A method, device, electronic device and storage medium for determining the edge line of a denture

Abstract

The present invention provides a denture edge determination method, device, electronic device, and storage medium, belonging to the technical field of denture restoration. The method comprises: obtaining three-dimensional oral mesh data; and using an AI model to identify the denture edge based on the three-dimensional oral mesh data to obtain the denture edge. The denture edge determination method of the present invention utilizes an AI model to automatically identify the denture edge based on the three-dimensional oral mesh data, which is highly intelligent and improves the accuracy and robustness of denture edge identification.

Description

Technical Field

[0001] The present invention relates to the technical field of denture restoration, and in particular to a denture edge line determination method, device, electronic equipment and storage medium. Background Art

[0002] In dentistry, especially when constructing complete or removable partial dentures, identification of the myostatic line is crucial to ensure denture retention and function.

[0003] Currently, when creating complete dentures or removable partial dentures in dental clinics, doctors typically use a manual impression-taking process to shape the edges and obtain the denture margins. However, this manual impression-taking method has several drawbacks, including potential discomfort for patients and low impression efficiency. Furthermore, it requires high technical skills from the doctor, who must manually draw the lines based on the impression. This results in significant uncertainty in the denture margins obtained by different doctors and is significantly influenced by subjective factors. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a denture edge line determination method, device, electronic device and storage medium to alleviate the technical problems of poor accuracy of denture edge lines determined by traditional technologies and low intelligence of the determination process.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a denture edge line, comprising:

[0006] Obtaining oral three-dimensional mesh data obtained by scanning the oral cavity with an oral scanning device;

[0007] An AI model is used to identify the denture edge line of the oral three-dimensional grid data to obtain the denture edge line.

[0008] Furthermore, an AI model is used to identify the denture edge line of the oral three-dimensional mesh data, including:

[0009] Segmenting the oral three-dimensional mesh data using a region segmentation model to obtain a denture coverage area;

[0010] The denture margin line is determined based on the denture coverage area.

[0011] Furthermore, the denture coverage area includes a myostatic area, and determining the denture edge line based on the denture coverage area includes:

[0012] A target three-dimensional myostatic line is determined based on the myostatic area, and a denture edge line is determined based on the target three-dimensional myostatic line.

[0013] Furthermore, after obtaining the three-dimensional mesh data of the oral cavity obtained by scanning the oral cavity with an oral scanning device, the method further includes:

[0014] Preprocessing the oral cavity three-dimensional mesh data to obtain preprocessed oral cavity three-dimensional mesh data;

[0015] The AI model is used to identify the denture edge line of the preprocessed oral three-dimensional grid data to obtain the denture edge line.

[0016] Furthermore, after obtaining the oral cavity three-dimensional mesh data obtained by scanning the oral cavity with an oral scanning device, and before preprocessing the oral cavity three-dimensional mesh data, the method further includes:

[0017] Determining whether the oral three-dimensional grid data completely contains the specified area;

[0018] If not all of them are included, a prompt message is issued, and the oral three-dimensional mesh data obtained by scanning the oral cavity with the oral scanning device is re-acquired until the specified area is completely included in the oral three-dimensional mesh data.

[0019] Furthermore, the method further comprises:

[0020] determining the degree of alveolar ridge resorption based on the oral three-dimensional mesh data;

[0021] The accuracy of the denture edge line is determined according to the degree of alveolar ridge absorption, and the accuracy is output to a display screen or voice broadcast.

[0022] Furthermore, determining a target three-dimensional myostatic line based on the myostatic area includes:

[0023] Performing a smoothing optimization process on the edge of the myostatic area to obtain a myostatic area with a smooth contour;

[0024] drawing a three-dimensional myostatic line according to the edge of the myostatic area with a smooth contour;

[0025] The three-dimensional myostatic line is smoothed to obtain the target three-dimensional myostatic line.

[0026] Furthermore, the edge of the myostatic area is smoothed and optimized, including:

[0027] A graph cut algorithm is used to perform smoothing optimization processing on the edge of the myostatic region to obtain the myostatic region with a smooth contour;

[0028] Alternatively, the three-dimensional myostatic lines are smoothed, comprising:

[0029] Determining a target smoothing algorithm according to the morphological characteristics and processing technology of the denture, wherein the target smoothing algorithm includes any one of the following: a Laplace smoothing algorithm, a least squares smoothing algorithm, and a curvature-based feature smoothing algorithm;

[0030] The target smoothing algorithm is used to smooth the three-dimensional myostatic line to obtain the target three-dimensional myostatic line.

[0031] Furthermore, the training process of the region segmentation model includes:

[0032] Acquire an oral three-dimensional mesh data sample, wherein the oral three-dimensional mesh data sample is marked with a denture coverage area;

[0033] Preprocessing the oral cavity three-dimensional mesh data sample to obtain a preprocessed oral cavity three-dimensional mesh data sample;

[0034] The preprocessed oral three-dimensional grid data samples are used to train the original region segmentation model to obtain the region segmentation model.

[0035] Furthermore, the oral three-dimensional mesh data sample is preprocessed, including:

[0036] performing data processing on the oral cavity three-dimensional mesh data sample to obtain a data-processed oral cavity three-dimensional mesh data sample;

[0037] performing vertex optimization on the oral three-dimensional mesh data sample after the data processing to obtain a vertex-optimized oral three-dimensional mesh data sample;

[0038] Performing feature calculation on the oral cavity three-dimensional mesh data sample after vertex optimization to obtain the oral cavity three-dimensional mesh data sample after feature calculation;

[0039] The oral cavity three-dimensional mesh data sample after the feature calculation is used as the preprocessed oral cavity three-dimensional mesh data sample.

[0040] Furthermore, data processing is performed on the oral three-dimensional grid data sample, including:

[0041] performing data denoising on the oral cavity three-dimensional mesh data sample to obtain a denoised oral cavity three-dimensional mesh data sample;

[0042] Projecting the denoised three-dimensional oral cavity mesh data sample in the same direction to obtain a two-dimensional depth image;

[0043] Obtaining a dental arch curve by fitting the depth values in the two-dimensional depth image;

[0044] performing data alignment on the corresponding denoised oral three-dimensional mesh data samples with reference to the dental arch curve to obtain aligned oral three-dimensional mesh data samples;

[0045] The aligned oral three-dimensional mesh data samples are used as the oral three-dimensional mesh data samples after data processing.

