Osteotomy guide plate manufacturing method and device and related equipment

Abstract

The invention provides an osteotomy guide plate manufacturing method and device and related equipment, and relates to the technical field of medical instruments. The method comprises the following steps: acquiring magnetic resonance imaging data of a first target part and computed tomography data of a second target part; generating a target skeleton model according to the magnetic resonance imaging data and the computed tomography data; and manufacturing the osteotomy guide plate according to the target skeleton model. The target skeleton model can be generated by combining the magnetic resonance imaging data and the computed tomography data at the same time, the manufacturing precision and integrity of the target skeleton model are improved, the manual participation degree in the target skeleton model generation process is reduced, and the precision of the osteotomy guide plate is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, and in particular to a method and device for manufacturing an osteotomy guide plate and related equipment. BACKGROUND

[0002] An osteotomy guide plate is a tool used in orthopedic surgery, which is mainly used to guide the spatial position and angle of the osteotomy site in orthopedic surgery, so as to improve the degree of fit between the prosthesis or implant and the affected area, restore the physiological force line, and accurately remove the lesion, thereby helping the surgeon to achieve more accurate osteotomy operation during the surgery.

[0003] At present, most of the existing technologies are based on single data to manufacture the osteotomy guide plate, which has the problems of single data entry, poor precision of the osteotomy guide plate, and complex operation in the use of the osteotomy guide plate, etc., resulting in the risks of large amount of bleeding and long operation time during the surgery. SUMMARY

[0004] The embodiments of the present application provide a method and device for manufacturing an osteotomy guide plate and related equipment, which generate a bone model based on an AI (Artificial Intelligence) system in combination with multiple image data, and manufacture the osteotomy guide plate according to the bone model, so as to improve the precision of the osteotomy guide plate, simplify the use steps of the osteotomy guide plate, and thus reduce the amount of bleeding during the surgery and improve the surgical efficiency.

[0005] In a first aspect, the present application provides a method for manufacturing an osteotomy guide plate, which includes: obtaining magnetic resonance imaging data of a first target site and computed tomography data of a second target site; generating a target bone model according to the magnetic resonance imaging data and the computed tomography data; and manufacturing the osteotomy guide plate according to the target bone model.

[0006] In some possible implementations, the above method further includes: generating a segmentation result of the magnetic resonance imaging data based on the component structure information of the first target site; and generating a first target site bone model according to the segmentation result, wherein the first target site bone model includes a component structure model of the first target site.

[0007] In some possible implementations, the above method further includes: extracting a cartilage bone model from the first target site bone model; generating a second target site bone model according to the computed tomography data; and generating the target bone model according to the cartilage bone model and the second target site bone model.

[0008] In some possible implementation manners, the osteotomy guide plate is manufactured according to the target bone model, including: determining data information required for manufacturing the osteotomy guide plate according to the target bone model, wherein the data information includes: femoral head rotation center data, intercondylar notch data, tibial tuberosity data, ankle hole center data, femoral external rotation angle data, and tibial posterior inclination angle data; and determining size information and positioning point information of the osteotomy guide plate to manufacture the osteotomy guide plate according to the data information.

[0009] In some possible implementation manners, the method further includes: obtaining personal information of the patient; determining compensation information of the osteotomy guide plate according to the personal information and the data information; and determining the size information and the positioning point information of the osteotomy guide plate to manufacture the osteotomy guide plate according to the compensation information.

[0010] In some possible implementation manners, the method further includes: obtaining budget information of the patient; determining first material information of the osteotomy guide plate according to the budget information, wherein the first material information is material information of a first preset part of the osteotomy guide plate; and determining second material information of the osteotomy guide plate according to the compensation information, wherein the second material information is material information of a second preset part of the osteotomy guide plate.

[0011] In some possible implementation manners, an area of the first preset part is greater than an area of the second preset part.

[0012] In some possible implementation manners, an area of the first preset part is greater than an area of the second preset part.

[0013] In a second aspect, the present application provides an osteotomy guide plate manufacturing device, which is suitable for the osteotomy guide plate manufacturing method described in any one of the above aspects, and the device includes: an acquisition unit configured to acquire magnetic resonance imaging data of a first target part and computed tomography data of a second target part; a generation unit configured to generate a target bone model according to the magnetic resonance imaging data and the computed tomography data; and a manufacturing unit configured to manufacture the osteotomy guide plate according to the target bone model.

[0014] In a third aspect, the present application provides an electronic device including a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are used to execute the osteotomy guide plate manufacturing method described in any one of the above aspects when the computer program instructions are run by the processor.

[0015] The method for manufacturing the osteotomy guide plate provided in the application comprises the following steps: acquiring magnetic resonance imaging data of a first target part and computer tomography data of a second target part; generating a target bone model according to the magnetic resonance imaging data and the computer tomography data; and manufacturing the osteotomy guide plate according to the target bone model. The target bone model is generated by combining the magnetic resonance imaging data and the computer tomography data, so that the manufacturing precision and completeness of the target bone model can be improved, the degree of human participation in the generation of the target bone model can be reduced, and the precision of the osteotomy guide plate can be improved.

