time of each silicone layer is 20min.

[0077] In S3 of this embodiment, constructing the printing model of part B specifically includes the following steps:

[0078] S32, extracting the data of the printed model of part B from the three-dimensional model data obtained in S2, and reserving fixed holes and inlets and outlets matching the vascular structure on the liver contour, thereby establishing a liver contour model;

[0079] S33, segmenting the liver contour model, and using 3D printing to make an upper mold and a lower mold of the liver contour.

[0080] The 3D printing material of the printed model of part B in this embodiment is photosensitive resin.

[0081] S4 of this embodiment specifically includes the following steps:

[0082] S41, making a liver solid negative mold outside the upper mold and the lower mold of the liver outline;

[0083] S42, taking out the liver solid positive model with the vascular structure from the liver solid negative model.

[0084] S41 of this embodiment specifically includes the following steps:

[0085] S411, providing a mold for preparing the liver solid negative mold, preparing the bottom of the mold with water-based mud and glass beads, and surrounding the edges with hot melt glue and cardboard to prevent leakage at the edges of the mold;

[0086] S412, applying vaseline on the outer surface of the upper mold of the liver outline and placing it in the mold;

[0087] S413. Pour epoxy resin 815 and gypsum powder in a ratio of 1:4 on the outer surface of the upper mold of the liver outline, and insert screws to increase the connection strength of the outer mold. Repeat twice, and each layer takes 2 hours to dry thoroughly. For the third layer, pour a mixture of gypsum powder, straw and water on the resin surface to increase the thickness of the outer mold. The drying time is 6 hours.

[0088] S414, after the plaster solidifies, the mold is turned over, and the bottom of the mold is removed to prepare a first parting surface of the liver solid negative mold;

[0089] S415, applying vaseline on the outer surface of the lower mold of the liver outline and placing it in the mold, and then repeating steps S413 and S414 to prepare a second parting surface of the liver solid negative mold;

[0090] S416, placing a vascular structure in the negative mold of the liver entity and positioning it, applying vaseline on the first parting surface and the second parting surface of the negative mold of the liver entity, then merging the first parting surface and the second parting surface, applying hot melt glue to fill the seam and reserving a glue inlet, and injecting a solution for generating a liver entity into the glue inlet until it is full and overflows;

[0091] S417. After 6 hours, the solution is completely solidified, and the liver solid positive model with vascular structure is taken out. Finally, silicone glue is used to bond the ligament and the simulated liver.

[0092] It should be noted that before the vascular structure is placed in the negative model of the liver entity, the holes on it need to be sealed with silicone, and the drying time is 2 hours to prevent the subsequent injection of the solution to generate the liver entity from entering the vascular structure.

[0093] The solution for generating liver entity in this embodiment comprises silica gel, silicone oil, curing agent and coloring agent, wherein the ratio of silica gel to silicone oil is 1:6, the ratio of coloring agent to silica gel is 1:100, the curing agent accounts for 4% of the solution for generating liver entity, and the solution for generating liver entity needs to be evacuated for 3 minutes using a vacuum extractor to remove bubbles.

[0094] See also Figure 3 The external shape and size of the liver entity prepared in this embodiment are similar to those of a real liver, and it has appropriate softness and elasticity, and can simulate the texture, elasticity and cutting feeling of a real liver, so that medical students and surgeons can obtain a touch and feedback similar to actual surgery when performing surgery on the simulated liver model.

[0095] In addition, the liver prepared in this embodiment has a built-in hollow interconnected vascular structure, and through an external circulation pump, a blood flow model can be designed, including optimization of parameters such as flow rate and flow resistance. According to the needs of different lesions, a system that can simulate different inflow and outflow states is designed, so that the simulated liver can accurately reflect the function of a real liver during operation.

[0096] The simulated liver model prepared in this embodiment can clearly observe and measure the positions of blood vessels and lesions in the liver during preoperative planning; intraoperative navigation can flip the model as the liver entity changes, accurately locate important blood vessels inside and outside the liver, and help surgeons choose a suitable surgical approach and liver section plane, avoid important blood vessel areas in the liver, predict blood supply and reflux in the surgical area, reduce surgical complications and errors, and ensure that the operation is carried out efficiently and smoothly.

[0097] Example 2

[0098] See also Figure 4 and 5 The present invention provides a method for accurately customizing a simulated gallbladder for clinical and teaching purposes. For specific steps, see S1-S4 in Example 1, and finally a simulated gallbladder with a bile duct structure is obtained.

[0099] Example 3

[0100] See also Figure 6 and 7 The present invention provides a method for accurately customizing a simulated stomach for clinical and teaching purposes. For specific steps, see S1-S4 in Example 1, and finally a simulated stomach with a vascular structure is obtained.

[0101] The simulated gallbladder and stomach prepared in Examples 2 and 3 can also be reliably used as teaching and surgical training tools to provide a realistic surgical simulation platform.

