Method and apparatus for generating dental consistency data

Abstract

A method for generating dental alignment data according to one embodiment of the present disclosure may include the steps of acquiring oral cavity scan images of a patient's dental region, the oral cavity scan images including an upper jaw scan image and a lower jaw scan image, acquiring CT scan images including the patient's dental region, generating first sub-alignment data by aligning the upper jaw scan image with the CT scan image, generating first alignment data by aligning the lower jaw scan image with the first sub-alignment data, generating second sub-alignment data by aligning the lower jaw scan image with the CT scan image, and generating second alignment data by aligning the first sub-alignment data with the second sub-alignment data.

Description

Technical Field

[0001] Method for Generating Integrated Dental Data

[0002] The present disclosure relates to a method for generating integrated dental data, and more specifically, to a method for generating integrated dental data that integrates oral scan images and CT images to more intuitively show the dental condition of dental patients.

Background Art

[0003] In dental consultations and treatments, relying solely on the oral explanations of dentists has limitations. Therefore, there is a need to more intuitively show patients the current dental condition and the dental condition after treatment.

[0004] In contrast, with the development of digital imaging technology, various types of image data are widely used in dental treatment or consultations. The utilization of such various image data enables improved dental diagnosis, treatment plan establishment, patient management, etc. Representative image data used in dental consultations and treatments include CT images obtained via a dental CT (Computerized Tomography) device, face images obtained by photographing a patient's face via a face photographing device, and oral scan images obtained by scanning a patient's oral cavity using a 3D oral scanner.

[0005] However, in the case of CT imaging, in order to fix the patient's position, the patient has to hold a specific part of the CT imaging device (e.g., mouthpiece, bite block) in the mouth for imaging. The CT images obtained in this way are images taken with the mouth slightly open, and there may be a difference from the actual oral occlusion state in normal times. Therefore, appropriately loading and utilizing image data with different uses according to the treatment or consultation purpose has some troublesome aspects from the perspective of users such as dentists.

[0006] For example, when determining the implant placement position and designing a surgical guide for implant procedures, it is crucial to accurately confirm the location of the nerve canal. Therefore, an oral scan image aligned with the CT scan image in a CT occlusal state may be necessary. On the other hand, when planning prosthetic treatments such as crowns, it is necessary to confirm the contact area between the tooth to be treated and the opposing tooth. Therefore, an oral scan image aligned with the CT scan image in a occlusal state may be necessary.

[0007] In response to this, there is a demand for a method to generate and display appropriate, consistent data according to the desired type of treatment, regardless of whether it is for dental treatment or consultation. [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] In contrast, one objective of this disclosure is to provide a method for generating and displaying appropriate consistent data according to the desired type of treatment, regardless of whether it is for dental treatment or consultation purposes.

[0009] According to one embodiment of the present disclosure, when aligning (or matching) maxillary scan images and mandibular scan images with a CT scan image, the mandibular scan image can be matched based on the maxillary scan image that has been matched with the CT scan image. In this case, a set of oral scan images aligned with the CT scan can be obtained in the occlusal state of the oral scan.

[0010] According to one embodiment of the present disclosure, when aligning (or matching) maxillary scan images and mandibular scan images with a CT scan image, the maxillary scan image aligned with the CT scan image and the mandibular scan image aligned with the CT scan image can be matched with each other. In this case, a set of oral scan images aligned with the CT can be obtained in a CT occlusal state.

[0011] According to one embodiment of this disclosure, the technical problem to be solved is to provide at least one of the following: consistent data showing the occlusal state of an oral scan and consistent data showing the occlusal state of a CT scan, depending on the purpose of treatment.

[0012] However, the problems that this disclosure seeks to solve are not limited to those mentioned above, and may include objectives that are not mentioned but can be clearly understood by a person with ordinary skill in the art to which this disclosure pertains from the following descriptions. [Means for solving the problem]

[0013] The following describes the specific means by which the objectives of this disclosure will be achieved.

[0014] A method for generating dental alignment data according to one embodiment of the present disclosure includes the steps of: acquiring an oral scan image of a patient's tooth region - the oral scan image includes a maxillary scan image and a mandibular scan image; acquiring a CT scan image including the patient's tooth region; generating first sub-alignment data by aligning the maxillary scan image with the CT scan image; generating first alignment data by aligning the mandibular scan image with the first sub-alignment data; generating second sub-alignment data by aligning the mandibular scan image with the CT scan image; and generating second alignment data by aligning the first sub-alignment data with the second sub-alignment data.

[0015] The method for generating dental alignment data described above may further include the steps of receiving user input to select one of the first alignment data and the second alignment data, and displaying the selected alignment data in response to the receipt of the user input.

[0016] The method for generating dental alignment data described above may further include the steps of: receiving user input regarding the treatment method for the patient's tooth area; selecting one alignment data from the first alignment data and the second alignment data that corresponds to the user input; and displaying the selected alignment data.

[0017] The method for generating dental matching data described above may further include the step of obtaining a CT segmentation image in which at least one of the maxillary teeth, mandibular teeth, maxilla, mandible, and mandibular nerve canal is segmented by processing the CT scan image.

[0018] In the method for generating dental alignment data described above, the step of generating the first sub-alignment data includes the step of generating the first sub-alignment data by aligning the maxillary scan image with the CT segmentation image, and the step of generating the second sub-alignment data may include the step of generating the second sub-alignment data by aligning the mandibular scan image with the CT segmentation image.

[0019] In the method for generating dental alignment data described above, the step of generating the first alignment data may include the step of calculating a conversion matrix between the first alignment data and the second sub-alignment data, and the step of applying an inverse conversion matrix to the conversion matrix to at least one of the segmented mandibular teeth, mandible bone, and mandibular nerve canal.

[0020] The method for generating dental matching data described above may further include the steps of: acquiring a three-dimensional facial image of the patient's face including the tooth area; and matching the three-dimensional facial image with one of the CT scan image, the first matching data, and the second matching data to generate a dental 3D avatar as final matching data.

[0021] In the method for generating dental matching data described above, the step of generating the first sub-matching data includes the step of generating the first sub-matching data from the maxillary scan image and the CT scan image using a first deep learning model, wherein the first deep learning model may be an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of maxillary scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matching datasets in which each of the plurality of maxillary scan images is matched to each of the plurality of CT scan images as output data.

[0022] In the method for generating dental matching data described above, the step of generating the second sub-matching data includes the step of generating the second sub-matching data from the mandibular scan image and the CT scan image using a second deep learning model, wherein the second deep learning model may be an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using multiple mandibular scan images and multiple CT scan image sets for multiple patients as input data, and multiple sub-matching datasets in which each of the multiple mandibular scan images is matched to each of the multiple CT scan images as output data.

