Denture margin line determination method and device, computer device, and storage medium

Abstract

The application provides a denture edge line determination method and device, computer equipment and a storage medium, and relates to the technical field of denture design. The method comprises the following steps: obtaining first oral cavity data and second oral cavity data corresponding to a target object; the first oral cavity data is used to represent the oral cavity of the target object in a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object in a second pressure state; the pressure in the second pressure state is smaller than the pressure in the first pressure state; performing denture reference line identification on the first oral cavity data according to a trained AI model to obtain a first denture reference line; and mapping the first denture reference line to the second oral cavity data to obtain a target denture edge line. The first oral cavity data and the second oral cavity data under different pressure states are combined, the denture edge line of the second oral cavity data with high precision is automatically and accurately identified, and subsequent digital storage and design are facilitated.

Description

Technical Field

[0001] This invention relates to the field of denture design technology, and in particular to a method, apparatus, computer device, and storage medium for determining the edge line of a denture. Background Technology

[0002] In dentistry, especially in the fabrication of complete dentures or removable partial dentures, identifying the myostatic line is crucial for ensuring denture retention and function. The myostatic zone refers to the area related to masticatory muscle activity that needs to be considered during denture restoration. In these areas, the mucosa does not move during physiological activities such as chewing and speaking. The boundary between the myostatic zone and the muscular zone is the myostatic line. Based on the myostatic line, the denture margin can be determined, allowing for the design of the corresponding denture. Therefore, correctly identifying the myostatic line contributes to designing more stable and comfortable dentures.

[0003] Currently, myostatic lines are usually marked manually. For example, they can be marked directly on the plaster model, or they can be marked on the digital oral impression or the scanned plaster model using digital drawing software. The digital oral impression is obtained by scanning the inside of the mouth with a scanner. The plaster model is a physical model of plaster material obtained by traditional molding methods, and the scanned plaster model is obtained by scanning the plaster model.

[0004] However, the aforementioned methods of manually annotating myostatic lines rely on the clinical experience of designers and other relevant personnel, making them susceptible to subjective human factors, difficult to achieve consistency and standardization, and requiring significant time and effort from relevant personnel, resulting in high labor costs and low efficiency. Specifically, in the method of manually annotating myostatic lines based on plaster models, the plaster models require physical storage, and sharing and transmission are not as convenient as digital dental impressions. Furthermore, shrinkage of the impression material, expansion of the plaster, or other physical factors can cause deviations between the plaster model and the actual oral condition, making the plaster model less accurate than a digital dental impression. The method of manually annotating myostatic lines based on digital drawing software tools requires the support of relevant marking tools and software, incurring a learning cost and being inconvenient to operate. Additionally, digital dental impressions are taken under near-pressure-free conditions; during intraoral scanning, the patient's oral cavity is essentially still, making it impossible to distinguish between myostatic and dynamic zones and obtain accurate denture margin lines. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, computer device, and storage medium for determining the edge line of a denture, so as to automatically and accurately identify the edge line of the denture, while facilitating subsequent digital storage and design.

[0006] In a first aspect, embodiments of the present invention provide a method for determining the edge line of a denture, including:

[0007] Acquire first oral cavity data and second oral cavity data corresponding to the target object; wherein, the first oral cavity data is used to represent the oral cavity of the target object under a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state;

[0008] The first denture reference line is obtained by identifying the denture reference line based on the trained AI model of the first oral cavity data.

[0009] Based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line.

[0010] Furthermore, based on the trained AI model, denture reference lines are identified from the first oral cavity data to obtain the first denture reference lines, including:

[0011] The first oral cavity data is preprocessed to obtain preprocessed first oral cavity data; wherein, the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation and surface feature fusion;

[0012] The preprocessed first oral cavity data is input into the AI ​​model to obtain the denture reference line recognition result output by the AI ​​model. The AI ​​model is trained on a three-dimensional deep learning model based on multiple sample oral cavity data with denture reference line annotation data. The sample oral cavity data is used to represent the oral cavity of the sample object under the first pressure state.

[0013] Based on the identification results of the denture reference line, the first denture reference line is determined.

[0014] Furthermore, the first denture reference line includes the first myostatic line, and the denture reference line annotation data includes myostatic annotation data, which includes myostatic line data or myostatic region data; or,

[0015] The first denture reference line includes the edge line of the first denture, and the denture reference line annotation data includes myostatic annotation data or denture edge line annotation data.

[0016] Furthermore, based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line, including:

[0017] Using a preset rigid body region, the first oral cavity data and the second oral cavity data are registered to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla;

[0018] Based on the registration information, the first denture reference line is mapped onto the second oral cavity data to obtain the second denture reference line;

[0019] Determine the edge line of the target denture based on the second denture reference line.

[0020] Furthermore, based on the registration information, the first denture reference line is mapped onto the second oral cavity data to obtain the second denture reference line, including:

[0021] Multiple sampling points were obtained from the first denture reference line;

[0022] Based on the registration information, the matching points of each sampling point on the second oral cavity data are determined;

[0023] Three-dimensional curve fitting is performed on each matching point to obtain the second denture reference line.

[0024] Furthermore, based on the second denture reference line, the edge line of the target denture is determined, including:

[0025] The second denture reference line is smoothed to obtain the smoothed denture reference line;

[0026] Determine the edge line of the target denture based on the reference line after smoothing.

[0027] Furthermore, the above method also includes:

[0028] Acquire oral cavity data from multiple samples with denture reference line annotations;

[0029] The three-dimensional deep learning model is trained based on oral cavity data of each sample to obtain the AI ​​model; the three-dimensional deep learning model includes any of the following: PointNet model, graph convolutional neural network model, MeshCNN model, multi-view model that analyzes three-dimensional information from multiple perspectives, convolutional neural network model based on graph theory, and diffusion network DiffusionNet model.

[0030] Furthermore, multiple sample oral data with denture reference line annotations were obtained, including:

[0031] Acquire multiple initial oral cavity data with denture reference line annotations; wherein, the initial oral cavity data is used to represent the oral cavity of the sample object under the first pressure state;

[0032] Multiple initial oral cavity data are preprocessed to obtain multiple sample oral cavity data; the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, multi-scale data augmentation, sparse coding representation and surface feature fusion.

[0033] Furthermore, the first oral cavity data is obtained by three-dimensional scanning of the plaster model obtained by taking an impression of the oral cavity of the target object under the first pressure, or by three-dimensional scanning of the target object's historical dentures, or by scanning the oral cavity of the target object under air pressure.

[0034] The second oral data is obtained by scanning the oral cavity of the target subject directly without physical pressure or standard atmospheric pressure.

[0035] Furthermore, after obtaining the first and second oral cavity data corresponding to the target object, the above method also includes:

[0036] The integrity of the first and second oral cavity data is assessed to determine the degree of integrity.

[0037] The accuracy of the target denture edge line is determined based on the degree of integrity.

[0038] Furthermore, the data integrity of the first and second oral cavity data is assessed to determine the degree of integrity, including:

[0039] Determine whether the first oral cavity data and / or the second oral cavity data contain a preset key region, and obtain a determination result; wherein, the key region corresponding to the first oral cavity data includes a first designated region, and the key region corresponding to the second oral cavity data includes a second designated region;

[0040] Determine the degree of completeness based on the inclusion judgment result;

[0041] After assessing the integrity of the first and second oral cavity data and determining the degree of integrity, the above method further includes:

[0042] Determine whether the integrity level is less than a preset integrity threshold;

[0043] If the data is less than the integrity threshold, a data incompleteness alert will be issued.

[0044] Furthermore, based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line. The method further includes:

[0045] When the first command is detected, the first pose change of the target denture edge line relative to the initial position is determined. The first command is the user's command to adjust the pose of the target denture edge line in the second oral cavity data.

[0046] Based on the first pose change and preset reference conditions, the first adjustment pose of the target denture edge line is determined, and the pose of the target denture edge line is updated and displayed based on the first adjustment pose of the target denture edge line. The preset reference conditions include one or more of the following: the target denture edge line fits the outer surface of the second oral cavity data, the target denture edge line conforms to simulated physiological movement, or the target denture edge line is located within the denture coverage area.

[0047] Specifically, when a change in the first pose is detected that causes the edge line of the target denture to deviate from the preset reference conditions, the pose of the target denture edge line is adaptively updated according to the preset reference conditions; or, the pose of the target denture edge line is not updated, and the user's confirmation result is obtained and the pose of the target denture edge line is adjusted according to the user's confirmation result; and / or

[0048] When it is detected that the first adjustment pose causes the edge line of the target denture to not conform to the preset reference conditions, the pose of the edge line of the target denture is adaptively updated according to the preset reference conditions, or the pose of the edge line of the target denture is not updated and the result of user confirmation is obtained and the pose of the edge line of the target denture is adjusted according to the result of user confirmation.

