KR102566311B1 - Method and system for generating endo guide for neurotherapy - Google Patents

Abstract

The present disclosure relates to a method for generating an endo guide for neurological treatment, executed by at least one processor. A method for generating an endo guide for nerve treatment includes the steps of receiving, from a user terminal, a dental image including a tooth and a location code for at least one tooth among a plurality of teeth to which different location codes are assigned, the received dental image Identifying a nerve region of a tooth in the tooth, the nerve region of the tooth including a tooth and surrounding nerve tissue of the tooth, and endo guide data for a tooth corresponding to the nerve region based on the location code and the identified nerve region. It includes the step of outputting.

Description

Endo guide generation method and system for nerve treatment {METHOD AND SYSTEM FOR GENERATING ENDO GUIDE FOR NEUROTHERAPY}

The present disclosure relates to a method and system for generating an endo guide for nerve treatment, and specifically, by generating endo guide data based on a position code for a tooth and a dental image, to insert an endo for nerve treatment. It relates to a method and system for providing a guide.

'Dental root canal therapy', known as root canal treatment, is a procedure that removes the tissue when the endodontic is damaged due to gingivitis or periodontal disease, inserts special materials, and makes it function as a natural tooth without pain. Dental conditions such as medical history, fracture, caries, etc. are observed, and inflammatory conditions for root canal treatment and apical disease can be diagnosed through examination procedures such as radiographic examination, percussion and thermal examination, and cavity formation examination. The process of root canal treatment includes 1) anesthesia so that the tooth does not feel pain during the treatment process, 2) a rubber dam is attached to the tooth to prevent infection in the root canal due to bacteria or saliva during treatment, and the treatment procedure 'Moisture-proof' process to promote the convenience of teeth, 3) 'root canal cavity formation' process to create a passage to access the inside of the tooth by making a hole in the tooth after figuring out the shape of the tooth and inner canal, 4) root canal expansion and root canal cleaning The process consists of 'root canal enlargement and cleaning', which removes inflammation or tissue residues through the process, and 'root canal filling', which uses materials for filling and sealing materials to fill and seal the canal after symptoms have disappeared and formation has been completed. can

In the case of the 'root canal cavity formation' process, which can be considered the most important process of root canal treatment, the doctor himself analyzes the CT image or uses a dental microscope to determine the shape of the tooth and the inner neural tube, and then drills a hole in the tooth. A passage for accessing the inside of the tooth is made, but in this way, it is difficult to accurately grasp the neural tube, and there is a problem that secondary damage to the tooth may occur due to unnecessary tooth removal in order to find the neural tube.

The present disclosure provides a method for generating an endo guide for nerve treatment, a computer program stored in a recording medium, and an apparatus (system) to solve the above problems.

The present disclosure may be implemented in a variety of ways, including a method, apparatus (system) or computer program stored on a readable storage medium.

According to an embodiment of the present disclosure, a method for generating an endo guide for nerve treatment, executed by at least one processor, includes a location code for at least one tooth among a plurality of teeth to which different location codes are assigned from a user terminal. and receiving a dental image including the tooth, identifying a nerve region of the tooth in the received dental image, the nerve region of the tooth including a tooth and surrounding nerve tissue of the tooth, and location code and identification. and outputting endo guide data for a tooth corresponding to the nerve region of the tooth based on the nerve region of the tooth.

According to one embodiment of the present disclosure, a computer program stored in a computer readable recording medium is provided to execute the above-described method on a computer.

An information processing system according to an embodiment of the present disclosure includes a memory and a processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program is different from each other. Receiving a location code for at least one tooth among a plurality of teeth to which the location code is assigned and a dental image including the tooth, and identifying a nerve area of the tooth in the received dental image - the nerve area of the tooth is a tooth and the surrounding nervous tissue of the tooth - includes instructions for outputting endo guide data for a tooth corresponding to the nerve region of the tooth based on the location code and the identified nerve region of the tooth.

According to some embodiments of the present disclosure, an objective and accurate guide for endo insertion may be provided by generating endo guide data for a tooth by inputting a tooth position code and a nerve region of the tooth to a machine learning model.

According to some embodiments of the present disclosure, by identifying a nerve region of a tooth from a dental image and generating endo guide data based thereon, an unnecessary region for generating an endo guide is removed from the image, thereby improving processing speed and accuracy. .