[0023] In the method for generating dental alignment data described above, the first sub-alignment data and the second sub-alignment data are generated based on at least three landmarks of the patient's three-dimensional data, the at least three landmarks being extracted by an application for generating dental alignment data, the at least three landmarks being extracted using an artificial neural network module built into the application or connected via a network, the artificial neural network module being pre-trained via pre-training data.

[0024] In an apparatus for generating dental alignment data according to an embodiment of the present disclosure, it includes a communication circuit, a memory, and a processor. The processor is configured to obtain an oral scan image of a patient's dental area, where the oral scan image includes a maxillary scan image and a mandibular scan image, obtain a CT scan image including the patient's dental area, generate first sub-alignment data by aligning the maxillary scan image with the CT scan image, generate first alignment data by aligning the mandibular scan image with the first sub-alignment data, generate second sub-alignment data by aligning the mandibular scan image with the CT scan image, and may be configured to generate second alignment data by aligning the first sub-alignment data with the second sub-alignment data.

[0025] In the apparatus for generating dental alignment data described above, it further includes an input device and a display. The processor is configured to receive a user input for selecting one of the first alignment data and the second alignment data via the input device, and in response to receiving the user input, display the selected one of the alignment data via the display.

[0026] In the apparatus for generating dental alignment data described above, it further includes an input device and a display. The processor is configured to receive a user input regarding a treatment method for the patient's dental area via the input device, select one of the first alignment data and the second alignment data corresponding to the user input, and display the selected one of the alignment data via the display.

[0027] In the apparatus for generating dental alignment data described above, the processor may be configured to obtain a CT segmentation image in which at least one of the maxillary teeth, mandibular teeth, maxilla, mandible, and mandibular canal is segmented by performing image processing on the CT scan image.

[0028] In the apparatus for generating the dental alignment data described above, the processor may be configured to generate the first sub-alignment data by aligning the upper jaw scan image with the CT segmentation image, and generate the second sub-alignment data by aligning the lower jaw scan image with the CT segmentation image.

[0029] In the apparatus for generating the dental alignment data described above, in the step of generating the first alignment data, the processor may calculate a transformation matrix between the first alignment data and the second sub-alignment data, and apply an inverse transformation matrix with respect to the transformation matrix to at least one of the segmented lower jaw teeth, lower jaw bone, and lower jaw nerve canal.

[0030] In the apparatus for generating the dental alignment data described above, the processor may be configured to obtain a three-dimensional face image of a face including the dental part of the patient, and align the three-dimensional face image with one of the CT scan image, the first alignment data, and the second alignment data to generate a dental 3D avatar as the final alignment data.

[0031] In the apparatus for generating the dental alignment data described above, the processor may be configured to generate the first sub-alignment data from the upper jaw scan image and the CT scan image using a first deep learning model, and the first deep learning model is an artificial intelligence model constructed by modeling a correlation relationship between input data, which is a plurality of upper jaw scan images and a plurality of CT scan image sets for a plurality of patients, and output data, which is a plurality of sub-alignment data sets in which each of the plurality of upper jaw scan images is aligned with each of the plurality of CT scan images, based on a deep learning algorithm.

[0032] In the apparatus for generating the aforementioned dental matching data, the processor is configured to generate the second sub-matching data from the mandibular scan image and the CT scan image using a second deep learning model, and the second deep learning model may be an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of mandibular scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matching datasets as output data, each of which is matched to each of the plurality of CT scan images.

[0033] In the apparatus for generating dental alignment data described above, the first sub-alignment data and the second sub-alignment data are generated based on at least three landmarks of the patient's three-dimensional data, the at least three landmarks being extracted by an application for generating dental alignment data, the at least three landmarks being extracted using an artificial neural network module built into the application or connected via a network, the artificial neural network module being pre-trained via pre-training data. [Effects of the Invention]

[0034] According to one embodiment of the present disclosure, at least one of the following can be provided depending on the purpose of treatment: consistent data showing the occlusal state of an oral scan and consistent data showing the occlusal state of a CT scan.

[0035] However, the effects obtained from this disclosure are not limited to those mentioned above, and other effects not mentioned can be clearly understood by a person with ordinary skill in the art to which this disclosure pertains from the following description. [Brief explanation of the drawing]

[0036] The following drawings accompanying this specification illustrate preferred embodiments of the Disclosure and, together with the detailed description of the invention, serve to further illustrate the technical concept of the Disclosure; therefore, this Disclosure should not be construed as being limited solely to what is depicted in such drawings. [Figure 1] This figure provides a schematic explanation of the basic configuration of an apparatus for generating dental compatibility data according to one embodiment of the present disclosure. [Figure 2] This is a block diagram of an apparatus for generating dental consistency data according to one embodiment of the present disclosure. [Figure 3] This figure schematically illustrates the principle of generating dental matching data using a matching module according to one embodiment of the present disclosure. [Figure 4] This is an operation flowchart illustrating a method for generating dental consistency data according to one embodiment of the present disclosure. [Figure 5a] This is a CT scan image taken according to one embodiment of the present disclosure. [Figure 5b] This is a maxillary scan image from an oral scan image according to one embodiment of the present disclosure. [Figure 5c] This is first sub-aligned data according to one embodiment of the present disclosure. [Figure 6a] This is a mandibular scan image from an oral scan image according to one embodiment of the present disclosure. [Figure 6b] This is the first harmonized data according to one embodiment of the present disclosure. [Figure 7] This is a second sub-aligned data according to one embodiment of the present disclosure. [Figure 8] This is a second harmonized data according to one embodiment of the present disclosure. [Figure 9] This is an operation flowchart illustrating a method for generating dental consistency data according to one embodiment of the present disclosure. [Figure 10a] This is a CT segmentation image according to one embodiment of the present disclosure. [Figure 10b] This is a CT segmentation image according to one embodiment of the present disclosure. [Figure 11]This is the first harmonized data according to one embodiment of the present disclosure. [Figure 12] This is a second harmonized data according to one embodiment of the present disclosure. [Modes for carrying out the invention]

[0037] The various embodiments described herein are illustrative for the purpose of clearly illustrating the technical concept of this disclosure and are not intended to limit it to any particular embodiment. The technical concept of this disclosure includes various modifications, equivalents, alternatives, and examples that selectively combine all or part of the embodiments described herein. Furthermore, the scope of rights of the technical concept of this disclosure is not limited to the various embodiments presented below or their specific descriptions.

[0038] Unless otherwise defined, terms used herein, including technical or scientific terms, may have meanings that are generally understood by a person with ordinary skill in the art to which this disclosure pertains.

[0039] Expressions used herein such as “includes,” “may include,” “equip,” “may be equipped,” “possess,” and “may have” mean that the feature in question (e.g., function, operation, or component) exists, but do not exclude the existence of other additional features. In other words, such expressions should be understood as open-ended terms that imply the possibility of including a second embodiment.

