Method, device, medium and product for positioning and generating titanium plates for internal fixation of jawbones

Abstract

The present application discloses a method, device, medium, and product for positioning and generating a titanium plate for mandibular internal fixation, relating to the field of medical technology. The method performs a CT or CBCT scan on a patient's bone surface to determine a digital model of the bone surface; performs surface smoothing processing on the mesh data to determine each grid point corresponding to the processed mesh data; determines the normal vector of each triangular facet based on the adjacent triangular facets formed by each grid point, and further determines the bone surface thickness value corresponding to each grid point; maps the bone surface thickness value corresponding to each grid point to the digital model of the bone surface to generate a thickness map, identifies the mesh area corresponding to the maximum bone surface thickness value, and determines the target triangular facet corresponding to the mesh area on the digital model of the bone surface; generates the location of each titanium plate nail hole based on the normal vector of the target triangular facet; and generates a titanium plate for mandibular internal fixation based on the location of each titanium plate nail hole using a bone surface mesh facet offset method. The method improves the matching accuracy between the titanium plate for mandibular internal fixation and the patient's mandible.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method, device, medium and product for positioning and generating a titanium plate for mandibular internal fixation. Background Art

[0002] Titanium plates are important instruments for maxillofacial surgery in the treatment of maxillofacial developmental deformities and trauma. They are widely used in the treatment of adolescent and adult patients and are suitable for cases of jaw deformities caused by genetics, developmental abnormalities, or trauma. The titanium alloy material gives titanium plates the characteristics of lightness, high strength, and corrosion resistance. They have good biocompatibility and can effectively reduce postoperative complications, ensure the stability of treatment effects, and have little impact on patient comfort after surgery. Currently, standard titanium plates are mainly used, and doctors need to manually bend them during internal fixation during surgery. During treatment, titanium plates are surgically fixed to the cortical areas of the maxillary and mandibular bones to fix the bone ends and provide stress support.

[0003] With the advancement of orthognathic surgery, the shape of titanium plates has been continuously optimized. However, it is impossible to automatically generate titanium plates personalized for each patient and ensure that they adhere closely to the bone surface, and manual design and operation are complex. Currently, the positioning and placement methods of standard titanium plates still have significant flaws. They mainly rely on the surgeon's subjective judgment of the thickest bone wall during surgery, and then manually bend and attach a titanium plate of uniform specifications to the bone surface. This process specifically involves the surgeon first using experience to locate the area on the patient's jaw where the bone surface is believed to be thickest. The surgeon then manually bends the standard titanium plate to make it fit the bone surface as closely as possible, and finally completes the fixation of the titanium plate. However, this method lacks precise quantitative basis and is prone to human error. It is difficult to ensure that each nail hole is located at the thickest point of the bone surface, which greatly reduces the accuracy of the match between the titanium plate and the patient's jaw. In addition, the morphology of standard titanium plates is limited, which affects the accuracy of the surgery and the treatment effect, and prolongs the patient's recovery time. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium and product for positioning and generating a mandibular internal fixation titanium plate, which can solve the problem of inaccurate generation and positioning of a mandibular internal fixation titanium plate caused by manual operation.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In the first aspect, the present application provides a method for positioning and generating a titanium plate for internal fixation of the mandible, comprising: performing a CT or CBCT scan on the patient's bone surface to determine a digital model of the bone surface; the digital model of the bone surface is stored in the form of grid data; the bone surface includes the mandible; performing surface smoothing processing on the grid data to determine each grid point corresponding to the processed grid data; determining the normal vector of each triangular facet formed by each grid point; determining the bone surface thickness value corresponding to each grid point according to the normal vector of each triangular facet; mapping the bone surface thickness value corresponding to each grid point to the digital model of the bone surface to generate a thickness map; based on the thickness map, identifying the grid area corresponding to the maximum bone surface thickness value, and inversely mapping it to the digital model of the bone surface to determine the target triangular facet corresponding to the grid area on the digital model of the bone surface; generating the positioning of each titanium plate nail hole based on the normal vector of the target triangular facet and a preset thickness threshold range; generating a titanium plate for internal fixation of the mandible by utilizing the bone surface grid facet offset method according to the positioning of each titanium plate nail hole.