According to some embodiments of the present disclosure, by detecting a tooth region from a dental image and identifying a tooth nerve region based thereon, the accuracy of identifying the tooth nerve region is improved, and furthermore, an auxiliary index for more accurate diagnosis is provided. can provide

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to those skilled in the art (referred to as "ordinary technicians") from the description of the claims. will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein like reference numbers indicate like elements, but are not limited thereto.
1 is a view showing an example in which an endo guide for nerve treatment according to an embodiment of the present disclosure is provided.
2 is a block diagram showing the internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
3 is a diagram showing a position code of each of a plurality of teeth according to an embodiment of the present disclosure.
4 is a block diagram showing a detailed configuration of a processor 234 according to an embodiment of the present disclosure.
5 is a flowchart illustrating a method for generating an endo guide for nerve treatment according to an embodiment of the present disclosure.
6 is a diagram illustrating an example of identifying a plurality of tooth regions and a nerve region from a dental image according to an embodiment of the present disclosure.
7 is a diagram illustrating an example of identifying a plurality of tooth regions and a nerve region from a dental image according to an embodiment of the present disclosure.
8 is a diagram illustrating an example of generating endo guide data by inputting a location code and a dental image to a machine learning model according to an embodiment of the present disclosure.
9 is an exemplary diagram illustrating an artificial neural network model according to an embodiment of the present disclosure.
10 is an exemplary diagram illustrating thresholding according to an embodiment of the present disclosure.

Hereinafter, specific details for the implementation of the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding elements are given the same reference numerals. In addition, in the description of the following embodiments, overlapping descriptions of the same or corresponding components may be omitted. However, omission of a description of a component does not intend that such a component is not included in an embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the following embodiments in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, but only the present embodiments make the present disclosure complete, and the present disclosure does not extend the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but they may vary according to the intention of a person skilled in the related field, a precedent, or the emergence of new technology. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the invention. Therefore, the terms used in the present disclosure should be defined based on the meaning of the terms and the general content of the present disclosure, not simply the names of the terms.

Expressions in the singular number in this specification include plural expressions unless the context clearly dictates that they are singular. Also, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a certain part includes a certain component in the entire specification, this means that it may further include other components without excluding other components unless otherwise stated.

Also, the term 'module' or 'unit' used in the specification means a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a 'module' or 'unit' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, and attributes. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. Functions provided within components and 'modules' or 'units' may be combined into a smaller number of components and 'modules' or 'units', or further components and 'modules' or 'units'. can be further separated.

According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and a memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some circumstances, 'processor' may refer to an application specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or a combination of any other such configurations. You may. Also, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' includes 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), It may also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor can read information from and/or write information to the memory. Memory integrated with the processor is in electronic communication with the processor.

In the present disclosure, a 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated by configuring a server device and a cloud device together.

In the present disclosure, a 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include a plurality of nodes. In the present disclosure, a plurality of machine learning models, such as a first machine learning model, a second machine learning model, and a third machine learning model, are described as separate machine learning models, but are not limited thereto, and some or The whole can be implemented as a single machine learning model. Also, in the present disclosure, a machine learning model may refer to an artificial neural network model, and an artificial neural network model may refer to a machine learning model.

In this disclosure, 'display' may refer to any display device associated with a computing device. For example, it may refer to any display device capable of displaying any information/data controlled by or provided from a computing device.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A's, or each of some components included in a plurality of A's. .

In the present disclosure, 'neurotherapy' may refer to a procedure in which, when the pulp is damaged due to gingivitis or periodontal disease, the tissue is removed and a special material is inserted to function as a natural tooth without pain.

In the present disclosure, a 'dental image' may refer to any image, video, etc. related to diagnosis, treatment, examination, or procedure of periodontal disease or the like. For example, it may include, but is not limited to, a panoramic radiographic image, an apex image, a computerized tomography (CT) image, a cone beam CT image, a 3D CT image, an intraoral radiographic image, and a head size radiographic image.

In the present disclosure, 'the nerve area of a tooth' may refer to an arbitrary area for generating an endo guide. For example, it may include a tooth and a region (eg, a rectangular region) including nerve tissue around the tooth surrounding the tooth.

In the present disclosure, 'endo guide data' may include data that may be an auxiliary indicator of an endo that should be inserted into the neural canal for root canal treatment of a tooth. For example, it may include, but is not limited to, the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube.

In the present disclosure, 'patient information' may include information about a patient that can affect neurotherapy. For example, the patient's age, gender, history of nerve treatment, implant status, and disease information may be included, but is not limited thereto.

1 is a view showing an example in which an endo guide for nerve treatment according to an embodiment of the present disclosure is provided. As shown, the user 120 may check the endo guide for nerve treatment through the display of the user terminal 110 . In one embodiment, the user terminal 110 may receive dental images from a photographing device or a storage device. In FIG. 1 , a panoramic radiographic image is shown as a dental image, but is not limited thereto, and the dental image includes an apical image, a computerized tomography (CT) image, a cone beam CT (CBCT) image, and a 3D CT. It may include images, intraoral radiographic images, and the like. Additionally or alternatively, the user terminal 110 may receive an image in which a tooth region is detected or an image in which a nerve region of a tooth is identified.