[0040] In this specification, singular expressions include plural expressions unless the context clearly identifies them as singular. Conversely, plural expressions include singular expressions unless the context clearly identifies them as plural. When a part of the specification is described as containing a component, this means that, unless otherwise specifically stated, it may contain other components rather than excluding them.

[0041] Furthermore, the terms “module” or “part” as used in this specification mean a software or hardware component, and a “module” or “part” performs a certain role. However, the meaning of “module” or “part” is not limited to software or hardware. A “module” or “part” may be configured to reside on an addressable storage medium, or to regenerate one or more processors. Thus, as an example, a “module” or “part” may include components such as software components, object-oriented software components, class components, and task components, and at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within a “module” or “part” may be combined with fewer components and “module” or “part”, or further separated into additional components and “module” or “part”.

[0042] According to one embodiment of the present disclosure, “module” or “part” may be embodied in a processor and memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In some environments, “processor” may also refer to application-specific semiconductors (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc. “Processor” may also refer to combinations of processing devices such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled with a DSP core, or any other combination of such configurations. “Memory” should also be broadly interpreted to include any electronic component capable of storing electronic information. "Memory" can also refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage devices, and registers. Memory is said to be in electronic communication with the processor when the processor can read information from and / or write information to it. Memory integrated into a processor is in electronic communication with the processor.

[0043] As used herein, expressions such as “first,” “second,” or “first,” “second,” etc., refer to multiple similar objects and are used to distinguish one object from others, unless the context otherwise indicates otherwise. They do not imply any order or importance between such objects.

[0044] Expressions used herein such as “A, B, and C,” “A, B, or C,” “A, B, and / or C,” or “at least one of A, B, and C,” “at least one of A, B, or C,” “at least one of A, B, and / or C,” “at least one selected from A, B, and C,” “at least one selected from A, B, or C,” and “at least one selected from A, B, and / or C” can mean each listed item or all possible combinations of listed items. For example, “at least one selected from A and B” can mean (1) A, (2) at least one of A, (3) B, (4) at least one of B, (5) at least one of A and at least one of B, (6) at least one of A and B, (7) at least one of B and A, and (8) both A and B.

[0045] As used herein, the expression "based on" is used to describe one or more factors that influence an act or action of decision, judgment, or action described in the phrase or sentence containing the expression, and this expression does not exclude any additional factors that influence such act or action of decision.

[0046] As used herein, the expression that a component (e.g., a first component) is “connected” or “linked” to another component (e.g., a second component) may mean not only that the first component is directly connected to the other component, but also that it is connected or linked through a new other component (e.g., a third component).

[0047] As used herein, the expression "configured to" can mean, depending on the context, "set to do," "capable of doing," "modified to do," "made to do," or "capable of doing." The expression is not limited to the meaning of "specifically designed in hardware," and for example, a processor configured to perform a specific operation may mean a generic-purpose processor that can perform that specific operation by running software.

[0048] Various embodiments of this disclosure will be described below with reference to the attached drawings. In the attached drawings and descriptions thereof, identical or substantially equivalent components may be given the same reference numerals. Furthermore, in the descriptions of the various embodiments below, identical or corresponding components may be omitted from the description; however, this does not mean that the component is not included in that embodiment.

[0049] Basic structure of a device for generating dental consistency data

[0050] Figure 1 is a diagram illustrating the basic configuration of an apparatus for generating dental consistency data according to one embodiment of the present disclosure.

[0051] Referring to Figure 1, the apparatus (100) for generating dental consistency data may include a memory (110) and a processor (120).

[0052] The memory (110) can store various applications, including applications for generating each patient's personal information and treatment data, as well as dental consistency data. In one embodiment, the patient's treatment data may include at least one of the following: intraoral scan (IOS) images (15), three-dimensional (3D) facial images (25), and CT (computed tomography) images (35).

[0053] The processor (120) can execute an application stored in memory (110) for generating dental matching data. In one embodiment, the acquisition module (130), matching module (140), and display module (150) may be implemented in software in response to the execution of the application for generating dental matching data. However, depending on the design of the device, some modules may be implemented in hardware, and are not limited to this.

[0054] In one embodiment, the intraoral scan (IOS) image (15) may be acquired by scanning the patient's teeth, i.e., the inside of the oral cavity, using a three-dimensional oral scanner (10). The three-dimensional oral scanner (10) is a non-contact scanner that collects surface data of the patient's oral cavity structure and generates a 3D intraoral scan (IOS) image based on this data using a confocal method or optical triangulation method. When acquiring a 3D intraoral scan (IOS) image as a digital impression via a three-dimensional oral scanner (10) in this way, there is an advantage that it eliminates the need to go through the troublesome procedure of physically acquiring the patient's oral cavity structure by performing an impression (molding) using impression material placed in a tray. The intraoral scan (IOS) image (15) may be acquired via various types of commercially available three-dimensional oral scanners, but is not limited to these.

[0055] Oral scan images may include maxillary scan images and mandibular scan images. Maxillary scan images may be obtained by scanning the patient's maxillary teeth, and mandibular scan images may be obtained by scanning the patient's mandibular teeth.

[0056] The 3D facial image (25) may be obtained by photographing the patient's face using a 3D facial imaging device (20). In one embodiment, the 3D facial imaging device (20) may include multiple cameras, and the multiple cameras may be driven in a manner that captures the patient's face in a single shot to acquire the 3D facial image (25). However, the 3D facial imaging device (20) may also be driven in a manner that captures the patient's face with a time delay while fixing or moving the patient's face.

[0057] Furthermore, according to one embodiment, the multiple cameras of the 3D facial imaging device (20) may include at least one dental imaging camera for imaging the patient's teeth, along with the multiple facial imaging cameras. The multiple facial imaging cameras may be installed on the front, left, right, and bottom of the 3D facial imaging device (20) so as to be able to image the front, left, right, and bottom of the patient's face. According to one embodiment, the dental imaging camera may be different from the facial imaging camera, for example, having a different lens magnification. The dental imaging camera may be installed on the left and right sides of the 3D facial imaging device (20) and configured to be able to image the left and right sides of the patient's teeth, but the number and installation position of the dental imaging cameras are not limited. Providing a separate dental imaging camera in this way not only allows for a more accurate representation and presentation of the patient's teeth, but also has the aspect of enabling more precise and smoother matching with other data (e.g., CT scan images or oral scan (IOS) images) in the generation of consistent data. This will be described in detail later.

[0058] In one embodiment, the 3D facial image (25) may be taken with the patient's teeth exposed. That is, when photographing a patient's face with the 3D facial imaging device (20), the patient can fix their face in a fixed position and be guided to expose their teeth before the imaging is performed. For this reason, the 3D facial imaging device (20) may be designed to have a guiding function. This makes it possible to more accurately photograph not only the patient's face but also their teeth to generate the 3D facial image (25). However, in some cases, it may not be necessary to acquire the 3D facial image (25) with the patient's teeth exposed, in which case the 3D facial image (25) may or may not have exposed teeth.

