WO2023282405A1 - Procédé de calcul de position optimale de stimulation à l'aide d'un modèle d'intelligence artificielle, procédé de simulation de modèle d'électrode, serveur, et programme informatique - Google Patents

Procédé de calcul de position optimale de stimulation à l'aide d'un modèle d'intelligence artificielle, procédé de simulation de modèle d'électrode, serveur, et programme informatique Download PDF

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WO2023282405A1
WO2023282405A1 PCT/KR2021/018145 KR2021018145W WO2023282405A1 WO 2023282405 A1 WO2023282405 A1 WO 2023282405A1 KR 2021018145 W KR2021018145 W KR 2021018145W WO 2023282405 A1 WO2023282405 A1 WO 2023282405A1
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model
electrode
optimal
information
coordinate data
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PCT/KR2021/018145
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English (en)
Korean (ko)
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김동현
고봉석
민대규
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뉴로핏 주식회사
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Priority claimed from KR1020210089051A external-priority patent/KR102373759B1/ko
Priority claimed from KR1020210089050A external-priority patent/KR102373758B1/ko
Application filed by 뉴로핏 주식회사 filed Critical 뉴로핏 주식회사
Publication of WO2023282405A1 publication Critical patent/WO2023282405A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/08—Arrangements or circuits for monitoring, protecting, controlling or indicating
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/20—Applying electric currents by contact electrodes continuous direct currents
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • Various embodiments of the present invention relate to a method for calculating an optimal magnetic pole position using an artificial intelligence model, a server, and a computer program.
  • invasive brain stimulation which treats diseases by applying electrical signals directly into the brain
  • invasive brain stimulation techniques such as deep brain stimulation (DBS) are being used to treat neurological symptoms such as hand tremors in Parkinson's disease.
  • DBS deep brain stimulation
  • non-invasive brain stimulation which is less expensive and less risky, has recently been in the limelight.
  • Non-invasive brain stimulation does not require a procedure that injures nerve tissue like surgery, guarantees improved stability, and has the advantage of not requiring hospitalization.
  • non-invasive brain stimulation applies sufficient brain stimulation by repeating a single treatment procedure performed in a relatively short time dozens of times, it is also a great advantage that there is no device to be always carried or managed.
  • Representative examples of such non-invasive brain stimulation include transcranial magnetic stimulation (TMS) and transcranial DC stimulation (tDCS).
  • TMS transcranial magnetic stimulation
  • tDCS transcranial DC stimulation
  • This non-invasive brain stimulation technique has the function of enhancing or inhibiting the activity of brain nerve cells, so it can be used not only for rehabilitation treatment of brain diseases such as stroke, but also for depression, epilepsy, dementia, Parkinson's, tic disorder, tinnitus, addiction, and chronic pain. It has been reported to be effective for many brain neurological diseases such as , anxiety disorders and sleep disorders.
  • a scalp model is implemented, and the position is slightly changed based on the initial starting position on the scalp model to create many candidate positions on the head, and then a test is performed at each candidate position. There is a way to select the optimal location through .
  • a human head is an unstructured surface, it may be difficult to continuously change or move coordinates on a head model corresponding to a corresponding head in order to select a location of a stimulus.
  • the corresponding position may be the skull rather than the scalp, and thus may not be an appropriate position for disposing the electrode.
  • a head model is obtained based on MRI information of each user, a spherical coordinate system fitted to the head model is obtained, and various stimuli are based on this.
  • a method for calculating an optimal stimulation position using an artificial intelligence model according to an embodiment of the present invention to solve the above problems includes generating a head model based on user diagnostic information, and a spherical model based on the head model. generating, identifying a plurality of transcriptional coordinate data corresponding to each of a plurality of spherical coordinate data related to the spherical model, and processing the plurality of transcriptional coordinate data as an input of an optimal positioning model to obtain optimal magnetic pole position information.
  • the method may include obtaining, wherein the plurality of transcriptional coordinate data is data related to orthogonal coordinates that can be expressed on the head model.
  • the user diagnostic information is information based on the generation of the head model, and includes medical image information on the brain or head of the user, and the head model includes three information related to the brain or head of the actual user. It is a dimensional brain map model, and the spherical model is implemented through at least a part of a three-dimensional sphere model having a specific radius, and the plurality of spherical coordinate data is data related to spherical coordinates that can be expressed in the spherical model. can be done with
  • the generating of the head model based on the user diagnostic information may include obtaining one or more brain region images by processing the user diagnostic information as an input of an image region classification model; and generating the head model based on the preprocessed one or more brain region images, wherein the image region classification model is a convolutional neural network (CNN)-based neural network model, and includes a plurality of It may be characterized in that learning is performed through learning data including learning input data related to user diagnostic information of and learning output data related to brain region classification information corresponding to each user diagnostic information.