[0049] Furthermore, based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line. The method further includes:

[0050] Based on the target denture edge line and the second oral cavity data, a three-dimensional denture model is generated and sent to a 3D printing device for printing the three-dimensional denture model.

[0051] When a user request to modify the 3D denture model is received, the revised target denture edge line and / or the revised 3D denture model are output based on the user request, the target denture edge line, and the second oral cavity data.

[0052] Secondly, embodiments of the present invention also provide a denture edge line determining device, comprising:

[0053] The data acquisition module is used to acquire first oral cavity data and second oral cavity data corresponding to the target object; wherein, the first oral cavity data is used to represent the oral cavity of the target object under a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state;

[0054] The denture reference line recognition module is used to recognize the denture reference line based on the trained AI model of the first oral cavity data, and obtain the first denture reference line.

[0055] The denture edge mapping module is used to map the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data, so as to obtain the target denture edge line.

[0056] Thirdly, embodiments of the present invention also provide a computer device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the denture edge line determination method of the first aspect.

[0057] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the denture edge line determination method of the first aspect.

[0058] The method, apparatus, computer equipment, and storage medium for determining denture edge lines provided in this invention can acquire first and second oral cavity data corresponding to the target object when determining the denture edge line. The first oral cavity data represents the target object's oral cavity under a first pressure state, and the second oral cavity data represents the target object's oral cavity under a second pressure state, where the pressure under the second pressure state is less than the pressure under the first pressure state. A denture reference line is identified on the first oral cavity data using a trained AI model to obtain a first denture reference line. Based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line. Since the impression pressure corresponding to the first oral cavity data is greater than that corresponding to the second oral cavity data, the accuracy of the second oral cavity data is higher than that of the first oral cavity data. The first denture reference line corresponding to the first oral cavity data is less susceptible to interference from the diversity of dental morphology and the quality of the mesh surface. Through artificial intelligence learning, the first denture reference line corresponding to the first oral cavity data can be accurately identified. Mapping the first denture reference line onto the more accurate second oral cavity data facilitates subsequent data storage and design. By combining the first and second oral cavity data under different pressure conditions, the edge trimming effect is achieved. The denture edge line of the second oral cavity data is automatically and accurately identified, which also facilitates subsequent digital storage and design. Attached Figure Description

[0059] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0060] Figure 1A flowchart illustrating a method for determining the edge line of a denture according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the appearance of a digital dental impression provided in an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of the appearance of a target three-dimensional plaster model provided in an embodiment of the present invention;

[0063] Figure 4 A schematic diagram of a target three-dimensional plaster model for marking muscle static lines provided in an embodiment of the present invention;

[0064] Figure 5 A schematic diagram of a myostatic line mapping provided in an embodiment of the present invention;

[0065] Figure 6 This is a schematic diagram showing the display of the edge line of a target denture on an interactive interface, provided by an embodiment of the present invention.

[0066] Figure 7 This is a schematic diagram of a device for determining the edge line of a denture, provided in an embodiment of the present invention.

[0067] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0068] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Currently, myostatic lines are typically manually marked directly on digital dental impressions or scanned plaster models using digital drawing software tools to obtain the denture margins. However, this method has the following drawbacks: a) It requires clinical experience to ensure that the design allows for flexibility; b) It requires a significant investment of time and effort; c) It requires the support of relevant marking tools and software, incurring a learning curve; d) Compared to the traditional method of marking directly on plaster models, marking and editing on 3D models within digital drawing software tools is a considerable challenge for designers; e) It is susceptible to the influence of subjective human factors, making it difficult to achieve consistency and standardization.

[0070] With the development of image processing technology, artificial intelligence can be used to automatically identify myostatic lines in digital dental impressions (such as intraoral edentulous digital impressions). However, because digital dental impressions are taken under near-pressure-free conditions, directly identifying myostatic lines on these impressions cannot yield accurate myostatic boundaries, resulting in inaccurate denture margins and making them unsuitable for pressure fabrication of the embankment area. Furthermore, material shrinkage, plaster expansion, or other physical factors can cause deviations between traditional impression methods and the actual oral cavity conditions in plaster models, resulting in lower accuracy compared to digital dental impressions.

[0071] Based on this, the present invention provides a method, apparatus, computer device, and storage medium for determining denture edge lines. Based on the application of deep learning technology in three-dimensional meshes, it can automatically identify denture edge lines on three-dimensional digital oral impressions. This method is highly robust and unaffected by the diversity of dentition morphology or the quality of mesh surfaces, providing solid technical support for subsequent denture design and manufacturing processes. For example, when acquiring denture edge lines, a combination of digital oral impressions and traditional impression-taking methods can be used. A scanner is used to scan a plaster model obtained through traditional impression-taking methods (e.g., a plaster model of an edentulous jaw). Through artificial intelligence learning, myostatic lines or denture edge lines are identified on the scanned plaster model, and then these myostatic lines or denture edge lines are mapped onto the digital oral impression acquired by the intraoral scanner.

[0072] To facilitate understanding of this embodiment, a method for determining the edge line of a denture disclosed in this embodiment of the invention will first be described in detail.

[0073] This invention provides a method for determining the edge line of a denture, which can be executed by a computer device with data processing capabilities. See [link to documentation]. Figure 1 The diagram shows a method for determining the edge line of a denture, which mainly includes the following steps S110 to S140:

[0074] Step S110: Obtain the first oral cavity data and the second oral cavity data corresponding to the target object.

[0075] The first oral cavity data represents the oral cavity of the target object under a first pressure state, and the second oral cavity data represents the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state.

[0076] Optionally, the first oral cavity data can be obtained by three-dimensional scanning of a plaster model of the target subject's oral cavity obtained under a first pressure, or by three-dimensional scanning of the target subject's historical dentures (old dentures), or by scanning the target subject's oral cavity under air pressure. The first pressure state can refer to the pressure state of the impression on the oral cavity, the pressure state of the historical denture on the oral cavity, or the pressure state of air pressure on the oral cavity. The second oral cavity data can be obtained by directly scanning the target subject's oral cavity under no physical pressure or standard atmospheric pressure (the second oral cavity data can be called a digital oral impression). The second pressure state can refer to no physical pressure or standard atmospheric pressure.

[0077] Specifically, the target group can be individuals who require denture installation. The first oral cavity data can be obtained by scanning the plaster model corresponding to the target group in three dimensions using scanning technology. In this case, the first oral cavity data can be called the target three-dimensional plaster model (i.e., the three-dimensional plaster model corresponding to the target group). The first oral cavity data and the second oral cavity data can also be obtained by scanning the target group's oral cavity using an intraoral scanner. The difference lies in the pressure exerted on the target group's oral cavity during the scanning process.

[0078] The oral cavity of the target subject can be scanned using scanning devices such as intraoral scanners and extraoral scanners to obtain results such as... Figure 2 The digital dental impression shown can be scanned using scanning devices such as extraoral scanners, intraoral scanners, and desktop scanners. Figure 3 The plaster model shown yields the target three-dimensional plaster model. Figure 2 and Figure 3 The digital dental impression and the target 3D plaster model are shown respectively. Both the digital dental impression and the target 3D plaster model can be obtained as meshed data after 3D scanning, through 3D reconstruction, point cloud registration, and meshing. The specific acquisition process of the digital dental impression and the target 3D plaster model can be found in relevant existing technologies, and will not be elaborated here.

[0079] Digital dental impressions can be obtained using high-precision optical dental scanning technology. This process does not involve physical pressure, making it difficult to accurately record the myostatic line characteristics under functional conditions (i.e., muscle movement), thus affecting the denture margin. In contrast, traditional plaster models are affected by functional pressure from the oral soft tissues during fabrication, which more accurately reflects the anatomical morphology of the myostatic line, resulting in a more accurate denture margin.

[0080] Step S120: Based on the trained AI model, the first oral cavity data is used to identify the denture reference line to obtain the first denture reference line.

[0081] The aforementioned first denture reference line can be either the first myostatic line on the first oral cavity data or the first denture edge line on the first oral cavity data. For example, three-dimensional deep learning technology can be used to identify the myostatic line / denture edge line in the scanned target three-dimensional plaster model to obtain the first myostatic line / first denture edge line, such as... Figure 4 As shown, the line indicated by the arrow is the first myostatic line.

[0082] In some possible embodiments, the AI ​​model can be a denture reference line recognition model. This model is trained on a 3D deep learning model using multiple sample oral cavity data with denture reference line annotations. The sample oral cavity data represents the oral cavity of the sample object under a first pressure state. The training process of the AI ​​model will be described in detail later. Therefore, step S120 may include: using the denture reference line recognition model to identify the first oral cavity data to obtain a first denture reference line.