[0046] Furthermore, feature calculation is performed on the oral cavity three-dimensional mesh data sample after vertex optimization, including:

[0047] Using the Laplacian operator of the grid to extract the features of different frequency domains of the oral three-dimensional grid data sample after the vertex optimization, to obtain the oral three-dimensional grid data sample with Laplacian features;

[0048] Performing multi-scale enhancement processing on the oral three-dimensional mesh data sample with Laplace features to obtain a multi-scale oral three-dimensional mesh data sample;

[0049] Using sparse coding to represent the multi-scale oral three-dimensional mesh data samples to obtain sparsely coded oral three-dimensional mesh data samples;

[0050] Calculating geometric features of the oral cavity three-dimensional mesh data sample represented by the sparse coding to obtain an oral cavity three-dimensional mesh data sample with geometric features, wherein the geometric features include: surface curvature and normal vector;

[0051] The oral three-dimensional mesh data sample with the geometric features is used as the oral three-dimensional mesh data sample after the feature calculation.

[0052] Furthermore, after the denture edge line is identified by using the AI model on the oral three-dimensional mesh data to obtain the denture edge line, the method further includes:

[0053] When a first instruction is detected, determining a first pose change of the denture edge line relative to the initial position, wherein the first instruction is an instruction from a user to adjust the pose of the denture edge line in the oral three-dimensional mesh data;

[0054] Determining a first adjusted posture of the denture edge line based on the first posture change and a preset reference condition, and updating and displaying the posture of the denture edge line based on the first adjusted posture of the denture edge line, wherein the preset reference condition includes one or more of: the denture edge line is aligned with the outer surface of the oral three-dimensional mesh data, the denture edge line conforms to simulated physiological movement, or the denture edge line is located within the denture coverage area;

[0055] When it is detected that the first posture change causes the denture edge line to not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or the posture of the denture edge line is not updated and a result of user reconfirmation is obtained and the posture of the denture edge line is adjusted according to the result of user reconfirmation; and / or

[0056] When it is detected that the first adjustment posture causes the denture edge line to not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or the posture of the denture edge line is not updated and the result of user re-confirmation is obtained and the posture of the denture edge line is adjusted according to the result of user re-confirmation.

[0057] Furthermore, the method further comprises:

[0058] generating a three-dimensional denture model according to the denture edge line and the oral three-dimensional mesh data, and sending the three-dimensional denture model to a 3D printing device for printing;

[0059] receiving a user request for modifying the three-dimensional denture model in real time;

[0060] According to the user request, the denture edge line and the oral three-dimensional mesh data, a revised denture edge line and / or a revised three-dimensional denture model is output.

[0061] In a second aspect, an embodiment of the present invention further provides a denture edge line determination device, comprising:

[0062] An acquisition unit, configured to acquire oral three-dimensional mesh data obtained by scanning the oral cavity with an oral scanning device;

[0063] The denture edge line recognition unit is used to use an AI model to perform denture edge line recognition on the oral three-dimensional grid data to obtain the denture edge line.

[0064] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0065] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0066] In an embodiment of the present invention, a method for determining a denture edge line is provided, comprising: obtaining three-dimensional oral mesh data obtained by scanning an oral cavity using an oral scanning device; and identifying a denture edge line using an AI model on the three-dimensional oral mesh data to obtain a denture edge line. As can be seen from the foregoing description, the method for determining a denture edge line of the present invention utilizes an AI model to automatically identify the denture edge line using the three-dimensional oral mesh data, which is highly intelligent and improves the accuracy and robustness of denture edge line identification. This alleviates the technical issues of poor accuracy and low intelligence in the determination process of denture edges determined using traditional techniques. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0068] Figure 1 A flowchart of a method for determining a denture edge line provided by an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of a target three-dimensional myostatic line provided by an embodiment of the present invention;

[0070] Figure 3 A schematic diagram of an oral 3D mesh data sample provided by an embodiment of the present invention;

[0071] Figure 4 A schematic diagram of another oral 3D mesh data sample provided by an embodiment of the present invention;

[0072] Figure 5 A schematic diagram of a denture edge line determination device provided by an embodiment of the present invention;

[0073] Figure 6 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] The accuracy of denture edge lines determined by traditional technology is poor and the determination process is not very intelligent.

[0076] Based on this, in the denture edge line determination method of the present invention, an AI model is used to automatically identify the denture edge line of the oral three-dimensional grid data, which is highly intelligent and improves the accuracy and robustness of denture edge line recognition.

[0077] To facilitate understanding of this embodiment, a denture edge line determination method disclosed in an embodiment of the present invention is first introduced in detail.

[0078] Example 1:

[0079] According to an embodiment of the present invention, an embodiment of a method for determining a denture edge line is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0080] Figure 1 FIG. 1 is a flow chart of a method for determining a denture edge line according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0081] Step S102, obtaining oral cavity three-dimensional mesh data obtained by scanning the oral cavity with an oral cavity scanning device;

[0082] Specifically, the three-dimensional oral mesh data is an intraoral digital impression, which includes the structure of the teeth, gums, and surrounding soft tissue. The oral cavity can be a patient's oral cavity, which may or may not contain some teeth. The oral scanning device can be an intraoral scanner or an extraoral scanner.

[0083] Step S104, using an AI model to identify the denture edge line of the oral three-dimensional mesh data to obtain the denture edge line;

[0084] Specifically, the above-mentioned AI model can be obtained by training the original AI model using oral three-dimensional mesh data samples marked with denture edge lines, and it has the ability to recognize denture edge lines; the above-mentioned AI model can be obtained by training the original AI model using oral three-dimensional mesh data samples marked with anatomical points, and it has the ability to recognize anatomical points; the above-mentioned AI model can also be other models, such as region segmentation models, anatomical point recognition models, etc.; the process will be described in detail below and will not be repeated here.

[0085] In an embodiment of the present invention, a method for determining a denture edge line is provided, comprising: obtaining three-dimensional oral mesh data obtained by scanning an oral cavity using an oral scanning device; and identifying a denture edge line using an AI model on the three-dimensional oral mesh data to obtain a denture edge line. As can be seen from the foregoing description, the method for determining a denture edge line of the present invention utilizes an AI model to automatically identify the denture edge line using the three-dimensional oral mesh data, which is highly intelligent and improves the accuracy and robustness of denture edge line identification. This alleviates the technical issues of poor accuracy and low intelligence in the determination process of denture edges determined using traditional techniques.

[0086] The above content briefly introduces the denture edge line determination method of the present invention. The specific contents involved are described in detail below.

[0087] In an optional embodiment of the present invention, an AI model is used to identify denture edge lines on oral 3D mesh data, specifically comprising the following steps:

[0088] (1) Use the regional segmentation model to segment the oral 3D mesh data to obtain the denture coverage area;

[0089] Specifically, the region segmentation model is a deep learning network capable of processing three-dimensional mesh data. In the present invention, the region segmentation model is a deep learning network capable of performing region segmentation on three-dimensional oral mesh data. Furthermore, the region segmentation model can be a denture coverage region segmentation model or a myostatic region segmentation model. When the region segmentation model is a myostatic region segmentation model, the resulting denture coverage region can be a myostatic region.