The user terminal 110 may identify a nerve region corresponding to the received tooth information 140 from among a plurality of nerve regions in the received dental image 130 . A nerve region may refer to any region for generating an endo guide for neurotherapy. For example, it may be a region including a tooth and a nerve tissue surrounding the tooth. With this configuration, a region unnecessary for generating an endo guide is removed from an image, so that processing speed and accuracy can be improved.

In one embodiment, the dental image 130 in which the nerve region corresponding to the received tooth position code 142 is identified may be displayed on the display of the user terminal 110 . For example, as shown, the neural regions 134 and 138 are displayed in the form of a bounding box on the frontal image 132 and the tomographic image 136, respectively, on the display of the user terminal 110. can be output to According to one embodiment, user 120 may modify the neural region. For example, the user 120 may correct an erroneously identified neural region by moving the location of the displayed bounding box by dragging and dropping. In another embodiment, user 120 may select one or more nerve regions from among a plurality of nerve regions. In this case, the selected nerve region may be highlighted and displayed on the display of the user terminal 110 .

Additionally, the user terminal 110 may receive patient information 150 associated with dental images from the user 120 . The patient information may include information about the patient that may affect neurotherapy. In the present disclosure, the patient's age, gender, nerve treatment history, implant status, and disease information are shown as examples of patient information, but are not limited thereto, and in some embodiments, patient information includes surgery history, smoking status, and bone quality information. , family history information, etc.

The user terminal 110 may generate endo guide data for a tooth corresponding to the nerve region by inputting the nerve region to the machine learning model. At this time, the user terminal 110 may generate endo guide data by selecting a machine learning model associated with the received location code from among a plurality of machine learning models. Additionally, when patient information is received, the user terminal 110 may generate endo guide data 160 for a tooth corresponding to the nerve region by inputting the nerve region and patient information into the machine learning model. Here, the endo guide data 160 may include data that may be an auxiliary indicator of the endo to be inserted into the neural canal for root canal treatment of the tooth, and tooth removal for root canal treatment may be performed based on the endo guide data. there is. In the present disclosure, as examples of endo guide data, the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube are shown, but are not limited thereto. In some embodiments, the endo guide data may be output after being overlaid on each of the frontal photographed image 132 and the tomographic image 136 in which a nerve region of a tooth requiring nerve treatment is displayed.

In another embodiment, the user terminal 110 may generate endo guide data by applying thresholding to a dental image. For example, the user terminal 110 may binarize the neural region to a value of 1 or 0 by applying the OTSU algorithm to the neural region. Here, the binarized nerve region may be made of black and white in order to mark the neural tube in the tooth more clearly, and may include endo guide data.

With this configuration, the present invention can provide an endo guide for objective and accurate nerve treatment by generating endo guide data based on a tooth position code and a dental image. In addition, according to some embodiments of the present disclosure, by generating endo guide data by additionally considering patient information, an endo guide tailored to the individual patient is provided, so that the accuracy of neurological treatment can be improved. The user 120 may proceed with tooth removal for nerve treatment by referring to the endo guide data provided by the user terminal 110 .

It has been described above that the user terminal 110 receives and processes various types of information to provide endo guide data to the user 120, but is not limited thereto. For example, the user terminal 110 transmits various information/data received to an external device (eg, an information processing system), and the external device transmits endo device based on the information/data received from the user terminal 110. Guide data can be created. In this case, the external device may transmit the generated endo guide data to the user terminal 110 . Alternatively, the user terminal 110 and the external device may be configured to divide and process tasks for generating endo guide data.

Figure 2 is a block diagram showing the internal configuration of the user terminal 110 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 110 may refer to any computing device capable of executing an endo guide application and the like and capable of wired/wireless communication, and may include, for example, a mobile phone terminal, a tablet terminal, a PC terminal, and the like. As shown, the user terminal 110 may include a memory 212 , a processor 214 , a communication module 216 and an input/output interface 218 . Similarly, information processing system 230 may include memory 232 , processor 234 , communication module 236 and input/output interface 238 . As shown in FIG. 2, the user terminal 110 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 216 and 236. It can be. In addition, the input/output device 219 may be configured to input information and/or data to the user terminal 110 through the input/output interface 218 or output information and/or data generated from the user terminal 110.

The memories 212 and 232 may include any non-transitory computer readable media. According to one embodiment, the memories 212 and 232 are non-perishable mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSDs), flash memory, and the like. (permanent mass storage device) may be included. As another example, a non-perishable mass storage device such as ROM, SSD, flash memory, disk drive, etc. may be included in the user terminal 110 or the information processing system 230 as a separate permanent storage device distinct from memory. In addition, an operating system and at least one program code (eg, code for an endo guide application) may be stored in the memories 212 and 232 .