[0083] In other possible embodiments, considering that specific anatomical points within the oral cavity can be used to locate denture reference lines such as myostatic lines or denture margins, the AI ​​model can be an anatomical point recognition model. This model is trained on a three-dimensional deep learning model using multiple sample oral data with anatomical point annotations. The sample oral data represents the oral cavity of the sample object under a first pressure state. Based on this, step S120 can include: using the anatomical point recognition model to identify the first oral data to obtain the target anatomical point; and determining the first denture reference line based on the target anatomical point. In one possible implementation, the target anatomical point can first be curve-fitted to obtain an anatomical point curve, and then the anatomical point curve can be pre-adjusted to obtain the first denture reference line. In another possible implementation, the reference point corresponding to the target anatomical point can be determined first, and then the reference point can be curve-fitted to obtain the first denture reference line.

[0084] Step S130: Based on the registration information between the first oral cavity data and the second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line.

[0085] Taking the first oral cavity data as the target three-dimensional plaster model and the second oral cavity data as the oral digital impression as an example, we can first use the common rigid body regions such as the alveolar ridge and / or the maxilla to obtain the registration information between the oral digital impression and the target three-dimensional plaster model. The registered oral digital impression and the target three-dimensional plaster model are in the same coordinate system, so the first denture reference line can be mapped onto the oral digital impression to obtain the edge line of the target denture.

[0086] The method for determining the edge line of a denture provided in this invention can acquire first oral cavity data and second oral cavity data corresponding to the target object when determining the edge line of the denture. The first oral cavity data represents the oral cavity of the target object under a first pressure state, and the second oral cavity data represents the oral cavity of the target object under a second pressure state, where the pressure under the second pressure state is less than the pressure under the first pressure state. A denture reference line is identified on the first oral cavity data using a trained AI model to obtain a first denture reference line. Based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line. Since the impression pressure corresponding to the first oral cavity data is greater than that corresponding to the second oral cavity data, the accuracy of the second oral cavity data is higher than that of the first oral cavity data. The first denture reference line corresponding to the first oral cavity data is less susceptible to interference from the diversity of dental morphology and the quality of the mesh surface. Through artificial intelligence learning, the first denture reference line corresponding to the first oral cavity data can be accurately identified. Mapping the first denture reference line onto the more accurate second oral cavity data facilitates subsequent data storage and design. By combining the first and second oral cavity data under different pressure conditions, the edge trimming effect is achieved. The denture edge line of the second oral cavity data is automatically and accurately identified, which also facilitates subsequent digital storage and design.

[0087] To facilitate understanding, the specific details involved in the above method for determining the edge line of dentures will be explained in detail below.

[0088] To improve computational efficiency and recognition accuracy, the step S120, which involves identifying the denture reference line from the first oral cavity data using the trained AI model, may include the following sub-steps:

[0089] Sub-step S121 involves preprocessing the first oral cavity data to obtain preprocessed first oral cavity data. The preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation, and surface feature fusion. These preprocessing procedures will be described in detail later.

[0090] Sub-step S122: Input the preprocessed first oral cavity data into the AI ​​model to obtain the denture reference line recognition result output by the AI ​​model; wherein, the AI ​​model is obtained by training a three-dimensional deep learning model based on multiple sample oral cavity data with denture reference line annotation data, and the sample oral cavity data is used to represent the oral cavity of the sample object under the first pressure state.

[0091] The first denture reference line can be the first myostatic line. At this time, the denture reference line annotation data can be myostatic annotation data, which includes myostatic line data or myostatic region data. The denture reference line recognition result output by the AI ​​model is the myostatic recognition result, which can be the identified myostatic line or the identified myostatic region.

[0092] The first denture reference line can also be the first denture edge line. In this case, the denture reference line annotation data can be myostatic annotation data or denture edge line annotation data. The denture reference line recognition result output by the AI ​​model can be the myostatic recognition result or the denture edge line recognition result.

[0093] Sub-step S123: Determine the first denture reference line based on the denture reference line recognition result.

[0094] When the first denture reference line is the first myostatic line and the denture reference line recognition result is the myostatic recognition result, if the myostatic recognition result is the identified myostatic line, then the identified myostatic line can be directly used as the first myostatic line; if the myostatic recognition result is the identified myostatic area, then the boundary line of the identified myostatic area can be directly determined as the first myostatic line.

[0095] When the reference line for the first denture is the edge line of the first denture, and the denture reference line identification result is the myostatic identification result, if the myostatic identification result is the identified myostatic line, then the identified myostatic line can be used as the first myostatic line, and the edge line of the first denture can be determined based on the first myostatic line; if the myostatic identification result is the identified myostatic area, then the boundary line of the identified myostatic area can be first determined as the first myostatic line, and then the edge line of the first denture can be determined based on the first myostatic line. Optionally, the step of determining the edge line of the first denture based on the first myostatic line may include: determining an equidistant line approximately 2mm-5mm from the boundary of the first myostatic line towards the gum line as the edge line of the first denture.

[0096] When the reference line for the first denture is the edge line of the first denture, and the denture reference line recognition result is the denture edge line recognition result, the denture edge line in the denture edge line recognition result can be directly determined as the edge line of the first denture.

[0097] This will give you the reference line for the first denture.

[0098] To improve the accuracy of the mapping, the step S130, which maps the first denture reference line onto the second oral data based on the registration information between the first and second oral data to obtain the edge line of the target denture, may include the following sub-steps:

[0099] Sub-step S131: Using a preset rigid body region, register the first oral cavity data and the second oral cavity data to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla.

[0100] The specific registration process can be found in existing technologies, and will not be elaborated here.

[0101] Sub-step S132: Based on the registration information, the first denture reference line is mapped onto the second oral cavity data to obtain the second denture reference line.

[0102] Sampling points can be obtained from the first denture reference line. Based on the registration information, neighboring points (i.e., matching points) are found on the second oral data. The neighboring points are fitted into a three-dimensional curve, which is the second denture reference line. Based on this, sub-step S132 can be implemented through the following process: obtaining multiple sampling points from the first denture reference line; determining the matching points of each sampling point on the second oral data according to the registration information; and performing three-dimensional curve fitting on each matching point to obtain the second denture reference line.

[0103] When the reference line for the first denture is the first myostatic line, the reference line for the second denture is the second myostatic line; when the reference line for the first denture is the edge line of the first denture, the reference line for the second denture is the edge line of the second denture.

[0104] Sub-step S133: Determine the edge line of the target denture based on the second denture reference line.

[0105] To improve data quality and enhance the precision and accuracy of data processing, the second denture reference line can be smoothed to obtain a smoothed denture reference line; based on the smoothed denture reference line, the edge line of the target denture can be determined.

[0106] Optionally, the above-mentioned smoothing process of the second denture reference line to obtain a smoothed denture reference line may include: smoothing the second denture reference line using a target smoothing algorithm to obtain a smoothed denture reference line; wherein, the target smoothing algorithm is determined according to the preset morphological characteristics and processing requirements of the denture, and the target smoothing algorithm includes one or more of the following: Laplace smoothing algorithm, least squares smoothing algorithm, and curvature-based feature smoothing algorithm.

[0107] Optionally, determining the target denture edge line based on the smoothed denture reference line may include: when the second denture reference line is the second myostatic line, the target denture edge line can be determined based on the smoothed second myostatic line; when the second denture reference line is the second denture edge line, the smoothed second denture edge line can be directly determined as the target denture edge line. Optionally, the step of determining the target denture edge line based on the smoothed second myostatic line may include: determining an equidistant line approximately 2mm-5mm from the boundary of the smoothed second myostatic line towards the gingiva as the target denture edge line.

[0108] In some possible embodiments, the above AI model can be trained through the following two steps:

[0109] A1. Obtain oral data from multiple samples with denture reference line annotations.

[0110] The initial oral data, such as the scanned 3D plaster model, can be manually marked with myostatic lines, myostatic regions, and denture edge lines. Then, preprocessing such as mesh optimization can be performed to reduce the amount of computation while preserving geometric features. Finally, the preprocessed data can be fed into the constructed 3D deep learning model for training.

[0111] In one possible implementation, multiple sample oral cavity data can be obtained by: acquiring multiple initial oral cavity data with denture reference line annotation data; wherein the initial oral cavity data is used to represent the oral cavity of the sample object under the first pressure state; preprocessing the multiple initial oral cavity data to obtain multiple sample oral cavity data; wherein the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, multi-scale data augmentation, sparse coding representation and surface feature fusion.

[0112] The above preprocessing methods will be described in detail below:

[0113] (a) Data denoising: Data denoising can be performed on the buccal and lingual sides of the data. After denoising, smoothing can be performed to improve the robustness of the algorithm.