[0059] According to one embodiment, a 3D facial imaging device (20) can acquire a 3D facial image (25) based on multiple images acquired by photographing the patient's face using multiple cameras while irradiating the patient's face with a structural light pattern. However, the method for acquiring the 3D facial image (25) can include, but is not limited to, a laser scanning method, a depth sensor method, or a method for reconstructing the 3D facial image using artificial intelligence (AI), in addition to the method using structural light.

[0060] The CT image (35) may be obtained by imaging the facial region including the patient's teeth via a CT scanner (30). The CT scanner (30) is a dental CT scanner, and may be, for example, a cone-beam CT scanner. A cone-beam CT scanner has the advantage of being able to acquire a CT image of the patient's teeth while minimizing radiation exposure to the patient by scanning a cone-shaped X-ray beam. However, the CT scanner (30) may also be a fan-beam CT scanner, which scans a fan-shaped X-ray beam. In other words, the CT image (35) according to the embodiments of this disclosure may be obtained via various CT scanners, and is not limited thereto.

[0061] According to one embodiment, the acquisition module (130) of the device (100) for generating dental consistency data can acquire oral scan (IOS) images (15), 3D facial images (25), and CT scan images (35) as described above from a 3D oral scanner (10), a 3D facial imaging device (20), and a CT scanner (30), respectively. However, at least one of the oral scan (IOS) images (15), 3D facial images (25), and CT scan images (35) may also be acquired by the acquisition module (130) by downloading them from other storage media or the cloud.

[0062] The oral scan (IOS) images (15), 3D facial images (25), and CT scan images (35) acquired by the acquisition module (130) may be stored in the memory (110) of the device (100) for generating dental consistency data. The memory (110) may store the oral scan (IOS) images (15), 3D facial images (25), and CT scan images (35) separately for each patient, and if there are multiple copies of the same type of data taken at different times, they may be stored separately by time. In addition, the memory (110) may store personal information of each patient, treatment data such as comments from dentists, and various applications, including applications for generating dental consistency data.

[0063] Figure 2 is a block diagram of an apparatus (hereinafter referred to as the dental alignment data generation apparatus) (100) for generating dental alignment data according to one embodiment of the present disclosure.

[0064] Referring to Figure 2, according to one embodiment, the dental matching data generation device (100) may be a device that matches oral scan images with CT scan images. The dental matching data generation device (100) according to one embodiment may include a memory (110), a processor (120), and a communication circuit (160). The processor (120) of the dental matching data generation device (100) can execute software (e.g., a program) to control at least one other component (e.g., a hardware component, a software component) of the dental matching data generation device (100) connected to the processor (120), and can perform various data processing or calculations. As at least part of the data processing or calculations, the processor (120) can load instructions or data received from the other component into the memory (110), process the instructions or data stored in the memory (110), and store the resulting data in the non-volatile memory (110). The memory (110) of the dental integration data generation device (100) can store information related to the method described above, or a program embodying the method described above. The memory (110) may be volatile memory or non-volatile memory.

[0065] A processor (120) of a dental data matching device (100) according to one embodiment can execute a program and control the dental data matching device (100). The code of the program executed by the processor (120) can be stored in memory (110). The dental data matching device (100) can be connected to external devices (e.g., a 3D oral scanner (10), a 3D facial imaging device (20), a CT scanner (30)) via input / output devices (not shown) to exchange data. The processor (120) can be operationally connected to components of the dental data matching device (100). The processor (120) can load instructions or data received from other components of the dental data matching device (100) into memory (110), process the instructions or data stored in memory (110), and store the resulting data.

[0066] A communication circuit (160) of a dental integration data generation device (100) according to one embodiment can establish a communication channel with an external device (e.g., a 3D oral scanner (10), a 3D facial scanner (20), a CT scanner (30)) and send and receive various data with the external device. According to various embodiments, the communication circuit (160) may include a cellular communication module and be configured to connect to a cellular network (e.g., 3G, LTE, 5G, Wibro, or WiMAX). According to various embodiments, the communication circuit (160) may include a short-range communication module and can send and receive data with an external device using short-range communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), UWB), but is not limited to these.

[0067] According to one embodiment, the dental matching data generation device (100) may further include an input device (not shown). The input device can receive commands or data used for components of the server device from an external source. The input device may include, for example, a microphone, a mouse, or a keyboard.

[0068] According to one embodiment, the dental integration data generation device (100) may further include a display (not shown). The display can display various screens under the control of a processor (120).

[0069] Figure 3 is a diagram illustrating the principle of generating dental consistency data according to one embodiment of the present disclosure.

[0070] Referring to Figures 1 and 2, the matching module (140) can generate matched data by matching oral scan (IOS) images (15) and CT scan images (35). The oral scan images can include maxillary (upper teeth) scan images and mandibular (lower teeth) scan images. The matching module (140) can generate two types of matched data in two ways, depending on the treatment objective. The specific methods for generating the two types of matched data will be described later.

[0071] Furthermore, the matching module (140) can match the 3D face image (25) with the CT scan image (35) or the matching data to generate a dental 3D avatar (dental3Davatar) as the final matching data. According to one embodiment, for such matching, a method can be used in which landmarks from the patient's 3D data (e.g., data of the face and / or tooth area) are extracted and used to perform matching between images. However, other methods besides the method using landmarks can also be used, and the invention is not limited to this.

[0072] According to one embodiment, the alignment module (140) can extract three or more landmarks from the patient's 3D data. In the embodiments of this disclosure, the oral scan (IOS) image (15), 3D facial image (25), and CT scan image (35) are all 3D, or at least three or more are 3D data. By using three or more landmarks, the x, y, and z axes can all be aligned in the 3D data.

[0073] According to one embodiment of the method for generating dental consistency data, the extraction of landmarks can be manually specified by a user, such as a dentist or dental laboratory manager. For example, the user can specify landmarks using a pointer such as a mouse through the user interface of the application for generating dental consistency data presented to the user by the display module (150). According to another embodiment, three or more landmarks can be extracted by the application for generating dental consistency data.

[0074] On the other hand, according to another embodiment, the application for generating dental integration data can also extract landmarks on an artificial intelligence basis by incorporating an artificial neural network module pre-trained through a large amount of data, or by being connected to such an artificial neural network module via a network.

[0075] According to one embodiment, in the case of an oral scan (IOS) image (15), it corresponds to a 3D image generated by scanning the inside of the patient's mouth and does not include facial data, so landmarks of the tooth area can be utilized when matching with other images (25, 35). Also, when matching a 3D facial image (25) with a CT scan image (35), landmarks may be landmarks of the facial region excluding the patient's tooth area, or both landmarks of the patient's tooth area and landmarks of the facial region may be utilized.

