Method for generating visual indicator representing degree of impact of lesion for each tooth, and computer program and electronic device supporting same

Abstract

A method for generating a visual indicator representing the degree of impact of a lesion for each tooth, according to an embodiment of the present disclosure, may comprise the steps of: generating, on the basis of dental image data, tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion; determining, for each of the at least one lesion, at least one tooth related to the lesion from among the plurality of teeth, on the basis of spatial relationship information determined from the tooth segmentation information and the lesion segmentation information; calculating, for each of the at least one tooth, the degree of impact of the lesion, on the basis of feature information of the lesion determined from the spatial relationship information and the lesion segmentation information; normalizing the degree of impact to a predetermined scale to determine a score for each of the at least one tooth; and generating, on the basis of the score, a visual indicator for each of the at least one tooth.

Description

Method for generating a visual indicator showing the degree of lesion impact per tooth, and a computer program and electronic device supporting the same

[0001] Various embodiments of the present disclosure relate to a method for generating a visual indicator indicating the degree of lesion influence for each tooth, and a computer program and electronic device supporting the same.

[0002] In dental clinical practice, the process of interpreting dental images, such as 3D CT images or 2D radiographic images, may be performed to evaluate lesions related to the patient's teeth and jawbone condition, and the results of the interpretation may be used for various clinical decisions.

[0003] However, commercially available dental image interpretation methods are limited to confirming the presence and / or approximate location of lesions, making it difficult to derive objective information regarding which teeth are actually affected and to what extent. For example, even if a lesion is observed in the area surrounding a tooth or in the alveolar bone, it may be difficult to determine, based on consistent criteria, whether the lesion affects a specific tooth locally or across multiple adjacent teeth.

[0004] Furthermore, although a comprehensive consideration of the size of the lesion and its spatial relationship with the teeth can play a major role in determining the necessity or priority of treatment, commercially available dental image interpretation methods do not sufficiently reflect such comprehensive considerations. Consequently, there may be a lack of a standardized system capable of quantifying the impact of the lesion at the tooth level or enabling comparisons between multiple teeth.

[0005] The foregoing is provided as background technology for the purpose of aiding understanding of the present disclosure, and no claim or determination is made as to whether it can be applied as prior art related to the present disclosure.

[0006] The present disclosure provides a method for generating a visual indicator showing the degree of lesion influence for each tooth to solve the above-mentioned problem.

[0007] The present disclosure provides a computer program that supports a method for generating a visual indicator showing the degree of lesion influence for each tooth to solve the above-mentioned problem.

[0008] The present disclosure provides an electronic device that supports a method for generating a visual indicator showing the degree of lesion influence for each tooth to solve the above-mentioned problem.

[0009] The technical problems that this disclosure aims to solve are not limited to those described above, and other unmentioned problems will be clearly understood by those skilled in the art from the various embodiments described below.

[0010] The present disclosure may be implemented in various ways, including methods, electronic devices, and / or computer programs stored on computer-readable recording media.

[0011] A method for generating a visual indicator indicating the degree of lesion influence for each tooth according to one embodiment of the present disclosure may include: generating tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion based on dental image data; for each of the at least one lesion, determining at least one tooth among the plurality of teeth associated with the lesion based on spatial relationship information determined from the tooth segmentation information and lesion segmentation information; for each of the at least one tooth, calculating the degree of influence caused by the lesion based on characteristic information of the lesion determined from the spatial relationship information and lesion segmentation information; normalizing the degree of influence to a predetermined scale to determine a score for each of the at least one tooth; and generating a visual indicator for each of the at least one tooth based on the score.

[0012] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of mapping and displaying a visual indicator for each of at least one tooth in a rendering view representing the structure of a plurality of teeth.

[0013] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of providing information on the amount of change of a score determined at each of a plurality of time points for a first tooth in response to receiving a user input selecting a first tooth among a plurality of teeth in a rendering view.

[0014] According to one embodiment of the present disclosure, the step of generating tooth segmentation information may include generating at least one of a mask, a label map, or a probability map in which each region of a plurality of teeth is divided into pixel or voxel units.

[0015] According to one embodiment of the present disclosure, the step of generating lesion segmentation information may include generating at least one of a mask, a label map, or a probability map that divides each region of at least one lesion into pixel or voxel units.

[0016] According to one embodiment of the present disclosure, the step of determining at least one tooth may include: calculating a first center point for each of a plurality of teeth based on tooth division information; calculating a second center point for each of at least one lesion based on lesion division information; and generating spatial relationship information including the Euclidean distance between the first center point and the second center point.

[0017] According to one embodiment of the present disclosure, the step of determining at least one tooth may further include the step of determining a predetermined number of teeth, determined in order of decreasing Euclidean distance from a second center point of the lesion, as at least one tooth associated with the lesion.

[0018] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may include the step of generating characteristic information of the lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion.

[0019] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may further include the step of calculating the degree of influence caused by a lesion based on an influence calculation rule configured to be proportional to the characteristic information of the lesion and inversely proportional to the spatial relationship information.

[0020] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may further include the step of calculating the degree of influence caused by a lesion by applying characteristic information of the lesion to a weighting function that decreases with increasing spatial relationship information.

[0021] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may include, for a first tooth associated with each of a first lesion of a first attribute and a second lesion of a second attribute different from the first attribute among a plurality of teeth, a step of calculating an integrated degree of influence by combining the first degree of influence caused by the first lesion and the second degree of influence caused by the second lesion.

[0022] According to one embodiment of the present disclosure, the step of determining the score may include the step of determining the score by normalizing the influence degree based on a continuous numerical scale in the range of 0 to 100.

[0023] According to one embodiment of the present disclosure, the step of determining the score may include the step of determining the score by normalizing the influence level based on a discrete grade scale divided into a plurality of intervals.

[0024] According to one embodiment of the present disclosure, the step of generating a visual indicator may include generating at least one of a color code, a numerical label, grade text, a grade symbol, a grade symbol, or a grade icon as a visual indicator corresponding to the score of each of at least one tooth.

[0025] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of configuring data of the visual indicator into a packet or a message and the step of transmitting the packet or message to at least one external electronic device.

[0026] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of generating a file containing data of a visual indicator and the step of transmitting access information for the file to at least one external electronic device.

[0027] According to one embodiment of the present disclosure, a computer program stored on a computer-readable recording medium may be provided for executing a method for generating a visual indicator indicating the degree of lesion influence for each tooth on a computer.

[0028] An electronic device according to one embodiment of the present disclosure may include a memory for storing instructions and at least one processor.

[0029] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may generate tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion based on dental image data, determine at least one tooth among a plurality of teeth associated with the lesion based on spatial relationship information determined from the tooth segmentation information and lesion segmentation information for each of at least one lesion, calculate the degree of influence caused by the lesion based on the lesion characteristic information determined from the spatial relationship information and lesion segmentation information for each of at least one tooth, determine a score for each of at least one tooth by normalizing the degree of influence to a predetermined scale, and generate a visual indicator for each of at least one tooth based on the score.