[0016] Other advantages, objects, and features of the application will be apparent from the following detailed description, and will be appreciated by those skilled in the art. BRIEF DESCRIPTION OF DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description. The accompanying drawings are included to provide a further understanding of the application and are incorporated into and constitute a part of this specification. In the drawings:

[0018] Figure 1 A flowchart of a method for manufacturing an osteotomy guide plate according to an embodiment of the application is shown in FIG. 1;

[0019] Figure 2 A knee joint model generated based on MRI data according to an embodiment of the application is shown in FIG. 2;

[0020] Figure 3 A tibia model generated based on CT data according to an embodiment of the application is shown in FIG. 3;

[0021] Figure 4 A femur model generated based on CT data according to an embodiment of the application is shown in FIG. 4;

[0022] Figure 5 A target bone model according to an embodiment of the application is shown in FIG. 5;

[0023] Figure 6 Another target bone model according to an embodiment of the application is shown in FIG. 6;

[0024] Figure 7 A structural diagram of an apparatus for manufacturing an osteotomy guide plate according to an embodiment of the application is shown in FIG. 7;

[0025] Figure 8 A structural diagram of an electronic device according to an embodiment of the application is shown in FIG. 8.

[0026] Wherein, Figures 2 to 6 The correspondence between the reference signs and the structural names in the drawings is as follows:

[0027] 20 knee joint; 200 tibial cartilage; 300 tibia; 400 femur; 500 femoral cartilage. DETAILED DESCRIPTION

[0028] In order to better understand the technical solutions provided by the embodiments of the present specification, the technical solutions of the embodiments of the present specification will be described in detail below through the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present specification and the embodiments are detailed descriptions of the technical solutions of the embodiments of the present specification, and are not limitations of the technical solutions of the present specification. In the case of no conflict, the technical features in the embodiments of the present specification and the embodiments can be combined with each other.

[0029] In this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... " does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element. The term "two or more" includes two or more than two.

[0030] In the related art, an osteotomy guide plate is mostly made based on single data. In total knee arthroplasty, the following two methods are mostly used to make an osteotomy guide plate: one is to make an osteotomy guide plate based on CT (Computed Tomography) data; the other is to make an osteotomy guide plate based on MRI (Magnetic Resonance Imaging) data. Among them, only based on CT data to make an osteotomy guide plate, due to the lack of cartilage tissue data in CT data, it is impossible to accurately predict the specific position of the positioning hole of the osteotomy guide plate before operation, and the cartilage and osteophytes and other substances need to be scraped out in the operation to realize the positioning operation of the osteotomy guide plate, resulting in problems such as large amount of bleeding and long operation time in the operation process. On the other hand, only based on MRI data to make an osteotomy guide plate, on the one hand, MRI is based on the imaging of the number of hydrogen ions in the tissue, and the imaging precision of bone is poor, and the MRI image coil is small, which cannot completely scan the full length of the bone, resulting in poor precision of the osteotomy guide plate made based on the MRI data. On the other hand, the identification process of the bone based on the MRI data needs to consume a lot of manual annotation operation, resulting in a time-consuming and laborious process of making an osteotomy guide plate based on MRI data.

[0031] Therefore, the embodiments of the present application provide a method and device for manufacturing an osteotomy guide plate, and related equipment, which are especially suitable for the manufacturing process of the osteotomy guide plate. The embodiments of the present application generate a bone model based on an AI system (Artificial Intelligence) in combination with various image data, and manufacture the osteotomy guide plate according to the bone model, which is beneficial to improve the precision of the osteotomy guide plate, simplify the use steps of the osteotomy guide plate, and thus reduce the amount of bleeding in the surgical process and improve the surgical efficiency.

[0032] In a first aspect, the embodiments of the present application provide a method for manufacturing an osteotomy guide plate. Figure 1 A flowchart of a method 100 for manufacturing an osteotomy guide plate is provided in the embodiments of the present application. As shown in the figure, the method 100 includes the following steps: Figure 1

[0033] In step S110, MRI (Magnetic Resonance Imaging) data of a first target site and CT (Computed Tomography) data of a second target site are acquired.

[0034] Exemplarily, the first target site and the second target site are both located on the affected side.

[0035] Exemplarily, the first target site can include a soft tissue site, a joint site structure, etc. The soft tissue site can include articular cartilage, meniscus, ligament, tendon, etc. The joint site structure can include a shoulder joint, an elbow joint, a hip joint, a knee joint, an ankle joint, etc.

[0036] Exemplarily, the second target site can be the full-length skeleton of the lower limbs of the human body, and the second target site can include a pelvis, a thigh bone, a calf bone, a knee bone, etc.

[0037] Exemplarily, the MRI data and the CT data can be DICOM (Digital Imaging and Communications in Medicine) data.

[0038] ​Exemplarily, the above MRI data of the first target site of the patient and the CT data of the second target site can be obtained after scanning the corresponding site of the patient, or can be obtained after the patient has completed the image recording and analysis, wherein the above recording process can be manual recording, or can be obtained by querying and calling the HIS system (Hospital Information System) based on the basic information of the patient, which is not limited here.

