Complete denture generation method, electronic equipment and computer readable storage medium

Abstract

The invention provides a complete denture generation method, electronic equipment and a computer readable storage medium, and relates to the technical field of digital oral cavities. The method comprises the following steps: acquiring an edentulous jaw three-dimensional model, face scanning data and oral cavity scanning data of a user; extracting upper and lower jaw feature information of the edentulous jaw three-dimensional model; based on the upper and lower jaw feature information of the edentulous jaw three-dimensional model, obtaining a target natural dentition model from a natural dentition database; and based on the face scanning data and the oral cavity scanning data, calibrating the target natural dentition model to obtain a complete denture three-dimensional model. The corresponding dentition is matched in the natural dentition database, and the complete denture is manufactured on the basis of the matched and screened dentition, so that compared with a traditional complete denture manufacturing method, the complete denture manufacturing method is quicker and more accurate, clinical operation links are reduced, the manufacturing period is shortened, and the manufacturing efficiency is improved. And the manufacturing efficiency of the complete denture and the fitness of the generated complete denture and a user are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of digital oral technology, and more specifically, to a method for generating complete dentures, an electronic device, and a computer-readable storage medium. Background Technology

[0002] With the increasing incidence of edentulous jaws and missing teeth, especially the growing oral health needs of the elderly, the demand for complete dentures has also increased significantly.

[0003] Currently, the traditional method of making complete dentures requires going through a series of steps, including initial impression, final impression, recording of jaw position relationship, manual tooth arrangement, and multiple clinical fittings. This usually requires the user to visit the clinic multiple times, takes a long time, and because the tooth arrangement process mainly relies on manual experience, it is difficult to guarantee the fit between the complete denture and the user.

[0004] Therefore, traditional methods of making complete dentures are not only time-consuming, but also have low efficiency and low fit. Summary of the Invention

[0005] The purpose of this application is to address the shortcomings of the prior art by providing a method for producing complete dentures, an electronic device, and a computer-readable storage medium, so as to solve the problems that the existing methods for producing complete dentures are not only time-consuming, but also have low efficiency and adaptability.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for generating complete dentures, the method comprising: Acquire the user's edentulous jaw 3D model, facial scan data, and oral scan data; Extract the maxillary and mandibular feature information of the edentulous three-dimensional model; Based on the maxillary and mandibular feature information of the edentulous three-dimensional model, the target natural dentition model is obtained from the natural dentition database, which includes multiple natural dentition models and the maxillary and mandibular feature information of each natural dentition model. Based on the facial scan data and the oral scan data, the target natural dentition model is calibrated to obtain a three-dimensional model of complete dentures. The three-dimensional model of complete dentures is used for 3D printing to obtain the user's complete dentures.

[0007] As one possible implementation, the extraction of maxillary and mandibular feature information from the edentulous 3D model includes: Determine the first occlusal plane of the edentulous three-dimensional model; The line connecting the incisor mastoid process and the mid-palatal suture is taken as the maxillary midline. The straight line passing through the incisor mastoid process at a predetermined distance and parallel to the first occlusal plane is taken as the anterior boundary line. The line connecting the maxillary alveolar ridge crest line is taken as the maxillary alveolar ridge crest curve. The maxillary midline is projected onto the mandible as the mandibular midline. The line connecting the midpoints of the left and right mandibular molar pads is taken as the posterior boundary line. The line connecting the mandibular alveolar ridge crest line is taken as the mandibular alveolar ridge crest curve.

[0008] As one possible implementation, the method further includes: Collect multiple natural tooth arch models; The natural dentition models are segmented by tooth position, and key points are determined based on the segmented tooth position information. A second occlusal plane is obtained by fitting the key points. Based on the second occlusal plane corresponding to each of the natural dentition models, the maxillary and mandibular feature information of each of the natural dentition models is determined, and the maxillary and mandibular feature information includes: maxillary feature line and mandibular feature line; The natural dentition models and their maxillary and mandibular feature information are associated and stored to construct the natural dentition database.

[0009] As one possible implementation, determining key points based on segmented tooth position information includes: Obtain point cloud data corresponding to the first molar, and calculate the Gaussian curvature corresponding to the first molar based on the point cloud data; Based on the Gaussian curvature, multiple high curvature points with curvature greater than a preset threshold are selected from the point cloud data; Clustering and deduplication are performed on multiple high curvature points to determine the near-mid tongue point.

[0010] As one possible implementation, the method of obtaining the second occlusal plane based on the key points includes: The second occlusal plane is obtained by fitting the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor.

[0011] As one possible implementation, the maxillary feature line includes: the maxillary midline, the anterior boundary line, and the maxillary dentition curve; the mandibular feature line includes: the mandibular midline, the posterior boundary line, and the mandibular dentition curve. The step of obtaining the target natural dentition model from the natural dentition database based on the maxillary and mandibular feature information of the edentulous 3D model includes: The maxillary and mandibular feature information of the edentulous 3D model is matched with the maxillary and mandibular feature information of each natural dentition model in the natural dentition database to select the target natural dentition model from multiple natural dentition models.

[0012] As one possible implementation, the step of performing feature matching between the maxillary and mandibular feature information of the edentulous 3D model and the maxillary and mandibular feature information of each natural dentition model in the natural dentition database, and selecting the target natural dentition model from multiple natural dentition models, includes: The maxillary midline, anterior boundary line, and maxillary alveolar ridge crest curve of the edentulous 3D model are aligned with the maxillary midline, anterior boundary line, and maxillary dentition curve of each of the natural dentition models, respectively. The mandibular midline, posterior boundary line, and mandibular alveolar ridge crest curve of the edentulous 3D model are aligned with the mandibular midline, posterior boundary line, and mandibular dentition curve of each of the natural dentition models, respectively, to determine the characteristic geometric deviation between the edentulous 3D model and each of the natural dentition models. The target natural dentition model is determined based on the characteristic geometric deviations between the edentulous 3D model and each of the natural dentition models.

[0013] As one possible implementation, the calibration of the target natural dentition model based on the facial scan data and the oral scan data to obtain a three-dimensional model of a complete denture includes: Based on the facial scan data and the oral scan data, the user's bite information is determined; Based on the occlusion information, the spatial position between the upper and lower jaws in the target natural dentition model is adjusted to obtain the three-dimensional model of the complete denture.