[0114] (b) Data Alignment: By referencing oral features such as the dental arch curve, the 3D tooth mesh is initially aligned to reduce overfitting of the training data and facilitate the improvement of the network's generalization ability. Initial oral data such as the 3D plaster model can be aligned with oral features such as the dental arch curve of a preset reference model. Specifically, the 3D plaster model can be projected from multiple angles to obtain multiple 2D depth images. The dental arch curve is obtained by fitting the depth data of the multiple 2D depth images. The dental arch curve can be a contour line obtained by curve fitting the highest point of the tooth based on the depth data in the multiple 2D depth images, and it is in the shape of a bent crescent moon.

[0115] (c) Mesh Simplification: The number of vertices in flat regions is reduced by optimizing the algorithm while preserving their geometric features as much as possible, thereby improving processing efficiency and reducing computational complexity. Flat regions refer to areas in an image with relatively uniform visual characteristics, i.e., areas with small curvature variations. These regions may lack obvious color, texture, or shape variations and appear relatively uniform. Flat regions do not contain much visual detail or information and have a relatively small impact on image analysis and understanding; therefore, the number of mesh vertices can be appropriately reduced.

[0116] (d) Laplacian feature calculation (obtaining data with Laplacian features): The Laplacian operator of the mesh is used to capture and extract the features of the mesh in different domains, providing key information for further analysis. Among them, different domains refer to high frequencies (better mesh details) and low frequencies (better overall shape) in the frequency domain.

[0117] (e) Multi-scale data augmentation: Augmenting data at different scale levels (i.e., data augmentation through scaling) increases the number of samples, which helps the model learn different levels of detail and enhances its generalization ability.

[0118] (f) Sparse coding representation: Sparse coding techniques are used to represent the grid to eliminate redundant features, highlight important information, and may improve the interpretability of the model.

[0119] (g) Surface Feature Fusion: Calculate and fuse geometric features such as surface curvature and normal vectors of the mesh to enrich the model's input information and enhance its understanding of complex structures. Through surface feature fusion, data with geometric features such as surface curvature and normal vectors can be obtained, making it easier for 3D deep learning networks to extract features more effectively.

[0120] Optionally, preprocessing can be performed in the order of (a), (b), (c), (d), and (g) to ensure high processing efficiency and good training results. Alternatively, preprocessing can be performed in the order of (a) to (g), which can improve the accuracy of the trained recognition model and enhance its robustness and generalization ability. It should be noted that the specific preprocessing method can be selected according to actual needs, and this embodiment does not limit it.

[0121] A2. Train the 3D deep learning model based on the oral cavity data of each sample to obtain the AI ​​model; wherein, the 3D deep learning model includes any of the following: PointNet model, graph convolutional neural network model, MeshCNN model, multi-view model that analyzes 3D information from multiple perspectives, convolutional neural network model based on graph theory, and DiffusionNet model.

[0122] The aforementioned 3D deep learning models can be deep learning models specifically designed for processing 3D mesh data, and may include any of the following: PointNet models that directly manipulate point cloud data; Graph CNNs (i.e., graph convolutional neural network models) and MeshCNN models that utilize graph convolutional networks to process mesh topology; multi-view models that analyze 3D information from multiple perspectives; Spectral CNN models based on spectral theory (i.e., convolutional neural network models based on graph theory); and DiffusionNet models designed for geometric data, etc. The 3D deep learning model can be comprehensively considered and decided upon based on the needs of the actual application scenario, such as computational resource consumption and real-time requirements.

[0123] For both complete dentures and removable partial dentures, multiple sample oral data points for complete dentures and removable partial dentures can be acquired separately. These two types of sample oral data can be used to train an AI model that can be applied to the identification of myostatic lines / myostatic regions / denture edges in both cases. Alternatively, multiple sample oral data points for complete dentures can be used separately to train a first AI model for complete dentures, which can be applied to the identification of myostatic lines / myostatic regions / denture edges in the case of complete dentures. Conversely, multiple sample oral data points for removable partial dentures can be used separately to train a second AI model for removable partial dentures, which can be applied to the identification of myostatic lines / myostatic regions / denture edges in the case of removable partial dentures.

[0124] To facilitate understanding, the following section uses the first oral cavity data as the target 3D plaster model and the second oral cavity data as the oral digital impression as examples to provide a detailed introduction to the methods for determining the denture edge line in two cases: when the first denture reference line is the first myostatic line and when the first denture reference line is the edge line of the first denture.

[0125] I. The reference line for the first denture is the first muscle static line.

[0126] The above method for determining the edge line of dentures may include the following steps:

[0127] Step a1: Obtain the target 3D plaster model and oral digital impression corresponding to the target object.

[0128] Step a2 involves preprocessing the target 3D plaster model to obtain a preprocessed target 3D plaster model. The preprocessing may include one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation, and surface feature fusion.

[0129] Step a3: Input the preprocessed target 3D plaster model into the first recognition model to obtain the myostatic force recognition result output by the first recognition model; wherein, the first recognition model is obtained by training a 3D deep learning model based on multiple first sample 3D plaster models with myostatic force annotation data, and the myostatic force annotation data includes myostatic force line data or myostatic force region data.

[0130] The above myostatic force recognition results correspond to the labeled data used during the training of the first recognition model, and can be the recognized myostatic force lines or the recognized myostatic force regions.

[0131] Step a4: Determine the first myostatic line based on the myostatic identification results.

[0132] In one possible implementation, when the myostatic force identification result is the identified myostatic force line, the identified myostatic force line can be directly used as the first myostatic force line; when the myostatic force identification result is the identified myostatic force region, the boundary line of the identified myostatic force region (i.e., the dividing line between the myostatic force region and the myodynamic force region) can be directly determined as the first myostatic force line.

[0133] In another possible implementation, the myostatic force identification results can be adjusted interactively: the myostatic force identification results are displayed on an interactive interface for users to view and adjust them; in response to the user's adjustment operation, the adjusted myostatic force identification result is determined, and a first myostatic force line is determined based on the adjusted myostatic force identification result. Specifically, when the myostatic force identification result is the identified myostatic force line, the adjusted myostatic force identification result is the adjusted myostatic force line, and this adjusted myostatic force line can be determined as the first myostatic force line; when the myostatic force identification result is the identified myostatic force region, the adjusted myostatic force identification result is the adjusted myostatic force region, and the boundary line of the adjusted myostatic force region can be determined as the first myostatic force line.

[0134] Step a5: Using a preset rigid body region, register the oral digital impression and the target three-dimensional plaster model to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla.

[0135] Step a6: Based on the registration information, map the first myostatic line onto the oral digital impression to obtain the second myostatic line.

[0136] See Figure 5 The diagram shows a principle of myostatic line mapping. After obtaining the oral digital impression and the first myostatic line on the target three-dimensional plaster model, the oral digital impression and the target three-dimensional plaster model can be registered according to the alveolar ridge region, and the myostatic line (i.e., the first myostatic line) can be mapped onto the digital impression (i.e., the oral digital impression).

[0137] Sampling points can be obtained from the first myostatic line. Based on the registration information, neighboring points (i.e., matching points) are found on the digital oral impression. The neighboring points are fitted into a three-dimensional curve, which is the second myostatic line. Based on this, sub-step S132 can be implemented through the following process: obtaining multiple sampling points from the first myostatic line; determining the matching points of each sampling point on the digital oral impression according to the registration information; and performing three-dimensional curve fitting on each matching point to obtain the second myostatic line.

[0138] This method maps the myostatic lines identified on the 3D plaster model corresponding to the traditional impression method onto the oral digital impression, ensuring a high-precision digital model while obtaining accurate myostatic boundary lines.

[0139] Step a7: Determine the target denture edge line based on the second myostatic line.

[0140] To improve data quality and enhance the precision and accuracy of data processing, the second myostatic line can be smoothed to obtain a smoothed second myostatic line; based on the smoothed second myostatic line, the edge line of the target denture can be determined.

[0141] Optionally, the above-mentioned smoothing process of the second myostatic line to obtain the smoothed second myostatic line may include: smoothing the second myostatic line using a target smoothing algorithm to obtain the smoothed second myostatic line; wherein, the target smoothing algorithm is determined according to the preset morphological characteristics and processing requirements of the denture, and the target smoothing algorithm includes one or more of the Laplace smoothing algorithm, the least squares smoothing algorithm, and the curvature-based feature smoothing algorithm.

[0142] In one possible implementation, the target denture edge line can be directly determined based on the smoothed second myostatic line. For example, an equidistant line approximately 2mm-5mm from the boundary of the smoothed second myostatic line towards the gum line can be defined as the target denture edge line. In another possible implementation, the smoothed second myostatic line can be adjusted interactively: the smoothed second myostatic line is displayed on an interactive interface for user viewing and adjustment; in response to user adjustments, the adjusted target myostatic line is determined, and the target denture edge line is determined based on this adjusted target myostatic line. For example, an equidistant line approximately 2mm-5mm from the boundary of the adjusted target myostatic line towards the gum line can be defined as the target denture edge line.