[0090] (2) Determine the denture edge line based on the denture coverage area.

[0091] Specifically, the denture coverage area includes: a myostatic area, and determining the denture edge line based on the denture coverage area specifically includes the following steps:

[0092] A target three-dimensional myostatic line is determined based on the myostatic area, and a denture margin line is determined based on the target three-dimensional myostatic line.

[0093] Specifically, after obtaining the target three-dimensional myostatic line, an equidistant line about 2 mm to 5 mm from the target three-dimensional myostatic line toward the gum is determined as the denture edge line.

[0094] Among them, the myostatic area can be divided into an upper and lower part by the denture edge line. The denture coverage area can refer to the entire myostatic area, or it can directly refer to the upper part of the myostatic area divided by the denture edge line. The upper part includes rigid areas such as gums or teeth. When wearing a complete denture (full denture) or a removable partial denture, the upper part is just covered by the denture. It should be noted that when the denture coverage area refers to the entire myostatic area, the myostatic area can be determined first, then the target three-dimensional myostatic line can be determined, and then the denture edge line can be obtained. When the denture coverage area refers to the upper part of the myostatic area divided by the denture edge line, the denture edge line can be directly determined by the denture coverage area.

[0095] The target myostatic line is the boundary between the static and dynamic myostatic regions. This is the area associated with masticatory muscle activity that needs to be considered during denture restoration. The myostatic region is where the mucosa remains stationary during physiological activities like chewing and speaking. Correctly identifying these areas helps design more stable and comfortable dentures.

[0096] In an optional embodiment of the present invention, an AI model is used to identify denture margins from three-dimensional oral mesh data. Specifically, the steps include: using an anatomical point recognition model to identify the three-dimensional oral mesh data to obtain anatomical points; and determining denture margins based on the anatomical points. The AI model automatically generates denture margins based on the anatomical points, eliminating the need for a region segmentation model.

[0097] In an optional embodiment of the present invention, after obtaining the three-dimensional mesh data of the oral cavity obtained by scanning the oral cavity with an oral scanning device, the method further includes the following steps:

[0098] (1) Preprocessing the oral cavity three-dimensional mesh data to obtain preprocessed oral cavity three-dimensional mesh data;

[0099] Specifically, the preprocessing here can include: data processing (including data denoising, data alignment), grid simplification, feature calculation (including Laplace feature calculation, geometric feature calculation) and sparse coding representation. The implementation process is the same as the relevant content below and will not be repeated here.

[0100] (2) The AI model is used to identify the denture edge line of the preprocessed oral three-dimensional mesh data to obtain the denture edge line.

[0101] In an optional embodiment of the present invention, after obtaining the three-dimensional mesh data of the oral cavity obtained by scanning the oral cavity with an oral scanning device, and before preprocessing the three-dimensional mesh data of the oral cavity, the method further includes the following steps:

[0102] (1) Determine whether the oral 3D mesh data contains all the specified areas;

[0103] Specifically, it can be done by automatically determining through AI whether the oral 3D mesh data contains all the specified areas, and then displaying a prompt to indicate whether the scan is complete. It can also be done by manually determining whether the oral 3D mesh data contains all the specified areas, to ensure that the oral 3D mesh data contains the complete myostatic line.

[0104] The above-mentioned designated areas include: lip frenulum, buccal frenulum, vestibular mucosal fold, inferior zygomatic line, buccal side of maxillary tuberosity, or the above-mentioned designated areas include: lip frenulum, buccal frenulum, maxillary tuberosity, pterygomaxillary notch, and the area 2 mm behind the maxillary fossa.

[0105] (2) If not all of them are included, a prompt message is issued, and the oral three-dimensional mesh data obtained by scanning the oral cavity with the oral scanning device is re-acquired until the specified area is completely included in the oral three-dimensional mesh data.

[0106] In an optional embodiment of the present invention, the method further comprises the following steps:

[0107] (1) Determine the degree of alveolar ridge resorption based on oral 3D mesh data;

[0108] Specifically, the degree of alveolar ridge projection can be determined based on the three-dimensional mesh data of the oral cavity, and then the degree of alveolar ridge resorption can be determined based on the degree of alveolar ridge projection. In addition, the degree of alveolar ridge resorption can also be manually input by the doctor. When implementing this, it is considered that gingival recession is often accompanied by alveolar bone resorption, which will affect the myostatic area and occlusal relationship. In order to accurately assess these changes, the dentist will use a mouth mirror and probe to perform a detailed examination to determine the condition of gingival recession and alveolar bone. The degree of alveolar ridge resorption is determined according to Atwood's grading system.

[0109] When the degree of alveolar ridge resorption is mild (e.g., when the degree of alveolar ridge resorption is at level one or two), it helps to achieve more accurate identification of the myostatic area, target three-dimensional myostatic line, and denture margin line.

[0110] (2) Determine the accuracy of the denture edge line based on the degree of alveolar ridge absorption, and output the accuracy to the display screen or voice broadcast.

[0111] Specifically, if the degree of alveolar ridge absorption is at level one or level two, the accuracy of the target three-dimensional myostatic line and denture edge line is determined to be accurate; if the degree of alveolar ridge absorption is greater than level two, the accuracy of the target three-dimensional myostatic line and denture edge line is determined to be inaccurate. Of course, the accuracy value can also be specifically displayed or voice broadcast, such as 90%, 80%, etc.

[0112] In an optional embodiment of the present invention, determining a target three-dimensional myostatic line based on the myostatic area specifically includes the following steps:

[0113] (1) Smoothing and optimizing the edge of the myostatic area to obtain a myostatic area with a smooth contour;

[0114] Specifically, the GraphCut algorithm is used to smooth and optimize the edges of the myostatic area to obtain a myostatic area with a smooth contour.

[0115] During implementation, graph cutting algorithms such as GraphCut are applied to finely smooth and optimize the edges of the myostatic area to ensure the smoothness and accuracy of the contour and obtain a myostatic area with a smooth contour.

[0116] (2) Draw three-dimensional myostatic lines based on the edges of the myostatic area with smooth contours;

[0117] Specifically, in the myostatic area with a smooth contour, the lower edge contour is the three-dimensional myostatic line.

[0118] (3) Smoothing the three-dimensional muscle static line to obtain the target three-dimensional muscle static line.

[0119] Specifically, the target smoothing algorithm is first determined according to the morphological characteristics and processing technology of the denture, wherein the target smoothing algorithm includes any one of the following: Laplace smoothing algorithm, least squares smoothing algorithm and curvature-based feature smoothing algorithm; then, the target smoothing algorithm is used to smooth the three-dimensional myostatic line to obtain the target three-dimensional myostatic line, such as Figure 2 As shown, the green curve is the target three-dimensional myostatic line, which shows two visualized oral three-dimensional mesh data and the corresponding target three-dimensional myostatic line.