[0007] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for positioning and generating a titanium plate for mandibular internal fixation.

[0008] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for positioning and generating a titanium plate for mandibular internal fixation.

[0009] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for positioning and generating a titanium plate for mandibular internal fixation.

[0010] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0011] The present application performs CT or CBCT scanning on the patient's bone surface, determines the digital model of the bone surface, performs surface smoothing processing on the mesh data corresponding to the digital model of the bone surface, determines each grid point corresponding to the processed mesh data, and realizes automatic analysis of the mesh data. Based on the adjacent triangular facets formed by each grid point, the normal vector of each triangular facet is determined, and based on the normal vector of the triangular facet, the bone surface thickness value corresponding to each grid point is determined, and the bone surface thickness value corresponding to each grid point is mapped to the digital model of the bone surface to generate a thickness map, and then through the thickness map, the mesh area corresponding to the maximum bone surface thickness value is automatically and accurately identified, and then through inverse mapping, the target triangular facet corresponding to the mesh area on the digital model of the bone surface is determined, and finally, based on the normal vector of the target triangular facet and the preset thickness threshold range, the positioning of each titanium plate nail hole is generated. Accurate positioning of the titanium plate is achieved. This application quickly and accurately identifies the grid area corresponding to the maximum bone surface thickness value, and then intelligently finds the corresponding target triangular facet on the digital bone surface model, thereby realizing and generating a mandibular internal fixation titanium plate design that precisely matches it, avoiding interference from human factors, and improving the matching accuracy between the mandibular internal fixation titanium plate and the patient's jaw, thereby improving the success rate of the operation and promoting better recovery of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of the process of generating and positioning a maxillary protraction titanium plate provided in an embodiment of the present application.

[0014] Figure 2 This is a schematic diagram of feature points in a grid area corresponding to the maximum bone surface thickness value identified based on the thickness map provided in an embodiment of the present application.

[0015] Figure 3 This is a schematic diagram of the generated hollow cylinder provided in the examples of this application.

[0016] Figure 4 This is a schematic diagram of the titanium plates formed after the bridge bodies provided in the embodiment of the present application are connected. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] like Figure 1 As shown, the present application provides a method for positioning and generating a titanium plate for mandibular internal fixation, comprising:

[0020] Step 101: Perform CT or CBCT scanning on the patient's bone surface to determine a digital model of the bone surface; the digital model of the bone surface is stored in the form of grid data; the bone surface includes the jaw.

[0021] Step 102: performing surface smoothing processing on the grid data, and determining each grid point corresponding to the processed grid data.

[0022] In some embodiments, step 102 specifically includes: for any grid point and a preset range, using a Gaussian kernel function to determine the weights of each neighboring grid point within the preset range; the Gaussian kernel function is determined based on the distance between each neighboring grid point and the grid point; the weights of each neighboring grid point are normalized; based on the weights of each processed neighboring grid point, the grid point and each neighboring grid point are weighted summed to determine the updated grid point and each neighboring grid point; each of the updated grid points and each neighboring grid point is used as each grid point corresponding to the processed grid data.

[0023] Step 103: Based on the adjacent triangular patches formed by each grid point, determine the normal vector of each triangular patch.

[0024] Step 104: Determine the bone surface thickness value corresponding to each grid point based on the normal vector of each triangular facet.

[0025] In some embodiments, step 104 specifically includes: for the normal vector of any triangular face and any vertex of the triangular face, determining the vertical distance from the vertex to the other side of the bone surface along the direction of the normal vector, and using the vertical distance as the bone surface thickness value corresponding to the vertex; using the bone surface thickness value corresponding to each vertex as the bone surface thickness value corresponding to each grid point.