[0039] Preferably, the MRI data is ultra-thin layer data obtained based on thin layer MRI scanning, and the layer thickness of the ultra-thin layer data is greater than or equal to 0.5 mm and less than or equal to 0.7 mm.

[0040] Step S120, generating a target bone model according to the MRI data and the CT data.

[0041] Exemplarily, thin layer MRI scanning can be performed on the knee joint of the patient to obtain MRI data of the knee joint of the affected side of the patient. CT scanning is performed on the full length of the lower limbs of the patient to obtain lower limb data of the patient. Specifically, the target bone model can be generated based on the AI medical image three-dimensional reconstruction software according to the above knee joint MRI data and lower limb data. The target bone model can include a knee joint model and a lower limb full length model.

[0042] Specifically, high-field MRI equipment can be selected to perform thin layer scanning on the knee joint of the patient to obtain 0.7 mm ultra-thin layer MRI data of the knee joint, so as to obtain detailed soft tissue and bone structure information of the patient. Full length CT scanning is performed on the lower limbs of the patient to obtain high-resolution bone structure information. Then, a three-dimensional model of the target bone is generated based on a deep learning method, wherein the three-dimensional model of the target bone can include soft tissue information and bone information.

[0043] It should be noted that the knee joint model and the lower limb full length model can be registered to generate the target bone model based on the AI medical image three-dimensional reconstruction software and the point cloud registration algorithm according to the MRI data and the CT data. Specifically, the target bone model can also be tested and adjusted according to actual needs to ensure its accuracy. The adjustment operation can be based on the original MRI data and CT data to remove artifacts, correct shape, or add missing parts, etc. to improve the accuracy of the target bone model.

[0044] Step S130, making an osteotomy guide plate according to the target bone model.

[0045] Exemplarily, the osteotomy guide plate can be designed based on an AI medical image three-dimensional reconstruction software according to the target bone model, and the osteotomy guide plate is manufactured by using a 3D printing technology.

[0046] Specifically, the osteotomy scheme can be generated according to the target bone model, and a prosthesis model can be simulated and generated, and the osteotomy guide plate positioning point information can be determined according to the prosthesis model and the target bone model.

[0047] The osteotomy guide plate manufacturing method provided in the present application includes: acquiring MRI data of a first target part and CT data of a second target part; generating a target bone model according to the MRI data and the CT data; and manufacturing an osteotomy guide plate according to the target bone model. Based on this, the present application can generate a target bone model by combining MRI data and CT data, improve the manufacturing accuracy and completeness of the target bone model, reduce the degree of human participation in the generation of the target bone model, and improve the accuracy of the osteotomy guide plate.

[0048] In some possible implementations, the method further includes: generating a segmentation result of the MRI data based on the component structure information of the first target part; and generating a first target part bone model according to the segmentation result, wherein the first target part bone model includes a component structure model of the first target part.

[0049] Exemplarily, the component structure information of the first target part can be determined based on orthopedic knowledge graph data. It should be noted that the orthopedic knowledge graph data can include triple information, wherein the triple information can include: an entity structure, an associated entity structure, and a relationship between the entity structure and the associated entity structure, for example: (femur, located, lower limb). The orthopedic knowledge graph can be stored by using a graph database neo4j, and a plurality of independent knowledge graph databases can be established.

[0050] Exemplarily, the bone knowledge graph data corresponding to the first target part can be called based on the entity name of the first target part, the MRI data is segmented and stored according to the structure entity name corresponding to the bone knowledge graph data, a single MRI data package corresponding to the single structure entity is generated, and the single MRI data package is called based on an AI medical image three-dimensional reconstruction software to generate a refined model corresponding to each component structure included in the first target part.