[0120] When determining the target smoothing algorithm, the design requirements and material properties of the restoration (e.g., denture) must be considered. The Laplace smoothing algorithm performs well in global smoothing and is suitable for processing complex surfaces, thereby improving the efficiency of toolpath generation. The least-squares smoothing algorithm, by reducing data point oscillation, is suitable for denture processing scenarios that require high precision and adaptability. The curvature-based feature smoothing algorithm, by precisely controlling the curvature distribution, is suitable for scenarios where the fit of the restoration to the oral tissue must be ensured.

[0121] In an optional embodiment of the present invention, the training process of the region segmentation model includes the following steps:

[0122] (1) Obtaining an oral three-dimensional mesh data sample, wherein the oral three-dimensional mesh data sample is marked with a denture coverage area;

[0123] Specifically, if the region segmentation model is a myostatic region segmentation model, then the acquired oral 3D mesh data sample is marked with a myostatic region, such as Figure 3 and Figure 4 As shown, the figure on the left is the original oral 3D mesh data sample ( Figure 3 The left image shows the original oral 3D mesh data sample with a higher alveolar ridge absorption than the Figure 4 The image on the left shows the original oral 3D mesh data sample with a milder degree of alveolar ridge absorption). The image on the right shows the oral 3D mesh data sample with the myostatic area marked (the white area in the image on the right is the myostatic area).

[0124] (2) Preprocessing the oral three-dimensional mesh data sample to obtain a preprocessed oral three-dimensional mesh data sample;

[0125] The specific steps include:

[0126] 21) Processing the oral cavity three-dimensional mesh data sample to obtain a processed oral cavity three-dimensional mesh data sample;

[0127] The specific steps include:

[0128] 211) performing data denoising on the oral cavity three-dimensional mesh data sample to obtain a denoised oral cavity three-dimensional mesh data sample;

[0129] Specifically, some data floating around the oral three-dimensional grid data samples, some small blocks on the buccal side (i.e., buccal and lingual data), and unnecessary data can be denoised and smoothed to improve the robustness of the model.

[0130] 212) Projecting the denoised oral 3D mesh data samples in the same direction to obtain a 2D depth image;

[0131] 213) The dental arch curve is obtained by fitting the depth values in the two-dimensional depth image.

[0132] During implementation, after projection in the same direction, a strip-shaped area with a depth value different from other areas can be obtained. This strip-shaped area is the alveolar ridge, and these depth data are fitted to obtain the dental arch curve.

[0133] 214) performing data alignment on the corresponding denoised oral 3D mesh data samples with reference to the dental arch curve to obtain aligned oral 3D mesh data samples;

[0134] Specifically, data alignment reduces overfitting of training data and facilitates improving the generalization ability of the network.

[0135] 215) The aligned oral 3D mesh data samples are used as the oral 3D mesh data samples after data processing.

[0136] 22) performing vertex optimization on the oral 3D mesh data sample after data processing to obtain a vertex-optimized oral 3D mesh data sample;

[0137] Specifically, the number of vertices of the mesh is reduced through optimization algorithms while preserving its geometric features as much as possible to improve processing efficiency and reduce computational complexity.

[0138] During implementation, the present invention focuses on the myostatic area, which is a raised position. The curvature of the raised position will become faster and more prominent, and the number of vertices here will not be reduced. The extended part below is flat, and the curvature here will be relatively small. The vertices here can be optimized and deleted to ensure that the shape of the entire tooth model does not change significantly, but the number of vertices is reduced.

[0139] 23) Performing feature calculation on the oral cavity three-dimensional mesh data sample after vertex optimization to obtain the oral cavity three-dimensional mesh data sample after feature calculation.

[0140] The specific steps include:

[0141] 231) Using the Laplacian operator of the grid to extract the features of different frequency domains of the oral 3D grid data sample after vertex optimization, the oral 3D grid data sample with Laplacian features is obtained;

[0142] Specifically, the Laplacian operator of the mesh is used to capture and extract the features of the oral 3D mesh data samples in different domains after vertex optimization, providing key information for further analysis.

[0143] 232) Performing multi-scale enhancement processing on the oral 3D mesh data sample with Laplace features to obtain a multi-scale oral 3D mesh data sample;

[0144] Specifically, oral 3D mesh data samples with Laplacian features are enhanced at different scale levels, which helps the model learn details at different levels and enhances its generalization ability.

[0145] 233) Using sparse coding to represent multi-scale oral 3D mesh data samples, to obtain sparsely coded oral 3D mesh data samples;

[0146] Specifically, sparse coding technology is used to represent multi-scale oral three-dimensional mesh data samples to eliminate redundant features, highlight important information, and improve the interpretability of the model.

[0147] 234) Calculating geometric features of the oral cavity three-dimensional mesh data sample represented by sparse coding to obtain an oral cavity three-dimensional mesh data sample with geometric features, wherein the geometric features include: surface curvature and normal vector;

[0148] Specifically, geometric features can enrich the model's input information and enhance its understanding of complex structures.

[0149] 235) The oral cavity three-dimensional mesh data sample with geometric features is used as the oral cavity three-dimensional mesh data sample after feature calculation.

[0150] 24) The oral 3D mesh data sample after feature calculation is used as the preprocessed oral 3D mesh data sample.

[0151] (3) The preprocessed oral 3D mesh data samples are used to train the original region segmentation model to obtain the region segmentation model.

[0152] Specifically, the original region segmentation model mentioned above focuses on a series of deep learning models specifically designed to process 3D mesh data. These models include: PointNet, which directly operates on point cloud data; Graph CNNs and MeshCNN, which utilize graph convolutional networks to process mesh topology; multi-view models that interpret 3D information from multiple perspectives; SpectralCNN, based on spectral theory; and DiffusionNet, designed for geometric data. These models can be comprehensively considered and decided based on the needs of the actual application scenario, such as computing resource consumption and real-time requirements.

[0153] In an optional embodiment of the present invention, after the denture edge line is obtained through the AI model, the denture edge line is displayed on an interactive interface, where the user can adjust the denture edge line. During the adjustment process, since the user adjusts the movement of the denture edge line on a two-dimensional display interface, if the denture edge line is adjusted directly according to the user's two-dimensional instructions, the denture edge line may move too much, leaving the myostatic zone or getting too close to the myodynamic zone. A complete denture or removable partial denture designed based on a denture edge line that is outside the myostatic zone or too close to the myodynamic zone may easily shift during wear or collide during physiological movements such as chewing, causing discomfort to the patient. Furthermore, since the patient's oral environment is not a regular cylinder, if the denture edge line is simply translated or enlarged or reduced according to the user's two-dimensional instructions, the denture edge line may deviate from the model surface, causing the denture edge line to be inconsistent with the patient's actual oral condition and making subsequent denture design operations impossible.