  • CNN convolutional neural network
  • the generating of the spherical model based on the user diagnosis information may include developing one or more spheres based on the central point of the head model, and determining the relationship between the surface of the one or more spheres and the head model among the developed one or more spheres.
  • the method may include identifying a first sphere having a minimum surface distance and having a maximum radius, and generating the spherical model based on the identified first sphere.
  • the optimal positioning model derives brain activity prediction information corresponding to each of the plurality of transcriptional coordinate data, and derives each of the derived brain activity prediction information and the reference brain corresponding to each of the plurality of transcriptional coordinate data. Identifying optimal transcriptional coordinate data whose loss function related to the difference with each activity information is less than or equal to a predetermined reference value, and outputting the optimal stimulation position information based on the identified optimal transcriptional coordinates data can do.
  • the method may include determining whether at least two or more spherical coordinate data among the plurality of spherical coordinate data overlap and indicate a point on the head model, and determining whether the at least two or more spherical coordinate data indicate the head image
  • a step of modifying some of the at least two or more spherical coordinate data may be included.
  • the modifying of some of the at least two or more spherical coordinate data may include removing at least one of the at least two or more spherical coordinate data indicating overlapping points on the head model. or correcting the coordinates of at least one spherical coordinate data among at least two or more spherical coordinate data indicating overlapping points on the head model.
  • the method may include disposing an electrode model at a first position corresponding to the optimal stimulation position information, and gradually moving the electrode model located at the first position to a position corresponding to the optimal stimulation position information.
  • the method may further include performing electrode attachment simulation on the head model by moving the head model, and the first position may be a position in a direction of a normal vector of the optimal stimulation position information.
  • An optimal magnetic pole position calculation server using an artificial intelligence model using an artificial intelligence model according to another embodiment of the present invention for solving the above problems is a processor, a network interface, a memory, and loaded into the memory, A computer program executed by the processor, the computer program including instructions for generating a head model based on user diagnosis information and instructions for generating a spherical model based on the head model, the spherical model An instruction for identifying a plurality of transcriptional coordinate data corresponding to each of a plurality of related spherical coordinate data and an instruction for obtaining optimal magnetic pole position information by processing the plurality of transcriptional coordinate data as an input of an optimal positioning model, wherein the plurality of The transfer coordinate data may be data related to orthogonal coordinates that can be expressed on the head model.
  • a computer program recorded on a computer-readable recording medium for solving the above problems is combined with a computing device to generate a head model based on user diagnostic information; Generating a spherical model based on the head model, a plurality of transcribed coordinate data corresponding to each of a plurality of spherical coordinate data related to the spherical model—the plurality of transcribed coordinate data is at Cartesian coordinates expressible on the head model Characterized in that it is related data—to be stored in a computer-readable recording medium to execute the steps of identifying and processing the plurality of transcriptional coordinate data as inputs of an optimal positioning model to obtain optimal magnetic pole position information.
  • Normal Vector the optimal stimulation position information
  • the method may include obtaining one or more brain region images by processing the user diagnostic information as an input of an image region classification model, performing preprocessing on the one or more brain region images, and performing preprocessing on the one or more brain region images.
  • Generating the head model based on one or more brain region images wherein the image region classification model is a neural network model based on a convolutional neural network (CNN), and learning input data related to a plurality of user diagnostic information and It may be characterized in that learning is performed through learning data including learning output data related to brain region classification information corresponding to each user diagnostic information.
  • CNN convolutional neural network
  • the electrode model includes a first surface contactable with the head model and a second surface corresponding to the first surface, and the coordinates forming each of the first surface and the second surface are: It may be characterized as having directivity in the same direction.
  • the performing of the electrode attachment simulation may include stopping the movement of the electrode model when the first surface and the head model are in contact with each other, and attaching the electrode model to the first surface of the electrode model where the movement is stopped.
  • the method may include calculating a movement distance of each of the plurality of first coordinates and moving each of the plurality of second coordinates related to the second surface based on the movement distance of each of the plurality of first coordinates.
  • the method further includes attaching a candidate electrode model based on an electrode model in contact with the head model, wherein the candidate electrode model is predetermined from the electrode model on the head model. It may be characterized in that it is attached within the separation distance.