[0014] Secondly, embodiments of this application provide a complete denture fabrication device, the device comprising: The acquisition module is used to acquire the user's edentulous jaw 3D model, facial scan data, and oral scan data; The extraction module is used to extract the maxillary and mandibular feature information of the edentulous three-dimensional model; The matching module is used to obtain a target natural dentition model from a natural dentition database based on the maxillary and mandibular feature information of the edentulous 3D model. The natural dentition database includes multiple natural dentition models and maxillary and mandibular feature information of each natural dentition model. The generation module is used to calibrate the target natural dentition model based on the facial scan data and the oral scan data to obtain a three-dimensional model of the complete denture. The three-dimensional model of the complete denture is used for 3D printing to obtain the user's complete denture.

[0015] As one possible implementation, the extraction module is specifically used for: Determine the first occlusal plane of the edentulous three-dimensional model; The line connecting the incisor mastoid process and the mid-palatal suture is taken as the maxillary midline. The straight line passing through the incisor mastoid process at a predetermined distance and parallel to the first occlusal plane is taken as the anterior boundary line. The line connecting the maxillary alveolar ridge crest line is taken as the maxillary alveolar ridge crest curve. The maxillary midline is projected onto the mandible as the mandibular midline. The line connecting the midpoints of the left and right mandibular molar pads is taken as the posterior boundary line. The line connecting the mandibular alveolar ridge crest line is taken as the mandibular alveolar ridge crest curve.

[0016] As one possible implementation, the matching module is also used for: Collect multiple natural tooth arch models; The natural dentition models are segmented by tooth position, and key points are determined based on the segmented tooth position information. A second occlusal plane is obtained by fitting the key points. Based on the second occlusal plane corresponding to each of the natural dentition models, the maxillary and mandibular feature information of each of the natural dentition models is determined, and the maxillary and mandibular feature information includes: maxillary feature line and mandibular feature line; The natural dentition models and their maxillary and mandibular feature information are associated and stored to construct the natural dentition database.

[0017] As one possible implementation, the matching module is also used for: Obtain point cloud data corresponding to the first molar, and calculate the Gaussian curvature corresponding to the first molar based on the point cloud data; Based on the Gaussian curvature, multiple high curvature points with curvature greater than a preset threshold are selected from the point cloud data; Clustering and deduplication are performed on multiple high curvature points to determine the near-mid tongue point.

[0018] As one possible implementation, the matching module is also used for: The second occlusal plane is obtained by fitting the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor.

[0019] As one possible implementation, the maxillary feature line includes: the maxillary midline, the anterior boundary line, and the maxillary dentition curve; the mandibular feature line includes: the mandibular midline, the posterior boundary line, and the mandibular dentition curve; the matching module is specifically used for: The maxillary and mandibular feature information of the edentulous 3D model is matched with the maxillary and mandibular feature information of each natural dentition model in the natural dentition database to select the target natural dentition model from multiple natural dentition models.

[0020] As one possible implementation, the matching module is specifically used for: The maxillary midline, anterior boundary line, and maxillary alveolar ridge crest curve of the edentulous 3D model are aligned with the maxillary midline, anterior boundary line, and maxillary dentition curve of each of the natural dentition models, respectively. The mandibular midline, posterior boundary line, and mandibular alveolar ridge crest curve of the edentulous 3D model are aligned with the mandibular midline, posterior boundary line, and mandibular dentition curve of each of the natural dentition models, respectively, to determine the characteristic geometric deviation between the edentulous 3D model and each of the natural dentition models. The target natural dentition model is determined based on the characteristic geometric deviations between the edentulous 3D model and each of the natural dentition models.

[0021] As one possible implementation, the generation module is specifically used for: Based on the facial scan data and the oral scan data, the user's bite information is determined; Based on the occlusion information, the spatial position between the upper and lower jaws in the target natural dentition model is adjusted to obtain the three-dimensional model of the complete denture.

[0022] Thirdly, embodiments of this application provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the complete denture generation method as described in any of the first aspects above.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the complete denture generation method as described in any of the first aspects above.

[0024] In the complete denture generation method, electronic device, and computer-readable storage medium provided in this application embodiment, maxillary and mandibular feature information that quantitatively represents the user's personalized morphology is extracted based on the user's edentulous jaw 3D model. Then, based on a pre-constructed natural dentition database, feature matching is used to accurately select the target natural dentition model that most closely matches the user's jawbone morphology from multiple natural dentition models included in the database, thereby avoiding the subjectivity and repeated adjustments of manual trial tooth arrangement. Furthermore, by combining the user's facial scan data and oral scan data, personalized spatial calibration is performed on the target natural dentition model to ensure that the complete denture is highly compatible with the user in terms of both function and aesthetics. The entire process of complete denture generation provided in this application embodiment does not require multiple clinical trials, significantly shortening the user's treatment cycle. At the same time, the model matching method using a constructed natural dentition database significantly improves the accuracy of tooth arrangement and the fit of the complete denture, achieving efficient, accurate, and personalized digital generation of complete dentures. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a complete denture fabrication method provided in an embodiment of this application is shown. Figure 2 This illustration shows a schematic diagram of a feature line of an edentulous maxilla provided in an embodiment of this application; Figure 3 This illustration shows a schematic diagram of a feature line of an edentulous mandible provided in an embodiment of this application; Figure 4 A flowchart illustrating a method for constructing a natural dentition database according to an embodiment of this application is shown; Figure 5 This illustration shows a schematic diagram of a maxillary feature line of a natural dentition provided in an embodiment of this application; Figure 6 This illustration shows a schematic diagram of a mandibular feature line of a natural dentition provided in an embodiment of this application; Figure 7 A flowchart illustrating a key point determination method provided in an embodiment of this application is shown; Figure 8 This illustration shows a schematic diagram of a tooth arrangement effect provided in an embodiment of this application; Figure 9 This illustration shows a structural schematic diagram of a complete denture generation device provided in an embodiment of this application; Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0028] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0029] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0030] Traditional methods for making complete dentures typically require patients to visit the clinic 4-5 times, taking several weeks, and include the following procedures: Consultation and oral examination: Understand the user's needs and overall health status, examine the morphology and fullness of the alveolar ridge, the health status of the mucosa, the size and position of the tongue, and the saliva secretion, and make a preliminary assessment of the relationship between the upper and lower jaws.