[0051] Specifically, taking the knee joint as an example, the bone knowledge graph data corresponding to the knee joint can include the following entity structure data: femur, tibia, patella, intercondylar fossa, medial meniscus, lateral meniscus, knee cartilage, and suprapatellar bursa, etc.; can include the following associated entity structure data: anterior cruciate ligament, posterior cruciate ligament, medial collateral ligament, lateral collateral ligament, etc.; and can also include the following relationship data: containing relationship, superior-inferior relationship, parent-child relationship, etc., which are not limited here. The MRI data of the knee joint is divided into femur MRI data, tibia MRI data, patella MRI data, intercondylar fossa MRI data, medial meniscus MRI data, lateral meniscus MRI data, knee cartilage MRI data, suprapatellar bursa MRI data, anterior cruciate ligament MRI data, posterior cruciate ligament MRI data, medial collateral ligament MRI data, and lateral collateral ligament MRI data, etc., and corresponding femur MRI data packets, tibia MRI data packets, patella MRI data packets, intercondylar fossa MRI data packets, medial meniscus MRI data packets, lateral meniscus MRI data packets, knee cartilage MRI data packets, suprapatellar bursa MRI data packets, anterior cruciate ligament MRI data packets, posterior cruciate ligament MRI data packets, medial collateral ligament MRI data packets, and lateral collateral ligament MRI data packets, etc. are generated. The above data packets are independently stored to generate a knee joint MRI data packet library, and then based on the femur MRI data packet, tibia MRI data packet, patella MRI data packet, intercondylar fossa MRI data packet, medial meniscus MRI data packet, lateral meniscus MRI data packet, knee cartilage MRI data packet, suprapatellar bursa MRI data packet, anterior cruciate ligament MRI data packet, posterior cruciate ligament MRI data packet, medial collateral ligament MRI data packet, and lateral collateral ligament MRI data packet, the above single data packet is retrieved based on the AI medical image three-dimensional reconstruction software, and femur model, tibia model, patella model, intercondylar fossa model, medial meniscus model, lateral meniscus model, knee cartilage model, suprapatellar bursa model, anterior cruciate ligament model, posterior cruciate ligament model, medial collateral ligament model, and lateral collateral ligament model, etc. are generated. It should be noted that after the above segmentation operation of the MRI data is completed, the target MRI data packet can be individually retrieved to generate a single bone model of each component structure as needed to make an osteotomy guide. The above model can also be independently stored to generate a knee joint model database for subsequent retrieval as needed. The single bone model of each component structure can also be registered according to the relationship information between the above entity structure and the above associated entity structure in the orthopedic knowledge graph data to generate a complete bone model of the first target part.

[0052] As shown in Figure 2 , Figure 2 a knee joint model generated based on MRI data is provided for the embodiments of the present application. As shown inFigure 2 As shown, the knee joint model generated according to the MRI data can include a knee joint 20, a tibial cartilage 200, wherein the tibial cartilage 200 covers the surface position of the tibia. It should be noted that the MRI data package of the tibial cartilage 200 can be stored separately according to actual storage requirements, or the bone model of the tibial cartilage 200 can be stored separately in the bone model database of the knee joint 20, so as to be recalled at any time according to requirements subsequently.

[0053] Therefore, the above method can realize automatic segmentation of MRI data based on the composition structure information of the first target site, reduce the manual participation in the MRI data labeling process, reduce the labor cost, accurately generate the refined model corresponding to each composition structure of the first target site according to the segmentation result of the MRI data, improve the refinement and accuracy of the target bone model, and further improve the manufacturing precision and efficiency of the osteotomy guide plate.

[0054] In some possible embodiments, the above method further includes: extracting a cartilage bone model in the first target site bone model; generating a second target site bone model according to computed tomography data; and generating a target bone model according to the cartilage bone model and the second target site bone model.

[0055] It should be noted that the cartilage bone model can include a tibial cartilage model and a femoral cartilage model. The second target site bone model can include a tibial bone model and a femoral bone model.

[0056] Specifically, the tibial cartilage model and the femoral cartilage model can be extracted in the first target site bone model. The second target site bone model is generated based on the AI medical image three-dimensional reconstruction software according to the CT data, and the tibial bone model and the femoral bone model are extracted. Then, the tibial cartilage model is registered with the tibial bone model, and the femoral cartilage model is registered with the femoral bone model, so as to generate the target bone model, thereby ensuring the accuracy of the relative position and proportion of the tibial cartilage model and the tibial bone model, and the femoral cartilage model and the femoral bone model, and further improving the accuracy of the target bone model.

[0057] It should be noted that for the first target site which needs to image the cartilage site, the MRI data can be selected to generate the first target site bone model, and for the second target site which has higher requirements for bone imaging, the CT data can be selected to generate the second target site bone model.

[0058] The tibial cartilage model and the femoral cartilage model in the first target site bone model can be generated based on the AI medical image three-dimensional reconstruction software according to the relationship data between the entity structure and the associated entity structure in the skeleton knowledge graph based on the foregoing manner by calling the tibial cartilage data package and the femoral cartilage data package of each component structure of the first target site segmented based on the skeleton knowledge graph data, or can be obtained by directly calling the tibial cartilage model and the femoral cartilage model stored independently in the first target site bone model database, which is not specifically limited here.

[0059] It should be noted that whether to perform a segmentation operation on the CT data can be selected according to actual needs. In the case where the CT data is not finely segmented, the tibial cartilage model and the tibial bone model, and the femoral cartilage model and the femoral bone model can be registered to generate a target bone model based on the AI medical image three-dimensional reconstruction software according to the relationship data between the entity structure and the associated entity structure in the skeleton knowledge graph. In the case where the CT data is finely segmented, the CT data can be segmented based on the skeleton knowledge graph data, and then the fine models corresponding to each component structure contained in the second target site are generated based on the CT data package of each component structure of the segmented second target site. The CT data package and the fine model corresponding to each component structure of the second target site can be stored independently to generate a CT data package library and a fine model database of the second target site, and the specific process is not described here. Then, the independently stored tibial cartilage model and tibial bone model, and femoral cartilage model and femoral bone model are registered to generate a target bone model based on the AI medical image three-dimensional reconstruction software according to the relationship data between the entity structure and the associated entity structure in the skeleton knowledge graph.