  • attaching a candidate electrode model based on an electrode model in contact with the head model may include locating the candidate electrode model based on an attachment position of the electrode model, and an orientation angle of the candidate electrode model. sequentially changing, and obtaining a plurality of direction vectors according to the change in the orientation angle, and the candidate electrode on the head model based on a comparison between the obtained plurality of direction vectors and the first direction vector of the electrode model. A step of determining the attachment direction of the model may be included.
  • the optimal stimulation position information includes one or more optimal stimulation position sub-information
  • the method performs one or more electrode attachment simulations on the head model in correspondence with each of the one or more optimal stimulation position sub-information. identifying whether at least one electrode model overlaps on the head model as a result of the one or more electrode attachment simulations; and modifying at least one optimal stimulation position sub-information based on the identified overlapping electrode model.
  • a server for simulating an electrode model according to another embodiment of the present invention for solving the above problems includes a processor, a network interface, a memory, and a computer program loaded into the memory and executed by the processor.
  • the computer program includes an instruction for arranging an electrode model at a first position based on the head model based on optimal stimulation position information, and the electrode model located at the first position corresponds to the optimal stimulation position information and an instruction for performing an electrode attachment simulation on the head model by gradually moving to a position where the first position is a position in a direction of a normal vector of the optimal stimulation position information.
  • a computer program recorded on a computer-readable recording medium for solving the above-mentioned problems is combined with a computing device, and based on optimal stimulation position information, based on the head model, first arranging an electrode model at a position—the first position being a position in the direction of a normal vector of the optimal magnetic pole position information—and the electrode model located at the first position in the optimal magnetic pole position information It may be stored in a computer-readable recording medium in order to execute a step of gradually moving to a corresponding position and performing electrode attachment simulation on the head model.
  • treatment efficiency through non-invasive brain stimulation can be improved by providing information on optimal stimulation positions corresponding to each user.
  • FIG. 1 is a diagram illustrating an optimal magnetic pole position calculation system using an artificial intelligence model according to an embodiment of the present invention.
  • FIG. 2 is a hardware configuration diagram of a server for calculating optimal magnetic pole positions using an artificial intelligence model according to another embodiment of the present invention.
  • FIG. 3 is a flowchart exemplarily illustrating a method for calculating an optimal magnetic pole position using an artificial intelligence model according to another embodiment of the present invention.
  • FIG. 4 is a flowchart exemplarily illustrating a process of generating a head model based on user diagnostic information, in various embodiments.
  • FIG. 5 is a flowchart exemplarily illustrating a process of determining an optimum location in various embodiments.
  • FIG. 6 is a flowchart exemplarily illustrating a process of performing electrode attachment simulation in various embodiments.
  • FIG. 7 is an exemplary diagram illustrating a process of obtaining a spherical model based on an MRI image in various embodiments.
  • FIG. 8 shows an exemplary diagram for explaining a spherical model in various embodiments.
  • FIG. 9 is an exemplary view illustrating an electrode model and a spherical model in various embodiments.
  • FIG. 10 is an exemplary view illustrating an electrode placement simulation process in various embodiments.
  • FIG. 11 is an exemplary view illustrating a process of arranging candidate electrodes according to various embodiments.
  • unit or “module” used in the specification means a hardware component such as software, FPGA or ASIC, and "unit” or “module” performs certain roles. However, “unit” or “module” is not meant to be limited to software or hardware.
  • a “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors.
  • a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and “units” or “modules” may be combined into smaller numbers of components and “units” or “modules” or may be combined into additional components and “units” or “modules”. can be further separated.
  • spatially relative terms “below”, “beneath”, “lower”, “above”, “upper”, etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as “below” or “beneath” another component will be placed “above” the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.
  • a computer means any kind of hardware device including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment.
  • a computer may be understood as including a smartphone, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device, but is not limited thereto.
  • each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least a part of each step may be performed in different devices according to embodiments.
  • the optimal stimulation position calculation method using an artificial intelligence model provides information on the optimal stimulation position corresponding to each of a plurality of users in brain stimulation treatment through non-invasive brain stimulation. can do.
  • a head model may be generated based on user diagnosis information, and a spherical model corresponding to the head model may be generated and provided.
  • the spherical model may be a model in which coordinates are always moved on the user's scalp even in a process of continuously changing or moving coordinates. That is, by providing a spherical model corresponding to each user's head, convenience can be provided in the process of selecting an optimal stimulation position.
  • the present invention has an effect of maximizing brain stimulation efficiency by providing detailed visualization information related to electrode placement by providing simulations of attachment positions of electrodes of various shapes on a three-dimensional head model including a curved surface. there is.