[0031] Initial Imprint Taking: Using finished trays and imprinting materials such as alginate, a rough research model is quickly taken to produce personalized individual trays.

[0032] Taking the final impression: Based on the preliminary model, a custom-made tray, made of self-curing or light-curing resin, is created to perfectly fit the user's oral cavity morphology. Further, using the individual tray and fine impression materials, functional impression techniques are employed, allowing the user to perform functional movements of the lips, cheeks, and tongue to shape the impression margins. This obtains the precise morphology of the alveolar ridge and surrounding soft tissues in a functional state, ensuring a perfect fit between the denture margins and the soft tissues, thus obtaining a functional impression.

[0033] Jawline Relationship Recording: Fabricate the denture base and wax rim. A temporary denture base and wax rim are fabricated on a precise final impression model. Determine Vertical Distance: Use the wax rim to restore the lower third of the face (distance from the base of the nose to the chin) in a relaxed state, ensuring facial harmony and preventing an aged appearance or an overly long chin. Determine Horizontal Jawline Relationship: Guide the user to locate and record their physiological mandibular position, ensuring precise alignment of the upper and lower dentures. Determine Midline and Smile Line: Mark the facial midline, corner of the mouth line, and incisal edge lines of future teeth on the wax rim as an aesthetic reference for tooth arrangement.

[0034] Trial tooth arrangement: Artificial teeth are the part that restores chewing function and aesthetics in edentulous patients. The arrangement of artificial teeth should follow certain principles, ensuring not only good occlusion but also stable oral function of the complete denture. Furthermore, the arranged artificial teeth should harmonize with the patient's facial features to achieve aesthetic requirements. The line connecting the incisal edges of the upper anterior teeth should form a natural downward convex arc, harmonizing with the curve of the lower lip when smiling. The posterior teeth should form a normal occlusal curve. Tooth arrangement should adhere to functional, aesthetic, and biomechanical principles. These principles include: the contact points of the left and right central incisors should be located on the facial midline; and the position of the artificial teeth should be symmetrical and coordinated with the curvature of the dental arch. From the occlusal perspective, the incisal edge of the upper anterior teeth and the central groove of the upper posterior teeth, as well as the incisal edge of the lower anterior teeth and the central groove of the occlusal surface of the lower posterior teeth, should form a natural and continuous curve. The upper and lower artificial teeth should achieve uniform and extensive contact; generally, the mesiobuccal groove of the mandibular first molar should be opposite the mesiobuccal cusp of the maxillary first molar; the incisal edge of the upper central incisor should be approximately 2 mm below the lip. The mechanical principles of artificial tooth arrangement in complete dentures include: the occlusal plane should roughly bisect the intermaxillary distance; in special cases, to ensure the stability and retention of the mandibular denture, the plane can be lowered slightly; the occlusal plane should be roughly parallel to the alveolar ridge; artificial teeth should be arranged on the crest of the alveolar ridge; artificial teeth with a small cusp inclination should be selected; the anterior teeth should be arranged to form a shallow overbite as much as possible; and the posterior teeth should be arranged to form a suitable transverse and longitudinal occlusal curve.

[0035] Clinical trial fitting: The pre-aligned wax-type denture is placed into the user's mouth. Jaw position: Verify the vertical and horizontal alignment. Aesthetics: Assess the color, shape, alignment, and smile line of the teeth. Pronunciation: Have the user pronounce the teeth to check for any issues. Joint confirmation: The user can look in the mirror and provide feedback. Once confirmed, the final stage begins.

[0036] Denture Placement: After a trial fitting, the dental lab will box the wax-patterned dentures, remove the wax by heating, and replace it with pink resin for the base, which will then be cured under heat and pressure. After being removed from the box, the dentures will undergo fine grinding and polishing. The completed complete dentures will then be placed in the patient's mouth. Inspection: Check for excessively long margins and any pressure points. Occlusal Adjustment: Check the occlusal contact, mark high points with occlusal paper, and make fine adjustments to ensure all teeth make even and balanced contact during occlusion.

[0037] Based on the above, traditional methods of fabricating complete dentures typically require multiple visits from the patient and are time-consuming. Furthermore, because the tooth alignment process relies heavily on manual experience, it is difficult to guarantee a proper fit between the complete denture and the patient. Therefore, traditional methods of fabricating complete dentures are not only time-consuming but also inefficient and have low fit.

[0038] To address the aforementioned issues, this application provides a method for fabricating complete dentures. This method utilizes digital technology to accurately collect user information in a single step. Based on this, a corresponding dentition is matched in a natural dentition database. Then, complete dentures are fabricated based on the matched and selected dentition. Compared to traditional complete denture fabrication methods, this approach is faster and more precise. It not only reduces clinical procedures and shortens the fabrication cycle but also significantly improves the fabrication efficiency of complete dentures and the fit between the fabricated dentures and the user.

[0039] The following will provide a detailed description of the complete denture generation method provided in the embodiments of this application.

[0040] Figure 1 A schematic flowchart of a complete denture fabrication method according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the method specifically includes the following steps: S101. Obtain the user's edentulous jaw 3D model, facial scan data, and oral scan data.

[0041] Optionally, a high-precision 3D point cloud or mesh model of the edentulous areas of the user's upper and lower jaws is acquired using devices such as an intraoral scanner to form a 3D model of the edentulous jaw. Simultaneously, a 3D facial scanner is used to scan the user's face in a resting jaw position. Then, a jaw fork and wax occluder are used to maintain the user in this resting jaw position for another facial scan, obtaining the user's facial scan data. The jaw fork is 3D printed from a digital model and its structure includes a fixedly connected handle and a stacking plate, as well as a horseshoe-shaped occlusal fork detachably connected to the stacking plate. The occlusal fork has multiple impression material overflow holes. Finally, an oral scan is performed on the user, and the oral scan data of the user in a resting jaw position is recorded using the jaw fork and wax occluder.

[0042] S102. Extract the maxillary and mandibular feature information of the edentulous 3D model.