[0154] To solve the above problem, after obtaining the denture edge line, the method further includes the following steps: (1) when a first instruction is detected, determining a first pose change of the denture edge line relative to an initial position, wherein the first instruction is an instruction for the user to adjust the pose of the denture edge line in the three-dimensional mesh data of the oral cavity; (2) based on the first pose change and a preset reference condition, determining a first adjusted pose of the denture edge line, and updating and displaying the pose of the denture edge line based on the first adjusted pose of the denture edge line; wherein, when it is detected that the first pose change makes the denture edge line not conform to the preset reference condition, the pose of the denture edge line is adaptively updated according to the preset reference condition, or, the pose of the denture edge line is not updated and a result of user reconfirmation is obtained and the pose of the denture edge line is adjusted according to the result of user reconfirmation; and / or when it is detected that the first adjusted pose makes the denture edge line not conform to the preset reference condition, the pose of the denture edge line is adaptively updated according to the preset reference condition, or, the pose of the denture edge line is not updated and a result of user reconfirmation is obtained and the pose of the denture edge line is adjusted according to the result of user reconfirmation.

[0155] It can be understood that not updating the denture edge line's pose means not moving or offsetting the denture edge line, and not updating the display of the denture edge line's pose after the move or offset; updating the denture edge line's pose can be understood as continuing to move or offset the denture edge line, and updating the display of the denture edge line's pose after the move or offset. Adjusting the denture edge line's pose based on the user's reconfirmation result can mean not updating the denture edge line's pose or updating the denture edge line's pose based on the user's choice. When the denture edge line's pose is not updated, a prompt message such as a prompt box can be displayed to provide the user with certain feedback.

[0156] Therefore, in this embodiment, the first adjustment posture can be recalculated based on the first posture change in the user's first instruction, including the movement distance of the denture edge line relative to its initial placement posture and / or the offset angle of the denture edge line relative to its initial placement posture, combined with the outer surface of the oral three-dimensional grid data, the preset reference conditions (medical reference requirements) such as the denture coverage area or simulated physiological movement, etc., and some adjustment instructions in the first posture change are ignored and some adjustment instructions in the first posture change are executed, that is, the instructions in the first posture change that do not meet the medical reference requirements are ignored, and the instructions in the first posture change that meet the medical reference requirements are executed, so that the denture edge line posture (first adjustment posture) on the display interface always meets the preset reference conditions, that is, meets the basic medical requirements, such as the denture edge line should be within the denture coverage area, or conform to the simulated physiological movement, and during the chewing process or physiological movements such as mandibular movement, the denture edge line will not move or the movement amplitude of the denture edge line is within the range.

[0157] Among them, the first posture change can be decomposed according to the preset reference conditions, and some or all instructions in the first posture change that meet the preset reference conditions are determined as the first adjustment posture of the denture edge line, and some or all instructions in the first posture change that do not meet the preset reference conditions are ignored; among them, for example: the preset reference conditions may include: the denture edge line should fit the outer surface of the oral three-dimensional grid data, the denture edge line should be within the denture coverage area, or the denture edge line should conform to one or more simulated physiological movements.

[0158] For example, in the first instruction, if the user clicks the mouse to move the denture edge line horizontally or diagonally by an X distance, and if the X distance causes the denture edge line to not align with the outer surface of the oral 3D mesh data, is not within the denture coverage area, or does not conform to simulated physiological movement, then the instruction for the horizontal or diagonal movement of the denture edge line will be ignored and not executed. This indicates that the first pose change does not meet the preset reference conditions. In this case, two operation methods are possible. First, the computer automatically enlarges or reduces the first pose change according to a preset ratio to make it conform to simulated physiological movement or fall within the denture coverage area, and determines this as the first adjustment pose, displaying the updated denture edge line. Second, the adjusted denture edge line is not displayed initially, but a prompt box is displayed to inform the user that this adjustment may be problematic and to request reconfirmation. Based on the result of the user's reconfirmation, the first adjustment pose is determined. If the user indicates "Confirm", this adjustment method is used as the first adjustment pose, and the denture edge line's pose is updated according to the user's instructions. If the user indicates "Discard modification", the first adjustment pose is set to 0, and the denture edge line's pose is not updated according to the user's instructions.

[0159] For example, in the first instruction, the user clicks the mouse to move the denture edge line diagonally. The X distance is decomposed into vertical distances (such as Figure 2 The Z direction in the vertical direction refers to the up and down movement along the gum surface) and the horizontal direction (such as Figure 2 In the X direction, the horizontal movement refers to the forward and backward movement along the normal of the outer surface of the oral 3D mesh data) moving the C distance, where the horizontal movement C distance will be ignored and not executed, and the vertical movement B distance will be executed, which is equivalent to projecting onto the vertical plane (such as Figure 2 The first adjustment position of the denture edge line is to move the denture edge line up and down by a distance B along the gingival surface, and automatically calculate and generate the latest denture edge line based on the denture edge line after vertical movement of the distance B and the outer surface of the oral 3D mesh data, so that the denture edge line fits the outer surface of the model.

[0160] For example, after determining the first adjustment posture, it can be judged again whether it conforms to the simulated physiological movement or is located in the denture coverage area. If so, it means that the first adjustment posture conforms to the medical reference conditions, and it can be adjusted according to the first adjustment posture and the adjusted denture edge line is displayed on the interactive interface; if not, it means that the first adjustment posture does not conform to the medical reference conditions. At this time, there are two operation methods. The first is that the computer automatically enlarges or reduces the first adjustment posture according to a preset ratio so that it conforms to the simulated physiological movement or is located in the denture coverage area; the second is that the adjusted denture edge line is not displayed first, and a prompt box is issued to remind the user that there may be problems with such adjustment. The user is asked to confirm again "whether to use this adjustment method to modify". If the user indicates "confirm", this adjustment method is used to modify, and the posture of the denture edge line is updated according to the user's instructions. If the user indicates "cancel modification", the posture of the denture edge line is not updated according to the user's instructions.

[0161] In one embodiment, based on the first instruction, the denture edge line can be controlled to rotate to the position indicated by the first instruction based on the current coordinate point of the mouse. At this time, the interactive interface shows that the denture edge line falls at the position where the mouse is clicked. At the same time, the angle or distance of the denture edge line rotating around the denture edge line is the projection of the mouse movement distance onto the vertical plane (such as Figure 2 The XZ plane in the image is determined.