[0026] In some embodiments, for the normal vector of any triangular face and any vertex of the triangular face, determining the vertical distance from the vertex along the direction of the normal vector to the other side of the bone surface specifically includes: moving the vertex along the direction of the normal vector according to a preset magnification to form a query point; using KDTree to find the nearest neighbor of the query point, determine the distance between the query point and the nearest neighbor, and use the distance between the query point and the nearest neighbor as the vertical distance from the vertex along the direction of the normal vector to the other side of the bone surface.

[0027] Step 105: Mapping the bone surface thickness value corresponding to each grid point to the bone surface digital model to generate a thickness map.

[0028] In some embodiments, step 105 specifically includes: based on the bone surface thickness value corresponding to each grid point, using the viridis color mapping method, mapping the bone surface thickness value corresponding to each grid point to the bone surface digital model to generate a thickness map.

[0029] Step 106: Based on the thickness map, identify the mesh area corresponding to the maximum bone surface thickness value, and reversely map it to the bone surface digital model to determine the target triangle patch corresponding to the mesh area on the bone surface digital model.

[0030] Among them, through the preset maximum bone surface thickness threshold, the grid area corresponding to the maximum bone surface thickness value can be identified in the thickness map.

[0031] Step 107: Generate the location of each titanium plate nail hole based on the normal vector of the target triangle facet and a preset thickness threshold range.

[0032] Step 108: Based on the positioning of the nail holes of each titanium plate, a bone surface mesh patch offset method is used to generate a mandibular internal fixation titanium plate.

[0033] In some embodiments, step 107 specifically includes: based on the normal vector of the target triangular facet, in a bottom-up order and a preset spacing, within a preset thickness threshold range, using a constructive solid geometry algorithm to generate a preset number of hollow cylinders; and positioning the preset number of hollow cylinders as nail holes for each titanium plate.

[0034] In practical applications, the thickest area is found on the bone surface, and then four points are generated from top to bottom in this thickest area. The four points are on four triangular facets (the four points are generated randomly). According to the normal vector of each triangular facet, a hollow cylinder with an axis parallel to the normal vector is generated in that direction, and the triangular facet is at the center of the hollow cylinder.

[0035] In some embodiments, step 108 specifically includes: based on the positioning of each titanium plate nail hole, using the bone surface mesh patch offset method to generate each titanium plate nail hole connected to the bridge body between each titanium plate nail hole; based on the each titanium plate nail hole connected to the bridge body, generating a mandibular internal fixation titanium plate.

[0036] Specifically, in steps 107 and 108, based on the generated titanium plate holes, a bridge connecting the holes is generated using a bone mesh offset method, resulting in a personalized titanium plate. Because the constructed geometric entity penetrates the jaw model, the personalized titanium plate must be removed from the jaw to generate an intelligent titanium plate for jaw internal fixation. This intelligent titanium plate is then 3D printed and placed in place within the jaw.

[0037] In practice, existing titanium plate reconstruction and production rely entirely on manual work by doctors or technicians. Manual identification of the thickest bone wall lacks objective criteria, leading to significant variability between operators. The resulting design of the titanium plate's holes and morphology also varies from person to person, making the process tedious and tiring. These factors inevitably lead to human error, ultimately compromising the precision of the titanium plate's fit with the patient's jawbone, impacting surgical accuracy and treatment effectiveness, and prolonging recovery time.

[0038] From identifying the thickest bone wall to completing titanium plate production, every step requires manual intervention. This complex manual process, including finding the location, designing the shape, and bending the titanium plate, consumes a significant amount of time. During emergency surgeries, this inefficiency can hinder the timely provision of appropriate titanium plates, potentially delaying optimal treatment and adversely affecting the patient's condition.

[0039] Manual design and production of titanium plates requires specialized technicians, resulting in high labor costs. Furthermore, they rely on expensive, high-precision equipment. Furthermore, long production cycles increase time costs. These factors contribute to the high cost of titanium plate production, increasing the operational burden on medical institutions and indirectly leading to increased treatment costs and financial pressure on patients.