[0030] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may map and display a visual indicator for each of at least one tooth in a rendering view representing the structure of a plurality of teeth.

[0031] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may calculate a first center point for each of a plurality of teeth based on tooth division information, calculate a second center point for each of at least one lesion based on lesion division information, generate spatial relationship information including the Euclidean distance between the first center point and the second center point, and determine a predetermined number of teeth determined in order of smallest Euclidean distance from the second center point of the lesion as at least one tooth associated with the lesion.

[0032] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, an electronic device may generate characteristic information of a lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion, and calculate the degree of influence caused by the lesion by applying the characteristic information of the lesion to a weighting function that decreases with increasing spatial relationship information.

[0033] According to various embodiments of the present disclosure, a mechanism may be provided that supports intuitive grasp and improved understanding of the impact of lesions on each tooth by quantifying the degree of impact of lesions on a tooth-by-tooth basis based on dental imaging data and generating and providing a visual indicator based on the quantification result.

[0034] According to various embodiments of the present disclosure, quantitative progress observation and tracking management for the same tooth can be supported by storing and providing quantification results and visual indicators at different time points in a time series.

[0035] According to various embodiments of the present disclosure, teeth significantly affected by lesions can be objectively identified based on a visual indicator, thereby supporting the establishment of an effective treatment plan and determination of treatment priorities for the teeth, and preventing overdiagnosis.

[0036] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description in the claims.

[0037] Various embodiments of the present disclosure will be described with reference to the drawings below, and identical or similar reference numbers may be assigned to identical or corresponding components in connection with the description of the drawings.

[0038] FIG. 1 is a drawing illustrating an exemplary electronic device in a network environment according to one embodiment of the present disclosure.

[0039] FIG. 2 is a drawing illustrating an exemplary memory of an electronic device according to one embodiment of the present disclosure.

[0040] FIG. 3 is a diagram illustrating an exemplary processing pipeline for generating spatial relationship information between a tooth and a lesion and characteristic information of a lesion according to one embodiment of the present disclosure.

[0041] FIG. 4 is a drawing illustrating a visual example of dental image data according to one embodiment of the present disclosure.

[0042] FIG. 5 is a drawing illustrating a visual example of tooth and lesion division according to one embodiment of the present disclosure.

[0043] FIG. 6 is a diagram illustrating an exemplary processing pipeline for calculating the degree of lesion influence on a tooth according to one embodiment of the present disclosure.

[0044] FIG. 7 is a diagram illustrating an exemplary processing pipeline for generating and providing a visual indicator for a tooth according to one embodiment of the present disclosure.

[0045] FIG. 8 is a drawing illustrating a visual example of a visual indicator according to one embodiment of the present disclosure.

[0046] FIG. 9 is a diagram exemplarily illustrating a method for generating a visual indicator showing the degree of lesion influence per tooth according to one embodiment of the present disclosure.

[0047] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions regarding well-known functions or configurations will be omitted if there is a risk that the gist of the present disclosure may be unnecessarily obscured.

[0048] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Additionally, in the description of the following embodiments, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0049] The advantages and features of the disclosed embodiments and the methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms, and the embodiments provided are merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the invention.

[0050] The terms used in this disclosure will be briefly explained, and the disclosed embodiments will be described in detail. The terms used in this disclosure have been selected to be as generally used as possible, taking into account their functions within the disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should be defined not merely by their names, but based on their meanings and the content throughout this disclosure.

[0051] In this disclosure, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout this disclosure, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0052] As used in this disclosure, the terms “module” or “part” refer to software or hardware components, and the “module” or “part” performs certain roles. However, the meaning of “module” or “part” is not limited to software or hardware. The “module” or “part” may be configured to reside in an addressable storage medium or may be configured to operate one or more processors. Accordingly, as an example, the “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 the “module” or “part” may be combined into a smaller number of components and “modules” or “parts,” or further separated into additional components and “modules” or “parts.”

[0053] According to one embodiment, a 'module' or 'part' may be implemented as a processor and memory. The term 'processor' should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc.

[0054] In some contexts, 'processor' may refer to an Application-Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), etc. 'Processor' may also refer to a combination 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 combined with a DSP core, or any other combination of such configurations.

[0055] Furthermore, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' may 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), eraseable-programmable read-only memory (EPROM), electrically eraseable PROM (EEPROM), flash memory, magnetic or marked data storage devices, registers, etc.

[0056] If a processor can read information from or write information to memory, the memory is said to be in a state of electronic communication with the processor. Memory integrated into the processor is in a state of electronic communication with the processor.

[0057] The terms 1, 2, A, B, (a), (b), etc. used in this disclosure are used merely to distinguish one component from another, and the essence, order, or sequence of said component is not limited by such terms.

[0058] Where in this disclosure it is stated that one component is 'connected,' 'coupled,' or 'connected' to another component, it should be understood that the component may be directly connected or connected to the other component, but that another component may also be 'connected,' 'coupled,' or 'connected' between each component.

[0059] As used in this disclosure, “comprising” and / or “comprising” does not exclude the presence or addition of one or more other components, steps, actions, and / or elements to the mentioned components, steps, actions, and / or elements.

[0060] FIG. 1 is a drawing illustrating an exemplary electronic device in a network environment according to one embodiment of the present disclosure.

[0061] Referring to FIG. 1, an electronic device (100) according to one embodiment may include various types of devices capable of storing, providing, and executing computer-executable programs and data related to a data processing service (e.g., a service that analyzes tooth areas and lesion areas based on dental image data, quantifies the degree of impact of the lesion on a tooth-by-tooth basis, and provides a visual indicator). For example, the electronic device (100) may include at least one of a system device, a server device, a cloud service-based distributed computing device, or a mobile communication device.

[0062] In one embodiment, the electronic device (100) can communicate with at least one external electronic device (300) through a network (200). According to various embodiments, the network (200) may be configured, depending on the installation environment, as a wired network including at least one of Ethernet, a wired home network (power line communication), a telephone line communication device, or RS-serial communication, a mobile communication network, a wireless network including at least one of a WLAN (wireless LAN), Wi-Fi, Bluetooth, or ZigBee, or a combination of a wired network and a wireless network.

[0063] In the present disclosure, the communication method between the electronic device (100) and at least one external electronic device (300) is not limited and may include a communication method utilizing a communication network (e.g., mobile communication network, wired internet, wireless internet, broadcasting network and / or satellite network) that the network (200) can support, as well as a short-range wireless communication method between the electronic device (100) and at least one external electronic device (300).