[0143] II. The reference line for the first denture is the edge line of the first denture.

[0144] The above method for determining the edge line of dentures may include the following steps:

[0145] Step b1: Obtain the target 3D plaster model and oral digital impression corresponding to the target object.

[0146] Step b2 involves preprocessing the target 3D plaster model to obtain a preprocessed target 3D plaster model. The preprocessing may include one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation, and surface feature fusion.

[0147] Step b3: Input the preprocessed target 3D plaster model into the first recognition model to obtain the myostatic recognition result output by the first recognition model, and determine the first denture edge line according to the first myostatic line corresponding to the myostatic recognition result; or, input the preprocessed target 3D plaster model into the second recognition model to obtain the denture edge line recognition result output by the second recognition model, and determine the denture edge line in the denture edge line recognition result as the first denture edge line.

[0148] The first recognition model is obtained by training a three-dimensional deep learning model on multiple first-sample three-dimensional plaster models with myostatic annotation data, including myostatic line data or myostatic region data; the second recognition model is obtained by training a three-dimensional deep learning model on multiple second-sample three-dimensional plaster models with denture edge line annotation data.

[0149] Step b4: Using a preset rigid body region, register the oral digital impression and the target three-dimensional plaster model to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla.

[0150] Step b5: Based on the registration information, map the edge line of the first denture onto the digital dental impression to obtain the edge line of the second denture.

[0151] Step b6: Determine the edge line of the target denture based on the edge line of the second denture.

[0152] The process and mapping method for obtaining the above registration information can be referred to the corresponding content of the foregoing embodiments, and will not be repeated here. The step of determining the target denture edge line based on the edge line of the second denture may include: smoothing the edge line of the second denture to obtain a smoothed edge line of the second denture; and determining the target denture edge line based on the smoothed edge line of the second denture.

[0153] In one possible implementation, the smoothed edge line of the second denture can be obtained as follows: the edge line of the second denture is smoothed using a target smoothing algorithm to obtain the smoothed edge line of the second denture; wherein, the target smoothing algorithm is determined according to the preset morphological characteristics and processing requirements of the denture, and the target smoothing algorithm includes one or more of the following: Laplace smoothing algorithm, least squares smoothing algorithm, and curvature-based feature smoothing algorithm.

[0154] In one possible implementation, the smoothed edge line of the second denture can be directly determined as the target denture edge line; alternatively, the target denture edge line can be determined as follows: display the smoothed edge line of the second denture on the interactive interface; in response to the user's adjustment operation on the smoothed edge line of the second denture, determine the adjusted denture edge line and set the adjusted denture edge line as the target denture edge line.

[0155] In this embodiment, based on the traditional impression method, three-dimensional deep learning technology is used to accurately mark / identify the myostatic line / denture edge line, ensuring better robustness for different dentition shapes; by combining the plaster model obtained by the traditional impression method with the oral digital impression, a high-precision digital model is ensured while obtaining an accurate myostatic boundary line, thereby obtaining an accurate denture edge line.

[0156] In an optional embodiment, after the steps of obtaining the first oral cavity data and the second oral cavity data corresponding to the target object, the method further includes: judging the data integrity of the first oral cavity data and the second oral cavity data to obtain the degree of integrity; and determining the accuracy of the edge line of the target denture based on the degree of integrity.

[0157] Optionally, the degree of completeness can be determined as follows: determine whether the first oral data and / or the second oral data contain a preset key region, and obtain a containment determination result; wherein, the key region corresponding to the first oral data includes a first designated region, and the key region corresponding to the second oral data includes a second designated region; determine the degree of completeness based on the containment determination result.

[0158] In one possible implementation, both the first and second designated regions may include at least two of the following: the labial frenulum, the buccal frenulum, the maxillary tuberosity, the pterygomaxillary notch, and the region 2 mm posterior to the maxillary fossa.

[0159] In another possible implementation, considering that digital oral impressions need to confirm the complete scanning of key areas such as the labial frenulum, buccal frenulum, vestibular mucosal folds, lower zygomatic border, and buccal aspect of the maxillary tuberosity, to ensure that the scan data includes complete myostatic lines; the target 3D plaster model needs to completely cover key areas such as the alveolar ridge, jaw structure, palate, labial, buccal, and lingual mucosa, frenulum, and salivary gland openings, to ensure accurate occlusal relationships and clear edges, reflecting the detailed oral structure of the target subject and ensuring accurate extraction of myostatic lines or denture margins. Based on this, the first designated area may include at least two of the alveolar ridge, jaw structure, palate, labial, buccal, and lingual mucosa, frenulum, and salivary gland openings; the second designated area may include at least two of the labial frenulum, buccal frenulum, vestibular mucosal folds, lower zygomatic border, and buccal aspect of the maxillary tuberosity.

[0160] AI can be used to determine whether a preset key area is included. The inclusion determination result can include the inclusion status of different key areas, such as whether the oral digital impression includes other key areas besides the labial frenulum. Based on the inclusion determination result, the inclusion percentage corresponding to the first oral data and / or the inclusion percentage corresponding to the second oral data can be determined, wherein the inclusion percentage corresponding to the first oral data and the inclusion percentage corresponding to the second oral data are calculated separately. Based on the inclusion percentage corresponding to the first oral data and / or the inclusion percentage corresponding to the second oral data, the degree of completeness can be determined.

[0161] When determining the inclusion percentage of key regions, the percentage of each key region can be preset, and the inclusion percentage can be determined based on the percentage and inclusion status of each key region. The percentages of different key regions can be the same or different; when the percentages of different key regions are the same, the inclusion percentage can be obtained by dividing the number of included key regions by the total number of key regions; when the percentages of different key regions are different, the inclusion percentages of the included key regions can be summed to obtain the inclusion percentage. If only the inclusion percentage corresponding to the first oral cavity data or the inclusion percentage corresponding to the second oral cavity data exists, then the inclusion percentage corresponding to the first oral cavity data or the inclusion percentage corresponding to the second oral cavity data is directly used as the completeness degree; if there are inclusion percentages corresponding to the first oral cavity data and the inclusion percentages corresponding to the second oral cavity data, then the two (i.e., the two inclusion percentages) can be weighted and summed to obtain the completeness degree. The weights of the two can be set according to actual needs and are not limited here.

[0162] It should be noted that the above-mentioned key areas can be selected according to actual needs, and this embodiment does not limit this.

[0163] Optionally, when determining the accuracy of the target denture edge line based on the degree of integrity, the accuracy can be obtained by looking up the corresponding integrity level in a preset correspondence between integrity and accuracy. Accuracy can be divided into multiple levels according to actual needs, such as two levels (accurate and inaccurate), or three levels (high, medium, and low); accuracy can also be expressed as a percentage, for example, the accuracy of the target denture edge line is 80%.

[0164] Optionally, after determining the integrity of the first oral cavity data and the second oral cavity data and obtaining the integrity level, the above method further includes: determining whether the integrity level is less than a preset integrity threshold; if it is less than the integrity threshold, issuing a data incompleteness reminder.

[0165] The integrity threshold can be set according to actual needs and is not limited here. When issuing a data incompleteness alert, it can only indicate which data is incomplete, for example, the first oral cavity data is incomplete; it can also further indicate which part of which data is incomplete, for example, the maxillary tuberosity buccal side of the second oral cavity data is incomplete. This allows users to easily understand the data incompleteness situation and replace the data in a timely manner.

[0166] In one optional embodiment, the target denture edge line is displayed on the interactive interface, where the user can adjust it. However, since the adjustment is performed on a two-dimensional display interface (i.e., the interactive interface), directly adjusting the edge line according to the user's two-dimensional commands can easily lead to excessive movement, causing it to deviate from the myostatic zone or become too close to the myodynamic zone. Complete dentures or removable partial dentures designed based on these edge lines, which may deviate from the myostatic zone or become too close to the myodynamic zone, are prone to displacement or collision during chewing and other physiological movements, causing discomfort for the patient. Furthermore, since the patient's oral cavity is not a regular cylinder, simply translating or enlarging / shrinking the target denture edge line according to the user's two-dimensional commands can cause it to detach from the model surface (i.e., the surface of the second oral data), resulting in a mismatch between the target denture edge line and the patient's actual oral condition, making subsequent denture design impossible.