[0076] The display module (150) can display the alignment data generated by the alignment module (140) via a display. The display module (150) can display the dental 3D avatar generated by the alignment module (140) via the display of the device (100). According to one embodiment, the display module (150) can display the alignment data on a user interface provided by an application for generating dental alignment data. Furthermore, the display module (150) can adjust the transparency of some of the three images (15, 25, 35) that constitute the dental 3D avatar, or enable the display of some of them, so that users such as dentists and dental laboratory managers can see the appropriate representation as needed. In addition, in the dental 3D avatar displayed via the display module (150), the user can adjust some of the data, for example, the position and shape of the oral scan image (15).

[0077] Method for generating dental consistency data

[0078] Figure 4 is a flowchart illustrating a method for generating dental consistency data according to one embodiment of the present disclosure.

[0079] First, referring to Figure 4, in a method for generating dental alignment data according to one embodiment of the present disclosure, the processor (120) of the dental alignment data generation device (100) can acquire an oral scan (IOS) image of the patient's tooth area in step S410. The oral scan image may include maxillary scan images and mandibular scan images. For example, a user (e.g., a dentist, dental technician) can acquire an oral scan image of the patient's tooth area using a three-dimensional oral scanner (20). The user can acquire a maxillary scan image of the patient's maxillary tooth area using a three-dimensional oral scanner (20). The user can acquire a mandibular scan image of the patient's mandibular tooth area using a three-dimensional oral scanner (20).

[0080] The dental integration data generation device (100) can acquire maxillary scan images and mandibular scan images obtained from a 3D oral scanner (20). Alternatively, the dental integration data generation device (100) can download and acquire maxillary scan images and mandibular scan images stored on other storage media or in the cloud. The dental integration data generation device (100) can generate an oral scan image by aligning the maxillary scan image and mandibular scan image with each other. The oral scan image obtained by aligning the maxillary scan image and mandibular scan image in this way represents the state in which the patient's mouth is normally in occlusion, and this occlusal state of the oral scan image (the state in which the patient bites down) can be called the oral scan occlusal state (or oral occlusal state).

[0081] In one embodiment, the processor (120) can acquire a CT scan image including the patient's teeth in step S420. For example, a user can acquire a CT scan image including the patient's teeth using a CT scanner (30). The patient can perform the CT scan by fixing the mouthpiece (or bite block) of the CT scanner (30) in their mouth. The occlusal state of the oral cavity during CT scanning can be called the CT occlusal state.

[0082] In one embodiment, the processor (120) can generate first sub-aligned data in step S430 by aligning the maxillary scan image with the CT scan image. The first sub-aligned data is necessary to generate both first aligned data corresponding to the oral scan occlusal state and second aligned data corresponding to the CT occlusal state. The method for generating the first sub-aligned data will be explained with reference to Figures 5a to 5c.

[0083] Figure 5a is a CT scan image including the patient's teeth. The CT scan image may be a three-dimensional CT scan image. Image (510a) in Figure 5a may be a CT scan image viewed from the front, and image (510b) may be a CT scan image viewed from the side. Figure 5b is a maxillary scan image of the patient's maxillary teeth. The maxillary scan image may be a three-dimensional oral scan image. Image (520a) in Figure 5b may be a maxillary scan image viewed from the front, and image (520b) in Figure 5b may be a maxillary scan image viewed from the side. Figure 5c is the first sub-aligned data obtained by aligning the maxillary scan image with the CT scan image. Figure 5c may be three-dimensional data generated by aligning a three-dimensional maxillary scan image with a three-dimensional CT scan image. Image (530a) in Figure 5c may be the first sub-aligned data viewed from the front, and image (530b) may be the first sub-aligned data viewed from the side.

[0084] Referring to Figures 5a to 5c, a processor (120) according to one embodiment can generate first sub-matched data by matching maxillary scan images and CT scan images based on at least three landmarks. Since each image corresponds to three-dimensional data, there may be three or more landmarks used to generate the first sub-matched data, and these can be extracted from the patient's face and / or tooth regions. For example, the processor (120) can extract three landmarks from the CT scan image and three corresponding landmarks from the maxillary scan data. Landmarks can refer to, for example, points located on the maxillary teeth and gums. However, the locations of the landmarks described above are illustrative, and they can be extracted from various locations, such as the intermediate area of ​​the anterior teeth or points located on the interdental boundary. Precise matching of tooth regions is important for generating matched data for use in dental treatment, so it is necessary to accurately extract landmarks from the patient's tooth regions. Therefore, if possible, it is more effective if precise matching is possible using only the landmarks of the patient's tooth regions, as this eliminates the need to extract additional landmarks from facial regions other than the patient's teeth.

[0085] A processor (120) according to one embodiment can generate first sub-matched data from maxillary scan images and CT scan images using a first deep learning model. The first deep learning model may be an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with multiple maxillary scan images and multiple CT scan image sets for multiple patients, and multiple sub-matched datasets as output data, each of which is matched to each of the multiple CT scan images.

[0086] In this disclosure, a deep learning algorithm may mean that computer software improves its data processing capabilities through learning using data and experience in processing that data. Deep learning can be performed by a deep learning model. A deep learning model is constructed by modeling the correlations between data, and these correlations can be represented by multiple parameters. A deep learning model extracts and analyzes features from given data to derive correlations between the data, and deep learning can be said to be the process of optimizing the parameters of the deep learning model by repeating this process. For example, if data is given as input and output pairs for a deep learning model, the deep learning model can learn the mapping (correlation) between the input and output. Alternatively, even if only input data is given, the learning model can derive regularities between the given data and learn their relationships. In one embodiment, the deep learning algorithm may be at least one selected from deep neural networks, recurrent neural networks, convolutional neural networks, machine learning models for classification-regression analysis, reinforcement learning models, decision tree learning methods, association rule learning methods, genetic programming, inductive logic programming, support vector machines, clustering, Bayesian networks, or identity measurement learning methods.

[0087] Through the process described above, the first sub-matched data disclosed in Figure 5c can be generated by aligning the maxillary scan image disclosed in Figure 5b with the CT scan image disclosed in Figure 5a.

[0088] In one embodiment, the processor (120) can generate first aligned data in step S440 by aligning the mandibular scan image with first sub-aligned data. The first aligned data can represent aligned data based on the oral scan occlusal state. Specifically, the processor (120) can generate first aligned data by aligning the mandibular scan image with the maxillary scan image on the first sub-aligned data. For example, the processor (120) can align the mandibular scan image and the maxillary scan image on the first sub-aligned data so that they are in an oral scan occlusal state relative to each other. The processor (120) can align the mandibular scan image with the first sub-aligned data by moving the mandibular scan image to follow the maxillary scan image by the amount that the maxillary scan image is aligned with the CT scan image.