[0043] Optionally, the maxillary and mandibular feature information of the edentulous 3D model includes: maxillary feature lines and mandibular feature lines. Among them, referencing... Figure 2 The maxillary feature lines shown include: the maxillary midline, the anterior boundary line, and the maxillary alveolar ridge crest curve. (Refer to...) Figure 3 The mandibular feature lines shown include: the mandibular midline, the posterior boundary line, and the mandibular alveolar ridge crest curve.

[0044] Optionally, in this embodiment of the application, extracting the maxillary and mandibular feature information of the edentulous three-dimensional model includes: determining the first occlusal plane of the edentulous three-dimensional model; using the line connecting the incisor papillae and the midpalatal suture as the maxillary midline; using a straight line passing a predetermined distance in front of the incisor papillae and parallel to the first occlusal plane as the front boundary line; and connecting the maxillary alveolar ridge crest line as the maxillary alveolar ridge crest curve. Projecting the maxillary midline onto the mandible as the mandibular midline; using the line connecting the midpoints of the left and right mandibular molar retromolar pads as the rear boundary line; and connecting the mandibular alveolar ridge crest line as the mandibular alveolar ridge crest curve.

[0045] S103. Based on the maxillary and mandibular feature information of the edentulous three-dimensional model, obtain the target natural dentition model from the natural dentition database.

[0046] The natural dentition database includes multiple natural dentition models and their maxillary and mandibular feature information. Optionally, the extracted maxillary and mandibular feature lines of the edentulous jaw are matched with the corresponding maxillary and mandibular feature lines of each natural dentition model in the database. By calculating the geometric deviation between the maxillary and mandibular feature lines of the edentulous 3D model and the corresponding maxillary and mandibular feature lines of each natural dentition model in the database, the morphological consistency between each natural dentition model and the user's edentulous jaw is evaluated. The natural dentition model with the smallest deviation and the highest overall matching degree is selected as the target natural dentition model, ensuring that the dental arch morphology and intermaxillary relationship of the selected target natural dentition model are highly adapted to the user's individual anatomical structure.

[0047] It is worth noting that, based on the patient's edentulous jaw data, the target natural dentition model is obtained by fitting the edentulous jaw feature lines and the normal dentition feature lines respectively in a pre-constructed natural dentition database. This process replaces the tooth arrangement step in the traditional complete denture fabrication, simplifies the complex tooth arrangement process, and the dentition data of the obtained target natural dentition model is more consistent with the patient's jaw arch morphology.

[0048] S104. Based on facial and oral scan data, the target natural dentition model is calibrated to obtain a three-dimensional model of complete dentures.

[0049] Optionally, the user's occlusal information is determined by integrating facial and oral scan data. This occlusal information is used to construct personalized occlusal constraints for the user. Based on these constraints, the acquired target natural dentition model is spatially calibrated to adjust the three-dimensional relative poses between the upper and lower dentitions in the target natural dentition model, so that the dentition arrangement meets the user's facial harmony and functional needs. Finally, a three-dimensional model of a complete denture is generated. This three-dimensional model of a complete denture has accurate occlusal relationships and can be directly exported for 3D printing to manufacture solid complete dentures.

[0050] Based on this, the complete denture generation method according to the embodiments of this application extracts and quantifies the maxillary and mandibular features that represent the user's personalized morphology from the user's edentulous 3D model. Then, based on a pre-constructed natural dentition database, feature matching is used to accurately select the target natural dentition model that most closely matches the user's jawbone morphology from multiple natural dentition models included in the database, thereby avoiding the subjectivity and repeated adjustments of manual trial tooth arrangement. Furthermore, by combining the user's facial scan data and oral scan data, personalized spatial calibration is performed on the target natural dentition model to ensure that the complete denture is highly compatible with the user in terms of both function and aesthetics. The entire process of complete denture generation provided by the embodiments of this application does not require multiple clinical trials, significantly shortening the user's treatment cycle. At the same time, the model matching method by constructing a natural dentition database significantly improves the accuracy of tooth arrangement and the fit of the complete denture, realizing efficient, accurate, and personalized digital generation of complete dentures.

[0051] Figure 4 A flowchart illustrating a method for constructing a natural dentition database according to an embodiment of this application is shown. (Refer to...) Figure 4 As shown, the method specifically includes the following steps: S401. Collect multiple natural dentition models.

[0052] Optionally, data on factory-made complete dentures that satisfy the patient are collected, as well as data on overbite covering normal natural dentition, to obtain multiple natural dentition models, each of which covers the complete upper and lower jaw teeth and their surrounding structures.

[0053] S402. Perform tooth position segmentation on each natural dentition model, determine key points based on the segmented tooth position information, and obtain the second occlusal plane based on the key points.

[0054] Optionally, the trimesh library can be used to convert each natural dentition model into a standard 3D model file format (Wavefront OBJ, OBJ), and then the converted natural dentition models can be imported in batches into the pre-trained deep learning model ToothGroupNetwork for automated tooth segmentation. The trimesh library is an open-source Python library that supports reading and writing various common 3D file formats, geometric calculations, Boolean operations, and is widely used in fields such as dentistry and 3D printing. The pre-trained model ToothGroupNetwork is a deep learning-based semantic segmentation network specifically designed for 3D dentition models. It is trained on a dataset containing a large amount of labeled dentition data and can accurately identify the individual boundaries of upper and lower jaw teeth, automatically segmenting the complete dentition into individual teeth and mucosal regions. Based on this, the pre-trained model ToothGroupNetwork automatically segments the teeth of the batch-imported 3D models of natural dentition. During the segmentation process, each tooth is assigned a unique semantic label according to its position number, so as to mark and separate the 14 teeth in the upper and lower jaws one by one. At the same time, the mucosal region is assigned a label of 0, so as to achieve precise separation of teeth and soft tissues.

[0055] Optionally, key points include the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor. One side is, for example, the left side, and the other side is, for example, the right side.

[0056] For example, after tooth segmentation is completed, key points are extracted for specific tooth positions. Specifically, for the first molar, Gaussian curvature is calculated on its segmented point cloud data, and high curvature points are initially screened using an adaptive threshold. Then, a clustering algorithm is applied to perform spatial clustering to eliminate redundancy caused by dense point clouds. Finally, the point with the largest Gaussian curvature or the geometric center is selected as the mesial lingual point. For the central incisor, the highest point of its incisal edge region is identified, and the midpoint between the highest points of the left and right central incisal edges is taken as the midpoint of the central incisal edge.