These software components may be loaded from a computer readable recording medium separate from the memories 212 and 232 . A recording medium readable by such a separate computer may include a recording medium directly connectable to the user terminal 110 and the information processing system 230, for example, a floppy drive, a disk, a tape, a DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card. As another example, the software components may be loaded into the memory 212 or 232 through the communication module 216 or 236 rather than a computer-readable recording medium. For example, the at least one program is a computer program (eg, Endo Guide application, etc.) installed by files provided by developers or a file distribution system that distributes application installation files through the network 220. Based on this, it can be loaded into the memories 212 and 232.

The processors 214 and 234 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processors 214 and 234 by memory 212 and 232 or communication modules 216 and 236 . For example, processors 214 and 234 may be configured to execute instructions received according to program code stored in a recording device such as memory 212 and 232 .

The communication modules 216 and 236 may provide configurations or functions for the user terminal 110 and the information processing system 230 to communicate with each other through the network 220, and the user terminal 110 and/or information processing. System 230 may provide configurations or functions for communicating with other user terminals or other systems (eg, separate cloud systems, etc.). For example, a request or data generated by the processor 214 of the user terminal 110 according to a program code stored in a recording device such as the memory 212 (eg, a request to generate endo guide-related data, etc.) is a communication module ( 216 may be transmitted to the information processing system 230 through the network 220. Conversely, a control signal or command provided under the control of the processor 234 of the information processing system 230 passes through the communication module 236 and the network 220 through the communication module 216 of the user terminal 110. It may be received by the user terminal 110 . For example, the user terminal 110 may receive an image in which a nerve region corresponding to a tooth position code is identified, endo guide data, and the like from the information processing system 230 through the communication module 216 .

The input/output interface 218 may be a means for interfacing with the input/output device 219 . As an example, the input device may include a device such as a camera, keyboard, microphone, mouse, etc. including an audio sensor and/or image sensor, and the output device may include a device such as a display, speaker, haptic feedback device, or the like. can As another example, the input/output interface 218 may be a means for interface with a device in which a configuration or function for performing input and output is integrated into one, such as a touch screen. Although the input/output device 219 is not included in the user terminal 110 in FIG. 2 , it is not limited thereto, and the user terminal 110 and the user terminal 110 may be configured as one device. In addition, the input/output interface 238 of the information processing system 230 is connected to the information processing system 230 or means for interface with a device (not shown) for input or output that the information processing system 230 may include. can be In FIG. 2, the input/output interfaces 218 and 238 are shown as separate elements from the processors 214 and 234, but are not limited thereto, and the input/output interfaces 218 and 238 may be included in the processors 214 and 234. there is.

The user terminal 110 and the information processing system 230 may include more components than those shown in FIG. 2 . However, there is no need to clearly show most of the prior art components. In one embodiment, the user terminal 110 may be implemented to include at least some of the aforementioned input/output devices 219 . In addition, the user terminal 110 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, when the user terminal 110 is a smartphone, it may include components that are generally included in a smartphone, for example, an acceleration sensor, a gyro sensor, a microphone module, a camera module, and various physical Various components such as a button, a button using a touch panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 110 .

According to one embodiment, the processor 214 of the user terminal 110 may be configured to operate an endo guide application or the like. At this time, the program code associated with the application may be loaded into the memory 212 of the user terminal 110 . While the application is running, the processor 214 of the user terminal 110 receives information and/or data provided from the input/output device 219 through the input/output interface 218 or through the communication module 216 the information processing system ( Information and/or data may be received from 230), and the received information and/or data may be processed and stored in the memory 212. Additionally, such information and/or data may be provided to information processing system 230 via communication module 216 .

While the endo guide application is operating, the processor 214 inputs inputs such as a touch screen connected to the input/output interface 218, a keyboard, a camera including an audio sensor and/or an image sensor, a radiographic imaging device, a tomography device, and a microphone. Voice data, text, image, video, etc. input or selected through the device may be received, and the received voice data, text, image, and/or video may be stored in the memory 212 or the communication module 216 and the network ( It can be provided to the information processing system 230 through 220). In one embodiment, the processor 214 may receive a dental image, endo guide data generation request, etc. through an input device 219 such as a keyboard or a mouse, and transmits the request to the communication module 216 and the network 220. It can be provided to the information processing system 230 through.

The processor 214 of the user terminal 110 may transmit and output information and/or data to the input/output device 219 through the input/output interface 218. For example, the processor 214 of the user terminal 110 processes information through an output device 219 such as a display output capable device (eg, a touch screen, a display, etc.) or an audio output capable device (eg, a speaker). and/or output data. In one embodiment, the processor 214 may display the endo guide data on the display of the user terminal 110 . Additionally, the processor 214 may convert text information among the endo guide data into voice and output it through a speaker of the user terminal 110 .