[0089] Figures 6a and 6b illustrate the method for generating the first aligned data. Specifically, Figure 6a is a mandibular scan image obtained by scanning the mandibular tooth region of a patient, and Figure 6b is a diagram showing the first aligned data. Image (610a) in Figure 6a is a mandibular scan image viewed from the front, and image (610b) is a mandibular scan image viewed from the side. Image (620a) in Figure 6b is the first aligned data viewed from the front, and image (620b) is the first aligned data viewed from the side.

[0090] Referring to Figures 6a and 6b, the processor (120) can generate first aligned data by aligning the mandibular scan image in Figure 6a with the first sub-aligned data in Figure 5c. The processor (120) can generate first aligned data by aligning the mandibular scan image and the maxillary scan image on the first sub-aligned data so that they represent the oral scan occlusal state. Since the first aligned data is aligned data generated by aligning the mandibular scan image based on the maxillary scan image of the first sub-aligned data, the first aligned data can represent the oral scan occlusal state. Therefore, the first aligned data can be used in dental treatments where the oral scan occlusal state must be confirmed (for example, prosthetic treatments such as crowns).

[0091] In one embodiment, the processor (120) can generate second sub-aligned data in step S450 by aligning the mandibular scan image with the CT scan image. The second sub-aligned data is necessary to generate second aligned data corresponding to the CT occlusal state. The method for generating the second sub-aligned data will be explained using Figures 5a, 6a, and 7. As described above, Figure 5a is a CT scan image including the patient's teeth, and Figure 6a is a mandibular scan image scanning the patient's mandibular teeth. Figure 7 is a diagram showing the second sub-aligned data. Image (710a) in Figure 7 is the second sub-aligned data viewed from the front, and image (710b) is the second sub-aligned data viewed from the side.

[0092] Referring to Figures 5a, 6a, and 7, the processor (120) can generate second sub-matched data by matching the mandibular scan image in Figure 6a with the CT scan image in Figure 5a. In one embodiment, the processor (120) can generate second sub-matched data by matching the mandibular scan image and the CT scan image based on at least three landmarks. For example, the processor (120) can extract three landmarks from the CT scan image and three corresponding landmarks from the mandibular scan data. The landmarks may refer to predetermined points related to mandibular teeth and gingiva, for example. However, the locations of the landmarks described above are illustrative, and they can be extracted from various locations, such as the intermediate area of ​​the anterior teeth or points located at the interdental boundary.

[0093] A processor (120) according to one embodiment can generate second sub-aligned data from mandibular scan images and CT scan images using a second deep learning model. The second deep learning model may be an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using multiple mandibular scan images and multiple CT scan image sets for multiple patients as input data, and multiple sub-aligned datasets in which each of the multiple mandibular scan images is aligned with each of the multiple CT scan images as output data.

[0094] In this disclosure, a deep learning algorithm may mean that computer software improves its data processing capabilities through learning using data and experience in processing that data. Deep learning can be performed by a deep learning model. A deep learning model is constructed by modeling the correlations between data, and these correlations can be represented by multiple parameters. A deep learning model extracts and analyzes features from given data to derive correlations between the data, and deep learning can be said to be the process of optimizing the parameters of the deep learning model by repeating this process. For example, if data is given as input and output pairs for a deep learning model, the deep learning model can learn the mapping (correlation) between the input and output. Alternatively, even if only input data is given, the learning model can derive regularities between the given data and learn their relationships. In one embodiment, the deep learning algorithm may be at least one selected from deep neural networks, recurrent neural networks, convolutional neural networks, machine learning models for classification-regression analysis, reinforcement learning models, decision tree learning methods, association rule learning methods, genetic programming, inductive logic programming, support vector machines, clustering, Bayesian networks, or identity measurement learning methods.

[0095] Through the process described above, the second sub-aligned data disclosed in Figure 7 can be generated by aligning the mandibular scan image disclosed in Figure 6a with the CT scan image disclosed in Figure 5a.

[0096] In one embodiment, the processor (120) can generate second aligned data in step S460 by aligning the first aligned data with the second aligned data. The second aligned data may represent aligned data based on the CT occlusal state. Specifically, the processor (120) can perform alignment based on the CT images of the first aligned data and the second aligned data, since the CT images of the first aligned data and the second aligned data are identical. That is, the processor (120) can align the first aligned data and the second aligned data with each other to achieve the CT occlusal state.

[0097] Figure 8 is a diagram showing the second aligned data. Image (810a) in Figure 8 is the second aligned data viewed from the front, and image (810b) is the second aligned data viewed from the side. The process of generating the second aligned data will be explained using Figures 5c, 7, and 8. The processor (120) can generate the second aligned data in Figure 8 by aligning the first sub-aligned data in Figure 5c and the second sub-aligned data in Figure 7 with each other. The processor (120) can generate the second aligned data by aligning the CT scan image of the first sub-aligned data and the CT scan image of the second sub-aligned data so that they overlap.

[0098] The user can select and use the necessary matching data from the first matching data and the second matching data according to the treatment purpose. In one embodiment, the processor (120) of the dental matching data generation device (100) can receive user input via the input device (100) to select one matching data from the first matching data and the second matching data. In response to receiving the user input, the processor (120) can display the selected matching data via the display. In another embodiment, the processor (120) of the dental matching data generation device (100) can receive user input regarding the treatment method for the patient's tooth area via the input device (100). The processor (120) can select one matching data from the first matching data and the second matching data corresponding to the user input. The processor (120) can also display the selected matching data via the display.

[0099] In one embodiment, the processor (120) can store the first and second consistent data in memory (110). Alternatively, the processor (120) can store the first and second consistent data in the cloud.

[0100] A processor (120) according to one embodiment can acquire a three-dimensional facial image of a patient's face, including the tooth area. The processor (120) can integrate the three-dimensional facial image with a CT scan image, the first integrated data, and the second integrated data to generate a dental 3D avatar as final integrated data. For example, the processor (120) can integrate the three-dimensional facial image with a CT scan image, integrate the three-dimensional facial image with the first integrated data, and integrate the three-dimensional facial image with the second integrated data. To generate a dental 3D avatar, the processor (120) can extract landmarks from the three-dimensional facial image that correspond to the landmarks extracted to generate the first integrated data. For example, landmarks corresponding to at least three landmarks extracted to generate the first sub-integrated data can be extracted from the three-dimensional facial image.

[0101] As described above, according to the method for generating dental alignment data according to other embodiments of the present disclosure, it is possible to generate first sub-alignment data generated on the basis of aligning the maxillary scan image from the oral scan images to a CT scan image acquired in the patient's oral occlusal state, and first alignment data generated on the basis of aligning the mandibular scan image. Furthermore, according to the method for generating dental alignment data according to other embodiments of the present disclosure, it is possible to generate first sub-alignment data generated on the basis of aligning the maxillary scan image from the oral scan images to a CT scan image acquired in the patient's oral occlusal state, and second alignment data generated on the basis of aligning second sub-alignment data generated on the basis of aligning the mandibular scan image to the CT scan image.

