Oral implant planning method, equipment, medium and product

Abstract

The invention discloses an oral implant planning method, equipment, a medium and a product, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring oral scanning data and CBCT data of a target object, a registration result of the oral scanning data and the CBCT data, and gingival margin line data and virtual dental crown data of an edentulous area in the registration result; and inputting the gingival margin line data, the virtual dental crown data, the CBCT data and the intentional brand identifier into a rule-based implant parameter decision model to obtain implant parameter data for the tooth missing area of the target object, the rules comprise a current oral implantology principle, a predetermined expert experience principle and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is realized based on an expert decision tree in combination with a constraint satisfaction planning algorithm, and the parameter data comprises an operation implantation direction and attribute data of the implant. According to the embodiment of the invention, the false tooth use experience of the target object can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, and in particular to an oral implant planning method, device, medium and product. BACKGROUND

[0002] The prior art usually takes the edentulous area in the oral CBCT (cone beam computed tomography) data as a region of interest, then determines the edentulous area contour corresponding to the region of interest, determines the maximum length and maximum depth corresponding to the edentulous area contour, and then selects a target implant from a database according to the maximum length and maximum depth. After the target implant is implanted in the user's oral cavity, the user often has various discomforts, and at least the target implant has the problem of low user experience. SUMMARY

[0003] The present application provides an oral implant planning method, device, medium and product to solve the problem of low user experience of the implant corresponding to the implant parameter data determined by the prior art.

[0004] According to an aspect of the present application, an oral implant planning method is provided, comprising:

[0005] obtaining oral scan data and CBCT data of a target object, a registration result of the oral scan data and the CBCT data, and gingival line data and virtual crown data for an edentulous area in the registration result;

[0006] inputting the gingival line data, the virtual crown data, the CBCT data and an intended brand identifier into a rule-based implant parameter decision model to obtain implant parameter data for the edentulous area of the target object, wherein the rules include current principles of oral implantology, predetermined expert experience principles and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is realized based on an expert decision tree combined with a constraint satisfaction planning algorithm, and the parameter data includes a surgical implant direction and attribute data of the implant.

[0007] According to another aspect of the present application, an oral implant planning method is provided, comprising:

[0008] a first module for obtaining oral scan data and CBCT data of a target object, a registration result of the oral scan data and the CBCT data, and gingival line data and virtual crown data for an edentulous area in the registration result;

[0009] a second module configured to input the gum line data, the virtual crown data, the CBCT data and the intended brand identification into a rule-based implant parameter decision model to obtain implant parameter data for the target object's edentulous region, wherein the rules include current principles of oral implantology, predetermined expert experience principles and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is implemented based on an expert decision tree combined with a constraint satisfaction planning algorithm, and the parameter data includes the implant's surgical implantation direction and attribute data.

[0010] According to another aspect of the present application, there is provided an electronic device comprising:

[0011] at least one processor; and

[0012] a memory connected to the at least one processor in communication; wherein

[0013] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the oral implant planning method according to any one of the embodiments of the present application.

[0014] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to perform the oral implant planning method according to any one of the embodiments of the present application when executed by the processor.

[0015] According to another aspect of the present application, there is provided a computer program product comprising a computer program for enabling a processor to perform the oral implant planning method according to any one of the embodiments of the present application when executed by the processor.

[0016] The technical solution provided by the embodiments of the present application can analyze the CBCT data, the gum line data and the virtual crown data of the target object based on the pre-determined rules, and determine implant parameter data that can meet the pre-determined rules and the oral conditions of the target object, because the implant parameter data includes implant direction and implant attribute data, the implant parameter data can ensure a high degree of adaptation between the implant and the oral cavity of the target object, thereby improving the experience of the target object in implanting dentures and the subsequent experience of using the dentures, and achieving denture implantation oriented to repair.