The processor 234 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 110 and/or a plurality of external systems. Information and/or data processed by processor 234 may be provided to user terminal 110 via communication module 236 and network 220 . In one embodiment, the processor 234 of the information processing system 230 may identify a nerve region from the received dental image and generate endo guide data based on a tooth position code and patient information. In addition, the generated endo guide data may be provided to the user terminal 110 through the communication module 236 and the network 220 .

For example, the processor 234 may generate endo guide data by inputting a dental image to a machine learning model associated with a received tooth position code among a plurality of machine learning models. Additionally or alternatively, the processor 234 may generate an endo guide by applying thresholding to the received dental image.

The internal configuration of the user terminal and information processing system shown in FIG. 2 is only an example, and is not limited thereto. In some embodiments, some of the illustrated components may be omitted. For example, an embodiment comprising only the information processing system 230 without the user terminal 110 is also possible, and in this case, the steps performed by the processor 214 of the user terminal may be performed by the processor 234 of the information processing system. there is. In addition, an embodiment consisting of only the user terminal 110 without the information processing system 230 is also possible, and in this case, the steps performed by the processor 234 of the information processing system may be performed by the processor 214 of the user terminal. .

3 is a diagram showing a position code of each of a plurality of teeth according to an embodiment of the present disclosure. The tooth position code is a number used by dental specialists to identify the position of a tooth, and may be assigned to each of a plurality of teeth so as not to overlap. All teeth are classified into maxilla, mandible, right, and left, and each classification can be divided into central incisors, lateral incisors, canines, premolars, and molars.

As shown, the position codes of the central incisor 310 structure are 11, 21, 31, 41, the position codes of the lateral incisor 320 structure are 12, 22, 32, 42, and the position codes of the canine 330 structure are 13, 23, 33, 43, the position codes of the premolar (340) structure are 14, 15, 24, 25, 34, 35, 44, 45, and the position codes of the molar (350) structure are 16, 17, 18, 26, 27, 28, 36, 37, 38, 46, 47, 48 may be assigned. In the present disclosure, a tooth position code may be used to select a tooth for root canal treatment or to select a machine learning model associated with a tooth structure corresponding to the position code.

4 is a block diagram showing a detailed configuration of a processor 234 according to an embodiment of the present disclosure. As shown, the processor 234 may include a pre-processing unit 410, a nerve region identifying unit 420, and an endo guide generating unit 430. The processor 234 may communicate with an external device (eg, a user terminal or an external device) and receive information necessary for generating an endo guide.

The preprocessing unit 410 may perform preprocessing on dental images. In one embodiment, the pre-processing unit 410 may crop a part of an image. Additionally or alternatively, the pre-processing unit 410 may perform resampling so that the length of one pixel in the image becomes a predetermined length. In addition, the pre-processing unit 410 may perform arbitrary pre-processing on the received dental image in a form suitable for identifying a nerve region or generating endo guide data. In some embodiments, the pre-processing unit 410 may be omitted.

The nerve region identification unit 420 may identify a nerve region corresponding to the received tooth position code after identifying a plurality of nerve regions in the received dental image. The nerve region of a tooth may include a tooth and surrounding nerve tissue of the tooth. According to some embodiments, the neural region identification unit 420 is a dental image received using a predetermined algorithm or machine learning model (eg, a convolutional neural network (CNN) among artificial neural network models). A plurality of neural regions can be identified from. Additionally or alternatively, the nerve region identification unit 420 detects a plurality of tooth regions from the received dental image, and then performs a tooth for nerve treatment based on the detected plurality of tooth regions and the received tooth position code. of neural regions can be identified. According to some embodiments, the nerve region identification unit 420 detects a plurality of tooth regions from the received dental image using a predetermined algorithm or machine learning model, and performs nerve treatment based on the received tooth position code. It is possible to identify the nerve area of the tooth for By further performing one step of detecting the tooth region from the dental image, the accuracy of nerve region identification is improved, and furthermore, a more accurate endo guide can be provided. In this case, the nerve region identification unit 420 may be composed of a machine learning model for detecting a tooth region and a separate machine learning model for identifying a nerve region based on the detected tooth region, or a single machine learning model. can

The endo guide generating unit 430 may generate endo guide data based on a position code of a tooth for nerve treatment and a nerve region corresponding to the position code. According to an embodiment, the endo guide generator 430 inputs the neural region of the corresponding tooth to a machine learning model (eg, a CNN among artificial neural network models) associated with the received position code of the tooth to generate endo guide data. can create According to another embodiment, the endo guide generator 430 generates endo guide data by applying thresholding (eg, OTSU algorithm) to a nerve region corresponding to a position code of a tooth for nerve treatment. can The endo guide data may include, but is not limited to, the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube. According to one embodiment, the endo guide data may include data for nerve treatment for a tooth corresponding to a nerve region.