[0040] This application introduces automatic thickness detection of digital models, thickness map generation, and reverse titanium plate automatic modeling algorithms, using computer programs and mathematical models to automatically analyze and process digital bone surface model data. Through precise algorithmic calculations, the thickest bone wall location is automatically identified and a titanium plate design precisely matched to it is generated, avoiding human interference and improving the matching accuracy between the titanium plate and the patient's jaw, thereby increasing the success rate of surgery and promoting better patient recovery.

[0041] By using automated design and 3D printing technology, the entire process, from obtaining the digital model of the bone surface to generating the titanium plate, is automated and quickly processed. Compared with traditional manual operations, the cycle from design to production is greatly shortened. In emergency surgery situations, the required titanium plates can be quickly provided to the clinic to ensure that patients receive timely and effective treatment. Automated production reduces dependence on a large number of professional and technical personnel and reduces labor costs; at the same time, it reduces the need for some expensive equipment, shortens the production cycle, and reduces time costs. These changes make the production of titanium plates more economical and efficient, reducing the operating costs of medical institutions and the economic burden on patients. This application mainly uses the thickness map of the digital model to reversely generate and position the titanium plate to achieve the matching accuracy between the titanium plate and the patient's jaw. The specific process is as follows.

[0042] First, some technical terms are explained:

[0043] Digital bone surface model: A three-dimensional model of the bone surface obtained through CT or CBCT scanning, presented in the form of mesh data (point cloud or triangular mesh), accurately records the geometric shape, position and other information of the bone surface, and is the basis for subsequent analysis and processing.

[0044] Surface smoothing: The scanning process generates noise, which can cause irregularities on the mesh surface. Surface smoothing involves applying a specific algorithm to the mesh to eliminate this noise, resulting in a smoother surface and accurate thickness calculations. Common algorithms, such as the Laplace smoothing algorithm, achieve surface smoothing by adjusting vertex positions to approximate the average position of their neighboring vertices.

[0045] Normal vector: A vector perpendicular to the plane of the bone surface triangular mesh of the digital bone surface model, reflecting the directional characteristics of the bone surface at that point. In this application, the normal vector is calculated by the cross product of the vectors of adjacent triangular facets to determine the direction of thickness calculation.

[0046] Thickness Map: This image displays the thickness values of each point on the bone surface as a color map on the digital bone model. Different colors represent different thicknesses, making it easy to visually observe the thickness distribution of the bone surface. For example, warm colors indicate thicker areas, while cool colors indicate thinner areas.

[0047] CSG algorithm (Constructive Solid Geometry): A solid modeling algorithm that combines simple geometric entities (such as cubes and spheres) through Boolean operations (union, intersection, and difference) to construct complex three-dimensional models. This application is used to design the shape of titanium plates.

[0048] KDTree (K-Dimensional Tree): A data structure that stores and quickly retrieves K-dimensional spatial data. In this application's thickness calculation, it's used to quickly find the nearest point along the normal vector, improving computational efficiency.

[0049] Step 1: Obtain a digital model of the bone surface.

[0050] Specifically, CT or CBCT scanning, along with laser scanning, structured light scanning, or CT / MRI, can be used to comprehensively scan the patient's bone surface (primarily the jawbone). Laser scanning uses a laser beam to measure surface distances, acquiring highly accurate three-dimensional data. Structured light scanning projects specific patterns and analyzes the image to reconstruct the object's shape. CT / MRI can capture information about the internal structure of bone tissue, providing a data foundation for subsequent processing. The scanned model is stored as a mesh, typically a point cloud or triangulated mesh, containing detailed information about the bone surface geometry, vertex coordinates, and connectivity.

[0051] Among them, the main one is CT scanning, with a scanning layer thickness of less than 1mm. The scanning range is the craniomaxillofacial area, including the entire mandible and maxilla. The CT scanning method with a small layer thickness can effectively and automatically reconstruct the jaw and avoid the loss of bone thickness during reconstruction.

[0052] Step 2: Mesh data processing - surface smoothing.