[0060] As shown in Figures 3-6 , Figure 3 a tibial model generated based on CT data provided by an embodiment of the present application, Figure 4 a femoral model generated based on CT data provided by an embodiment of the present application, and Figure 6 a target bone model provided by another embodiment of the present application. Wherein, Figures 3-4 The bone quality of the tibia 300 and the femur 400 can be clearly observed. As described above, the relevant data of the tibial cartilage 200 in the bone database of the knee joint 20 can be called, and the tibial cartilage 200 and the tibia 300 are registered to generate a target bone model based on the AI medical image three-dimensional reconstruction software according to the relationship data between the entity structure and the associated entity structure in the skeleton knowledge graph, and the femoral cartilage 500 and the femur 400 are registered to generate a target bone model.

[0061] Therefore, the application can generate a target bone model by extracting a cartilage bone model from a first target site bone model and generating a second target site bone model according to CT data, and performing corresponding registration on the cartilage bone model in the first target bone model and the second target bone model to generate a target bone model, so that the target bone model generated by the application has clear bone quality and covers the required cartilage structure, improves the model accuracy and completeness of the target bone model of the application, and thus can improve the accuracy of the osteotomy guide plate made based on the target bone model, so that the position of the positioning point of the osteotomy guide plate can be predicted before the operation, thereby eliminating the need for additional scraping operations on cartilage and osteophytes and other substances, reducing the amount of bleeding during the operation, reducing the risk of the operation, and improving the efficiency of the operation.

[0062] In some possible implementations, according to the target bone model, an osteotomy guide plate is manufactured, including: determining data information required for manufacturing the osteotomy guide plate according to the target bone model, wherein the data information includes: femoral head rotation center data, intercondylar notch data, tibial tuberosity data, ankle hole center data, femoral external rotation angle data, and tibial posterior inclination angle data; and determining size information and positioning point information of the osteotomy guide plate according to the data information to manufacture the osteotomy guide plate.

[0063] Exemplarily, the lower limb force line can be determined according to the femoral head rotation center data, the intercondylar notch data, the tibial tuberosity data, and the ankle hole center data to calculate the mechanical axis data of the lower limb, so as to assist in determining the position information, angle information, and size information of the osteotomy plane of the femur and the tibia. Based on the femoral external rotation angle and the tibial posterior inclination angle, the prosthesis model information is determined. Based on the position information, the angle information of the osteotomy plane, and the prosthesis model information, the size information and the positioning point information of the osteotomy guide plate are determined to manufacture the osteotomy guide plate.

[0064] Therefore, the above method can accurately determine the size information and the positioning point information of the osteotomy guide plate based on the femoral head rotation center data, the intercondylar notch data, the tibial tuberosity data, the ankle hole center data, the femoral external rotation angle data, and the tibial posterior inclination angle data, so as to ensure the accuracy of the osteotomy operation and ensure that the bone after the osteotomy can restore the correct force line and joint alignment.

[0065] In some possible implementations, the above method further includes: obtaining personal information of the patient; determining compensation information of the osteotomy guide plate according to the personal information and the data information; and determining the size information and the positioning point information of the osteotomy guide plate according to the compensation information to manufacture the osteotomy guide plate.

[0066] Exemplarily, the personal information of the patient can include: gender information, age information, medical history information, bone quality information, and the like of the patient. The compensation information can include: compensation position information of the osteotomy plane, compensation angle information of the osteotomy plane, and compensation size information of the osteotomy plane.

[0067] Specifically, for female, children, and the previous preset time within the osteotomy site injured or osteoporosis osteotomy site, the position information, angle information and size information of the osteotomy plane are compensated. Based on the above data information, in the case of ensuring that the position information of the osteotomy plane is within the target position range, the angle information is within the target angle range, and the size information is within the target size range, based on the personal information of the patient, such as the history of injury and bone quality of the osteotomy site, the compensation position information of the osteotomy plane, the compensation angle information of the osteotomy plane and the compensation size information of the osteotomy plane are determined to ensure that the selected osteotomy plane has good bone quality, the stress of the osteotomy site is lighter, and the size of the osteotomy site is smaller. Then, the size information and the positioning point information of the osteotomy guide plate are determined according to the above compensation information to manufacture the osteotomy guide plate, so that the osteotomy guide plate matches the position information, angle information and size information of the compensated osteotomy plane.

[0068] Therefore, the above method can compensate the size information and the positioning point information of the osteotomy guide plate according to the personal information and the data information of the patient, thereby compensating the position information, angle information and size information of the osteotomy plane based on the differences in bone structure between men and women, the differences in bone growth and degradation between children and the elderly, the differences in medical history such as previous surgery, fracture and arthritis, and the differences in bone quality. Then, the size information and the positioning point information of the osteotomy guide plate are designed based on the position information, angle information and size information of the compensated osteotomy plane, thereby optimizing the force line and ensuring the joint function.