In the present disclosure, the preprocessing unit 410, the neural region identification unit 420, and the endo guide generation unit 430 are illustrated as being included in the processor 234 of the information processing system, but are not limited thereto. For example, in some embodiments, at least some or all of the preprocessor 410, the nerve region identification unit 420, or the endo guide generator 430 may be included in the processor of the user terminal.

In FIG. 4 , the configuration of the processor 234 has been described by dividing it by function, but this does not necessarily mean that it is physically separated. For example, the pre-processing unit 410, the nerve region identification unit 420, and the endo guide generation unit 430 have been separately described above, but this is for helping understanding of the invention, and is not limited thereto. For example, the neural region identifying unit 420 and the endo guide generating unit 430 may be implemented through one artificial neural network model or implemented through a plurality of different artificial neural network models.

5 is a flowchart illustrating a method 500 for generating an endo guide for neurological treatment according to an embodiment of the present disclosure. According to one embodiment, the method 500 for generating an endo guide for nerve treatment may be performed by a processor (eg, a processor of a user terminal and/or at least one processor of an information processing system). As shown, the method 500 for generating an endo guide may be initiated (S510) by a processor receiving a tooth position code and a dental image. For example, a dental image may be received from the user terminal 110, an external device, or the like, or may be received from an internal memory.

The processor may identify the nerve region of the tooth in the received image (S520). According to an embodiment, the processor may identify a plurality of tooth regions from the received dental image using a predetermined algorithm or machine learning model (eg, CNN among artificial neural network models). For example, the processor may detect a plurality of tooth regions from the dental image and identify a nerve region from among the plurality of tooth regions based on the received tooth location code. In this case, one or more machine learning models may be used.

The method 500 may end when the processor outputs endo guide data for a tooth corresponding to the nerve region based on the location code and the identified nerve region ( S530 ). In one embodiment, the endo guide data may be generated through a machine learning model associated with a tooth position code. In another embodiment, endo guide data may be generated by applying the OTSU algorithm to a neural region. For example, the endo guide data may include, but is not limited to, the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube.

6 is a diagram illustrating an example of identifying a plurality of tooth regions and a nerve region from a dental image 610 according to an embodiment of the present disclosure. According to an embodiment, the processor may receive a dental image 610 . In FIG. 6, the dental image is shown as a panoramic radiographic image, but is not limited thereto, and may include apical images, computed tomography (CT) images, cone beam CT (CBCT) images, 3D CT images, intraoral radiographic images, and the like. .

In an embodiment, the processor may detect a plurality of tooth areas from the received dental image 610 and generate an image 620 in which the tooth areas are detected. Alternatively, the processor may receive the image 620 in which the tooth region is detected. Thereafter, the processor may identify a nerve region in the image 620 in which the tooth region is detected based on the received tooth position code. For example, the processor may detect a plurality of tooth areas by inputting the received dental image to a first machine learning model, and input the detected plurality of tooth areas and tooth position codes to a second machine learning model. A neural region 632 can be identified. In this case, the identified nerve region 632 may be displayed on a dental image, and the image 630 in which the nerve region is identified may be provided to the user. In another example, the identified nerve region 632 may include not only a tooth corresponding to the received location code, but also teeth located on both sides of the corresponding tooth. When the nerve area is configured to include the teeth located on both sides, it is possible to check whether the teeth located on both sides affect the nerve treatment, thereby further increasing the accuracy of the nerve treatment.

In FIG. 6 , the processor detects a tooth area from the dental image 610 and then identifies a nerve area, but is not limited thereto. For example, the processor may immediately identify a nerve region from the received dental image 610 .

7 is a diagram illustrating an example of identifying a plurality of tooth regions and a nerve region from a dental image 710 according to an embodiment of the present disclosure. According to an embodiment, the processor may receive a dental image 710 . In FIG. 7 , the dental image is shown as a computed tomography image, but is not limited thereto, and a panoramic radiographic image, an apex image, a cone beam CT image, a 3D CT image, an intraoral radiographic image, and the like may be used.

In an embodiment, the processor may detect a plurality of tooth areas from the received dental image 710 and generate an image 720 in which the tooth areas are detected. Alternatively, the processor may receive the image 720 in which the tooth region is detected. Thereafter, the processor may identify a nerve region in the image 720 in which the tooth region is detected based on the received tooth position code. In this case, the identified nerve region 732 may be displayed on a dental image, and the image 730 in which the nerve region 732 is identified may be provided to the user.