[0064] In one embodiment, the electronic device (100) may include components that support the execution and / or provision of data processing services. For example, the electronic device (100) may include a memory (110), a display (120), a communication module (130), and at least one processor (140). According to various embodiments, at least some of the components included in the electronic device (100) may be connected to each other via a bus, GPIO (general purpose input and output), SPI (serial peripheral interface) and / or MIPI (mobile industry processor interface) to exchange signals, data and / or information.

[0065] In one embodiment, the memory (110) may include a non-transient recording medium readable by any computer. According to one embodiment, the memory (110) may include a non-perishable permanent mass storage device comprising at least one of a read-only memory (ROM), a disk drive, a solid state drive (SSD), or a flash memory. Alternatively, such a non-perishable permanent mass storage device may be included in the electronic device (100) as a separate permanent storage device distinct from the memory (110).

[0066] In one embodiment, the memory (110) may store at least one of an operating system, at least one application program, or at least one program code. According to various embodiments, these software components may be loaded from a computer-readable recording medium distinct from the memory (110).

[0067] In various embodiments, the recording medium may include at least one of a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card that can be directly connected to the electronic device (100). Alternatively, software components may be loaded into memory (110) via a communication module (130). For example, at least one application program may be installed and loaded into memory (110) based on files provided by a file distribution system via a network (200).

[0068] In one embodiment, the memory (110) may store instructions related to the functional operation of the components of the electronic device (100). For example, the memory (110) may store instructions that cause the components of the electronic device (100) to perform a defined functional operation when executed by at least one processor (140).

[0069] In one embodiment, the display (120) may be exposed to the outside of the electronic device (100) in at least a portion so as to visually provide various content. For example, the display (120) may output a program execution screen and / or a user interface by processing image information (or a driving signal corresponding to the image information) provided from at least one processor (140).

[0070] In one embodiment, the communication module (130) may provide a configuration or function for the electronic device (100) to communicate with at least one external electronic device (300) through a network (200). For example, the communication module (130) may establish communication (or a communication channel) with at least one external electronic device (300) according to a defined communication protocol and perform the transmission and reception of signals, data, and / or information through said communication. According to one embodiment, the communication module (130) may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, and / or a GNSS (global navigation satellite system) communication module) and a wired communication module (e.g., a LAN (local area network) communication module and / or a power line communication module). In various embodiments, the wireless communication module and the wired communication module may be composed of separate chips or integrated into a single chip.

[0071] In one embodiment, at least one processor (140) may be configured to execute instructions of a computer program provided by memory (110) and / or a communication module (130) by performing arithmetic, logic, and input / output operations. For example, at least one processor (140) may be configured to execute instructions according to program code stored in memory (110) and / or instructions received from at least one external electronic device (300) through the communication module (130).

[0072] According to one embodiment, at least one processor (140) can execute a program to provide data processing services by processing instructions of a computer program. In one embodiment, at least one processor (140) can receive and process signals, data, and / or information transmitted from at least one external electronic device (300) through a communication module (130) while executing the program. Additionally, at least one processor (140) can store signals, data, and / or information generated while executing the program in a memory (110) or transmit them to at least one external electronic device (300) through the communication module (130).

[0073] The functional operation of the electronic device (100) according to the various embodiments described below can be implemented by at least one processor (140) executing (or processing) instructions of a computer program stored in (or loaded into) memory (110).

[0074] FIG. 2 is a drawing illustrating an exemplary memory of an electronic device according to one embodiment of the present disclosure.

[0075] Referring to FIG. 2, the memory (110) of an electronic device (e.g., the electronic device (100) of FIG. 1) according to one embodiment may include a program (111) and a database (113) that support a data processing service, for example, a service that analyzes tooth areas and lesion areas based on dental image data, quantifies the degree of influence of the lesion on a tooth-by-tooth basis, and provides a visual indicator to show this.

[0076] In one embodiment, the program (111) may include an artificial neural network model (111a) configured to perform an analysis of dental image data and an indicator generation module (111b) configured to generate a visual indicator indicating the degree of influence of a lesion on a tooth-by-tooth basis based on the analysis results of the artificial neural network model (111a).

[0077] According to one embodiment, the artificial neural network model (111a) may be implemented by a deep learning-based segmentation model, and the indicator generation module (111b) may be implemented using software logic, hardware logic and / or firmware logic. In one embodiment, at least one processor of the electronic device (100) (e.g., the processor (140) of FIG. 1) may be configured to control and / or execute the functions of the artificial neural network model (111a) and the indicator generation module (111b), respectively, by executing the program (111) to process instructions, algorithms, program code and / or parameters.

[0078] According to one embodiment, an artificial neural network model (111a) may be configured to receive dental image data as input and output an analysis result for the dental image data. For example, the artificial neural network model (111a) may estimate tooth regions and lesion regions from the dental image data and generate segmentation information (e.g., a mask, a label map, and / or a probability map) corresponding to each. In this regard, the artificial neural network model (111a) may include pre-trained model parameters (or weights). For example, the electronic device (100) inputs a training sample consisting of dental image data and ground truth labels representing the correct divisions for tooth regions and lesion regions in the dental image data into an artificial neural network model (111a), and can iteratively update the parameters of the artificial neural network model (111a) using a predetermined optimization method (e.g., gradient descent) so as to minimize the loss value between the ground truth labels and the inference result (e.g., a mask, label map, and / or probability map corresponding to the division result for tooth regions and lesion regions) output by the artificial neural network model (111a).

[0079] In various embodiments, the artificial neural network model (111a) may include a plurality of models, for example, a first model configured to generate tooth segmentation information by segmenting a tooth region from dental image data and a second model configured to generate lesion segmentation information by segmenting a lesion region. Alternatively, the artificial neural network model (111a) may include a single multi-task model configured to integrally generate tooth segmentation information for a tooth region and lesion segmentation information for a lesion region from dental image data.

[0080] In one embodiment, the indicator generation module (111b) may be configured to receive the analysis results of the artificial neural network model (111a) for dental image data (e.g., segmentation information for each tooth region and lesion region), quantify the degree of influence caused by the lesion on a tooth-by-tooth basis, and generate a visual indicator representing the quantification results. In this regard, the indicator generation module (111b) may determine the spatial relationship between the teeth and the lesion using the analysis results of the artificial neural network model (111a). For example, the indicator generation module (111b) may determine the centroids of each tooth and the lesion, and determine the teeth associated with the lesion based on the Euclidean distance between the centroids. Additionally, the indicator generation module (111b) may determine characteristic information of the lesion, such as the size of the lesion, using the analysis results of the artificial neural network model (111a).

[0081] In one embodiment, the indicator generation module (111b) can calculate the degree of influence of a specific lesion on a tooth based on determined spatial relationship information and lesion characteristic information, and can determine a score on a tooth-by-tooth basis by normalizing the degree of influence to a predetermined scale (e.g., a continuous numerical scale in the range of 0 to 100 and / or a discrete grade scale divided into multiple intervals) (e.g., min-max normalization, percentile-based normalization and / or sigmoid mapping normalization). Additionally, the indicator generation module (111b) can generate visual indicators that are distinguished from each other by tooth according to the score (e.g., a color code, numerical label, grade text, symbol, and / or icon corresponding to the score).