[0069] In some possible embodiments, the above method further includes: obtaining budget information of the patient; determining first material information of the osteotomy guide plate according to the budget information, wherein the first material information is material information of a first preset part of the osteotomy guide plate; and determining second material information of the osteotomy guide plate according to the compensation information, wherein the second material information is material information of a second preset part of the osteotomy guide plate.

[0070] In the case of ensuring the use function of the above osteotomy guide plate, the material of the osteotomy guide plate can be personalized selected according to the budget information and the compensation information of the patient.

[0071] For example, the first preset part can be a non-main working component, such as a handle, a connecting piece or an auxiliary structure, etc. The second preset part can be a main working component, such as a cutting surface, etc. The first material can be a low-cost material, such as an aluminum alloy, a plastic composite material, etc. The second material can be a material with high cost, good wear resistance and good biocompatibility, such as stainless steel, titanium alloy or cobalt-chromium alloy, etc.

[0072] Specifically, according to the budget information of the patient, the material of the non-working area component, that is, the first preset part, can be selected as the first material, according to the compensation information, the compensation position information, the compensation angle information and the compensation size information of the osteotomy plane are determined, the size information and the positioning point information of the compensated osteotomy guide plate are determined, and then based on the size information and the positioning point information of the compensated osteotomy guide plate, the specific position information and the size information of the main working component of the osteotomy guide plate, that is, the specific position information of the second preset part, that is, the size information, are determined, and based on the specific position information, the size information and the functional requirement of the second preset part, a material with greater strength and better wear resistance is selected as the second material to manufacture the osteotomy guide plate.

[0073] Therefore, the above method can meet the budget requirement while designing the osteotomy guide plate individually, reduce the manufacturing cost of the osteotomy guide plate and improve the osteotomy effect.

[0074] In some possible implementation manners, the area sum of the first preset part is greater than the area sum of the second preset part.

[0075] The area sum of the first preset part is greater than the area sum of the second preset part, that is, the total area of the working area component is less than the total area of the non-working area component.

[0076] Therefore, by limiting the area sum of the first preset part to be greater than the area sum of the second preset part, that is, the total area of the working area component is less than the total area of the non-working area component, the amount of the first material of the osteotomy guide plate is greater than the amount of the second material, and the manufacturing cost of the osteotomy guide plate is further reduced under the premise of ensuring the applicable function of the osteotomy guide plate.

[0077] In some possible implementation manners, the above method further includes:

[0078] According to the elastic modulus and the Poisson's ratio of the first material information and the second material information, the performance results of the first material information and the second material information under the stress condition are obtained.

[0079] The elastic modulus represents the modulus of the material and is a physical quantity describing the deformation degree of the material under stress. The elastic modulus reflects the elastic deformation ability of the material under stress. The Poisson's ratio represents the relationship between the transverse deformation and the longitudinal deformation of the material under the action of tensile or compressive force, and is a dimensionless ratio, defined as the ratio of the transverse strain to the longitudinal strain of the material under the action of tensile or compressive force. The performance results under the stress condition represent the mechanical properties and response conditions of the first material and the second material under the action of external force.

[0080] According to the fixed position of the osteotomy guide plate, the performance results of the first material information and the second material information under the stress condition, and the required load in the actual operation process, the material density distribution diagram is determined.

[0081] The fixed position of the osteotomy guide plate indicates the area to be osteotomized, i.e., the surgical region. The load required during the actual surgical procedure indicates the load applied corresponding to the actual surgical situation. This load can represent the fixing force applied to the first and second materials during the actual surgery, such as the force used to fix the osteotomy guide plate to the bone with screws; and the operational force applied to the first and second materials, such as the force applied by the surgeon during the operation, which can include cutting force, bone-sawing force, etc. The material density distribution map indicates the optimal material distribution within the space corresponding to the surgical area, specifically the first and second preset portions. The material density distribution map can include high-density and low-density regions.

[0082] It should be noted that, after determining the fixation position of the osteotomy guide plate and the performance results of the first and second material information under stress conditions, the surgical area, the information of the first and second materials can be obtained. This information may include the elastic modulus and Poisson's ratio. Then, the loads applied during actual surgery, such as fixation force and manipulation force, and boundary conditions are simulated. The boundary conditions may include geometric boundary conditions and force and load boundary conditions. Geometric boundary conditions can be expressed as the displacement at the point of contact between the osteotomy guide plate and the screw being 0. Force and load boundary conditions can be expressed as applying a uniformly distributed load to the surface area of ​​the osteotomy guide plate, such as applying a distributed load q to the surface area (A) of the osteotomy guide plate, such that 10 Newtons (N / mm²) are applied per square millimeter. 2 The process involves applying pressure perpendicular to the guide plate surface at the screw contact point, and using finite element analysis (FEA) to simulate the loads applied during actual surgery. This calculates the stress, strain, and displacement distribution of the osteotomy guide plate under stress conditions, determining which areas of the guide plate bear the greatest stress. Finally, color coding is used to visualize the density distribution of different areas. If the maximum stress range is 0 to 100 MPa, the color coding map covers this range. Color steps or intervals are selected to represent the frequency of color changes, forming a material density distribution map.