[0017] It is to be understood that the embodiments described herein are merely exemplary of the application and that a person skilled in the art can devise other embodiments without departing from the scope of the present application. It is also to be understood that not all of the benefits described herein need necessarily be realized in any particular embodiment of the application and that various embodiments of the present application can be directed to one or more particular benefits or be directed to no benefits at all. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0019] Figure 1 is a flow chart of the oral implant planning method provided according to an embodiment of the present application;

[0020] Figure 2 is another flow chart of the oral implant planning method provided according to an embodiment of the present application;

[0021] Figure 3 is a structural schematic diagram of the oral implant planning device provided according to an embodiment of the present application;

[0022] Figure 4 is a structural schematic diagram of an electronic device implementing the oral implant planning method according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the technical personnel in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of the present application.

[0024] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] Figure 1 A flowchart of a dental implant planning method is provided for an embodiment of the present application, which can be applicable to a case where the attribute data of the implant is determined automatically at least, and the method can be executed by a dental implant planning device which can be realized in the form of hardware and / or software and can be configured in an electronic device. As shown in Figure 1 the method comprises:

[0026] S110, obtaining the oral scanning data and the CBCT data of the target object, the registration result of the oral scanning data and the CBCT data, and the gum line data and the virtual crown data for the edentulous region in the registration result.

[0027] The target object is a patient who undergoes dental implantation.

[0028] Oral scanning data, in full, is a high-precision digital model obtained by directly inserting an intraoral scanner (an electronic device similar to a camera) into the patient's mouth to perform three-dimensional scanning on the soft and hard tissues such as teeth, gums, and occlusal surfaces.

[0029] CBCT (Cone Beam Computed Tomography) data is three-dimensional gray-scale volume data reconstructed by a CBCT device that performs rotational scanning around the patient's head to obtain hundreds of two-dimensional projection images at different angles. In simple terms, it is a three-dimensional X-scan data of the oral and maxillofacial region, which can clearly show the internal structure of tissues such as bone, teeth (including tooth roots), nerve canal, and jaw sinus. Therefore, the bone volume, bone density of the alveolar bone, and the position information of the adjacent tooth roots can be determined based on the CBCT data. At the same time, 2D slices (such as sagittal, coronal, and axial) or 3D voxel data related to the implant region can be extracted.

[0030] Gum line, commonly known as "gum line", is the contour line of the gum edge at the neck of the tooth (the junction of the crown and the root). It clearly outlines the boundary of the gum wrapping each tooth, which is the boundary line between the tooth and the soft tissue (gum).

[0031] Accurate registration of oral scanning data and CBCT data in three-dimensional space can ensure that the coordinate systems of the two are consistent. The present embodiment can use an algorithm based on surface matching or marker point matching to complete the image registration of oral scanning data and CBCT data.

[0032] Among them, before the image registration of the oral scanning data and the CBCT data, one or more of the gray value range unification processing, the noise removal processing, and the metal artifact removal processing are performed on the CBCT data.

[0033] In one embodiment, the doctor manually determines the gum line data for the target object's edentulous region based on the registration result of the mouth scan data and the CBCT data, and determines the virtual crown data for the target object's edentulous region according to the registration result and the gum line corresponding to the gum line data.

[0034] S120, input the gum line data, the virtual crown data, the CBCT data and the intended brand identification into a rule-based implant parameter decision model to obtain implant parameter data for the target object's edentulous region, wherein the rules include current principles of oral implantology, predetermined expert experience principles and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is realized based on an expert decision tree combined with a constraint satisfaction planning algorithm, and the parameter data includes the surgical implantation direction and attribute data of the implant.

[0035] The attribute data of the implant output by the rule-based implant parameter decision model includes the length, diameter and brand identification of the implant.

[0036] The implant attribute data in the rules includes the brand identification, model, length, diameter, connection method, surface treatment and other information of the implant.

[0037] The current principles of oral implantology include but are not limited to the repair-oriented principle, the bone integration requirement principle, the safety distance principle, the length and diameter selection principle, the biomechanical consideration principle and the aesthetic area consideration principle.

[0038] The repair-oriented principle refers to that the implant shoulder is usually located 1-3mm below the ideal gum line (adjusted according to the thickness of soft tissue and the requirements of aesthetic area), and the axial direction should pass through the central fossa or the lingual side of the restoration crown, so as to facilitate screw retention and mechanical distribution.

[0039] The bone integration requirement principle refers to that there needs to be at least 1-1.5mm of bone mass around the implant.