[0082] In one embodiment, the database (113) may store dental image data of various formats that are the target for generating a visual indicator. Additionally, the database (113) may store output information of an artificial neural network model (111a) (e.g., segmentation information for each tooth region and lesion region) and output information of an indicator generation module (111b) (e.g., information on the degree of impact of the lesion on each tooth, score information for each tooth, and visual indicator information for each tooth).

[0083] FIG. 3 is a diagram illustrating an exemplary processing pipeline for generating spatial relationship information between a tooth and a lesion and feature information of a lesion according to one embodiment of the present disclosure. FIG. 4 is a diagram illustrating a visual example of dental image data according to one embodiment of the present disclosure. FIG. 5 is a diagram illustrating a visual example of tooth and lesion segmentation according to one embodiment of the present disclosure.

[0084] Referring to FIGS. 3, 4, and 5, an electronic device according to one embodiment (e.g., the electronic device (100) of FIG. 1) can acquire dental image (411) data (410) representing the condition of a patient's teeth and jawbones (e.g., maxilla and mandible). For example, the electronic device (100) can acquire dental image (411) data by loading a three-dimensional CT image (e.g., cone-beam CT) and / or a two-dimensional radiographic image (e.g., panorama) from a database (e.g., the database (113) of FIG. 2). Alternatively, the electronic device (100) can acquire dental image (411) data by receiving a three-dimensional CT image and / or a two-dimensional radiographic image from an external electronic device (e.g., the external electronic device (300) of FIG. 1) that includes at least one of an imaging device or a medical imaging server. According to various embodiments, the electronic device (100) can establish a mutual correspondence by performing registration and / or projection transformation on a 3D CT image and a 2D radiographic image obtained for the same patient, and use the multimodality image generated based thereon as dental image (411) data.

[0085] In one embodiment, the electronic device (100) can generate (430) segmentation information for each tooth and lesion based on dental image (411) data. For example, the electronic device (100) can obtain tooth segmentation information for a plurality of teeth (e.g., a mask, a label map, and / or a probability map that divides tooth regions into pixels or voxels) and lesion segmentation information for at least one lesion (e.g., a mask, a label map, and / or a probability map that divides lesion regions including caries, periodontitis, and / or apical periodontitis into pixels or voxels) by inputting dental image (411) data (e.g., a 3D CT image, a 2D radiographic image, or a multimodality image) into a learned artificial neural network model (e.g., the artificial neural network model (111a)) by inputting the dental image (411) data (e.g., a 3D CT image, a 2D radiographic image, or a multimodality image) output by the said artificial neural network model (111a).

[0086] In one embodiment, the electronic device (100) may visually provide tooth segmentation information and lesion segmentation information for dental image (411) data generated using an artificial neural network model (111a). For example, the electronic device (100) may provide a rendering view (431) (e.g., a 3D or 2D rendering view) representing the patient's teeth and jawbone structure through a user interface supported by a program (e.g., the program (111) of FIG. 2) and / or an application related to a data processing service, and may overlay and display each tooth region (433) corresponding to the tooth segmentation information in a first color and each lesion region (435) corresponding to the lesion segmentation information in a second color on the rendering view (431). Alternatively, if the lesion segmentation information represents multiple lesion regions of different attributes (e.g., caries, periodontitis and / or apical periodontitis), the electronic device (100) may display each of the multiple lesion regions in the rendering view (431) in colors that are distinct from each other according to their attributes.

[0087] In one embodiment, the electronic device (100) can generate information (450) indicating the spatial relationship between the tooth and the lesion based on tooth division information and lesion division information. In this regard, the electronic device (100) can use an indicator generation module (e.g., the indicator generation module (111b) of FIG. 2) to calculate a first center point corresponding to the center coordinates of each tooth area and a second center point corresponding to the center coordinates of each lesion area, and calculate the Euclidean distance between the first center point and the second center point.

[0088] Additionally, the electronic device (100) can generate information (470) representing the characteristics of at least one lesion based on lesion segmentation information. For example, the electronic device (100) can generate lesion characteristic information including at least one of the size of the lesion (e.g., three-dimensional volume or two-dimensional area) or the shape characteristics of the lesion (e.g., length, maximum diameter and / or distribution direction) using an indicator generation module (111b). Additionally, the electronic device (100) can further generate information representing the characteristics of a plurality of teeth based on tooth segmentation information. For example, the electronic device (100) can generate tooth characteristic information including the structural characteristics of the teeth (e.g., alveolar bone height, root length and / or periodontal ligament space) using an indicator generation module (111b).

[0089] In various embodiments, the electronic device (100) may generate spatial relationship information between a tooth and a lesion based on tooth division information and lesion division information, generate feature information of a lesion based on lesion division information, and generate feature information of a tooth based on tooth division information in a predetermined sequence, or may perform substantially simultaneously in parallel.

[0090] FIG. 6 is a diagram illustrating an exemplary processing pipeline for calculating the degree of lesion influence on a tooth according to one embodiment of the present disclosure.

[0091] Referring to FIG. 6, an electronic device according to one embodiment (e.g., the electronic device (100) of FIG. 1) can determine (510) at least one first tooth associated with each of at least one lesion based on spatial relationship information between a tooth and a lesion (e.g., Euclidean distance information between center points) using an indicator generation module (e.g., the indicator generation module (111b) of FIG. 2). For example, the electronic device (100) can identify a tooth having a first center point that is at least the distance from a second center point of the first lesion and determine that tooth as the first tooth associated with the first lesion. Alternatively, the electronic device (100) can identify a predetermined number (e.g., 2) of multiple teeth in order of smallest distance from the second center point of the first lesion to the first center point of the tooth and determine that multiple teeth as the first tooth associated with the first lesion. Alternatively, the electronic device (100) may identify at least one first center point located within a predetermined threshold distance from a second center point of the first lesion and determine at least one tooth corresponding to said at least one first center point as the first tooth associated with the first lesion.

[0092] That is, for each of at least one lesion, the electronic device (100) can determine at least one first tooth that the lesion may affect based on the distance between the second center point of the lesion and the first center point of each of the plurality of teeth.

[0093] In one embodiment, the electronic device (100) can calculate (530) the degree of lesion influence for each of at least one first tooth using an indicator generation module (111b). For example, the electronic device (100) can calculate the degree of lesion influence for each of at least one first tooth by quantifying the degree of influence that a lesion associated with the first tooth has on the first tooth. In this regard, the electronic device (100) can calculate the degree of lesion influence for the first tooth based on spatial relationship information between the tooth and the lesion and characteristic information of the lesion (e.g., volume or area information). For example, the electronic device (100) can calculate the degree of lesion influence for each of at least one first tooth associated with the lesion based on an influence calculation rule set to be proportional to the size of the lesion and inversely proportional to the distance between the lesion and the first tooth. Alternatively, the electronic device (100) may calculate the degree of lesion influence for each of at least one first tooth associated with the lesion by using mathematical formula 1 or mathematical formula 2, which combines the size information of the lesion with a weighting function that decreases with increasing distance parameters.