[0057] Optionally, the above steps are based on key point fitting to obtain the second occlusal plane, including: fitting the second occlusal plane based on the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor.

[0058] For example, after obtaining the mesial-lingual apex of the left first molar, the mesial-lingual apex of the right first molar, and the midpoint of the incisal edge of the central incisor, an optimal plane passing through these three points is fitted using the least squares method; this is the second occlusal plane. This second occlusal plane not only conforms to the standard description of an occlusal plane in dentistry, but also ensures spatial consistency because each natural dentition model undergoes automatic orientation calibration through geometric center alignment and principal component analysis before being imported into the pre-trained model in batches. This provides a reliable reference benchmark for the construction of a natural dentition database and edentulous jaw matching.

[0059] S403. Based on the second occlusal plane corresponding to each natural dentition model, determine the maxillary and mandibular feature information of each natural dentition model.

[0060] Optionally, the maxillary and mandibular feature information includes: maxillary feature lines and mandibular feature lines. The maxillary feature lines include: the maxillary midline, the anterior boundary line, and the maxillary dentition curve; the mandibular feature lines include: the mandibular midline, the posterior boundary line, and the mandibular dentition curve.

[0061] Optionally, for each natural dentition model, based on determining the second occlusal plane corresponding to the natural dentition model, key feature lines of the maxilla and mandible are further annotated. For example, refer to... Figure 5 The maxillary characteristic lines of the natural dentition shown are defined as follows: in the maxillary portion, the perpendicular line connecting the mesial incisal edges of the two central incisors is defined as the maxillary midline; the line parallel to the second occlusal plane and located at the incisal edge of the maxillary central incisors is defined as the anterior boundary line; and the continuous curve connecting the incisal edges of the anterior teeth and the central fossa of the posterior teeth is considered as the maxillary dentition curve. For example, refer to... Figure 6 The natural dentition features shown are defined as follows: in the mandibular region, the perpendicular line connecting the mesial incisal ends of the two central incisors is defined as the mandibular midline, and the line connecting the distal points of the left and right second molars is defined as the posterior boundary line. Similarly, the continuous curve extending from the incisal end of the anterior teeth to the central fossa of the posterior teeth constitutes the mandibular dentition curve.

[0062] S404. Link and store the maxillary and mandibular feature information of each natural dentition model to construct a natural dentition database.

[0063] Optionally, the natural dentition models and their related maxillary and mandibular features are efficiently stored in a relational database according to a unified data format and standard, forming a natural dentition database that can be queried, analyzed, and applied later. This natural dentition database can support various practical needs in the field of oral medicine, such as personalized denture design and edentulous jaw restoration plan formulation.

[0064] Based on this, the embodiments of this application construct a natural dentition database, providing a high-quality matching template library for personalized complete denture design for edentulous users, avoiding the tooth arrangement deviation caused by traditional reliance on subjective experience or general templates, thereby comprehensively optimizing the digital restoration effect of complete dentures in terms of function, aesthetics and adaptability.

[0065] Figure 7 A flowchart illustrating a key point determination method provided in an embodiment of this application is shown. (Refer to...) Figure 7 As shown, the above steps determine key points based on the segmented tooth position information, specifically including the following steps: S701. Obtain the point cloud data corresponding to the first molar, and calculate the Gaussian curvature corresponding to the first molar based on the point cloud data.

[0066] Optionally, an intraoral scanner or a 3D optical scanning device can be used to scan the user's oral cavity, acquiring complete 3D point cloud data of the user's maxillary or mandibular first molar. The acquired point cloud data is then preprocessed, such as through noise reduction and resampling, to obtain preprocessed point cloud data. Based on this, a local surface fitting method, such as Moving Least Squares (MLS), can be used. For each point in the point cloud data, a differential geometric model is constructed in the neighborhood of each point, and the Gaussian curvature of that point is calculated based on differential geometry theory, thus obtaining the Gaussian curvature corresponding to the first molar.

[0067] S702. Based on Gaussian curvature, select multiple high curvature points from point cloud data whose curvature is greater than a preset threshold.

[0068] Optionally, the mean Gaussian curvature is calculated based on the Gaussian curvature value of each point in the point cloud data. and Gaussian curvature standard deviation And based on the mean Gaussian curvature and Gaussian curvature standard deviation Set a preset threshold; specifically, you can set a preset threshold. Based on this, the Gaussian curvature values ​​at each point are compared with a preset threshold. By comparison, values ​​with Gaussian curvature greater than a preset threshold are selected. Multiple high curvature points. Since the tooth cusp vertex typically exhibits a local positive Gaussian curvature maxima, based on a preset threshold... Screening can initially locate multiple candidate cusps, including mesial lingual cusp, distal lingual cusp, and mesial buccal cusp, in order to further determine the mesial lingual cusp.

[0069] S703. Perform clustering and deduplication on multiple high curvature points to determine the near-mid tongue point.

[0070] Optionally, in this embodiment, a clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) can be used to spatially cluster and deduplicate multiple high-curvature points to determine the mesial lingual cusp point from among them. Specifically, a distance parameter and a minimum number of cluster samples are set to merge multiple neighboring high-curvature points formed at the same cusp top due to point cloud density, thereby achieving deduplication and generating several independent clusters. Each independent cluster represents a potential cusp. Then, the independent cluster closest to the midline and located on the lingual side is selected, and the point with the largest Gaussian curvature or the geometric center within that independent cluster is taken as the mesial lingual cusp point.

[0071] Based on this, the embodiments of this application accurately calculate the Gaussian curvature based on high-density point clouds, effectively capture key features such as tooth cusps, and use a preset threshold to dynamically filter high curvature points. Furthermore, the DBSCAN clustering algorithm is used to spatially deduplicate the multiple filtered high curvature points, eliminating duplicate detections caused by point cloud redundancy, thereby achieving accurate identification and positioning of the mesial lingual cusp of the first molar.