[0102] In other words, since two sets of consistent data are generated based on two different occlusal states, users can selectively utilize the data necessary according to the patient's dental treatment objectives. Furthermore, this results in a more effective representation of the tooth condition when the patient maintains the oral occlusal state.

[0103] More specifically, depending on the treatment method, the first alignment data may be more appropriate, the second alignment data may be more appropriate, and sometimes both are necessary. For example, when designing prosthetics such as crowns, inlays, and onlays, the occlusal relationship with other teeth is more important, so the first alignment data based on the oral scan occlusal state can be used. On the other hand, when designing a surgical guide, it is necessary to confirm the position of the patient's tooth roots and nerves, so the second alignment data, which is data on the patient's tooth occlusal state (i.e., CT occlusal state) based on CT scan images, may be more appropriate. Furthermore, when designing prosthetics and surgical guides simultaneously, both sets of data (i.e., the first dental 3D avatar and the second dental 3D avatar) may be necessary.

[0104] Figure 9 is an operation flowchart of a dental alignment data generation device (100) according to one embodiment of the present disclosure. Content that overlaps with that described in Figure 4 will be omitted.

[0105] Referring to the operation flowchart 900, in one embodiment, the processor (120) of the dental matching data generation device (100) can acquire an oral scan image of the patient's tooth area in step S910. In one embodiment, the processor (120) can acquire a CT scan image including the patient's tooth area in step S920.

[0106] In one embodiment, the processor (120) can obtain a CT segmentation image in which at least one of the maxillary teeth, mandibular teeth, maxilla, mandible, and mandibular nerve canal is segmented by processing the CT image in step S930. The processor (120) can identify and extract at least one of the maxillary teeth, mandibular teeth, maxilla, mandible, and mandibular nerve canal from the CT image. When creating mesh data, each part can be segmented while maintaining the CT occlusal state.

[0107] Figures 10a and 10b are diagrams showing CT segmentation images. Image (1010a) in Figure 10a is a CT segmentation image viewed from the front, and image (1010b) is a CT segmentation image viewed from the side. Image (1010c) in Figure 10b is an image of image (1010a) in Figure 10a displayed semi-transparently.

[0108] Referring to Figures 10a to 10c, the CT segmentation image may include teeth (1011), maxilla (1013), mandible (1015), and mandibular nerve canal (1017). That is, the processor (120) can identify and extract teeth (1011), maxilla (1013), mandible (1015), and mandibular nerve canal (1017) from the CT image.

[0109] Returning to Figure 9, the processor (120) according to one embodiment can generate first sub-matched data in step S940 by matching the maxillary scan image to the CT segmentation image. The processor (120) according to one embodiment can generate first matched data in step S950 by matching the mandibular scan image to the first sub-matched data. The difference from the method disclosed in Figure 4 is that the method in Figure 9 does not use the CT scan image as is, but uses a CT segmentation image in which the teeth, maxilla, mandible and / or mandibular nerve canal are segmented for matching.

[0110] Figure 11 is a diagram showing the first alignment data according to one embodiment. Image (1110a) in Figure 11 is the first alignment data viewed from the front, and image (1110b) is the first alignment data viewed from the side. That is, the processor (120) can sequentially align the maxillary scan image and the mandibular scan image to the CT segmentation image. At this time, the processor (120) can align the mandibular scan image with the maxillary scan image of the first sub-alignment data as a reference. That is, the processor (120) can generate the first alignment data by moving the mandibular scan image by the amount that the maxillary scan data has been aligned with the CT segmentation image. Therefore, the first alignment data can show the oral scan occlusal state.

[0111] In one embodiment, the segmented mandible, mandibular teeth, and mandibular nerve canal can be adjusted to the occlusal state of the oral scan. The processor (120) can calculate the conversion relationship between the first aligned data and the second sub-aligned data. For example, the processor (120) can generate the second sub-aligned data by aligning the mandibular scan image of the first aligned data with the CT segmentation image. The processor (120) can calculate a conversion matrix from the first aligned data to the second sub-aligned data. The processor (120) can calculate an inverse conversion matrix for the conversion matrix. By applying the inverse conversion matrix to the segmented mandible, mandibular teeth, and mandibular nerve canal, the processor (120) can accurately calculate and represent the positions of the segmented mandible, mandibular teeth, and mandibular nerve canal in the occlusal state of the oral scan.

[0112] Returning to Figure 9, the processor (120) according to one embodiment can generate second sub-matched data in step S960 by matching the mandibular scan image with the CT segmentation image. The processor (120) according to one embodiment can generate second matched data in step S970 by matching the first sub-matched data with the second sub-matched data. The difference from the method disclosed in Figure 4 is that the method in Figure 9 does not use the CT scan image as is, but uses a CT segmentation image in which the teeth, maxilla, mandible and / or mandibular nerve canal are segmented for matching.

[0113] Figure 12 is a diagram showing second alignment data according to one embodiment. Image (1210a) in Figure 12 is the second alignment data viewed from the front, and image (1210b) is the first alignment data viewed from the side. That is, the processor (120) can generate first sub-alignment data by aligning the maxillary scan image with the CT segmentation image, and generate second sub-alignment data by aligning the mandibular scan image with the CT segmentation image. At this time, the processor (120) can align the first sub-alignment data and the second sub-alignment data based on the CT segmentation image. Therefore, the second alignment data can show the CT occlusal state.

[0114] Computer-readable recording media

[0115] It is obvious that each step and operation of the methods according to the embodiments of this disclosure may be performed by a computer including one or more processors in response to the execution of a computer program stored on a computer-readable recording medium.

[0116] The computer-executable instructions stored on the aforementioned recording media can be implemented through computer programs programmed to perform each corresponding step. Such computer programs can be stored on computer-readable recording media and executed by a processor. Computer-readable recording media can be non-transitory-readable mediums. In this case, a non-transitory-readable medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short moment, such as registers, caches, and memory. Specifically, programs for performing the various methods described above can be stored and provided on non-transitory-readable mediums such as semiconductor memory devices like erasable-programmable-readable-only memory (EPROM), electrically erasable-programmable-readable-only memory (EEPROM), and flash memory devices; magnetic disks like internal hard disks and removable disks; magneto-optical disks; and non-volatile memory including CD-ROMs and DVD-ROMs.

[0117] The various illustrative methods disclosed herein may be provided in a computer program product. A computer program product may be distributed in the form of a device-readable storage medium (e.g., compactdiscreadonlymemory (CD-ROM)) or online via an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated on a storage medium such as the memory of the manufacturer's server, the application store's server, or an intermediary server.

[0118] As described above, a person ordinary in the art to which this disclosure belongs will understand that this disclosure may be implemented in other specific forms without altering its technical idea or essential features. Therefore, the embodiments described above should be understood to be illustrative and not restrictive in all respects. The scope of this disclosure is indicated by the claims set forth below rather than by the detailed description, and all modified or altered forms derived from the meaning and scope of the claims and equivalent concepts should be interpreted as being included within the scope of this disclosure.