[0040] The safety distance principle refers to maintaining a safety distance of at least 2mm from the inferior alveolar nerve canal, a safety distance of at least 1mm from the maxillary sinus bottom and the nasal bottom, and a safety distance of at least 1.5mm from the adjacent tooth root.

[0041] The length and diameter selection principle refers to selecting as long and thick an implant as possible to obtain better initial stability and stress distribution under the premise of meeting the safety distance, but it needs to be coordinated with the bone mass and anatomical structure. Generally, a wider diameter implant is selected for the posterior region, and the diameter and length are considered for the anterior region under the permission of aesthetics.

[0042] The biomechanical consideration principle refers to performing rule-based stress analysis to ensure that the direction of resultant force under dynamic load is as consistent as possible with the long axis of the implant.

[0043] The special consideration principle of the esthetic zone refers to the great influence of the labial-lingual position and implant depth of the anterior teeth on the final gingival papilla and the subgingival contour.

[0044] The predetermined expert experience includes but is not limited to the implant type preference for different bone density areas, the axial angle fine-tuning strategy under specific anatomical conditions, etc.

[0045] In one embodiment, the predetermined expert experience principle is determined by analyzing the implant case data completed by industry experts through a pre-trained experience extraction model.

[0046] Specifically, the experience extraction model is an existing machine learning model. By analyzing the case data completed by experienced experts through the pre-trained experience extraction model, the implicit experience principle for the anatomical characteristics of the target group is extracted as the predetermined expert experience principle. The target group can be selected as a male population, a female population, an elderly population, a population in a certain geographical area, such as an Asian population, etc.

[0047] For example, by analyzing the case data determined by industry experts in the Hong Kong Special Administrative Region of China through a pre-trained experience extraction model, the experience principle for the anatomical characteristics of the population in the Hong Kong Special Administrative Region of China is extracted as the predetermined expert experience principle for the population in the Hong Kong Special Administrative Region of China.

[0048] In one embodiment, the rule-based implant parameter decision model is configured to: determine the preliminary position of the shoulder of the implant according to the axis of the virtual crown corresponding to the gingival margin line and the axis of the crown corresponding to the gingival margin line data; take the preliminary position as the starting point and the axis as the initial direction, adjust the axial direction of the implant based on the bone contour corresponding to the CBCT data and the target anatomical structure, so that the implant is located in the bone, maintains a safe distance from the target anatomical structure, and the stress of the crown is transmitted along the long axis direction of the implant; based on the adjusted axial direction and the target shoulder position corresponding to the adjusted axial direction, determine the available bone height and the spatial distance between the target shoulder position and the contralateral jaw teeth; and determine the implant parameter data that meets the rules according to the available bone height and the spatial distance.

[0049] Wherein, after the adjusted axial direction and the target shoulder position corresponding to the adjusted axial direction are determined, the available bone height is measured downward (toward the root direction), and the spatial distance between the target shoulder position and the contralateral jaw teeth is measured upward (toward the crown direction).

[0050] The implant parameter decision model is realized based on an expert decision tree and a constraint satisfaction programming (CSP) algorithm. The model is used to convert implant design rules into structured constraint conditions and decision paths, and use the CSP algorithm to efficiently and accurately find all or the optimal feasible solution in a huge parameter space, thereby providing personalized and compliant implant parameter solutions for doctors and patients.

[0051] The expert decision tree is used for the formalization and structuring of implant design rules and expert experience. It includes decision nodes and leaf nodes. The leaf nodes represent one or more implant parameters to be determined, such as implant diameter, implant length, etc., or point to a CSP sub-problem to be started; the decision nodes are conditional branches, such as, under the premise of meeting the safety distance, the longer and thicker implant is selected as much as possible to obtain better initial stability and stress distribution, but it needs to be coordinated with the bone volume and anatomical structure. The introduction of the expert decision tree can quickly narrow the search range, decompose the complex global problem into a series of more manageable sub-problems, and determine high-level strategies in advance, such as giving priority to stability or aesthetics.