[0167] To address the aforementioned issues, after the step of mapping the first denture reference line onto the second oral data based on the registration information between the first and second oral data to obtain the target denture edge line, the method further includes: when a first command is detected, determining the first pose change of the target denture edge line relative to its initial position, wherein the first command is a user command to adjust the pose of the target denture edge line in the second oral data; based on the first pose change and preset reference conditions, determining the first adjusted pose of the target denture edge line, and updating and displaying the pose of the target denture edge line based on the first adjusted pose of the target denture edge line, wherein the preset reference conditions include the fit between the target denture edge line and the outer surface of the second oral data, and the target denture edge line conforming to... The method involves simulating physiological movement or the target denture edge line being located within the denture coverage area, or in one or more of the following ways: when a first pose change is detected that causes the target denture edge line to deviate from the preset reference conditions, the pose of the target denture edge line is adaptively updated according to the preset reference conditions; or, the pose of the target denture edge line is not updated, and the user confirms the result again, and the pose of the target denture edge line is adjusted according to the user confirms the result again; and / or when a first adjusted pose is detected that causes the target denture edge line to deviate from the preset reference conditions, the pose of the target denture edge line is adaptively updated according to the preset reference conditions; or, the pose of the target denture edge line is not updated, and the user confirms the result again, and the pose of the target denture edge line is adjusted according to the user confirms the result again.

[0168] It's understandable that not updating the pose of the target denture edge line means not moving or offsetting the target denture edge line, and not updating the displayed pose after any movement or offset. Updating the pose of the target denture edge line can be understood as continuing to move or offset the target denture edge line, and updating the displayed pose after any movement or offset. Adjusting the pose of the target denture edge line based on the user's confirmation can either mean not updating the pose or updating it, depending on the user's choice. When not updating the pose, a prompt or other feedback message can be displayed to provide the user with some feedback.

[0169] Therefore, in this embodiment, the first adjustment pose can be recalculated based on the first pose change, such as the movement distance of the target denture edge line relative to its initial pose and / or the offset angle of the target denture edge line relative to its initial pose included in the user's first instruction, combined with the outer surface of the second oral data, and the preset reference conditions (medical reference requirements) such as the determined denture coverage area or simulated physiological movement. Some adjustment instructions in the first pose change are ignored and some adjustment instructions in the first pose change are executed. That is, instructions in the first pose change that do not meet the medical reference requirements are ignored, and instructions in the first pose change that meet the medical reference requirements are executed. This ensures that the pose of the target denture edge line (first adjustment pose) on the two-dimensional display interface always meets the preset reference conditions, that is, meets the basic medical requirements, such as the target denture edge line should be within the denture coverage area, or meets the simulated physiological movement, that is, during chewing or mandibular movement, the target denture edge line will not move or the movement range of the target denture edge line is within a preset range. The preset range can be the range corresponding to the denture coverage area.

[0170] Specifically, the first position change can be decomposed according to preset reference conditions. Some or all of the instructions in the first position change that meet the preset reference conditions are determined as the first adjustment position of the target denture edge line, while some or all of the instructions in the first position change that do not meet the preset reference conditions are ignored. The preset reference conditions may include: the target denture edge line should fit the outer surface of the second oral cavity data, the target denture edge line should be within the denture coverage area, or the target denture edge line should conform to one or more of the simulated physiological movements.

[0171] For example, the first instruction includes the user clicking the target denture edge line with the mouse to move it horizontally or diagonally by an X distance. If the X distance causes the target denture edge line to not conform to the outer surface of the second oral data, not be within the denture coverage area, or not conform to the simulated physiological movement, then the instruction to move the target denture edge line horizontally or diagonally by an X distance will be ignored and not executed. This indicates that the first pose change does not meet the preset reference conditions. At this time, there are two operation methods: First, the computer automatically enlarges or reduces the first pose change according to a preset ratio to make it conform to the simulated physiological movement or be within the denture coverage area, and determines it as the first adjustment pose, and displays the updated target denture edge line; Second, the adjusted target denture edge line is not displayed first, and a prompt box is issued to inform the user that there may be a problem with this adjustment, and asks the user to confirm again. Based on the result of the user's confirmation, the first adjustment pose is determined; if the user indicates "confirm", this adjustment method is adopted as the first adjustment pose, and the pose of the target denture edge line is updated according to the user's instruction; if the user indicates "abandon modification", the first adjustment pose is 0, and the pose of the target denture edge line is not updated according to the user's instruction.

[0172] For example, the first instruction includes the X distance that the user moves diagonally along the edge of the target denture by clicking the mouse, and decomposing the X distance into vertical (e.g.) Figure 6 The Z-direction, vertical movement (referring to moving up and down along the gingival surface), and the B-distance (such as...) are all considered vertical movements. Figure 6 In the X direction, the lateral movement (referring to the forward and backward movement along the outer surface of the second oral cavity data) is a distance C. The lateral movement distance C is ignored and not executed, while the vertical movement distance B is executed. This is equivalent to projecting the diagonal X-distance movement onto the vertical plane (e.g., ...). Figure 6 In the XZ plane), the first adjustment pose of the target denture edge line is to move the target denture edge line up and down by a distance B along the direction of the gingival surface, and automatically calculate and generate the latest target denture edge line based on the target denture edge line after vertical movement of B distance and the outer surface of the second oral cavity data, so that the target denture edge line fits the outer surface of the model.

[0173] For example, after determining the first adjustment posture, it can be determined again whether the target denture edge line conforms to simulated physiological movement or is located within the denture coverage area. If so, it means that the first adjustment posture meets the medical reference conditions, and it can be adjusted according to the first adjustment posture, with the adjusted target denture edge line displayed on the interactive interface. If not, it means that the first adjustment posture does not meet the medical reference conditions, and there are two operation methods: First, the computer automatically enlarges or reduces the first adjustment posture according to a preset ratio to make it conform to simulated physiological movement or be located within the denture coverage area; Second, the adjusted target denture edge line is not displayed first, and a prompt box is issued, indicating to the user that there may be problems with this adjustment, and asking the user to confirm again "whether to use this adjustment method to modify"; If the user indicates "confirm", this adjustment method will be used to modify, and the posture of the target denture edge line will be updated according to the user's instructions; If the user indicates "abandon modification", the posture of the target denture edge line will not be updated according to the user's instructions.

[0174] In one embodiment, based on a first instruction, the edge line of the target denture can be controlled to rotate relative to the current mouse coordinates to the pose indicated by the first instruction. At this time, the interactive interface displays the edge line of the target denture at the mouse click position. Simultaneously, the angle or distance of rotation of the target denture edge line around the target denture edge line is the projection of the mouse movement distance onto a vertical plane (e.g., ...). Figure 6 The XZ plane in the middle is used to determine this.

[0175] It should be noted that the myostatic region can be divided into upper and lower parts by the edge line of the target denture. Therefore, the denture coverage area can refer to the entire myostatic region, or specifically the upper part of the myostatic region divided by the edge line of the target denture. This upper part includes rigid areas such as the gingiva or teeth. When wearing a complete denture or a removable partial denture, this upper part is exactly covered by the denture. Simulated physiological motion refers to simulated physiological motion automatically generated by inputting second oral data into a motion simulation deep learning model. This simulated physiological motion can be selectively displayed on the interactive interface based on user commands. When the user selects to display the simulated physiological motion on the interactive interface, the second oral data on the interface will change according to the simulated physiological motion. The user can visually observe whether the edge line of the target denture is suitable and can move or adjust the edge line. When the user chooses not to display the simulated physiological motion on the interactive interface, the interface can directly output the computer's judgment on whether the edge line of the target denture conforms to the simulated physiological motion.

[0176] The simulated physiological movements automatically generated by the deep learning model for motion simulation can be generated based on the average movement of multiple patient training samples, or based on the mandibular movement trajectory and chewing movement trajectory obtained by CBCT (cone beam computed tomography) equipment, facial scanners, extraoral scanners or intraoral scanners.

[0177] In an optional embodiment, a three-dimensional denture model can be obtained based on the edge line of the target denture. After quickly printing the three-dimensional denture model, a solid denture can be obtained and tested by the user. Based on the results of the user's test, the edge line of the target denture or the three-dimensional denture model can be further adjusted to obtain a denture that better meets the user's needs.

[0178] Based on this, after the step of mapping the first denture reference line onto the second denture data according to the registration information between the first and second oral data to obtain the target denture edge line, the method further includes:

[0179] Based on the target denture edge line and the second oral cavity data, a three-dimensional denture model is generated and sent to a 3D printing device for printing the three-dimensional denture model.

[0180] When a user request to modify the 3D denture model is received, the revised target denture edge line and / or the revised 3D denture model are output based on the user request, the target denture edge line, and the second oral cavity data.