[0094]

[0095] In mathematical formula 1, Influence represents the value of the influence that a specific lesion has on the first tooth, and Size may represent the three-dimensional volume value or two-dimensional area value of the lesion. Additionally, d represents the Euclidean distance value between the second center point of the lesion and the first center point of the first tooth, and ε may represent a small positive value to prevent the denominator from becoming zero when the distance value is zero or close to zero.

[0096]

[0097] In mathematical formula 2, Influence represents the value of the influence that a specific lesion has on the first tooth, and Size may represent the three-dimensional volume value or two-dimensional area value of the lesion. Additionally, d represents the Euclidean distance value between the second center point of the lesion and the first center point of the first tooth, and α may represent a positive parameter value for controlling the degree of reduction in influence as the distance value increases.

[0098] That is, based on mathematical formula 1 or mathematical formula 2, as the size of the lesion increases, the influence of the lesion on the first tooth increases, and as the distance between the lesion and the first tooth increases, the influence of the lesion on the first tooth may decrease.

[0099] According to various embodiments, when the same first tooth is associated with multiple lesions of different attributes (e.g., caries, periodontitis and / or apical periodontitis), the electronic device (100) may calculate an integrated influence on the first tooth by aggregating the lesion influences on the first tooth calculated for each of the multiple lesions on a tooth-by-tooth basis. For example, the electronic device (100) may calculate an integrated influence on the first tooth by combining the first lesion influence of the first tooth for the first lesion of the first attribute and the second lesion influence of the first tooth for the second lesion of the second attribute (e.g., simple summation, weighted sum with different weights applied according to the first attribute and the second attribute, or selection of a maximum value).

[0100] That is, the electronic device (100) can calculate a unified lesion influence (e.g., integrated influence) for a tooth even when multiple lesions of different attributes simultaneously affect the same tooth.

[0101] FIG. 7 is a diagram illustrating an exemplary processing pipeline for generating and providing a visual indicator for a tooth according to one embodiment of the present disclosure. FIG. 8 is a diagram illustrating a visual example of a visual indicator according to one embodiment of the present disclosure.

[0102] Referring to FIGS. 7 and 8, an electronic device according to one embodiment (e.g., the electronic device (100) of FIG. 1) can calculate a score (610) for each of at least one first tooth associated with each of at least one lesion. For example, the electronic device (100) can calculate a score based on the lesion influence (or integrated lesion influence) of each of at least one first tooth using an indicator generation module (e.g., the indicator generation module (111b) of FIG. 2). In this regard, the electronic device (100) can determine a score for each of at least one first tooth by normalizing the lesion influence calculated for each of at least one first tooth to a predetermined scale. For example, the electronic device (100) can determine a low score for a first tooth with a relatively small lesion influence and a high score for a first tooth with a relatively large lesion influence by normalizing based on a continuous numerical scale in the range of 0 to 100 and / or a discrete grade scale divided into multiple intervals so that the lesion influence of each of at least one first tooth associated with the lesion is converted into a form that can be compared with each other (e.g., min-max normalization, percentile-based normalization and / or sigmoid mapping normalization).

[0103] According to one embodiment, the electronic device (100) can generate a visual indicator (630) for each of at least one first tooth associated with a lesion using an indicator generation module (111b). For example, the electronic device (100) can generate a visual indicator corresponding to the score of the first tooth for each of at least one first tooth.

[0104] According to one embodiment, a visual indicator may be referenced as a result of converting the score of a first tooth into a visual representation that can be intuitively perceived by a user (e.g., medical staff and / or patient), and may include at least one of a color code, a numeric label, grade text, a symbol, or an icon. For example, the electronic device (100) may generate a visual indicator in a continuous or segmented color mapping manner that reflects a first color (e.g., a blue tone) for a first tooth with a relatively low score and a second color (e.g., a red tone) for a first tooth with a relatively high score. Alternatively, the electronic device (100) may generate a numeric label representing the score of each of at least one first tooth as a visual indicator. Alternatively, the electronic device (100) may determine the grade of the first tooth (e.g., normal, mild, moderate, severe, or critical) corresponding to the segment containing the score of the first tooth within a predetermined scale, and may generate text, a symbol, and / or an icon representing the grade as a visual indicator. That is, the electronic device (100) can generate a visual indicator that converts the score of each of the at least one first tooth associated with the lesion into a color, numerical value and / or grade.

[0105] According to one embodiment, the electronic device (100) may generate mapping information including at least some of the output information of the indicator generation module (111b) and store it in a database (e.g., the database (113) of FIG. 2). For example, the electronic device (100) may generate structured data in which at least one of identification information, lesion influence information, score information, or visual indicator information is associated with a first tooth determined to be associated with a specific lesion, and may store such mapping information in a time series at multiple points in time for the same patient in the database (113).

[0106] In one embodiment, the electronic device (100) may visually display (650) a visual indicator generated for at least one first tooth corresponding to each lesion in response to user input. In this regard, the electronic device (100) may provide a rendering view (651) (e.g., a three-dimensional or two-dimensional rendering view) representing the patient's teeth and jawbone structure through a user interface supported by a program (e.g., the program (111) of FIG. 2) and / or an application related to a data processing service, and may display a visual indicator by mapping (or overlaying) it on the rendering view (651). For example, the electronic device (100) may display a visual indicator corresponding to at least some of the plurality of teeth represented by the rendering view (651) based on mapping information stored in a database (113).

[0107] According to one embodiment, the electronic device (100) may display a color according to the score of the tooth as a visual indicator on a rendering view (651). For example, the electronic device (100) may display a visual indicator expressed in a first color (e.g., a blue or green color) on a tooth area (653) having a low score with a relatively small influence on at least one lesion, and may display a visual indicator expressed in a second color (e.g., a yellow, orange, or red color) on a tooth area (655) having a high score with a relatively large influence on at least one lesion.

[0108] Alternatively, the electronic device (100) may display a numeric label according to the score of the tooth as a visual indicator on the rendering view (651), display text, symbols, or icons indicating a grade according to the score of the tooth as a visual indicator, or display a combination of at least some of a color, numeric label, text, symbols, or icons as a visual indicator.

[0109] In various embodiments, the electronic device (100) may provide legend content (657) in one area of ​​the rendering view (651). For example, the electronic device (100) may display legend content (657) including at least one of a color bar, a grade table, symbol interpretation information, symbol interpretation information, or icon interpretation information, which indicates a score range of teeth and a corresponding color change or grade interval.