[0162] It should be noted that simulated physiological movement refers to the simulated physiological movement automatically generated by inputting oral 3D mesh data into the motion simulation deep learning model. The simulated physiological movement can be selectively displayed on the interactive interface based on user instructions. When the user chooses to display the simulated physiological movement on the interactive interface, the oral 3D mesh data on the interactive interface will simulate the physiological movement and change. The user can visually observe whether the denture edge line is suitable and can move it up and down or adjust the denture edge line. If the user chooses not to display the simulated physiological movement on the interactive interface, the interactive interface can directly output the computer's judgment on whether the denture edge line conforms to the simulated physiological movement result.

[0163] The simulated physiological motion automatically generated by the motion simulation deep learning model can be generated based on the average motion of multiple patient training samples, or based on the mandibular motion trajectory and chewing motion trajectory obtained by CBCT equipment, facial scanner, extraoral scanner or intraoral scanner.

[0164] In an optional embodiment of the present invention, the method further comprises the following steps:

[0165] (1) Generate a 3D denture model based on the denture edge line and oral 3D mesh data, and send the 3D denture model to a 3D printing device for printing;

[0166] (2) Receive user requests for modifying the three-dimensional denture model in real time;

[0167] (3) Outputting a revised denture edge line and / or a revised three-dimensional denture model based on the user's request, the denture edge line and the oral three-dimensional mesh data.

[0168] Therefore, after obtaining the denture edge line through the AI model, the present invention can quickly generate a three-dimensional denture model and perform 3D printing to obtain a physical denture. The user tries on the physical denture, and the denture edge line or the three-dimensional denture model is directly adjusted according to the user's trial results to obtain a denture that better meets the user's needs.

[0169] In summary, in the denture edge line determination method of the present invention, an AI model is used to segment the oral three-dimensional mesh data, which has high robustness and will not be affected by the diversity of dental and maxillary morphology and the quality of mesh surface. It provides solid technical support for the subsequent denture design and manufacturing process, and improves the efficiency of doctors' clinical diagnosis and treatment.

[0170] The present invention has the following advantages:

[0171] Using deep learning technology, complex patterns can be learned from large amounts of data, ensuring better robustness for different jaw shapes, improving the accuracy of myostatic line recognition, and reducing human errors.

[0172] The fully automated identification process can significantly reduce the dental technician's working time in identifying the myostatic line and improve overall work efficiency.

[0173] It can be processed directly on the digital impression, avoiding complicated steps such as plaster touch-up.

[0174] Example 2:

[0175] An embodiment of the present invention also provides a denture edge line determination device, which is mainly used to execute the denture edge line determination method provided in Example 1 of the present invention. The denture edge line determination device provided in the embodiment of the present invention is specifically introduced below.

[0176] Figure 5 Schematic diagram of a denture edge line determination device according to an embodiment of the present invention. Figure 5 As shown, the device mainly includes: an acquisition unit 10, a denture edge line recognition unit 20, wherein:

[0177] An acquisition unit, configured to acquire oral three-dimensional mesh data obtained by scanning the oral cavity with an oral scanning device;

[0178] The denture edge line recognition unit is used to use the AI model to identify the denture edge line of the oral three-dimensional grid data to obtain the denture edge line.

[0179] In an embodiment of the present invention, a device for determining a denture edge line is provided, comprising: obtaining three-dimensional oral mesh data obtained by scanning an oral cavity using an oral scanning device; and identifying a denture edge line using an AI model based on the three-dimensional oral mesh data to obtain a denture edge line. As can be seen from the foregoing description, the device for determining a denture edge line of the present invention utilizes an AI model to automatically identify the denture edge line based on the three-dimensional oral mesh data, thereby achieving high intelligence and improving the accuracy and robustness of denture edge line identification. This alleviates the technical issues associated with poor accuracy and low intelligence in the determination process of denture edges determined using conventional techniques.

[0180] Optionally, the denture edge line recognition unit is further used to: segment the oral three-dimensional grid data using a region segmentation model to obtain a denture coverage area; and determine the denture edge line based on the denture coverage area.

[0181] Optionally, the denture coverage area includes a myostatic area, and the denture edge line recognition unit is further used to determine a target three-dimensional myostatic line based on the myostatic area, and determine the denture edge line based on the target three-dimensional myostatic line.

[0182] Optionally, the device is also used to: preprocess the oral three-dimensional grid data to obtain preprocessed oral three-dimensional grid data; use the AI model to identify the denture edge line of the preprocessed oral three-dimensional grid data to obtain the denture edge line.

[0183] Optionally, the device is also used to: determine whether the oral three-dimensional grid data completely contains the specified area; if not, issue a prompt message and re-acquire the oral three-dimensional grid data obtained by scanning the oral cavity with an oral scanning device until the oral three-dimensional grid data completely contains the specified area.

[0184] Optionally, the device is also used to: determine the degree of alveolar ridge absorption based on oral three-dimensional grid data; determine the accuracy of the denture edge line based on the degree of alveolar ridge absorption, and output the accuracy to a display screen or voice broadcast.

[0185] Optionally, the denture edge line recognition unit is also used to: smooth and optimize the edge of the myostatic area to obtain a myostatic area with a smooth contour; draw a three-dimensional myostatic line based on the edge of the myostatic area with a smooth contour; and smooth the three-dimensional myostatic line to obtain a target three-dimensional myostatic line.

[0186] Optionally, the denture edge line recognition unit is further used to: use a graph cutting algorithm to perform smoothing optimization processing on the edge of the myostatic area to obtain a myostatic area with a smooth contour.

[0187] Optionally, the denture edge line recognition unit is also used to: determine a target smoothing algorithm based on the morphological characteristics and processing technology of the denture, wherein the target smoothing algorithm includes any one of the following: Laplace smoothing algorithm, least squares smoothing algorithm and curvature-based feature smoothing algorithm; use the target smoothing algorithm to smooth the three-dimensional myostatic line to obtain the target three-dimensional myostatic line.

[0188] Optionally, the device is also used to: obtain oral three-dimensional mesh data samples, wherein the oral three-dimensional mesh data samples are marked with denture coverage areas; preprocess the oral three-dimensional mesh data samples to obtain preprocessed oral three-dimensional mesh data samples; and use the preprocessed oral three-dimensional mesh data samples to train the original region segmentation model to obtain a region segmentation model.

[0189] Optionally, the device is also used to: perform data processing on the oral three-dimensional mesh data samples to obtain the data-processed oral three-dimensional mesh data samples; perform vertex optimization on the data-processed oral three-dimensional mesh data samples to obtain the vertex-optimized oral three-dimensional mesh data samples; perform feature calculation on the vertex-optimized oral three-dimensional mesh data samples to obtain the feature-calculated oral three-dimensional mesh data samples; and use the feature-calculated oral three-dimensional mesh data samples as the preprocessed oral three-dimensional mesh data samples.