[0102] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for accurately customizing a simulated organ for clinical and teaching purposes, characterized in that: include: S1. Establishment of deep learning model of simulated organs Based on the multimodal imaging data of the organ and the corresponding patient medical record information, a training data set is constructed, and a convolutional neural network model with the medical record information as input and the three-dimensional imaging data as output is trained using the training data set; S2. Acquisition of three-dimensional data of simulated organ models Based on the acquired medical record information of the target patient, the trained convolutional neural network model is used to output the three-dimensional model data corresponding to the pathological state of the target patient; S3. 3D printing data processing of simulated organ models Based on the three-dimensional model data obtained in S2, a part A printing model and a part B printing model are established. The part A printing model corresponds to the blood vessels, bile ducts or lesion structures of the simulated organ and can be connected to an external circulation pump. The part B printing model corresponds to the outline of the simulated organ. S4. Assembly of simulated organ models.

2. A method for accurately customizing a simulated organ for clinical and teaching use according to claim 1, characterized in that: S1 specifically includes the following steps: S11. Obtain detailed imaging data of the patient's organs from medical imaging equipment through the PACS system; S12. Use the trained convolutional neural network model to identify and optimize low-quality medical images and perform data fusion on multimodal imaging data.

3. A method for accurately customizing a simulated organ for clinical and teaching use according to claim 1, characterized in that: S2 specifically includes the following steps: S21, using the convolutional neural network model trained in S1, inputting the target patient's medical record information, and obtaining the medical imaging data of the organ model in DICOM format; S22, using multimodal diffeomorphic flexible registration to fuse the reconstruction results of multiple modalities into a unified voxel space; S23. In the process of converting voxels to a three-dimensional mesh model, the model is accurately calibrated in combination with DICOM internal references, and compared with the organ appearance reconstruction results in multiple dimensions, and finally the three-dimensional model data corresponding to the pathological state of the target patient is output.

4. The method for accurately customizing a simulated organ for clinical and teaching purposes according to claim 1, characterized in that: The construction of the printing model of part A in S3 specifically includes the following steps: S31. Extract the data of the printed model of part A from the three-dimensional model data obtained in S2, and then construct a structural model of blood vessels, bile ducts or lesions, and use 3D printing to make a positive model of blood vessels, bile ducts or lesions.

5. A method for accurately customizing a simulated organ for clinical and teaching use according to claim 4, characterized in that: The 3D printing material of the printed model of part A is a flexible photosensitive resin, which is brushed on the surface of the blood vessel, bile duct or lesion positive model to form a coating on the blood vessel wall or bile duct wall.

6. The method for accurately customizing a simulated organ for clinical and teaching purposes according to claim 1, characterized in that: The construction of the printing model of part B in S3 specifically includes the following steps: S32, extracting the data of the printed model of part B from the three-dimensional model data obtained in S2, and reserving fixed holes and inlets and outlets matching the blood vessels, bile ducts or lesion structures on the outline of the organ, thereby establishing an outline model of the organ; S33, segmenting the organ contour model, and using 3D printing to make an upper mold and a lower mold of the organ contour.

7. A method for accurately customizing a simulated organ for clinical and teaching use according to claim 6, characterized in that: The 3D printing material of the printed model of Part B is photosensitive resin.

8. The method for accurately customizing a simulated organ for clinical and teaching purposes according to claim 1, characterized in that: S4 specifically includes the following steps: S41, making a solid negative mold of the organ outside the upper mold and the lower mold of the outer contour of the organ; S42, taking out the organ solid positive mold with blood vessels, bile ducts or lesion structures from the organ solid negative mold.

9. The method for accurately customizing a simulated organ for clinical and teaching purposes according to claim 8, characterized in that: S41 specifically includes the following steps: S411, providing a mold for preparing the organ solid negative mold and a removable mold bottom; S412, applying a release agent to the outer surface of the upper mold of the organ outline, and placing the mold into the mold; S413, pouring gypsum into the mold; S414, waiting for the plaster to solidify, and removing the bottom of the mold to prepare a first parting surface of the organ solid negative mold; S415, applying a release agent on the outer surface of the lower mold of the organ outline and placing it in the mold, and then repeating steps S413 and S414 to prepare a second parting surface of the organ solid negative mold; S416, placing blood vessels, bile ducts or lesion structures in the organ entity negative mold and positioning them, combining the first parting surface and the second parting surface of the organ entity negative mold, applying hot melt glue to fill the seams and reserving a glue inlet, and then injecting a solution for generating the organ entity into the glue inlet; S417. After the solution is completely solidified, take out the organ solid positive mold with blood vessels, bile ducts or lesion structures, and finally use silicone glue to bond the ligaments and the simulated organ model.

10. The method for accurately customizing a simulated organ for clinical and teaching purposes according to claim 9, characterized in that: The solution for generating the organ entity comprises silica gel, silicone oil, a curing agent, and a colorant, wherein the ratio of the silica gel to the silicone oil is 1:5-10.

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