[0052] The constraint satisfaction problem is responsible for finding feasible solutions under given rules. In the process of determining the feasible solution, the parameters of the prosthesis to be determined are taken as variables, such as the diameter and length of the implant; the possible value range of each variable is determined; and the relationship that must be satisfied between the variables is defined, which directly comes from the aforementioned rules.

[0053] Since the rule-based implant parameter decision model in the embodiment is realized based on the decision tree and the constraint satisfaction problem algorithm, its decision logic is completely based on explicit rules, and the decision path of the implant parameter data output by the model is traceable, and is easier to understand and trust. Moreover, the implant parameter data output by the model definitely satisfies the built-in rules, greatly reducing the risk of surgical failure caused by improper parameter selection.

[0054] In addition, the rules relied on by the rule-based implant parameter decision model can be updated separately, that is, the knowledge base (rules) and the solving engine are separated. In this way, when new design preferences or design rules appear, only the rules corresponding to the constraint conditions relied on by the decision tree need to be updated, and the model updating cost is low.

[0055] In one embodiment, in response to a selection operation for a brand, an intended brand identifier is determined; the gum line data, the virtual crown data, the CBCT data, and the intended brand identifier are input into the rule-based implant parameter decision model to obtain implant parameter data for the target object's edentulous region, and the attribute data further includes a brand identifier of the implant.

[0056] Optionally, the interactive interface displays a data loading option and brand options corresponding to each of the at least two predetermined brands; in response to a triggering operation of the data loading option, the intraoral scan data and the CBCT data of the target object are loaded, and a registration result of the intraoral scan data and the CBCT data is determined, and then the gingival line data and the virtual crown data of the target object are determined based on the prior art; the user manually selects an intended brand; in response to a selection operation of the brand, a predetermined brand corresponding to the brand selection operation is identified as an intended brand of the target object, and then the gingival line data, the virtual crown data, the CBCT data, and the intended brand are input into the rule-based implant parameter decision model to obtain implant parameter data of the target object.

[0057] Optionally, the interactive interface displays a data loading option and brand options corresponding to each of the at least two predetermined brands; the user selects an intended brand in the interactive interface, completes a data loading operation, and then clicks or touches a confirmation option; in response to a triggering operation of the confirmation option, the intraoral scan data and the CBCT data of the target object are loaded, and a registration result of the intraoral scan data and the CBCT data is determined, and then the gingival line data and the virtual crown data of the target object are determined based on the prior art; a brand selected by the user is identified as an intended brand of the target object, and then the gingival line data, the virtual crown data, the CBCT data, and the intended brand are input into the rule-based implant parameter decision model to obtain implant parameter data of the target object.

[0058] In one embodiment, the gingival line data and the virtual crown data of the target object are obtained simultaneously / afterwards, and brand options corresponding to each of the at least two predetermined brand identifiers are displayed in the interactive interface.

[0059] Specifically, the first interactive interface only displays a data loading option, and the user loads the intraoral scan data and the CBCT data of the target object based on the data loading option; the processor determines the gingival line data and the virtual crown data of the target object in the missing tooth region according to a registration result of the intraoral scan data and the CBCT data, and displays the gingival line data and the virtual crown data in the second interactive interface, while displaying brand options corresponding to each of the at least two predetermined brands in the second interactive interface; the user selects a desired brand; the processor determines an intended brand identifier in response to a brand selection operation, and inputs the gingival line data, the virtual crown data, the CBCT data, and the intended brand identifier into the rule-based implant parameter decision model to obtain implant parameter data of the target object.

[0060] The technical scheme provided by the embodiment of the present application, since the implant parameter decision model is realized based on the expert decision tree combined with the constraint satisfaction planning algorithm, the model can analyze the CBCT data, the gum margin line data and the virtual crown data of the target object based on the predetermined rules, determine the implant parameter data that can meet the predetermined rules and the oral conditions of the target object, since the implant parameter data includes the implant direction and the implant attribute data, the implant parameter data can ensure that the implant has high adaptability with the oral cavity of the target object, thereby improving the experience of the target object in the implant denture and the subsequent experience of the target object in using the denture, and realizing the implant denture guided by repair.