[0053] Specifically, due to the influence of equipment accuracy, environmental factors, etc. during the scanning process, the acquired mesh data inevitably contains noise, which will cause irregular small fluctuations on the model surface. If the thickness calculation is performed directly based on such a noisy model, the calculation results will be inaccurate. Therefore, the mesh surface needs to be smoothed. Commonly used smoothing algorithms can be used: such as the Laplace smoothing algorithm and the Taubin algorithm. The Laplace smoothing algorithm calculates the average position of each vertex and its neighboring vertices, and moves the vertex to the average position by a certain proportion, thereby making the model surface smoother; the Taubin algorithm, based on Laplace smoothing, adjusts the iterative parameters to better balance the relationship between surface smoothness and model feature retention.

[0054] This embodiment uses Gaussian smoothing, which is also widely used in 3D mesh processing. Smoothing is achieved by taking a weighted average of each vertex of a triangle and its neighborhood, typically using a Gaussian kernel function to define the weights. The advantage of Gaussian smoothing is that it can retain important geometric features while removing noise, making it particularly suitable for smoother surfaces.

[0055] Among them, the key parameter of Gaussian smoothing is the standard deviation (σ, Sigma), which controls the width of the Gaussian kernel and thus affects the strength of the smoothing. Specifically, the formula of the Gaussian function is as follows:

[0056]

[0057] in: For the i The weight of the neighboring vertices; For the i The distance between the neighboring vertices and the current vertex; σ is the standard deviation of the Gaussian distribution, which determines the width of the weight distribution and thus affects the smoothing effect.

[0058] 1. σ Selection: A smaller standard deviation preserves more detail and is suitable for complex shapes where important features do not need to be lost. For example, in a digital bone model, it may be necessary to preserve more local details and edge information in the jaw mesh area. Typically, this value is set to 0.1-0.2 times the mesh size.

[0059] 2. Neighborhood size: The neighborhood size (i.e., the number of points that affect smoothing) is usually selected to be between 3 times σ. If the neighborhood size is too small, the smoothing effect will be insignificant, while if it is too large, the model may be over-smoothed, resulting in loss of details.

[0060] 3. Iterations: The number of Gaussian smoothing iterations (i.e., how many times smoothing is applied to each triangle vertex) also affects the final result. Generally, three iterations achieve good smoothing results. If noise is noticeable or strong denoising is required, increase the number of iterations appropriately.

[0061] Step 3: Calculate thickness.

[0062] Specifically, the normal vector calculation includes calculating the surface normal vector for each grid point by using the geometric information of its adjacent triangle patches through vector cross product.

[0063] The thickness calculation method defines bone thickness as the perpendicular distance from a point on a triangular patch along the normal vector of the triangular patch to the other side of the bone surface. To calculate thickness, after calculating the normal vector, a query point (search_point) is extended in the direction of the normal vector. Setting the normal vector magnification factor (currently set to 10) ensures accurate bone thickness measurements of the anterior wall of the maxillary sinus and ensures that bone thickness measurements near the zygomatic alveolar ridge do not include thickness of the posterior portion of the maxillary bone that is not part of the maxillary bone, thereby accurately generating a bone thickness map. A KDTree data structure is used to find the nearest point (R) in the model to the query point (Q). The distance from point (P) to point (R) is the bone thickness at that point. Choosing an appropriate normal vector magnification factor is crucial. A too small factor may prevent the query point from crossing the bone surface to find the nearest point on the other side, resulting in an underestimation of the thickness calculation result. A too large factor may introduce excessive extraneous points, affecting calculation efficiency and accuracy.

[0064] Step 4: Identify the thickest part.

[0065] Thickness Map Generation: The calculated thickness values for each point are mapped onto the 3D model to generate a thickness map. Choosing an appropriate color mapping scheme is crucial when generating a thickness map. This application utilizes the Viridis color mapping scheme. A key feature of the Viridis color mapping design is its perceptual uniformity. In other words, changes in data values are visually linear, with smooth and uniform color transitions. This is crucial for conveying subtle differences in data, particularly in scientific data visualizations requiring high precision. Furthermore, Viridis uses a color spectrum ranging from purple to yellow, ensuring that all color variations are easily discernible for users with diverse backgrounds and color blindness. This avoids the visual bias introduced by many traditional color mappings (such as jet), which often make the difference between high and low values difficult to discern. It maps thickness values from minimum to maximum using a gradient color scheme, from cool colors (e.g., blue) to warm colors (e.g., red). This allows for intuitive identification of thickness differences at different locations on the bone surface through color changes on the thickness map, facilitating subsequent analysis and processing.