[0181] Corresponding to the above-described method for determining the denture edge line, this embodiment of the invention also provides a device for determining the denture edge line, see [link to relevant documentation]. Figure 7The diagram shows a structural schematic of a denture edge line determining device, which includes:

[0182] The data acquisition module 701 is used to acquire first oral cavity data and second oral cavity data corresponding to the target object; wherein, the first oral cavity data is used to represent the oral cavity of the target object under a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state;

[0183] The denture reference line recognition module 702 is used to recognize the denture reference line of the first oral cavity data according to the trained AI model, and obtain the first denture reference line.

[0184] The denture edge mapping module 703 is used to map the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data to obtain the target denture edge line.

[0185] The denture edge line determination device provided in this embodiment of the invention can acquire first oral cavity data and second oral cavity data corresponding to the target object when determining the denture edge line. The first oral cavity data represents the target object's oral cavity under a first pressure state, and the second oral cavity data represents the target object's oral cavity under a second pressure state, where the pressure under the second pressure state is less than the pressure under the first pressure state. A denture reference line is identified on the first oral cavity data using a trained AI model to obtain a first denture reference line. Based on the registration information between the first and second oral cavity data, the first denture reference line is mapped onto the second oral cavity data to obtain the target denture edge line. Since the impression pressure corresponding to the first oral cavity data is greater than that corresponding to the second oral cavity data, the accuracy of the second oral cavity data is higher than that of the first oral cavity data. The first denture reference line corresponding to the first oral cavity data is less susceptible to interference from the diversity of dental morphology and the quality of the mesh surface. Through artificial intelligence learning, the first denture reference line corresponding to the first oral cavity data can be accurately identified. Mapping the first denture reference line onto the more accurate second oral cavity data facilitates subsequent data storage and design. By combining the first and second oral cavity data under different pressure conditions, the edge trimming effect is achieved. The denture edge line of the second oral cavity data is automatically and accurately identified, which also facilitates subsequent digital storage and design.

[0186] Furthermore, the aforementioned denture reference line recognition module 702 is specifically used for: preprocessing the first oral cavity data to obtain preprocessed first oral cavity data; wherein, the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation, and surface feature fusion; inputting the preprocessed first oral cavity data into the AI ​​model to obtain the denture reference line recognition result output by the AI ​​model; wherein, the AI ​​model is obtained by training a three-dimensional deep learning model based on multiple sample oral cavity data with denture reference line annotation data, and the sample oral cavity data is used to represent the oral cavity of the sample object under the first pressure state; and determining the first denture reference line based on the denture reference line recognition result.

[0187] Furthermore, the first denture reference line includes the first myostatic line, and the denture reference line annotation data includes myostatic annotation data, which includes myostatic line data or myostatic region data; or,

[0188] The first denture reference line includes the edge line of the first denture, and the denture reference line annotation data includes myostatic annotation data or denture edge line annotation data.

[0189] Furthermore, the aforementioned denture edge mapping module 703 is specifically used for: registering the first oral cavity data and the second oral cavity data using a preset rigid body region to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla; mapping the first denture reference line onto the second oral cavity data according to the registration information to obtain the second denture reference line; and determining the target denture edge line according to the second denture reference line.

[0190] Furthermore, the aforementioned denture edge mapping module 703 is also used to: acquire multiple sampling points from the first denture reference line; determine the matching point of each sampling point on the second oral data according to the registration information; and perform three-dimensional curve fitting on each matching point to obtain the second denture reference line.

[0191] Furthermore, the aforementioned denture edge mapping module 703 is also used to: smooth the second denture reference line to obtain a smoothed denture reference line; and determine the target denture edge line based on the smoothed denture reference line.

[0192] Furthermore, the aforementioned device also includes a training module for: acquiring multiple sample oral cavity data with denture reference line annotation data; training a three-dimensional deep learning model based on each sample oral cavity data to obtain an AI model; wherein the three-dimensional deep learning model includes any of the following: PointNet model, graph convolutional neural network model, MeshCNN model, multi-view model that parses three-dimensional information from multiple perspectives, convolutional neural network model based on graph theory, and diffusion network DiffusionNet model.

[0193] Furthermore, the aforementioned training module is specifically used to: acquire multiple initial oral cavity data with denture reference line annotation data; wherein the initial oral cavity data is used to represent the oral cavity of the sample object under the first pressure state; preprocess the multiple initial oral cavity data to obtain multiple sample oral cavity data; wherein the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, multi-scale data augmentation, sparse coding representation, and surface feature fusion.

[0194] Furthermore, the aforementioned first oral cavity data is obtained by three-dimensional scanning of a plaster model obtained by taking an impression of the target object's oral cavity under the first pressure, or by three-dimensional scanning of the target object's historical dentures, or by scanning the target object's oral cavity under air pressure.

[0195] The second oral data is obtained by scanning the oral cavity of the target subject directly without physical pressure or standard atmospheric pressure.

[0196] Furthermore, the aforementioned device also includes:

[0197] The first judgment module is used to judge the integrity of the first oral cavity data and the second oral cavity data to obtain the degree of integrity.

[0198] The accuracy determination module is used to determine the accuracy of the target denture edge line based on the degree of integrity.

[0199] Furthermore, the aforementioned first judgment module is specifically used to: determine whether the first oral data and / or the second oral data contain a preset key region, and obtain a inclusion judgment result; wherein, the key region corresponding to the first oral data includes a first designated region, and the key region corresponding to the second oral data includes a second designated region; and determine the degree of completeness based on the inclusion judgment result.

[0200] Furthermore, the aforementioned device also includes:

[0201] The second judgment module is used to determine whether the integrity level is less than a preset integrity threshold.

[0202] The reminder module is used to issue a reminder if the data is incomplete if it is less than the integrity threshold.

[0203] Furthermore, the aforementioned device also includes a user adjustment module, configured to: upon detecting a first instruction, determine a first pose change of the target denture edge line relative to its initial position, wherein the first instruction is a user instruction to adjust the pose of the target denture edge line in the second oral cavity data; based on the first pose change and preset reference conditions, determine a first adjusted pose of the target denture edge line, and update and display the pose of the target denture edge line based on the first adjusted pose of the target denture edge line, wherein the preset reference conditions include one of the following: the target denture edge line conforms to the outer surface of the second oral cavity data; the target denture edge line conforms to simulated physiological movement; or the target denture edge line is located within the denture coverage area. One or more; wherein, when a first pose change is detected that causes the edge line of the target denture to not conform to the preset reference conditions, the pose of the edge line of the target denture is adaptively updated according to the preset reference conditions, or, the pose of the edge line of the target denture is not updated and the result of user reconfirmation is obtained and the pose of the edge line of the target denture is adjusted according to the result of user reconfirmation; and / or, when a first adjusted pose is detected that causes the edge line of the target denture to not conform to the preset reference conditions, the pose of the edge line of the target denture is adaptively updated according to the preset reference conditions, or, the pose of the edge line of the target denture is not updated and the result of user reconfirmation is obtained and the pose of the edge line of the target denture is adjusted according to the result of user reconfirmation.

[0204] Furthermore, the above-mentioned device also includes: a generation module, used to generate a three-dimensional denture model based on the target denture edge line and the second oral cavity data, and send the three-dimensional denture model to a 3D printing device for printing the three-dimensional denture model; and a modification module, used to output a revised target denture edge line and / or a revised three-dimensional denture model based on the user request, the target denture edge line and the second oral cavity data when a user request for modifying the three-dimensional denture model is received.

[0205] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0206] like Figure 8 As shown, an embodiment of the present invention provides a computer device 800, including a processor 801, a memory 802 and a bus. The memory 802 stores machine-readable instructions that can be executed by the processor 801. When the computer device 800 is running, the processor 801 communicates with the memory 802 through the bus. The processor 801 executes the machine-readable instructions to perform the steps of the above-described method for determining the edge line of a denture.

[0207] Specifically, the memory 802 and processor 801 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 801 runs the computer program stored in the memory 802, it can execute the above-mentioned method for determining the edge line of the denture.