[0061] Figure 2 Another flowchart of the oral implant planning method provided by the embodiment of the present application is used to refine the determination rules of the gum margin line data and the virtual crown data in the foregoing embodiment. As shown in the figure, Figure 2 the method comprises:

[0062] S210, obtain the oral scanning data and the CBCT data of the target object, and the registration result of the oral scanning data and the CBCT data, input the registration result of the oral scanning data and the CBCT data into a pre-trained stable diffusion model, and obtain the gum margin line data and the virtual crown data for the edentulous region of the target object.

[0063] The gum margin line is represented in the form of a three-dimensional coordinate point cloud, and accurately defines the neck edge of the future repair crown. The virtual crown data is represented in the form of a point cloud, simulates the ideal shape, size and basic occlusal surface form of the final prosthesis.

[0064] The stable diffusion model is a deep learning model for generating images from text, and its core is a denoising network with a U-Net structure, which removes noise in the latent variable through multiple iterations and gradually generates target representations. It does not directly operate in the pixel space, but first uses a variational autoencoder to compress the image into a smaller latent space (such as 64x64x4), and then performs a diffusion process in this space.

[0065] The pre-trained stable diffusion model incorporates conditional information into each level of the U-Net through a cross-attention mechanism (Cross-Attention), wherein the conditional information is the registration result of the oral scanning data and the CBCT data.

[0066] During the training process of the stable diffusion model, the network parameters are lured by minimizing the difference (such as MSE loss) between the predicted noise and the actual added noise, and at the same time, in order to ensure the accuracy of the generated gum margin line data and virtual crown data, a perception loss based on expert data and / or an adversarial loss are introduced.

[0067] The perceptual loss and the adversarial loss based on expert data are both in the category of loss functions. The loss function is a "guide" for the neural network to learn, which measures the gap between the model output and the real target. "Based on expert data" means that these loss functions are not directly calculated in pixel level (such as L1, L2 loss), but use a model pre-trained on a large dataset (such as ImageNet) with "expert" knowledge to guide the training process, so as to generate high-quality results that conform to human perception.

[0068] The perceptual loss calculates the difference by comparing the feature representations of the generated image and the real image in the intermediate layers of a pre-trained deep convolutional network (such as VGG, ResNet). For example, select a model pre-trained on a large dataset (such as ImageNet), such as VGG19. The weights of this model are frozen during training and do not participate in training, only as a feature extractor; input the generated image (G) and the target real image (Y) into this pre-trained VGG network, then select some intermediate layers (such as relu2_2, relu3_3, etc.) to capture details and textures; calculate the difference between the feature maps of the two images in the selected layers. Usually use L2 loss (MSE) or L1 loss (MAE). This model training method can make the image texture of the virtual crown data output by the model more realistic and detailed, avoiding the blurring and mediocrity problems caused by L2 loss.

[0069] The adversarial loss introduces a "discriminator" network as an "expert" to judge the true or false of the generated image, instead of directly comparing with the target image. Specifically, set the generator and the discriminator. The generator tries to generate images that are difficult to distinguish from real images, trying to "deceive" the discriminator; the discriminator tries to distinguish whether the input image is from the real data or the generator, which is a binary classification "expert". Training principle and process: input a batch of real images, labeled as "true" (1); input a batch of generated images, labeled as "false" (0); then update the discriminator parameters to make it better distinguish true from false; use the generator to generate a batch of images, but this time try to make the discriminator judge them as "true" (1); update the generator parameters according to the discriminator's judgment errors to make it generate more realistic images; loop iteration: alternately, until the generator can generate high-quality images, and the discriminator is difficult to distinguish (accuracy close to 50%). The stable diffusion model trained by this method can generate extremely realistic, sharp, and detailed images.

[0070] If the training of the stable diffusion model is completed in a manner that combines the perception loss based on the expert data and the adversarial loss, the pre-trained stable diffusion model can ensure that the generated image is generally consistent in structure with the target and can also provide realistic textures and details to enhance the visual sharpness and realism of the image, thereby greatly improving the subjective visual quality of the generated image.

[0071] The training sample used in the stable diffusion model training process includes the registration result of the intraoral scan data and the CBCT data, and the gingival margin line data and the virtual crown data corresponding to the registration result.