[0066] Maximum thickness detection: By traversing all thickness values in the thickness map, find the grid area corresponding to the largest thickness value. This grid area is the thickest part of the bone surface. Select four feature points from top to bottom in this part, such as Figure 2 As shown in the figure, in actual programming, a loop structure can be used to compare the thickness map data point by point, recording the maximum value and its corresponding position information. To improve detection efficiency, optimization algorithms such as the divide-and-conquer method can also be used to divide the thickness map into multiple sub-regions, search for the maximum value in each sub-region, and finally merge the results to reduce the number of comparisons and increase the search speed.

[0067] Step 5: Automatic generation of titanium plates.

[0068] like Figure 3 As shown, after the thickest part of the bone surface is identified using the thickness map, the four points of the thickest part marked on the bone surface are reversely mapped as the implantation sites of the titanium nails. The titanium plate morphology is designed using a modeling algorithm (such as the CSG algorithm), and four hollow cylinders are automatically generated. For example, the cylinder height is 0.8 mm, the outer diameter is 5.5 mm, and the hollow inner diameter for placing the titanium nail is 3.0 mm. A bone surface offset cube is generated between the four hollow cylinders to connect the four hollow cylinders. The cube height is 0.8 mm, the width is 3 mm, and the length is automatically generated according to the cylinder spacing to achieve automated design.

[0069] Reference Figure 3 and Figure 4 Constructive Solid Geometry (CSG) technology first determines some basic geometric shapes, such as a hollow cylinder representing the titanium plate's holes and a rectangular parallelepiped representing the main body of the plate. Boolean operations (such as union, which combines the cylinder and the rectangular parallelepiped to form the basic shape of the titanium plate with the holes; intersection, which rounds the edges of the titanium plate to ensure a better fit to the bone surface and avoid damaging surrounding tissue; and difference, which removes unnecessary areas to optimize the plate structure) are then used to construct a titanium plate shape that meets clinical needs. The design process also considers factors such as the titanium plate's mechanical properties and conformity to the bone surface. Based on the curvature of the bone surface and the load conditions, the thickness of the titanium plate and the distribution of the holes (i.e., the hollow cylinders) are adjusted to ensure that the titanium plate provides sufficient traction while maintaining a stable fixation on the bone surface. The holes are located at the four thickest points.

[0070] Manufacturing and Printing: Based on the designed titanium plate model, 3D printing technology, such as Selective Laser Melting (SLM), is used to automatically produce the titanium plate. 3D printing technology can precisely produce the desired titanium plate by depositing materials layer by layer according to the digital model. During the printing process, selecting the appropriate printing material (such as a specific titanium alloy with good biocompatibility, strength, and corrosion resistance) and printing parameters (such as layer thickness, printing speed, and printing temperature) is crucial. Layer thickness affects the surface quality and precision of the titanium plate. Smaller layer thicknesses produce a smoother surface but increase printing time. Printing speed affects production efficiency, but excessively high speeds can lead to reduced print quality and problems such as loose interlayer bonding. Printing temperature affects the material's fluidity and curing properties. The appropriate temperature ensures uniform material deposition and improves the mechanical properties of the titanium plate.