[0190] Optionally, the device is also used to: perform data denoising on the oral three-dimensional mesh data samples to obtain denoised oral three-dimensional mesh data samples; project the denoised oral three-dimensional mesh data samples in the same direction to obtain a two-dimensional depth image; obtain a dental arch curve based on the depth values in the two-dimensional depth image; perform data alignment on the corresponding denoised oral three-dimensional mesh data samples with reference to the dental arch curve to obtain aligned oral three-dimensional mesh data samples; and use the aligned oral three-dimensional mesh data samples as the oral three-dimensional mesh data samples after data processing.

[0191] Optionally, the device is also used to: use the Laplace operator of the grid to extract features in different frequency domains of the oral three-dimensional mesh data samples after vertex optimization, and obtain oral three-dimensional mesh data samples with Laplace features; perform multi-scale enhancement processing on the oral three-dimensional mesh data samples with Laplace features, and obtain multi-scale oral three-dimensional mesh data samples; use sparse coding to represent the multi-scale oral three-dimensional mesh data samples, and obtain oral three-dimensional mesh data samples represented by sparse coding; calculate the geometric features of the oral three-dimensional mesh data samples represented by sparse coding, and obtain oral three-dimensional mesh data samples with geometric features, wherein the geometric features include: surface curvature, normal vector; and use the oral three-dimensional mesh data samples with geometric features as the oral three-dimensional mesh data samples after feature calculation.

[0192] Optionally, the device is also used to: when a first instruction is monitored, determine a first posture change of the denture edge line relative to the initial position, wherein the first instruction is an instruction for the user to adjust the posture of the denture edge line in the three-dimensional mesh data of the oral cavity; based on the first posture change and a preset reference condition, determine a first adjusted posture of the denture edge line, and update and display the posture of the denture edge line based on the first adjusted posture of the denture edge line, wherein the preset reference condition includes: the denture edge line fits the outer surface of the three-dimensional mesh data of the oral cavity, the denture edge line conforms to simulated physiological movement, or the denture edge line is located in the denture coverage area. or multiple; wherein, when it is detected that the first posture change makes the denture edge line not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or, the posture of the denture edge line is not updated and the result of user reconfirmation is obtained and the posture of the denture edge line is adjusted according to the result of user reconfirmation; and / or when it is detected that the first adjusted posture makes the denture edge line not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or, the posture of the denture edge line is not updated and the result of user reconfirmation is obtained and the posture of the denture edge line is adjusted according to the result of user reconfirmation.

[0193] Optionally, the device is also used to: generate a three-dimensional denture model based on the denture edge line and the oral three-dimensional mesh data, and send the three-dimensional denture model to a 3D printing device for printing; receive user requests for modifying the three-dimensional denture model in real time; output the revised denture edge line and / or output the revised three-dimensional denture model based on the user request, the denture edge line and the oral three-dimensional mesh data.

[0194] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0195] like Figure 6 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the denture edge line determination method as described above.

[0196] Specifically, the memory 602 and processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, the denture edge line determination method can be executed.

[0197] The processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or by instructions in the form of software. The above-mentioned processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 602, and processor 601 reads the information in memory 602 and performs the steps of the above method in conjunction with its hardware.

[0198] Corresponding to the above-mentioned denture edge line determination method, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned denture edge line determination method.

[0199] The denture edge line determination device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in the embodiment of the present application are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0200] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0201] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0202] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0203] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0204] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the denture margin determination method described in each embodiment of this application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0205] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0206] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for determining a denture edge line, characterized in that: include: Acquiring three-dimensional mesh data of an oral cavity obtained by performing a static scan of the oral cavity using an oral scanning device, wherein the oral cavity contains some teeth or no teeth; Determining whether the oral three-dimensional mesh data completely contains a specified area; the specified area includes: the lip frenulum, the buccal frenulum, the vestibular mucosal fold, the lower zygomatic line, and the buccal side of the maxillary tuberosity; or the specified area includes: the lip frenulum, the buccal frenulum, the maxillary tuberosity, the pterygomaxillary notch, and the area 2 mm behind the maxillary fossa; If not all of them are included, a prompt message is issued, and the oral cavity three-dimensional mesh data obtained by scanning the oral cavity with the oral scanning device is re-acquired until the oral cavity three-dimensional mesh data completely includes the specified area; If all are included, preprocessing the oral cavity three-dimensional mesh data to obtain preprocessed oral cavity three-dimensional mesh data; Using an AI model to perform denture edge line recognition on the preprocessed oral three-dimensional mesh data to obtain the denture edge line; The preprocessing includes at least data processing, which includes: performing data denoising on the oral 3D mesh data to obtain denoised oral 3D mesh data; performing same-direction projection on the denoised oral 3D mesh data to obtain a 2D depth image; obtaining a dental arch curve based on depth values in the 2D depth image; performing data alignment on the corresponding denoised oral 3D mesh data with reference to the dental arch curve to obtain aligned oral 3D mesh data; and using the aligned oral 3D mesh data as the preprocessed oral 3D mesh data. The AI model is used to identify the denture edge line of the pre-processed oral three-dimensional mesh data, including: The pre-processed three-dimensional oral mesh data is segmented using a regional segmentation model to obtain a denture coverage area, wherein the denture coverage area includes: a myostatic area; Determining the denture edge line based on the denture coverage area, wherein determining the denture edge line based on the denture coverage area comprises: determining a target three-dimensional myostatic line based on the myostatic area, and determining the denture edge line based on the target three-dimensional myostatic line; The method further comprises: determining the degree of alveolar ridge resorption based on the oral three-dimensional mesh data; Determining the accuracy of the denture edge line according to the degree of alveolar ridge resorption, and outputting the accuracy to a display screen or voice broadcast; The method further comprises: generating a three-dimensional denture model according to the denture edge line and the oral three-dimensional mesh data, and sending the three-dimensional denture model to a 3D printing device for printing; receiving a user request for modifying the three-dimensional denture model in real time; According to the user request, the denture edge line and the oral three-dimensional mesh data, a revised denture edge line and / or a revised three-dimensional denture model is output.

2. The method according to claim 1, characterized in that Determining a target three-dimensional myostatic line based on the myostatic area includes: Performing a smoothing optimization process on the edge of the myostatic area to obtain a myostatic area with a smooth contour; drawing a three-dimensional myostatic line according to the edge of the myostatic area with a smooth contour; The three-dimensional myostatic line is smoothed to obtain the target three-dimensional myostatic line.

3. The method according to claim 2, characterized in that The edge of the myostatic area is smoothed and optimized, including: A graph cut algorithm is used to perform smoothing optimization processing on the edge of the myostatic region to obtain the myostatic region with a smooth contour; Alternatively, the three-dimensional myostatic lines are smoothed, comprising: Determining a target smoothing algorithm according to the morphological characteristics and processing technology of the denture, wherein the target smoothing algorithm includes any one of the following: a Laplace smoothing algorithm, a least squares smoothing algorithm, and a curvature-based feature smoothing algorithm; The target smoothing algorithm is used to smooth the three-dimensional myostatic line to obtain the target three-dimensional myostatic line.