[0072] As one possible implementation, step S103 above obtains the target natural dentition model from the natural dentition database based on the maxillary and mandibular feature information of the edentulous three-dimensional model, including: performing feature matching between the maxillary and mandibular feature information of the edentulous three-dimensional model and the maxillary and mandibular feature information of each natural dentition model in the natural dentition database, and selecting the target natural dentition model from multiple natural dentition models.

[0073] Optionally, the maxillary and mandibular feature information extracted from the edentulous 3D model can be compared and matched with the corresponding maxillary and mandibular feature information of each natural dentition model in the natural dentition database. This allows for the automatic selection of the target natural dentition model with the most similar anatomical morphology and the most harmonious occlusal relationship from multiple natural dentition models, providing an ideal dentition arrangement reference that conforms to the individual jawbone characteristics of the user for subsequent edentulous restoration.

[0074] Optionally, the maxillary midline, anterior boundary line, and maxillary alveolar ridge crest curve of the edentulous 3D model are aligned with the maxillary midline, anterior boundary line, and maxillary dentition curve of each natural dentition model, respectively. Similarly, the mandibular midline, posterior boundary line, and mandibular alveolar ridge crest curve of the edentulous 3D model are aligned with the mandibular midline, posterior boundary line, and mandibular dentition curve of each natural dentition model, respectively, to determine the characteristic geometric deviations between the edentulous 3D model and each natural dentition model. Based on these characteristic geometric deviations, the target natural dentition model is determined.

[0075] For example, for the maxillary feature lines, the maxillary midline of the edentulous 3D model is rigidly or affinely aligned with the maxillary midlines of each natural dentition model, the anterior boundary line of the edentulous 3D model is rigidly or affinely aligned with the anterior boundary lines of each natural dentition model, and the maxillary alveolar ridge crest curve of the edentulous 3D model is rigidly or affinely aligned with the maxillary dentition curve of each natural dentition model, so that the two are aligned in a unified coordinate system. For the mandibular feature lines, the mandibular midline of the edentulous 3D model is rigidly or affinely aligned with the mandibular midlines of each natural dentition model, the posterior boundary line of the edentulous 3D model is rigidly or affinely aligned with the mandibular dentition curve of the edentulous 3D model, so that the two are aligned in a unified coordinate system. Based on this, the geometric deviation between each pair of aligned feature curves is calculated, and the natural dentition model with the smallest error is selected as the target natural dentition model. This target natural dentition model is closest to the user's edentulous jaw structure in terms of dental arch morphology, jaw contour and jaw relationship, thus providing a highly matching reference template for the user's personalized denture design.

[0076] Based on this, the embodiments of this application precisely select the target natural dentition model that best matches the individual user's jawbone morphology by finely aligning the 3D model of the edentulous jaw with each natural dentition model in the natural dentition database in terms of maxillary and mandibular features and quantifying the geometric deviation. This process avoids the tooth arrangement deviation caused by relying on manual experience in the traditional method, and significantly improves the adaptability of dental arch morphology, occlusal plane and occlusal relationship in denture restoration, providing reliable support for edentulous users to achieve personalized and high-precision digital full mouth restoration.

[0077] As one possible implementation, step S104 above calibrates the target natural dentition model based on facial scan data and oral scan data to obtain a three-dimensional model of complete dentures, including: determining the user's occlusal information based on facial scan data and oral scan data, and adjusting the spatial position between the upper and lower jaws in the target natural dentition model according to the occlusal information to obtain a three-dimensional model of complete dentures.

[0078] Optionally, facial scan data includes external aesthetic information such as the user's lip-tooth relationship, smile line, and occlusal plane reference, while oral scan data includes internal anatomical information such as the alveolar ridge morphology and relative positions of the upper and lower jaws. By fusing the user's facial and intraoral scan data and establishing a unified three-dimensional coordinate system through multimodal registration, personalized occlusal information is extracted through joint analysis. This occlusal information includes, but is not limited to, centric occlusion, occlusal plane inclination, vertical intermaxillary distance, and anterior overbite / overjet relationship. Furthermore, using this occlusal information as a constraint, the selected target natural dentition model is individually adjusted according to the oral condition of the edentulous patient. This includes adding or subtracting base wax patterns, and moving, enlarging, or reducing the position of artificial teeth, ensuring that the adjusted target natural dentition model fits the intraoral condition of the edentulous patient. Based on this, using the adjusted target natural dentition model as a reference, offset surfaces are generated to ensure adhesion and stability. Simultaneously, according to the morphology of the lips, cheeks, and tongue, the position of the teeth, and aesthetic requirements, outward-facing, smooth polished surfaces are generated to facilitate muscle retention and speech. This process creates the denture base, which is then precisely connected to the artificial teeth to form a complete three-dimensional model of the prosthesis. (Refer to...) Figure 8 The diagram showing the tooth arrangement effect demonstrates that the 3D model of the complete denture accurately matches the user's actual occlusion and can be directly used for digital denture design and manufacturing. In other words, by 3D printing this 3D model of the complete denture, the user's personalized complete denture can be obtained.

[0079] Based on this, the embodiments of this application achieve a precise fit between the target natural tooth arch model and the user's personalized occlusal state by integrating facial aesthetic information and oral anatomical data. This not only ensures that the complete denture has the correct jaw contact relationship, but also conforms to the user's facial features and smile curve. Compared with the traditional method of tooth arrangement that relies on experience, it significantly improves the personalization and wearing comfort of the complete denture.

[0080] As one possible implementation, this application embodiment roughly distinguishes the anterior and posterior regions of the upper and lower jaws using line segments in the natural dentition and edentulous jaw data, allowing for segmented matching. Specifically, segmented matching is achieved by matching the dental arch curve of the anterior region and fitting the projected area of ​​the posterior region.

[0081] For example, for arch curve matching in the anterior region, the central axis of the dental arch is extracted from the data of the natural dentition and the edentulous anterior region. This central axis is an arc connecting the mesial suture of the central incisor to the distal surfaces of the canines on both sides. The radius of curvature, arc length, and center coordinates of the central axis are calculated. Based on this, curve fitting is performed using Matlab software. Specifically, the B-spline curve fitting function of Matlab software can be used. Taking the curve of the natural dentition anterior region as a reference, the radius of curvature, arc length, and center coordinates of the edentulous anterior region curve are adjusted accordingly to make the overlap between the two greater than or equal to 95%. The fitted anterior region data is then saved.