8 is a diagram illustrating an example of generating endo guide data 830 by inputting a location code 810 and a dental image 820 to a machine learning model 800 according to an embodiment of the present disclosure. As noted above, a processor may receive location code 810 . The position code 810 is a code assigned to each of a plurality of teeth so as not to be duplicated, and a machine learning model for generating endo guide data may be selected by receiving a position code of a tooth for nerve treatment from a user. For example, a machine learning model may be selected according to a structure of at least one tooth among a central incisor, a lateral incisor, a canine, a premolar, and a molar corresponding to the received location code. In this case, the machine learning model may be trained to output endo guide data according to the tooth structure corresponding to the position code.

According to an embodiment of the present disclosure, the processor may generate endo guide data 830 based on the location code 810 and the dental image 820 using the machine learning model 800 . For example, the machine learning model 800 may receive the location code 810 and the dental image 820 and output endo guide data 830 . In one embodiment, the endo guide data 830 may include data that may be an auxiliary indicator of an endo to be inserted into the neural canal for tooth nerve treatment. For example, the endo guide data 830 may include, but is not limited to, the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube.

The machine learning model 800 may be trained with various training data. For example, the machine learning model 800 may be learned through training data consisting of pairs of location codes, dental images, and endo guide data. The endo guide data included in the learning data may include the coordinates of the entrance of the neural tube according to the structure of the tooth, the number of the neural tube, the length of the neural tube, and the shape of the neural tube. In one embodiment, the processor may receive endo guide data modified by a user. In this case, the machine learning model 800 may be re-learned based on the modified endo guide data.

9 is an exemplary diagram illustrating an artificial neural network model 900 according to an embodiment of the present disclosure. The artificial neural network model 900, as an example of a machine learning model, is a statistical learning algorithm implemented based on the structure of a biological neural network or a structure that executes the algorithm in machine learning technology and cognitive science.

According to an embodiment, the artificial neural network model 900, as in a biological neural network, is an artificial neuron nodes formed by synaptic coupling to repeatedly adjust synaptic weights, thereby correcting the correct response corresponding to a specific input. By learning to reduce the error between the output and the inferred output, it is possible to represent a machine learning model having problem solving ability. For example, the artificial neural network model 1100 may include an arbitrary probability model, a neural network model, and the like used in artificial intelligence learning methods such as machine learning and deep learning.

According to an embodiment, the neural region identification unit and the endo guide generation unit may be generated in the form of an artificial neural network model 900 as one form of a machine learning model. For example, a plurality of artificial neural network models 900 may be configured to generate endo guide data for each structure of teeth including central incisors, lateral incisors, canines, premolars, and molars. After selecting the artificial neural network model 900 corresponding to the received tooth position code from among the plurality of artificial neural network models 900 , endo guide data may be generated by inputting a neural region. In another example, the artificial neural network model 900 may be configured to receive a location code, a dental image, and patient information, and generate endo guide data in consideration of patient information.

The artificial neural network model 900 is implemented as a multilayer perceptron (MLP) composed of multi-layer nodes and connections between them. The artificial neural network model 900 according to this embodiment may be implemented using one of various artificial neural network model structures including MLP. As shown in FIG. 9, the artificial neural network model 900 includes an input layer 920 that receives an input signal or data 910 from the outside, and an output layer that outputs an output signal or data 950 corresponding to the input data. 940, located between the input layer 920 and the output layer 940, receives a signal from the input layer 920, extracts characteristics, and transfers n (where n is a positive integer) to the output layer 940. It is composed of hidden layers 930_1 to 930_n. Here, the output layer 940 receives signals from the hidden layers 930_1 to 930_n and outputs them to the outside.

The learning method of the artificial neural network model 900 includes a supervised learning method that learns to be optimized for problem solving by inputting a teacher signal (correct answer), and an unsupervised learning method that does not require a teacher signal. ) way. The information processing system may perform supervised learning and/or unsupervised learning of the artificial neural network model 900 learned to generate endo guide data from the location code and the dental image. The artificial neural network model 900 learned in this way may be stored in the memory of the information processing system.

According to an embodiment, input variables of the artificial neural network model 900 may be a tooth position code and a dental image. Additionally, input variables of the artificial neural network model 900 may include patient information and the like. That is, a vector representing or characterizing the above-described location code, image, and patient information may be input through the input layer 920 . In this way, when the above-described input variables are input through the input layer 920, the output variables output from the output layer 940 of the artificial neural network model 900 may be vectors representing or characterizing endo guide data.