[0083] For example, different colors in a colorimetric diagram can be used to represent different density regions; for instance, high-density regions can be represented by darker colors, and low-density regions by lighter colors. It is understood that high-density regions require higher stress, and low-density regions require lower stress. Therefore, a first preset portion corresponds to the high-density region, and a second preset portion corresponds to the low-density region, in order to achieve optimal material distribution. In some feasible embodiments, the first and second preset portions can also be made of the same material according to actual needs. In the above case, the amount of material used in the high-density region can be greater than that in the low-density region, so that the stress intensity in the high-density region is greater than that in the low-density region.

[0084] Based on the material density distribution diagram, determine the location and number of target positioning holes for the osteotomy guide plate.

[0085] It should be noted that the aforementioned target positioning holes are points in the osteotomy guide plate that can be initially planned for creating positioning holes.

[0086] For example, the target positioning holes of the osteotomy guide plate can be located in the high-density regions corresponding to the material density distribution map. It is understood that high-density regions correspond to higher stress intensity; placing the target positioning holes in high-density regions can reduce the weight of the osteotomy guide plate and increase its rigidity. A hybrid coding scheme is used to design the positions of the target positioning holes, resulting in multiple individual plates.

[0087] In this design, a scheme not only represents an individual but also a potential hole configuration and hole offset. Hybrid encoding represents the encoding of the number of holes, hole positions, and hole offsets as a hybrid vector (the number of holes is encoded as an integer; the hole positions are encoded as real numbers representing the coordinates (x, y); and the hole offsets are encoded as real numbers representing the offsets (Δx, Δy)). In other words, the scheme design for the target positioning position is encoded as a binary code. For example, with 10 target positioning positions, and aiming to minimize stress concentration, the number of holes is set between 2 and 5, with offsets within ±5mm. Thus, in one scheme design, if 0 represents no holes and 1 represents holes, the binary code is 0010100110, which represents a hole configuration, illustrative only.

[0088] An initial population is constructed based on multiple individuals.

[0089] For example, when the number of individuals is 50, the initial population can contain 50 different individuals, each with a different pore configuration and offset. For example, individual 1: number of pores = 3, pore position = [(10,10),(20,20),(30,30)], offset = [(1,-1),(-2,2),(0,0)]; individual 2: number of pores = 2, pore position = [(15,15),(25,25)], offset = [(-1,1),(2,-2)].

[0090] The fitness function is used to calculate the fitness of each individual.

[0091] The fitness function is calculated as follows: Fitness = 1 / Maximum Stress. Fitness represents fitness, a numerical value used to measure the quality of each design scheme (individual). Maximum Stress represents the maximum stress, calculated in the Finite Element Analysis (FEA) test. It indicates the maximum stress the design scheme can withstand under stress conditions. In other words, fitness is determined by the maximum stress; the lower the stress, the higher the fitness. Higher fitness indicates a better design scheme.

[0092] In the initial population, individuals with high fitness are selected for reproduction using the roulette wheel selection method to generate new individuals.

[0093] Roulette wheel selection refers to the probabilistic selection of individuals for reproduction based on their fitness values. This ensures that individuals with high fitness have a greater chance of being selected, while also preserving some opportunities for individuals with lower fitness to maintain population diversity. A new individual represents the crossover operation between two individuals with high fitness. For example, before crossing: Individual 1: Number of holes = 3, Hole position = [(10,10),(20,20),(30,30)], Offset = [(1,-1),(-2,2),(0,0)]; Individual 2: Number of holes = 2, Hole position = [(15,15),(25,25)], Offset = [(-1,1),(2,-2)]; After crossing: New Individual 1: Number of holes = 3, Hole position = [(10,10),(20,20),(25,25)], Offset = [(1,-1),(-2,2),(2,-2)].

[0094] Mutate one of the multiple individuals to obtain the mutated individual. Mutation means randomly adjusting the offset parameter of the individual within a certain range. The adjustment range of the offset parameter can be greater than or equal to -5mm and less than or equal to 5mm. For example, before mutation, individual 1: hole position: [(10,10),(20,20),(30,30)], offset: [(1,-1),(-2,2),(0,0)]; after mutation, individual 1: hole position: [(10,10),(20,20),(30,30)], offset = [(2,-1),(-2,1),(1,-2)].

[0095] Finite element models are established for the individual, the new individual, and the mutated individual, respectively, resulting in finite element models corresponding to the individual, the new individual, and the mutated individual. The finite element model represents the model constructed based on each design scheme, used to simulate and analyze its behavior under actual usage conditions.

[0096] Finite element analysis (FEA) was used to calculate the finite element models corresponding to the individual, the new individual, and the mutated individual, respectively, and the stress, strain, and displacement distribution of the individual, the new individual, and the mutated individual under stress conditions were obtained.

[0097] Based on the stress, strain, and displacement distribution of the individual, the new individual, and the variant individual under stress conditions, the finite element analysis results of the individual, the new individual, and the variant individual are obtained.