[0082] For example, for fitting the projected area of ​​the posterior teeth region, the data of the natural dentition and the edentulous posterior teeth region are projected onto a horizontal plane, such as the orbitoauricular plane. Then, the area measurement tool of Geomagic Studio software is used to calculate the projected area and centroid coordinates of the occlusal surface of the posterior teeth region. Based on this, the least squares method is used for area fitting. Specifically, using the projected area and centroid coordinates of the posterior teeth region of the natural dentition as a reference, the spatial position of the edentulous posterior teeth region data is adjusted so that the goodness of fit of the projected areas is greater than or equal to 90%, and the centroid offset distance is less than or equal to 0.5 mm. The fitted posterior teeth region data is then saved.

[0083] Based on the above, the complete denture generation method provided in the embodiments of this application can achieve the following technical effects: (1) Streamlined Process: Traditional complete denture fabrication methods require multiple clinical steps, including initial impression, final impression, jaw position recording, trial fitting, and denture placement, and rely on manual adjustments and repeated communication with the dental laboratory. In the embodiment of this application, only two visits are required: the first visit completes the digital oral scan and jaw position recording; the second visit is for denture placement. This reduces the number of clinical operation steps and decreases the accumulation of errors caused by multiple model transfers. Correspondingly, the number of visits for users is reduced, the treatment cycle is shortened, and costs are significantly saved, which is especially friendly to middle-aged and elderly users with limited mobility.

[0084] (2) Reduced adjustment burden: Complete dentures can achieve better shape, occlusion and positioning accuracy during the processing stage. When wearing the dentures, fine adjustments are mainly made, which greatly reduces the adjustment time and makes clinical operation more predictable and controllable.

[0085] (3) Efficiency improvement: Reduced model transportation time and physical loss, and automated processing reduced human error, improved the quality and fit rate of complete dentures, thereby reducing clinical rework and repetitive labor.

[0086] Based on the same inventive concept, this application also provides a complete denture generating device corresponding to the complete denture generating method. Since the principle of the complete denture generating device in this application is similar to the complete denture generating method described above in this application, the implementation of the complete denture generating device can refer to the implementation of the complete denture generating method, and the repeated parts will not be described again.

[0087] Reference Figure 9 The diagram shown is a structural schematic of a complete denture generation device provided in an embodiment of this application. The complete denture generation device 900 includes: an acquisition module 901, an extraction module 902, a matching module 903, and a generation module 904, wherein: The acquisition module is used to acquire the user's edentulous jaw 3D model, facial scan data, and oral scan data; The extraction module is used to extract the maxillary and mandibular feature information of the edentulous 3D model; The matching module is used to obtain the target natural dentition model from the natural dentition database based on the maxillary and mandibular feature information of the edentulous 3D model. The natural dentition database includes multiple natural dentition models and the maxillary and mandibular feature information of each natural dentition model. The generation module is used to calibrate the target natural dentition model based on facial and oral scan data to obtain a 3D model of the complete denture. The 3D model of the complete denture is used for 3D printing to obtain the user's complete denture.

[0088] Based on this, the complete denture generation device according to the embodiments of this application extracts and quantifies the maxillary and mandibular features that represent the user's personalized morphology based on the user's edentulous jaw 3D model. Then, based on a pre-constructed natural dentition database, it accurately selects the target natural dentition model that most closely matches the user's jawbone morphology from multiple natural dentition models contained in the database through feature matching, thereby avoiding the subjectivity and repeated adjustments of manual trial tooth arrangement. Furthermore, by combining the user's facial scan data and oral scan data, personalized spatial calibration is performed on the target natural dentition model to ensure that the complete denture is highly compatible with the user in terms of both function and aesthetics. The entire process of complete denture generation provided by the embodiments of this application does not require multiple clinical trials, significantly shortening the user's treatment cycle. At the same time, the model matching method of constructing a natural dentition database significantly improves the accuracy of tooth arrangement and the fit of the complete denture, realizing efficient, accurate, and personalized digital generation of complete dentures.

[0089] In one possible implementation, the extraction module 902 is specifically used for: Determine the first occlusal plane of the edentulous 3D model; The line connecting the incisor mastoid process and the mid-palatal suture is taken as the maxillary midline. The straight line passing through the incisor mastoid process at a predetermined distance and parallel to the first occlusal plane is taken as the anterior boundary line. The line connecting the maxillary alveolar ridge crest line is taken as the maxillary alveolar ridge crest curve. Projecting the maxillary midline onto the mandible serves as the mandibular midline. Connecting the midpoints of the retromolar pads of the left and right mandibular molars serves as the posterior boundary line. Connecting the mandibular alveolar ridge crest line serves as the mandibular alveolar ridge crest curve.

[0090] In one possible implementation, the matching module 903 is further configured to: Collect multiple natural tooth arch models; The tooth positions of each natural dentition model are segmented, and key points are determined based on the segmented tooth position information. The second occlusal plane is then obtained by fitting the key points. Based on the second occlusal plane corresponding to each natural dentition model, the maxillary and mandibular feature information of each natural dentition model is determined. The maxillary and mandibular feature information includes the maxillary feature line and the mandibular feature line. A natural dentition database is constructed by associating and storing the maxillary and mandibular feature information of each natural dentition model.

[0091] In one possible implementation, the matching module 903 is further configured to: Obtain the point cloud data corresponding to the first molar, and calculate the Gaussian curvature corresponding to the first molar based on the point cloud data; Based on Gaussian curvature, multiple high curvature points with curvature greater than a preset threshold are selected from point cloud data; Clustering and deduplication are performed on multiple high curvature points to determine the near-mid tongue point.

[0092] In one possible implementation, the matching module 903 is further configured to: The second occlusal plane is obtained by fitting the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor.

[0093] In one possible implementation, the maxillary feature line includes: the maxillary midline, the anterior boundary line, and the maxillary dentition curve; the mandibular feature line includes: the mandibular midline, the posterior boundary line, and the mandibular dentition curve; the matching module 903 is specifically used for: The maxillary and mandibular feature information of the edentulous 3D model is matched with the maxillary and mandibular feature information of each natural dentition model in the natural dentition database, and the target natural dentition model is selected from multiple natural dentition models.