[0208] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the denture edge line determination method described in the preceding method embodiments. The computer-readable storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0209] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0210] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0211] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0213] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0214] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the edge line of a denture, characterized in that, include: Acquire first oral cavity data and second oral cavity data corresponding to the edentulous target object; wherein, the first oral cavity data is used to represent the oral cavity of the target object under a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state; The first denture reference line is obtained by identifying the denture reference line based on the trained AI model of the first oral cavity data; the first denture reference line includes the first myostatic line or the first denture edge line, and the first myostatic line is the boundary line between the myostatic area and the myodynamic area. Based on the registration information between the first oral data and the second oral data, the first denture reference line is mapped onto the second oral data to obtain the target denture edge line; The step of mapping the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data to obtain the target denture edge line includes: Using a preset rigid body region, the first oral cavity data and the second oral cavity data are registered to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla; Based on the registration information, the first denture reference line is mapped onto the second oral data to obtain the second denture reference line; the second denture reference line includes a second myostatic line corresponding to the first myostatic line, or a second denture edge line corresponding to the first denture edge line. The edge line of the target denture is determined based on the second denture reference line; After acquiring the first and second oral cavity data corresponding to the edentulous target object, the method further includes: The integrity of the first and second oral cavity data is assessed to determine the degree of integrity. The accuracy of the target denture edge line is determined based on the degree of integrity. The process of determining the integrity of the first and second oral cavity data to obtain the degree of integrity includes: Determine whether the first oral data and / or the second oral data contain a preset key region to obtain a determination result; wherein, the key region corresponding to the first oral data includes a first designated region, and the key region corresponding to the second oral data includes a second designated region; both the first designated region and the second designated region include at least two of the following: labial frenulum, buccal frenulum, maxillary tuberosity, pterygomaxillary notch, and 2mm posterior to the maxillary fossa; or, the first designated region includes at least two of the following: alveolar ridge, jaw structure, palate, labial, buccal, and lingual mucosa, frenulum, and salivary gland opening; and the second designated region includes at least two of the following: labial frenulum, buccal frenulum, vestibular mucosal fold, lower border of the zygomatic bone, and buccal side of the maxillary tuberosity; The degree of completeness is determined based on the inclusion judgment result.

2. The method according to claim 1, characterized in that, The step of identifying the first denture reference line based on the trained AI model of the first oral data to obtain the first denture reference line includes: The first oral cavity data is preprocessed to obtain preprocessed first oral cavity data; wherein, the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, sparse coding representation and surface feature fusion; The preprocessed first oral cavity data is input into the AI ​​model to obtain the denture reference line recognition result output by the AI ​​model; wherein, the AI ​​model is obtained by training a three-dimensional deep learning model based on multiple sample oral cavity data with denture reference line annotation data, and the sample oral cavity data is used to represent the oral cavity of the sample object under the first pressure state; Based on the denture reference line identification results, the first denture reference line is determined.

3. The method according to claim 2, characterized in that, The first denture reference line includes a first myostatic line, and the denture reference line annotation data includes myostatic annotation data, which includes myostatic line data or myostatic region data; or... The first denture reference line includes the edge line of the first denture, and the denture reference line annotation data includes myostatic annotation data or denture edge line annotation data.

4. The method according to claim 1, characterized in that, The step of mapping the first denture reference line onto the second oral data based on the registration information to obtain the second denture reference line includes: Multiple sampling points were obtained from the first denture reference line; Based on the registration information, the matching points of each sampling point on the second oral cavity data are determined; Three-dimensional curve fitting is performed on each of the matching points to obtain the second denture reference line.

5. The method according to claim 1, characterized in that, Determining the edge line of the target denture based on the second denture reference line includes: The second denture reference line is smoothed to obtain a smoothed denture reference line. The edge line of the target denture is determined based on the smoothed denture reference line.

6. The method according to claim 1, characterized in that, The method further includes: Acquire oral cavity data from multiple samples with denture reference line annotations; The AI ​​model is obtained by training a three-dimensional deep learning model based on the oral cavity data of each sample; wherein the three-dimensional deep learning model includes any one of the following: PointNet model, graph convolutional neural network model, MeshCNN model, multi-view model that analyzes three-dimensional information from multiple perspectives, convolutional neural network model based on graph theory, and diffusion network DiffusionNet model.

7. The method according to claim 6, characterized in that, The acquisition of multiple sample oral data with denture reference line annotations includes: Acquire multiple initial oral cavity data with denture reference line annotation data; wherein, the initial oral cavity data is used to represent the oral cavity of the sample object under a first pressure state; The multiple initial oral cavity data are preprocessed to obtain the multiple sample oral cavity data; wherein, the preprocessing includes one or more of the following: data denoising, data alignment, mesh simplification, Laplacian feature calculation, multi-scale data augmentation, sparse coding representation and surface feature fusion.

8. The method according to claim 1, characterized in that, The first oral cavity data is obtained by three-dimensional scanning of a plaster model of the oral cavity of the target object under a first pressure, or by three-dimensional scanning of the historical dentures of the target object, or by scanning the oral cavity of the target object under an air pressure state; The second oral cavity data was obtained by directly scanning the oral cavity of the target object without physical pressure or standard atmospheric pressure.

9. The method according to claim 1, characterized in that, After determining the integrity level of the first and second oral cavity data, the method further includes: Determine whether the degree of integrity is less than a preset integrity threshold; If the data is less than the integrity threshold, an incomplete data alert will be issued.

10. The method according to claim 1, characterized in that, After mapping the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data to obtain the target denture edge line, the method further includes: When the first instruction is detected, the first pose change of the target denture edge line relative to the initial position is determined, wherein the first instruction is an instruction from the user to adjust the pose of the target denture edge line in the second oral cavity data; Based on the first pose change and preset reference conditions, the first adjusted pose of the target denture edge line is determined, and the pose of the target denture edge line is updated and displayed based on the first adjusted pose of the target denture edge line. The preset reference conditions include one or more of the following: the target denture edge line fits the outer surface of the second oral data, the target denture edge line conforms to simulated physiological movement, or the target denture edge line is located within the denture coverage area. Wherein, when the first pose change is detected to cause the target denture edge line to not conform to the preset reference condition, the pose of the target denture edge line is adaptively updated according to the preset reference condition; or, the pose of the target denture edge line is not updated and the user confirms again, and the pose of the target denture edge line is adjusted according to the user confirms again; and / or When it is detected that the first adjustment pose causes the edge line of the target denture to not conform to the preset reference conditions, the pose of the edge line of the target denture is adaptively updated according to the preset reference conditions, or the pose of the edge line of the target denture is not updated and the result of user reconfirmation is obtained and the pose of the edge line of the target denture is adjusted according to the result of user reconfirmation.

11. The method according to any one of claims 1-10, characterized in that, After mapping the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data to obtain the target denture edge line, the method further includes: Based on the target denture edge line and the second oral cavity data, a three-dimensional denture model is generated, and the three-dimensional denture model is sent to a 3D printing device for printing the three-dimensional denture model; When a user request to modify the three-dimensional denture model is received, the revised target denture edge line and / or the revised three-dimensional denture model are output based on the user request, the target denture edge line, and the second oral cavity data.

12. A device for determining the edge line of a denture, characterized in that, include: The data acquisition module is used to acquire first oral cavity data and second oral cavity data corresponding to the edentulous target object; wherein, the first oral cavity data is used to represent the oral cavity of the target object under a first pressure state, and the second oral cavity data is used to represent the oral cavity of the target object under a second pressure state, wherein the pressure under the second pressure state is less than the pressure under the first pressure state; The denture reference line recognition module is used to recognize the denture reference line of the first oral data according to the trained AI model to obtain the first denture reference line; the first denture reference line includes the first myostatic line or the first denture edge line, and the first myostatic line is the boundary line between the myostatic area and the myodynamic area. The denture edge mapping module is used to map the first denture reference line onto the second oral data based on the registration information between the first oral data and the second oral data to obtain the target denture edge line; The denture edge mapping module is specifically used for: registering the first oral cavity data and the second oral cavity data using a preset rigid body region to obtain registration information; wherein, the rigid body region includes the alveolar ridge and / or the maxilla; mapping the first denture reference line onto the second oral cavity data according to the registration information to obtain a second denture reference line; the second denture reference line includes a second myostatic line corresponding to the first myostatic line, or a second denture edge line corresponding to the first denture edge line; and determining the target denture edge line according to the second denture reference line. The device further includes: The first judgment module is used to judge the integrity of the first oral cavity data and the second oral cavity data to obtain the degree of integrity. An accuracy determination module is used to determine the accuracy of the target denture edge line based on the degree of integrity. The first judgment module is specifically used to: determine whether the first oral data and / or the second oral data contain a preset key region, and obtain a inclusion judgment result; wherein, the key region corresponding to the first oral data includes a first designated region, and the key region corresponding to the second oral data includes a second designated region; both the first designated region and the second designated region include at least two of the following: labial frenulum, buccal frenulum, maxillary tuberosity, pterygomaxillary notch, and 2mm posterior to the lesser maxillary fossa; or, the first designated region includes at least two of the following: alveolar ridge, jaw structure, palate, labial, buccal, and lingual mucosa, frenulum, and salivary gland opening; and the second designated region includes at least two of the following: labial frenulum, buccal frenulum, vestibular mucosal fold, lower zygomatic bone line, and buccal side of the maxillary tuberosity; and determine the degree of integrity based on the inclusion judgment result.

13. A computer device, characterized in that, It includes a memory and a processor; the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements the method for determining the denture edge line as described in any one of claims 1-11.

14. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the denture edge line determination method according to any one of claims 1-11.

Images