In this way, a plurality of output variables corresponding to a plurality of input variables are matched in the input layer 920 and the output layer 940 of the artificial neural network model 900, respectively, and the input layer 920, the hidden layers 930_1 to 930_n and By adjusting the synaptic value between nodes included in the output layer 940, learning can be performed so that a correct output corresponding to a specific input can be extracted. Through this learning process, it is possible to grasp the characteristics hidden in the input variables of the artificial neural network model 900, and the nodes of the artificial neural network model 900 so that the error between the output variable calculated based on the input variable and the target output is reduced. You can adjust the synaptic values (or weights) between them. Endo guide data may be output using the artificial neural network model 900 learned in this way.

10 is an exemplary diagram illustrating thresholding according to an embodiment of the present disclosure. Thresholding 1010 is a technique for binarizing an image or video based on a threshold value, and for example, the OTSU algorithm may be used. In one embodiment, when the OTSU algorithm is applied to the nerve region 1012 of a tooth, a color threshold may be determined based on the nerve region 1012 . In the case of the binarized neural region 1014 generated by binarizing the neural region to a value of 1 or 0 based on the determined color threshold, the shape of the neural tube is displayed more clearly, so that the accuracy of endo guide data generation can be improved. For example, the endo guide data may be displayed overlaid on the binarized neural region 1014 image.

The above method may be provided as a computer program stored in a computer readable recording medium to be executed on a computer. The medium may continuously store programs executable by a computer or temporarily store them for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or combined hardware, but is not limited to a medium directly connected to a certain computer system, and may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc. configured to store program instructions. In addition, examples of other media include recording media or storage media managed by an app store that distributes applications, a site that supplies or distributes various other software, and a server.

The methods, acts or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or combinations thereof. Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs) ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be incorporated into a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may be implemented or performed in any combination of those designed to perform the functions described in A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

In firmware and/or software implementation, the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM ( on a computer readable medium, such as programmable read-only memory (EPROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage device, or the like. It can also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

Although the embodiments described above have been described as utilizing aspects of the presently-disclosed subject matter in one or more stand-alone computer systems, the disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Further, aspects of the subject matter in this disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure that can be understood by those skilled in the art. Moreover, such modifications and variations are intended to fall within the scope of the claims appended hereto.

110: user terminal
120: user
130: dental image
132: front shot video
134: tomography image
140: tooth information
150: patient information
160: endo guide data

Claims (10)

In the endo guide generation method for neurotherapy, executed by at least one processor,
Receiving, from a user terminal, a location code for at least one tooth among a plurality of teeth to which different location codes are assigned and a dental image including the tooth;
detecting a plurality of tooth areas including the teeth from the dental image;
identifying a nerve region of the tooth based on the detected plurality of tooth regions and the received location code for at least one tooth, wherein the tooth nerve region includes a tooth and surrounding nerve tissue of the corresponding tooth;
Selecting a machine learning model associated with the position code of the tooth from among a plurality of machine learning models; and
Generating endo guide data for the tooth from the identified nerve region using the selected machine learning model.
Including, endo guide generation method for neurological treatment.
delete According to claim 1,
Identifying the nerve region of the tooth,
determining a color threshold of the nerve region of the tooth by applying the OTSU algorithm to the nerve region of the tooth; and
Generating a binarized neural region by binarizing the neural region to a value of 1 or 0 based on the determined color threshold.
Including, endo guide generation method for neurological treatment.
According to claim 3,
The endo guide data for the tooth includes the coordinates of the entrance of the neural tube in the tooth, the number of neural tubes, the length of the neural tube, and the shape of the neural tube,
Overlayed and output on the dental image,
A method for generating endo guides for neurological treatment.
According to claim 1,
classifying the plurality of teeth to which different position codes are assigned as at least one structure of a central incisor, a lateral incisor, a canine, a premolar, and a molar; and
Learning the plurality of machine learning models to output endo guide data according to the structure of the classified teeth.
Further comprising, endo guide generation method for neurological treatment.
delete According to claim 1,
Receiving patient information related to the dental image
Including more,
The patient information includes age, gender, nerve treatment history, implant status, and disease information, endo guide generation method for nerve treatment.
A computer program stored in a computer readable recording medium to execute the method according to any one of claims 1, 3 to 5, and 7 on a computer.
In the information processing system,
Memory; and
a processor connected to the memory and configured to execute at least one computer readable program contained in the memory
including,
The at least one program,
Receiving a location code for at least one tooth among a plurality of teeth to which different location codes are assigned and a dental image including the tooth;
Detecting a plurality of tooth regions including the tooth from the dental image;
Identifying a nerve region of the tooth based on the detected plurality of tooth regions and the received location code for at least one tooth, wherein the tooth nerve region includes a tooth and surrounding nerve tissue of the tooth;
Selecting a machine learning model associated with the position code of the tooth among a plurality of machine learning models,
Using the selected machine learning model, information processing system comprising instructions for outputting endo guide data for the tooth from the nerve region of the identified tooth.
delete

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