[0119] The features and advantages described herein are not exhaustive, and many additional features and advantages will become apparent to those skilled in the art in consideration of the drawings, specification, and claims. Furthermore, it should be noted that the language used herein has been chosen primarily for readability and teaching purposes and may not be chosen to describe or limit the subject matter of this disclosure.

[0120] The above description of the embodiments of this disclosure is provided for illustrative purposes only. It is not intended to limit this disclosure to the exact form disclosed or to make it complete. Those skilled in the art will understand that many modifications and variations are possible in light of the above disclosure.

[0121] Therefore, the scope of this disclosure is not limited by the detailed description, but by any claim of an application based thereon. Accordingly, the examples disclosed in this disclosure are illustrative and do not limit the scope of this disclosure as described in the following claims. [Explanation of Symbols]

[0122] 10: 3D oral scanner 15: Oral scan (IOS) image 20: 3D facial imaging device 25: 3D Face Image 30: Dental CT scanner 35: CT scan image 100: Dental Integrity Data Generation Device 110: Memory 120: Processor 130: Acquisition Module 140: Alignment Module 150: Display Module

Claims

1. In a method for generating dental compatibility data using a device including a communication circuit, memory, and processor, The method performed by the processor is, A step of acquiring an oral scan image of the patient's tooth area - the oral scan image includes an upper jaw scan image and a lower jaw scan image - The steps include: acquiring a CT scan image including the tooth area of ​​the patient; and aligning the maxillary scan image with the CT scan image to generate first sub-aligned data. The steps include: aligning the mandibular scan image with the first sub-aligned data to generate first aligned data; The steps include generating second sub-matching data by matching the mandibular scan image with the CT scan image, The steps include generating second aligned data by aligning the first sub-aligned data with the second sub-aligned data, A method for generating dental consistency data, including [specific data].

2. A method for generating dental matching data according to claim 1, further comprising the step of obtaining a CT segmentation image in which at least one of the maxillary teeth, mandibular teeth, maxilla, mandible and mandibular nerve canal is segmented by the processor performing image processing on the CT scan image.

3. The step of generating the first sub-matching data includes the step of generating the first sub-matching data by matching the maxillary scan image with the CT segmentation image obtained by processing the CT scan image, The method for generating dental alignment data according to claim 2, wherein the step of generating the second sub-alignment data includes the step of aligning the mandibular scan image with the CT segmentation image obtained by image processing of the CT imaging image to generate the second sub-alignment data.

4. A step of calculating a conversion matrix from the generated first aligned data to the second sub-aligned data after the first aligned data has been generated, The steps include applying the inverse transformation matrix to the transformation matrix to at least one of the segmented mandibular teeth, mandible, and mandibular nerve canal to correct the position of at least one of the mandibular teeth, mandible, and mandibular nerve canal to the occlusal state of the first alignment data, A method for generating dental consistency data according to claim 3, further comprising:

5. The step of generating the first sub-matching data includes the step of generating the first sub-matching data from the maxillary scan image and the CT scan image using a first deep learning model, A method for generating dental matching data according to claim 1, wherein the first deep learning model is an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of maxillary scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matching datasets in which each of the plurality of maxillary scan images is matched with each of the plurality of CT scan images as output data.

6. The step of generating the second sub-matching data includes the step of generating the second sub-matching data from the mandibular scan image and the CT scan image using a second deep learning model, A method for generating dental matching data according to claim 1, wherein the second deep learning model is an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of mandibular scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matching datasets in which each of the plurality of mandibular scan images is matched with each of the plurality of CT scan images as output data.

7. The first sub-aligned data and the second sub-aligned data are generated based on at least three landmarks of the patient's three-dimensional data. The aforementioned three landmarks are extracted by an application for generating dental consistency data. The method for generating dental matching data according to claim 1, wherein the at least three landmarks are extracted using an artificial neural network module embedded in the application or connected via a network, and the artificial neural network module is pre-trained via pre-training data.

8. A device for generating dental consistency data, It includes a communication circuit, memory, and a processor. The aforementioned processor, Acquire oral scan images of the patient's tooth area—the oral scan images include maxillary scan images and mandibular scan images— A CT scan image including the tooth area of ​​the aforementioned patient is obtained. By aligning the maxillary scan image with the CT scan image, first sub-aligned data is generated. By aligning the mandibular scan image with the first sub-aligned data, the first aligned data is generated. By aligning the mandibular scan image with the CT scan image, a second sub-aligned data is generated. A device configured to generate second aligned data by aligning the first sub-aligned data with the second sub-aligned data.

9. The system further includes an input device and a display, The aforementioned processor, The input device receives user input to select one of the first and second matching data sets. The apparatus according to claim 8, configured to display the selected matching data via the display in response to the receipt of the user input.

10. The system further includes an input device and a display, The aforementioned processor, The input device receives user input regarding the treatment method for the patient's tooth area. From the first and second matching data, select one matching data corresponding to the user input. The apparatus according to claim 8, configured to display the selected matching data via the display.

11. The apparatus according to claim 8, wherein the processor is configured to obtain a CT segmentation image in which at least one of the maxillary teeth, mandibular teeth, maxilla, mandible and mandibular nerve canal is segmented by processing the CT imaging image.

12. The aforementioned processor, The first sub-matching data is generated by matching the maxillary scan image with the CT segmentation image obtained by image processing of the CT scan image. The apparatus according to claim 11, configured to generate the second sub-matching data by matching the mandibular scan image with the CT segmentation image obtained by image processing of the CT scan image.

13. The aforementioned processor, After the first aligned data is generated, a conversion matrix is ​​calculated from the generated first aligned data to the second sub-aligned data. The apparatus according to claim 12, configured to apply an inverse transformation matrix to the transformation matrix to at least one of the segmented mandibular teeth, mandible, and mandibular nerve canal to correct the position of at least one of the mandibular teeth, mandible, and mandibular nerve canal to the occlusal state of the first alignment data.

14. The aforementioned processor, The system is configured to generate the first sub-matching data from the maxillary scan image and the CT scan image using a first deep learning model. The apparatus according to claim 8, wherein the first deep learning model is an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of maxillary scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matched datasets in which each of the plurality of maxillary scan images is matched with each of the plurality of CT scan images as output data.

15. The aforementioned processor, The system is configured to generate the second sub-matching data from the mandibular scan image and the CT scan image using a second deep learning model. The apparatus according to claim 8, wherein the second deep learning model is an artificial intelligence model constructed by modeling the correlation between the input data and the output data, using a deep learning algorithm as input data, with a plurality of mandibular scan images and a plurality of CT scan image sets for a plurality of patients, and a plurality of sub-matched datasets in which each of the plurality of mandibular scan images is matched with each of the plurality of CT scan images as output data.

Images