[0094] In one possible implementation, the matching module 903 is specifically used for: The maxillary midline, anterior boundary line, and maxillary alveolar ridge crest curve of the edentulous 3D model are aligned with the maxillary midline, anterior boundary line, and maxillary dentition curve of each natural dentition model, respectively. The mandibular midline, posterior boundary line, and mandibular alveolar ridge crest curve of the edentulous 3D model are aligned with the mandibular midline, posterior boundary line, and mandibular dentition curve of each natural dentition model, respectively, to determine the characteristic geometric deviation between the edentulous 3D model and each natural dentition model. The target natural dentition model is determined based on the characteristic geometric deviations between the edentulous 3D model and each natural dentition model.

[0095] In one possible implementation, the generation module 904 is specifically used for: Based on facial and oral scan data, determine the user's bite information; Based on the occlusal information, the spatial position between the upper and lower jaws in the target natural dentition model is adjusted to obtain a three-dimensional model of the complete denture.

[0096] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0097] This application also provides an electronic device 1000, such as... Figure 10 The diagram shown is a structural schematic of an electronic device 1000 provided in an embodiment of this application. It includes a processor 1001 and a memory 1002, and optionally, a bus 1003. The memory 1002 stores machine-readable instructions executable by the processor 1001. When the electronic device 1000 is running, the processor 1001 and the memory 1002 communicate via the bus 1003. When the machine-readable instructions are executed by the processor 1001, the steps of the complete denture generation method described in any of the preceding claims are performed.

[0098] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the complete denture generation method as described in any of the preceding claims.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0100] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0101] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for fabricating complete dentures, characterized in that, include: Acquire the user's edentulous jaw 3D model, facial scan data, and oral scan data; Extract the maxillary and mandibular feature information of the edentulous three-dimensional model; Based on the maxillary and mandibular feature information of the edentulous three-dimensional model, the target natural dentition model is obtained from the natural dentition database, which includes multiple natural dentition models and the maxillary and mandibular feature information of each natural dentition model. Based on the facial scan data and the oral scan data, the target natural dentition model is calibrated to obtain a three-dimensional model of complete dentures. The three-dimensional model of complete dentures is used for 3D printing to obtain the user's complete dentures.

2. The method according to claim 1, characterized in that, The extraction of maxillary and mandibular feature information from the edentulous 3D model includes: Determine the first occlusal plane of the edentulous three-dimensional model; The line connecting the incisor mastoid process and the mid-palatal suture is taken as the maxillary midline. The straight line passing through the incisor mastoid process at a predetermined distance and parallel to the first occlusal plane is taken as the anterior boundary line. The line connecting the maxillary alveolar ridge crest line is taken as the maxillary alveolar ridge crest curve. The maxillary midline is projected onto the mandible as the mandibular midline. The line connecting the midpoints of the left and right mandibular molar pads is taken as the posterior boundary line. The line connecting the mandibular alveolar ridge crest line is taken as the mandibular alveolar ridge crest curve.

3. The method according to claim 1, characterized in that, The method further includes: Collect multiple natural dentition models; The natural dentition models are segmented by tooth position, and key points are determined based on the segmented tooth position information. A second occlusal plane is obtained by fitting the key points. Based on the second occlusal plane corresponding to each of the natural dentition models, the maxillary and mandibular feature information of each of the natural dentition models is determined, and the maxillary and mandibular feature information includes: maxillary feature line and mandibular feature line; The natural dentition models and their maxillary and mandibular feature information are associated and stored to construct the natural dentition database.

4. The method according to claim 3, characterized in that, The determination of key points based on the segmented tooth position information includes: Obtain point cloud data corresponding to the first molar, and calculate the Gaussian curvature corresponding to the first molar based on the point cloud data; Based on the Gaussian curvature, multiple high curvature points with curvature greater than a preset threshold are selected from the point cloud data; Clustering and deduplication are performed on multiple high curvature points to determine the near-mid tongue point.

5. The method according to claim 3, characterized in that, The process of obtaining the second occlusal plane based on the key points includes: The second occlusal plane is obtained by fitting the mesial-lingual point of the first molar on one side, the mesial-lingual point of the first molar on the other side, and the midpoint of the incisal edge of the central incisor.

6. The method according to claim 3, characterized in that, The maxillary feature lines include: the maxillary midline, the anterior boundary line, and the maxillary dentition curve; the mandibular feature lines include: the mandibular midline, the posterior boundary line, and the mandibular dentition curve. The step of obtaining the target natural dentition model from the natural dentition database based on the maxillary and mandibular feature information of the edentulous 3D model includes: The maxillary and mandibular feature information of the edentulous 3D model is matched with the maxillary and mandibular feature information of each natural dentition model in the natural dentition database to select the target natural dentition model from multiple natural dentition models.

7. The method according to claim 6, characterized in that, The step of performing feature matching between the maxillary and mandibular feature information of the edentulous 3D model and the maxillary and mandibular feature information of each natural dentition model in the natural dentition database, and selecting the target natural dentition model from multiple natural dentition models, includes: The maxillary midline, anterior boundary line, and maxillary alveolar ridge crest curve of the edentulous 3D model are aligned with the maxillary midline, anterior boundary line, and maxillary dentition curve of each of the natural dentition models, respectively. The mandibular midline, posterior boundary line, and mandibular alveolar ridge crest curve of the edentulous 3D model are aligned with the mandibular midline, posterior boundary line, and mandibular dentition curve of each of the natural dentition models, respectively, to determine the characteristic geometric deviation between the edentulous 3D model and each of the natural dentition models. The target natural dentition model is determined based on the characteristic geometric deviations between the edentulous 3D model and each of the natural dentition models.

8. The method according to claim 1, characterized in that, The process of calibrating the target natural dentition model based on the facial scan data and the oral scan data to obtain a three-dimensional model of a complete denture includes: Based on the facial scan data and the oral scan data, the user's bite information is determined; Based on the occlusion information, the spatial position between the upper and lower jaws in the target natural dentition model is adjusted to obtain the three-dimensional model of the complete denture.

9. An electronic device, characterized in that, include: The device includes a processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when the electronic device is in operation, are executed by the processor to perform the steps of the complete denture generation method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the complete denture generation method as described in any one of claims 1 to 8.

Images