4. The method according to claim 1, wherein The training process of the region segmentation model includes: Acquire an oral three-dimensional mesh data sample, wherein the oral three-dimensional mesh data sample is marked with a denture coverage area; Preprocessing the oral cavity three-dimensional mesh data sample to obtain a preprocessed oral cavity three-dimensional mesh data sample; The preprocessed oral three-dimensional grid data samples are used to train the original region segmentation model to obtain the region segmentation model.

5. The method according to claim 4, characterized in that Preprocessing the oral three-dimensional mesh data sample includes: performing data processing on the oral cavity three-dimensional mesh data sample to obtain a data-processed oral cavity three-dimensional mesh data sample; performing vertex optimization on the oral three-dimensional mesh data sample after the data processing to obtain a vertex-optimized oral three-dimensional mesh data sample; Performing feature calculation on the oral cavity three-dimensional mesh data sample after vertex optimization to obtain the oral cavity three-dimensional mesh data sample after feature calculation; The oral cavity three-dimensional mesh data sample after the feature calculation is used as the preprocessed oral cavity three-dimensional mesh data sample.

6. The method according to claim 5, characterized in that The oral cavity three-dimensional grid data sample is processed, including: performing data denoising on the oral cavity three-dimensional mesh data sample to obtain a denoised oral cavity three-dimensional mesh data sample; Projecting the denoised three-dimensional oral cavity mesh data sample in the same direction to obtain a two-dimensional depth image; Obtaining a dental arch curve by fitting the depth values in the two-dimensional depth image; performing data alignment on the corresponding denoised oral three-dimensional mesh data samples with reference to the dental arch curve to obtain aligned oral three-dimensional mesh data samples; The aligned oral three-dimensional mesh data samples are used as the oral three-dimensional mesh data samples after data processing.

7. The method according to claim 5, characterized in that Performing feature calculation on the oral cavity three-dimensional mesh data sample after vertex optimization, including: Using the Laplacian operator of the grid to extract the features of different frequency domains of the oral three-dimensional grid data sample after the vertex optimization, to obtain the oral three-dimensional grid data sample with Laplacian features; Performing multi-scale enhancement processing on the oral three-dimensional mesh data sample with Laplace features to obtain a multi-scale oral three-dimensional mesh data sample; Using sparse coding to represent the multi-scale oral three-dimensional mesh data samples to obtain sparsely coded oral three-dimensional mesh data samples; Calculating geometric features of the oral cavity three-dimensional mesh data sample represented by the sparse coding to obtain an oral cavity three-dimensional mesh data sample with geometric features, wherein the geometric features include: surface curvature and normal vector; The oral three-dimensional mesh data sample with the geometric features is used as the oral three-dimensional mesh data sample after the feature calculation.

8. The method according to claim 1, characterized in that After the pre-processed oral three-dimensional mesh data is subjected to denture edge line recognition using the AI model to obtain the denture edge line, the method further includes: When a first instruction is detected, determining a first pose change of the denture edge line relative to the initial position, wherein the first instruction is an instruction from a user to adjust the pose of the denture edge line in the oral three-dimensional mesh data; Determining a first adjusted posture of the denture edge line based on the first posture change and a preset reference condition, and updating and displaying the posture of the denture edge line based on the first adjusted posture of the denture edge line, wherein the preset reference condition includes one or more of: the denture edge line is aligned with the outer surface of the oral three-dimensional mesh data, the denture edge line conforms to simulated physiological movement, or the denture edge line is located within the denture coverage area; When it is detected that the first posture change causes the denture edge line to not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or the posture of the denture edge line is not updated and a result of user reconfirmation is obtained and the posture of the denture edge line is adjusted according to the result of user reconfirmation; and / or When it is detected that the first adjustment posture causes the denture edge line to not meet the preset reference condition, the posture of the denture edge line is adaptively updated according to the preset reference condition, or the posture of the denture edge line is not updated and the result of user re-confirmation is obtained and the posture of the denture edge line is adjusted according to the result of user re-confirmation.

9. A denture edge line determination device, characterized in that: include: an acquisition unit, configured to acquire three-dimensional mesh data of an oral cavity obtained by performing a static scan of the oral cavity by an oral scanning device, wherein the oral cavity contains some teeth or no teeth; The device is further configured to determine whether the three-dimensional oral mesh data contains all designated areas; the designated areas include: the lip frenulum, the buccal frenulum, the vestibular mucosal fold, the lower zygomatic line, and the buccal side of the maxillary tuberosity; or the designated areas include: the lip frenulum, the buccal frenulum, the maxillary tuberosity, the pterygomaxillary notch, and an area 2 mm behind the maxillary fossa; if not all areas are contained, a prompt message is issued, and the three-dimensional oral mesh data obtained by scanning the oral cavity with an oral scanning device is reacquired until the three-dimensional oral mesh data contains all designated areas. a preprocessing unit, configured to preprocess the oral three-dimensional mesh data if all are included, to obtain preprocessed oral three-dimensional mesh data; a denture edge line recognition unit, configured to use an AI model to perform denture edge line recognition on the preprocessed oral three-dimensional mesh data to obtain a denture edge line; The preprocessing includes at least data processing, which includes: performing data denoising on the oral 3D mesh data to obtain denoised oral 3D mesh data; performing same-direction projection on the denoised oral 3D mesh data to obtain a 2D depth image; obtaining a dental arch curve based on depth values in the 2D depth image; performing data alignment on the corresponding denoised oral 3D mesh data with reference to the dental arch curve to obtain aligned oral 3D mesh data; and using the aligned oral 3D mesh data as the preprocessed oral 3D mesh data. The denture edge line recognition unit is further configured to: segment the pre-processed oral three-dimensional mesh data using a region segmentation model to obtain a denture coverage area, wherein the denture coverage area includes a myostatic area; determine the denture edge line based on the denture coverage area, wherein the denture edge line recognition unit is further configured to: determine a target three-dimensional myostatic line based on the myostatic area, and determine the denture edge line based on the target three-dimensional myostatic line; The device is further configured to: determine the degree of alveolar ridge resorption based on the oral three-dimensional grid data; determine the accuracy of the denture edge line based on the degree of alveolar ridge resorption, and output the accuracy to a display screen or voice broadcast; The device is also used to: generate a three-dimensional denture model based on the denture edge line and the oral three-dimensional grid data, and send the three-dimensional denture model to a 3D printing device for printing; receive user requests for modifying the three-dimensional denture model in real time; output revised denture edge lines and / or output revised three-dimensional denture models based on the user requests, the denture edge lines and the oral three-dimensional grid data.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute the method according to any one of claims 1 to 8.

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