[0110] In various embodiments, the electronic device (100) may receive user input selecting a specific tooth area on a rendering view (651) through a user interface. In this case, the electronic device (100) may, in response to the user input, provide additional information through a pop-up window that includes at least one of score information, grade information, related lesion information, or change information relative to a past point in time (e.g., information on the amount of change between scores determined at each of a plurality of points in time and / or indicator information indicating the degree of deterioration or improvement of the lesion).

[0111] In various embodiments, the electronic device (100) may receive a request for a visual indicator from at least one external electronic device (e.g., the external electronic device (300) of FIG. 1). For example, the electronic device (100) may receive a request for a visual indicator from an electronic medical record system, a picture archiving and communication system, and / or a user (e.g., patient) terminal. In one embodiment, the electronic device (100) may respond to a request from at least one external electronic device (300) by packetizing or configuring visual indicator data for a plurality of teeth into a message form and transmitting it to at least one external electronic device (300), or by storing visual indicator data for a plurality of teeth in a file form and transmitting access information (e.g., a file link) to the file to at least one external electronic device (300).

[0112] FIG. 9 is a diagram exemplarily illustrating a method for generating a visual indicator showing the degree of lesion influence per tooth according to one embodiment of the present disclosure.

[0113] The steps of the method (900) for generating a visual indicator indicating the degree of lesion influence per tooth, as described in the embodiment of FIG. 9, may be performed sequentially or non-sequentially. For example, the order of execution of the steps described in the embodiment of FIG. 9 may be changed, some steps may be performed repeatedly, some steps may be omitted, or at least two steps may be performed in parallel. Additionally, at least some of the steps described in the embodiment of FIG. 9 may include operations identical or similar to the operation of the electronic device (e.g., the electronic device (100) of FIG. 1) described above with reference to the drawings, and redundant descriptions of identical or similar operations may be omitted below.

[0114] Referring to FIG. 9, in step S910, an electronic device according to one embodiment (e.g., the electronic device (100) of FIG. 1) may generate tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion based on dental image data. In this regard, the electronic device (100) may acquire dental image data by loading a 3D CT image (e.g., cone-beam CT) and / or a 2D radiographic image (e.g., panorama) from a database (e.g., the database (113) of FIG. 2) or receiving it from an external electronic device (e.g., the external electronic device (300) of FIG. 1). Alternatively, the electronic device (100) may establish a mutual correspondence relationship by performing registration and / or projection transformation on the 3D CT image and the 2D radiographic image, and acquire a multimodality image generated based thereon as dental image data. Additionally, the electronic device (100) can input dental image data (e.g., 3D CT image, 2D radiographic image, or multimodality image) into a learned artificial neural network model (e.g., artificial neural network model (111a) of FIG. 2) to obtain tooth segmentation information for a plurality of teeth (e.g., a mask, label map, and / or probability map that divides tooth regions into pixels or voxels) and lesion segmentation information for at least one lesion (e.g., a mask, label map, and / or probability map that divides lesion regions including caries, periodontitis, and / or apical periodontitis into pixels or voxels) generated by the artificial neural network model (111a) based on the dental image data.

[0115] In step S920, an electronic device (100) according to one embodiment may determine, for each of at least one lesion, at least one first tooth associated with the lesion among a plurality of teeth based on spatial relationship information determined from tooth division information and lesion division information. In this regard, the electronic device (100) may use an indicator generation module (e.g., an indicator generation module (111b) of FIG. 2) to calculate a first center point corresponding to the center coordinates of each tooth area and a second center point corresponding to the center coordinates of each lesion area based on tooth division information and lesion division information, and generate the Euclidean distance between the first center point and the second center point as spatial relationship information between the tooth and the lesion.

[0116] According to one embodiment, the electronic device (100) can determine at least one first tooth associated with each of at least one lesion based on spatial relationship information between the tooth and the lesion. For example, the electronic device (100) can determine as at least one first tooth associated with the lesion a tooth having a first center point at a minimum distance from a second center point of a specific lesion, a predetermined number of teeth determined in order of decreasing distance from the second center point of a specific lesion to the first center point, or a tooth with a first center point located within a predetermined threshold distance from the second center point of a specific lesion.

[0117] In step S930, an electronic device (100) according to one embodiment may calculate the degree of influence caused by a lesion for each of at least one first tooth based on lesion feature information determined from spatial relationship information and lesion segmentation information. In this regard, the electronic device (100) may use an indicator generation module (111b) to generate feature information representing the features of each of at least one lesion based on lesion segmentation information. For example, the electronic device (100) may generate lesion feature information including at least one of the size of the lesion (e.g., three-dimensional volume or two-dimensional area) or the shape characteristics of the lesion (e.g., length, maximum diameter and / or distribution direction).

[0118] According to one embodiment, the electronic device (100) can calculate a lesion influence degree for each of at least one first tooth, based on spatial relationship information between the tooth and the lesion and characteristic information of the lesion, which quantifies the degree of influence that a lesion associated with the first tooth has on the first tooth. For example, the electronic device (100) can calculate the lesion influence degree for each of at least one first tooth using an indicator generation module (111b) according to an influence degree calculation rule set to be proportional to the size of the lesion and inversely proportional to the distance between the lesion and the first tooth, or calculate the lesion influence degree for each of at least one first tooth by combining the size information of the lesion with a weighting function that decreases as the distance parameter increases.

[0119] In step S940, an electronic device (100) according to one embodiment may determine a score for each of at least one first tooth by normalizing the degree of influence for each of at least one first tooth to a predetermined scale. For example, the electronic device (100) may determine a low score for a first tooth with a relatively small degree of influence and a high score for a first tooth with a relatively large degree of influence by using an indicator generation module (111b) to normalize the degree of influence of the lesion for each of at least one first tooth to a predetermined scale (e.g., a continuous numerical scale in the range of 0 to 100 and / or a discrete grade scale divided into multiple intervals).

[0120] In step S950, an electronic device (100) according to one embodiment may generate a visual indicator for each of at least one first tooth based on the score of each of at least one first tooth. In this regard, the visual indicator may be referenced as the result of converting the score of the first tooth into a visual representation that can be intuitively perceived by a user (e.g., medical staff and / or patient), and the electronic device (100) may generate a visual indicator represented by at least one of a corresponding color code, numeric label, grade text, symbol, or icon according to the score of the first tooth using an indicator generation module (111b). For example, the electronic device (100) may generate a visual indicator using a continuous or segmented color mapping method that reflects a first color (e.g., a blue tone) for a first tooth with a relatively low score and a second color (e.g., a red tone) for a first tooth with a relatively high score. Alternatively, the electronic device (100) may generate a numeric label representing the score of each of at least one first tooth as a visual indicator. Alternatively, the electronic device (100) may determine the grade of the first tooth corresponding to the section containing the score of the first tooth within a predetermined scale, and generate text, symbols, and / or icons representing the grade as a visual indicator.