[0098] Based on the finite element analysis results, individuals, new individuals, and mutated individuals are adjusted, and iterative optimization is performed to obtain the optimal solution. Specifically, iterative optimization refers to the process from step "in the initial population, using roulette wheel selection to select individuals with high fitness for reproduction to generate new individuals" to step "based on the finite element analysis results, individuals, new individuals, and mutated individuals are adjusted" to "based on the finite element analysis results, individuals, and mutated individuals are adjusted." The optimal solution may include information on the optimal number of holes, hole positions, and offset of the osteotomy guide plate.

[0099] Based on the optimal solution, determine the number of holes, hole positions, and offset of the osteotomy guide plate.

[0100] Using the above methods, the mechanical properties and stability of the osteotomy guide plate during surgery can be guaranteed, thereby improving the precision and safety of the procedure. The offset ensures that the final design is not only theoretically optimal but also provides the best performance and adaptability in actual operation.

[0101] Secondly, this application proposes an osteotomy guide plate fabrication apparatus applicable to the osteotomy guide plate fabrication method described in any of the preceding claims. Figure 7 This application provides a structural schematic diagram of an osteotomy guide plate fabrication device. Figure 7As shown, device 600 may include:

[0102] The acquisition unit 610 is used to acquire magnetic resonance imaging data of the first target area and computed tomography data of the second target area.

[0103] The generation unit 620 is used to generate data based on magnetic resonance imaging data and computed tomography data.

[0104] Fabrication unit 630 is used to fabricate osteotomy guide plates based on the target skeletal model.

[0105] Thirdly, this application proposes an electronic device. Figure 8 This is a structural schematic diagram of an electronic device 700 provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device 700 includes a processor 710 and a memory 720, wherein the memory 720 stores computer program instructions, which are executed by the processor 710 to perform the osteotomy guide plate fabrication method of any of the first aspects.

[0106] Fourthly, this application also provides a storage medium storing program instructions, which, when executed, perform the osteotomy guide plate fabrication method according to any one of the first aspects. The storage medium may, for example, include a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0107] Those skilled in the art can understand the specific details and beneficial effects of the osteotomy guide fabrication device, electronic equipment, and storage medium by reading the above description of the osteotomy guide fabrication method, and will not be repeated here for the sake of brevity.

[0108] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0110] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0111] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. An osteotomy guide fabrication method, characterized by, The method comprises: obtaining magnetic resonance imaging data of a first target site and computed tomography data of a second target site; generating a target bone model according to the magnetic resonance imaging data and the computed tomography data; manufacturing an osteotomy guide plate according to the target bone model.

2. The method of claim 1, wherein: Further comprising: generating a segmentation result of the magnetic resonance imaging data based on component structure information of the first target site; generating a first target site bone model according to the segmentation result, wherein the first target site bone model comprises a respective component structure model of the first target site.

3. The method of claim 2, wherein: Further comprising: extracting a cartilage bone model from the first target site bone model; generating a second target site bone model according to the computed tomography data; generating a target bone model according to the cartilage bone model and the second target site bone model.

4. The method of claim 3, wherein, The manufacturing of the osteotomy guide plate according to the target bone model comprises: determining data information required for manufacturing the osteotomy guide plate according to the target bone model, wherein the data information comprises femoral head rotation center data, intercondylar notch data, tibial tuberosity data, ankle hole center data, femoral external rotation angle data, and tibial posterior inclination angle data; determining size information and positioning point information of the osteotomy guide plate according to the data information to manufacture the osteotomy guide plate.

5. The method of claim 1-4, wherein, Further comprising: obtaining personal information of a patient; determining compensation information of the osteotomy guide plate according to the personal information and the data information; determining size information and positioning point information of the osteotomy guide plate according to the compensation information to manufacture the osteotomy guide plate.

6. The method of claim 5, wherein: Further comprising: obtaining budget information of a patient; determining first material information of the osteotomy guide plate according to the budget information, wherein the first material information is material information of a first preset part of the osteotomy guide plate; determining second material information of the osteotomy guide plate according to the compensation information, wherein the second material information is material information of a second preset part of the osteotomy guide plate.

7. The method of claim 6, wherein: The area of the first preset part is greater than the area of the second preset part.

8. An osteotomy guide fabrication device, characterized by, The device is suitable for the osteotomy guide plate manufacturing method as claimed in any one of claims 1 to 7, and the device comprises: an obtaining unit configured to obtain magnetic resonance imaging data of a first target site and computed tomography data of a second target site; a generating unit configured to generate a target bone model according to the magnetic resonance imaging data and the computed tomography data; a manufacturing unit configured to manufacture an osteotomy guide plate according to the target bone model.

9. An electronic device, comprising: The device comprises a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are used to execute the osteotomy guide plate manufacturing method as claimed in any one of claims 1 to 7 when executed by the processor.

10. A storage medium on which program instructions are stored, characterized in that, The program instructions are used to execute the osteotomy guide plate manufacturing method as claimed in any one of claims 1 to 7 when executed.

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