[0071] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0072] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0073] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0074] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0075] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0076] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0077] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for positioning and generating a titanium plate for mandibular internal fixation, characterized in that: include: Perform CT or CBCT scans on the patient's bone surface to determine the digital model of the bone surface; The digital bone surface model is stored in the form of grid data; The bone surface includes the jaw; Performing surface smoothing processing on the grid data, and determining each grid point corresponding to the processed grid data; Determine the normal vector of each triangle patch based on the adjacent triangle patches formed by each grid point; Determining the bone surface thickness value corresponding to each grid point according to the normal vector of each triangular facet, specifically comprising: for the normal vector of any triangular facet and any vertex of the triangular facet, determining the vertical distance from the vertex to the other side of the bone surface along the direction of the normal vector, and using the vertical distance as the bone surface thickness value corresponding to the vertex; using the bone surface thickness value corresponding to each vertex as the bone surface thickness value corresponding to each grid point; Mapping the bone surface thickness value corresponding to each grid point to the bone surface digitized model to generate a thickness map; Based on the thickness map, identifying the mesh area corresponding to the maximum bone surface thickness value, and reverse mapping it to the bone surface digital model, and determining the target triangular facet corresponding to the mesh area on the bone surface digital model; Based on the normal vector of the target triangular facet and the preset thickness threshold range, the positioning of each titanium plate nail hole is generated, specifically including: based on the normal vector of the target triangular facet, in a bottom-up order and a preset spacing, within the preset thickness threshold range, using a constructive solid geometry algorithm to generate a preset number of hollow cylinders, specifically including: finding the thickest area on the bone surface, generating four points from top to bottom in the thickest area according to the preset spacing, and the four points are respectively on the target triangular facet; based on the normal vector of the target triangular facet, generating a hollow cylinder with an axis parallel to the normal vector on the normal vector; wherein the preset number includes four; using the preset number of hollow cylinders as the positioning of each titanium plate nail hole; According to the positioning of the nail holes of each titanium plate, a bone surface mesh patch offset method is used to generate a mandibular internal fixation titanium plate; the bone surface mesh patch offset method includes a uniform offset method.

2. The method for positioning and generating a titanium plate for mandibular internal fixation according to claim 1, characterized in that: Performing surface smoothing processing on the grid data and determining each grid point corresponding to the processed grid data specifically includes: For any grid point and a preset range, a Gaussian kernel function is used to determine the weights of each neighboring grid point within the preset range; the Gaussian kernel function is determined based on the distance between each neighboring grid point and the grid point; Normalize the weights of each neighboring grid point; Based on the processed weights of each neighboring grid point, performing weighted summation on the grid point and each neighboring grid point to determine an updated grid point and each neighboring grid point; Each of the updated grid points and each of the neighboring grid points is used as each grid point corresponding to the processed grid data.

3. The method for positioning and generating a titanium plate for mandibular internal fixation according to claim 1, characterized in that: According to the positioning of the nail holes of each titanium plate, a bone surface mesh patch offset method is used to generate a mandibular internal fixation titanium plate, specifically including: Based on the positioning of the titanium plate nail holes, the titanium plate nail holes for bridge connection are generated between the titanium plate nail holes using a bone surface mesh patch offset method; Based on the nail holes of the titanium plates connected to the bridge body, a mandibular internal fixation titanium plate is generated.

4. The method for positioning and generating a titanium plate for mandibular internal fixation according to claim 1, characterized in that: Mapping the bone surface thickness value corresponding to each grid point to the bone surface digital model to generate a thickness map, specifically including: Based on the bone surface thickness value corresponding to each grid point, the viridis color mapping method is used to map the bone surface thickness value corresponding to each grid point to the bone surface digital model to generate a thickness map.

5. The method for positioning and generating a titanium plate for mandibular internal fixation according to claim 1, characterized in that: For any normal vector of a triangular face and any vertex of the triangular face, determining the vertical distance from the vertex to the other side of the bone surface along the direction of the normal vector specifically includes: Moving the vertex along the normal vector direction according to a preset magnification to form a query point; Using KDTree, find the nearest neighbor of the query point, determine the distance between the query point and the nearest neighbor, and use the distance between the query point and the nearest neighbor as the vertical distance from the vertex to the other side of the bone surface along the normal vector direction.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for positioning and generating a titanium plate for mandibular internal fixation according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for positioning and generating a titanium plate for mandibular internal fixation according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for positioning and generating a titanium plate for mandibular internal fixation according to any one of claims 1 to 5 is implemented.

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