[0072] In an embodiment, while / after obtaining the gingival margin line data and the virtual crown data for the target object, brand options corresponding to each of at least two predetermined brand identifiers are displayed.

[0073] After the gingival margin line data and the virtual crown data are determined, brand options corresponding to each of at least two predetermined brand identifiers are displayed in a visualization interface. A user selects a corresponding brand option according to the intended brand of the target object, and obtains intended brand information of the target object in response to the selection operation of the brand option.

[0074] In the stable diffusion model training process, "gold standard" gingival margin line and virtual crown data need to be extracted from the training sample. Among them, the gingival margin line in the training sample can be obtained by manual sketching or semi-automatic segmentation on the registered model; the crown shape can be directly obtained using the scanning data of the restoration or the virtual crown data designed by industry experts. In addition, in order to expand the training sample size and improve the model generalization ability, image data can be rotated, translated, scaled, and added with noise.

[0075] S220, input the gingival margin line data, the virtual crown data, the CBCT data and the intended brand identifier into the rule-based implant parameter decision model to obtain implant parameter data for the edentulous region of the target object, wherein the rules include current principles of oral implantology, predetermined expert experience principles and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is realized based on an expert decision tree combined with a constraint satisfaction planning algorithm, and the parameter data includes the surgical implantation direction and attribute data of the implant.

[0076] The technical scheme provided by the embodiment of the present application improves the speed and accuracy of determining the gingival margin line data and the virtual crown data by analyzing the registration result of the intraoral scan data and the CBCT data through the pre-trained stable diffusion model.

[0077] Figure 3 The structure diagram of the oral implant planning device provided by the embodiment of the present application is shown in the following figure.Figure 3 The device comprises:

[0078] The first module 31 is configured to acquire mouth scan data and CBCT data of a target object, a registration result of the mouth scan data and the CBCT data, and gingival line data and virtual crown data of a toothless region in the registration result.

[0079] The second module 32 is configured to input the gingival line data, the virtual crown data, the CBCT data and an intended brand identification into a rule-based implant parameter decision model to obtain implant parameter data of the toothless region of the target object, wherein the rules include current principles of oral implantology, predetermined expert experience principles and implant attribute data under at least one predetermined implant brand, the implant parameter decision model is implemented based on an expert decision tree combined with a constraint satisfaction planning algorithm, and the parameter data includes a surgical implantation direction and attribute data of the implant.

[0080] In one embodiment, the current principles of oral implantology include a restoration-oriented principle, a bone integration requirement principle, a safety distance principle, a length and diameter selection principle, a biomechanical consideration principle and an aesthetic area consideration principle.

[0081] The second module 32 is configured to:

[0082] In response to a selection operation for a brand, determine an intended brand identification;

[0083] Input the gingival line data, the virtual crown data, the CBCT data and the intended brand identification into a rule-based implant parameter decision model to obtain implant parameter data of the toothless region of the target object, and the attribute data further includes a brand identification of the implant.

[0084] The first module 31 is further configured to:

[0085] Display a brand option corresponding to each of at least two predetermined brand identifications.

[0086] In one embodiment, the first module 31 is configured to:

[0087] Input the mouth scan data and CBCT data into a pre-trained stable diffusion model to obtain gingival line data and virtual crown data of the target object.

[0088] In one embodiment, the rule-based implant parameter decision model is configured to:

[0089] Determine a preliminary position of an abutment of the implant according to an axis of a gingival line corresponding to the gingival line data and a crown corresponding to the virtual crown data.

[0090] Starting from the initial position and with the axis as the initial direction, the axial direction of the implant is adjusted based on the bone contour and target anatomical structure corresponding to the CBCT data, so that the implant is located within the bone, maintains a safe distance from the target anatomical structure, and transmits the force of the crown along the long axis of the implant.

[0091] Based on the adjusted axial direction and the target shoulder position corresponding to the adjusted axial direction, the available bone height and the spatial distance between the target shoulder position and the contralateral jaw tooth are determined;

[0092] Based on the available bone height and the spatial distance, determine the implant parameter data that meets the rules, select the target implant corresponding to the implant parameter data from the predetermined brand database, and output the implant parameter data.