[0121] According to one embodiment, the electronic device (100) may provide a rendering view (e.g., a three-dimensional or two-dimensional rendering view) representing the patient's teeth and jawbone structure through a user interface supported by a program (e.g., the program (111) of FIG. 2) and / or an application related to a data processing service, and may display a visual indicator on the rendering view.

[0122] A method for generating a visual indicator indicating the degree of lesion influence for each tooth according to one embodiment of the present disclosure may include: generating tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion based on dental image data; for each of the at least one lesion, determining at least one tooth among the plurality of teeth associated with the lesion based on spatial relationship information determined from the tooth segmentation information and lesion segmentation information; for each of the at least one tooth, calculating the degree of influence caused by the lesion based on characteristic information of the lesion determined from the spatial relationship information and lesion segmentation information; normalizing the degree of influence to a predetermined scale to determine a score for each of the at least one tooth; and generating a visual indicator for each of the at least one tooth based on the score.

[0123] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of mapping and displaying a visual indicator for each of at least one tooth in a rendering view representing the structure of a plurality of teeth.

[0124] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of providing information on the amount of change of a score determined at each of a plurality of time points for a first tooth in response to receiving a user input selecting a first tooth among a plurality of teeth in a rendering view.

[0125] According to one embodiment of the present disclosure, the step of generating tooth segmentation information may include generating at least one of a mask, a label map, or a probability map in which each region of a plurality of teeth is divided into pixel or voxel units.

[0126] According to one embodiment of the present disclosure, the step of generating lesion segmentation information may include generating at least one of a mask, a label map, or a probability map that divides each region of at least one lesion into pixel or voxel units.

[0127] According to one embodiment of the present disclosure, the step of determining at least one tooth may include: calculating a first center point for each of a plurality of teeth based on tooth division information; calculating a second center point for each of at least one lesion based on lesion division information; and generating spatial relationship information including the Euclidean distance between the first center point and the second center point.

[0128] According to one embodiment of the present disclosure, the step of determining at least one tooth may further include the step of determining a predetermined number of teeth, determined in order of decreasing Euclidean distance from a second center point of the lesion, as at least one tooth associated with the lesion.

[0129] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may include the step of generating characteristic information of the lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion.

[0130] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may further include the step of calculating the degree of influence caused by a lesion based on an influence calculation rule configured to be proportional to the characteristic information of the lesion and inversely proportional to the spatial relationship information.

[0131] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may further include the step of calculating the degree of influence caused by a lesion by applying characteristic information of the lesion to a weighting function that decreases with increasing spatial relationship information.

[0132] According to one embodiment of the present disclosure, the step of calculating the degree of influence caused by a lesion may include, for a first tooth associated with each of a first lesion of a first attribute and a second lesion of a second attribute different from the first attribute among a plurality of teeth, a step of calculating an integrated degree of influence by combining the first degree of influence caused by the first lesion and the second degree of influence caused by the second lesion.

[0133] According to one embodiment of the present disclosure, the step of determining the score may include the step of determining the score by normalizing the influence degree based on a continuous numerical scale in the range of 0 to 100.

[0134] According to one embodiment of the present disclosure, the step of determining the score may include the step of determining the score by normalizing the influence level based on a discrete grade scale divided into a plurality of intervals.

[0135] According to one embodiment of the present disclosure, the step of generating a visual indicator may include generating at least one of a color code, a numerical label, grade text, a grade symbol, a grade symbol, or a grade icon as a visual indicator corresponding to the score of each of at least one tooth.

[0136] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of configuring data of the visual indicator into a packet or a message and the step of transmitting the packet or message to at least one external electronic device.

[0137] According to one embodiment of the present disclosure, a method for generating a visual indicator may further include the step of generating a file containing data of a visual indicator and the step of transmitting access information for the file to at least one external electronic device.

[0138] According to one embodiment of the present disclosure, a computer program stored on a computer-readable recording medium may be provided for executing a method for generating a visual indicator indicating the degree of lesion influence for each tooth on a computer.

[0139] An electronic device according to one embodiment of the present disclosure may include a memory for storing instructions and at least one processor.

[0140] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may generate tooth segmentation information for a plurality of teeth and lesion segmentation information for at least one lesion based on dental image data, determine at least one tooth among a plurality of teeth associated with the lesion based on spatial relationship information determined from the tooth segmentation information and lesion segmentation information for each of at least one lesion, calculate the degree of influence caused by the lesion based on the lesion characteristic information determined from the spatial relationship information and lesion segmentation information for each of at least one tooth, determine a score for each of at least one tooth by normalizing the degree of influence to a predetermined scale, and generate a visual indicator for each of at least one tooth based on the score.

[0141] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may map and display a visual indicator for each of at least one tooth in a rendering view representing the structure of a plurality of teeth.

[0142] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, the electronic device may calculate a first center point for each of a plurality of teeth based on tooth division information, calculate a second center point for each of at least one lesion based on lesion division information, generate spatial relationship information including the Euclidean distance between the first center point and the second center point, and determine a predetermined number of teeth determined in order of smallest Euclidean distance from the second center point of the lesion as at least one tooth associated with the lesion.

[0143] According to one embodiment of the present disclosure, when instructions are executed by at least one processor, an electronic device may generate characteristic information of a lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion, and calculate the degree of influence caused by the lesion by applying the characteristic information of the lesion to a weighting function that decreases with increasing spatial relationship information.

[0144] The method described above may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may continuously store a computer-executable program, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or multiple hardware components, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Furthermore, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0145] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof.

[0146] Those skilled in the art will understand that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functions are implemented in hardware or in software depends on the design requirements imposed on the specific application and the overall system. Those skilled in the art may implement the described functions in various ways for each specific application, but such implementations should not be construed as departing from the scope of the disclosure.

[0147] In a hardware implementation, the processing units used to perform the techniques may be implemented in 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, other electronic units designed to perform the functions described in this disclosure, computers, or a combination thereof.

[0148] Accordingly, the various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors coupled with a DSP core, or any other combination of configurations.

[0149] In firmware and / or software implementations, techniques may be implemented as instructions stored on a computer-readable medium 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, compact disc (CD), magnetic or marked data storage device, etc. The instructions may be executable by one or more processors, and may cause the processor(s) to perform specific aspects of the functions described in this disclosure.

[0150] When implemented in software, the techniques described above may be stored on a computer-readable medium as one or more instructions or code, or transmitted through a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available media accessible by a computer.

[0151] As a non-limiting example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to transfer or store desired program code in the form of instructions or data structures and can be accessed by a computer. Additionally, any connection is appropriately referred to as a computer-readable medium.

[0152] For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium.

[0153] The disks and discs used in this disclosure include CDs, laser discs, optical discs, DVDs (digital versatile discs), floppy disks, and Blu-ray discs, wherein disks typically reproduce data magnetically, while discs reproduce data optically using a laser. The above combinations should also be included within the scope of computer-readable media.