[0093] In one embodiment, the predetermined expert experience principle is determined by analyzing implant case data completed by industry experts through a pre-trained experience extraction model.

[0094] The technical solution provided by this invention, because the implant parameter decision model is based on an expert decision tree combined with a constraint satisfaction planning algorithm, can analyze the CBCT data, gingival margin data, and virtual crown data of the target object based on predetermined rules. This allows the model to determine implant parameter data that satisfies both the predetermined rules and the target object's oral conditions. Since the implant parameter data includes implant direction and implant attribute data, it can ensure a high degree of fit between the implant and the target object's oral cavity, thereby improving the target object's denture implant experience and subsequent denture usage experience, and realizing restorative-oriented denture implantation.

[0095] The dental implant planning device provided in this embodiment of the invention can execute the dental implant planning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0096] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0097] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0098] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0099] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as dental implant planning methods.

[0100] In some embodiments, the dental implant planning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the dental implant planning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the dental implant planning method by any other suitable means (e.g., by means of firmware).

[0101] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0105] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0106] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0107] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the dental implant planning method as provided in any embodiment of this application.

[0108] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for planning dental implants, characterized in that, include: Acquire the oral scan data and CBCT data of the target object, the registration result of the oral scan data and the CBCT data, and the gingival margin data and virtual crown data of the edentulous area in the registration result; The gingival margin data, the virtual crown data, the CBCT data, and the intended brand identifier are input into a rule-based implant parameter decision model to obtain implant parameter data for the edentulous area of ​​the target patient. The rules include current oral implantology principles, pre-determined expert experience principles, and implant attribute data under at least one pre-determined implant brand. The implant parameter decision model is implemented based on an expert decision tree combined with a constraint satisfaction programming algorithm. The parameter data includes the surgical placement direction and attribute data of the implant.

2. The method according to claim 1, characterized in that, Current principles of dental implantology include the restoration-oriented principle, the osseointegration requirement principle, the safety distance principle, the length and diameter selection principle, the biomechanical consideration principle, and the aesthetic zone consideration principle.

3. The method according to claim 1, characterized in that, The process involves inputting the gingival margin data, the virtual crown data, the CBCT data, and the intended brand identifier into a rule-based implant parameter decision model to obtain implant parameter data for the edentulous area of ​​the target patient, including: In response to the brand selection process, determine the intended brand identity; The gingival margin data, the virtual crown data, the CBCT data, and the intended brand identifier are input into a rule-based implant parameter decision model to obtain implant parameter data for the edentulous area of ​​the target object. The attribute data also includes the brand identifier of the implant.

4. The method according to claim 3, characterized in that, The determination of the gingival margin data and the virtual crown data, simultaneously / afterwards, also includes: Display brand options corresponding to each of the at least two pre-defined brand identifiers.

5. The method according to claim 1, characterized in that, The following steps are used to obtain gingival margin data and virtual crown data for the edentulous region in the registration results: The registration results of the intraoral scan data and CBCT data are input into a pre-trained stable diffusion model to obtain gingival margin data and virtual crown data for the edentulous area of ​​the target object.

6. The method according to claim 1, characterized in that, The rule-based implant parameter decision model is configured as follows: The initial position of the implant shoulder is determined based on the gingival margin corresponding to the gingival margin data and the axis of the crown corresponding to the virtual crown data; Starting from the initial position and with the axis as the initial direction, the axial direction of the implant is adjusted based on the bone contour and target anatomical structure corresponding to the CBCT data, so that the implant is located within the bone, maintains a safe distance from the target anatomical structure, and transmits the force of the crown along the long axis of the implant. Based on the adjusted axial direction and the target shoulder position corresponding to the adjusted axial direction, the available bone height and the spatial distance between the target shoulder position and the contralateral jaw tooth are determined; Based on the available bone height and the spatial distance, determine the implant parameter data that meets the rules, select the target implant corresponding to the implant parameter data from the predetermined brand database, and output the implant parameter data.

7. The method according to claim 1, characterized in that, The predetermined expert experience principle is determined by analyzing implant case data completed by industry experts through a pre-trained experience extraction model.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the oral implant planning method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the dental implant planning method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the oral implant planning method according to any one of claims 1-7.

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