[0154] Software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other known form of storage media. An exemplary storage medium may be connected to a processor so that the processor can read information from the storage medium or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and the storage medium may exist within an ASIC. The ASIC may exist within an electronic device. Alternatively, the processor and the storage medium may exist as separate components within an electronic device.

[0155] Although the embodiments described above have been described as utilizing aspects of the subject matter disclosed herein in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in a plurality of processing chips or devices, and storage may be distributed across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0156] Although the present disclosure has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the present disclosure as understood by a person skilled in the art to which the invention of the present disclosure pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to the present disclosure.

Claims

1. A method for generating a visual indicator indicating the degree of lesion influence per tooth, executed by at least one processor of an electronic device, A step of generating tooth segmentation information for multiple teeth and lesion segmentation information for at least one lesion based on dental imaging data; For each of the above at least one lesion, a step of determining at least one tooth among the plurality of teeth associated with the lesion based on the tooth division information and spatial relationship information determined from the lesion division information; For each of the above at least one tooth, a step of calculating the degree of influence caused by the lesion based on the characteristic information of the lesion determined from the spatial relationship information and the lesion segmentation information; A step of determining a score for each of the at least one tooth by normalizing the above influence degree to a predetermined scale; and A step of generating a visual indicator for each of the at least one tooth based on the above score A method for generating a visual indicator including 2. In Paragraph 1, A step of mapping and displaying the visual indicator for each of the at least one tooth in a rendering view representing the structure of the plurality of teeth. A method for creating a visual indicator that further includes 3. In Paragraph 2, In response to receiving user input selecting a first tooth among the plurality of teeth in the rendering view, providing information on the amount of change of the score determined at each of the plurality of time points for the first tooth. A method for creating a visual indicator that further includes 4. In Paragraph 1, The step of generating the above tooth division information is, A step of generating at least one of a mask, a label map, or a probability map that divides each region of the plurality of teeth into pixel or voxel units. Includes, The step of generating the above-mentioned lesion segmentation information is, A step of generating at least one of a mask, a label map, or a probability map that divides each of the areas of each of the at least one lesion into pixel or voxel units. A method for generating a visual indicator including 5. In Paragraph 1, The step of determining at least one tooth above is, A step of calculating the first center point of each of the plurality of teeth based on the above tooth division information; A step of calculating a second center point for each of the at least one lesion based on the above lesion segmentation information; and A step of generating spatial relationship information including the Euclidean distance between the first center point and the second center point. A method for generating a visual indicator including 6. In Paragraph 5, The step of determining at least one tooth above is, A step of determining a predetermined number of teeth, determined in order of decreasing Euclidean distance from the second center point of the lesion, as the at least one tooth associated with the lesion. A method for creating a visual indicator that further includes 7. In Paragraph 1, The step of calculating the degree of influence caused by the above-mentioned lesion is, A step of generating characteristic information of the lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion A method for generating a visual indicator including 8. In Paragraph 7, The step of calculating the degree of influence caused by the above-mentioned lesion is, A step of calculating the degree of influence caused by the lesion based on an influence calculation rule configured to be proportional to the characteristic information of the lesion and inversely proportional to the spatial relationship information. A method for creating a visual indicator that further includes 9. In Paragraph 7, The step of calculating the degree of influence caused by the above-mentioned lesion is, A step of calculating the degree of influence caused by the lesion by applying the characteristic information of the lesion to a weighting function that decreases as the spatial relationship information increases. A method for creating a visual indicator that further includes 10. In Paragraph 1, The step of calculating the degree of influence caused by the above-mentioned lesion is, For a first tooth associated with each of the first lesion of a first attribute and the second lesion of a second attribute different from the first attribute among the plurality of teeth, a step of calculating an integrated influence degree by combining the first influence degree caused by the first lesion and the second influence degree caused by the second lesion. A method for generating a visual indicator including 11. In Paragraph 1, The step of determining the above score is, A step of determining the score by normalizing the influence level based on a continuous numerical scale in the range of 0 to 100. A method for generating a visual indicator including 12. In Paragraph 1, The step of determining the above score is, A step of determining the score by normalizing the above influence based on a discrete grade scale divided into multiple intervals. A method for generating a visual indicator including 13. In Paragraph 1, The step of generating the above visual indicator is, A step of generating at least one of a color code, numerical label, grade text, grade symbol, grade symbol, or grade icon as a visual indicator corresponding to the score of each of the at least one tooth. A method for generating a visual indicator including 14. In Paragraph 1, The step of configuring the data of the above visual indicator into a packet or message; and The step of transmitting the above packet or the above message to at least one external electronic device A method for creating a visual indicator that further includes 15. In Paragraph 1, A step of generating a file containing data of the above-mentioned visual indicator; and A step of transmitting access information for the above file to at least one external electronic device A method for creating a visual indicator that further includes 16. A computer program stored on a computer-readable recording medium for executing on a computer a method for generating a visual indicator indicating the degree of lesion influence for each tooth according to any one of claims 1 to 15.

17. In electronic devices, Memory for storing instructions; and At least one processor Includes, When the above instructions are executed by the above at least one processor, the electronic device, Based on dental imaging data, tooth segmentation information for multiple teeth and lesion segmentation information for at least one lesion are generated, and For each of the above at least one lesion, at least one tooth associated with the lesion among the plurality of teeth is determined based on the tooth division information and spatial relationship information determined from the lesion division information, and For each of the above at least one tooth, the degree of influence caused by the lesion is calculated based on the characteristic information of the lesion determined from the spatial relationship information and the lesion segmentation information, and The above influence is normalized to a predetermined scale to determine a score for each of the at least one tooth, and A method for generating a visual indicator for each of the at least one tooth based on the above score. Electronic device.

18. In Paragraph 17, When the above instructions are executed by the above at least one processor, the electronic device, A method for mapping and displaying the visual indicator for each of the at least one tooth in a rendering view representing the structure of the plurality of teeth. Electronic device.

19. In Paragraph 17, When the above instructions are executed by the above at least one processor, the electronic device, Based on the above tooth division information, the first center point of each of the plurality of teeth is calculated, and Based on the above lesion segmentation information, a second center point for each of the at least one lesion is calculated, and Generating spatial relationship information including the Euclidean distance between the first center point and the second center point, and A predetermined number of teeth determined in order of decreasing Euclidean distance from the second center point of the lesion are determined as the at least one tooth associated with the lesion. Electronic device.

20. In Paragraph 17, When the above instructions are executed by the above at least one processor, the electronic device, Generating characteristic information of the lesion including at least one of the volume, area, length, maximum diameter, or distribution direction of the lesion, and Calculating the degree of influence caused by the lesion by applying the characteristic information of the lesion to a weighting function that decreases with increasing spatial relationship information. Electronic device.

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