  • the optimal magnetic pole position calculation system using an artificial intelligence model according to an embodiment of the present invention includes an optimal magnetic pole position calculation server 100, a user terminal 200, and an external server 300. ) may be included.
  • the optimal magnetic pole position calculation system using the artificial intelligence model shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added or changed as necessary. or can be deleted.
  • the optimal magnetic pole position calculation server 100 may generate a head model based on user diagnostic information.
  • the optimal magnetic pole position calculation server 100 may receive user diagnosis information of a user.
  • the user diagnosis information is information based on the generation of the head model, and may be medical image information about the user's brain or head image.
  • the user diagnostic information may be diagnostic information related to Magnetic Resonance Imaging (MRI). That is, the optimal stimulation position calculation server 100 may generate a head model related to the 3D brain map of the user based on the MRI image of the user.
  • MRI Magnetic Resonance Imaging
  • the optimal stimulation position calculation server 100 may obtain one or more brain region images by processing the user's MRI information as an input of a pre-learned classification model.
  • the pre-learned classification model may be an image region classification model that classifies one or more brain region images included in the MRI image of the user as an input.
  • the image region classification model may acquire one or more brain region images related to various physical characteristics of the brain through segmentation of the corresponding MRI image.
  • the pre-learned image region classification model (eg, artificial intelligence model) is composed of one or more network functions, and the one or more network functions may be composed of a set of interconnected computational units, which may be generally referred to as 'nodes'. there is. These 'nodes' may also be referred to as 'neurons'.
  • One or more network functions include at least one or more nodes. Nodes (or neurons) that make up one or more network functions can be interconnected by one or more 'links'.
  • one or more nodes connected through a link may form a relative relationship of an input node and an output node.
  • the concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa.
  • the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.
  • the value of the output node may be determined based on data input to the input node.
  • a node interconnecting an input node and an output node may have a weight.
  • the weight may be variable, and may be variable by a user or an algorithm in order to perform a function desired by the artificial intelligence model. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes.
  • An output node value may be determined based on the weight.
  • one or more nodes are interconnected through one or more links to form an input node and output node relationship in the AI model.
  • Characteristics of the artificial intelligence model may be determined according to the number of nodes and links in the artificial intelligence model, the relationship between the nodes and links, and the value of weight assigned to each link. For example, when there are two artificial intelligence models having the same number of nodes and links and different weight values between the links, the two artificial intelligence models may be recognized as different from each other.
  • Some of the nodes constituting the artificial intelligence model may constitute one layer based on distances from the first input node.
  • a set of nodes having a distance of n from the first input node may constitute n layers.
  • the distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node.
  • the definition of such a layer is arbitrary for explanation, and the order of a layer in an artificial intelligence model may be defined in a different way from the above.
  • a layer of nodes may be defined by a distance from a final output node.
  • An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the artificial intelligence model.
  • an artificial intelligence model network in a relationship between nodes based on a link, it may mean nodes that do not have other input nodes connected by a link.
  • the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the artificial intelligence model.
  • the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node.
  • An artificial intelligence model according to an embodiment of the present invention may have more nodes of an input layer than nodes of a hidden layer close to an output layer, and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer.
  • AI models can include one or more hidden layers.
  • a hidden node of a hidden layer may use outputs of previous layers and outputs of neighboring hidden nodes as inputs.
  • the number of hidden nodes for each hidden layer may be the same or different.
  • the number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes.
  • Input data input to the input layer may be operated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.
  • FCL fully connected layer
  • the artificial intelligence model may be supervised learning using MRI images related to a plurality of users and brain region classification information related to the MRI images as learning data.
  • MRI images related to a plurality of users
  • brain region classification information related to the MRI images as learning data.
  • various learning methods may be applied.
  • supervised learning is a method of generating learning data by labeling specific data and information related to the specific data, and learning using the labeling. It means learning from data.
  • the optimal stimulus position calculation server 100 may generate an image region classification model by performing learning on one or more network functions constituting the artificial intelligence model using the labeled learning data. For example, the optimal stimulus position calculation server 100 inputs each of the learning input data to one or more network functions, and each of the output data calculated by the one or more network functions and the learning output data corresponding to the label of each of the learning input data An error can be derived by comparing each. That is, in learning an image region classification model, training input data may be input to an input layer of one or more network functions, and training output data may be compared with outputs of one or more network functions.
  • the optimal magnetic pole position calculation server 100 may train an artificial intelligence model based on an error between an operation result of one or more network functions for learning input data and learning output data (labels).
  • the optimal magnetic pole position calculation server 100 may adjust the weights of one or more network functions based on the error in a backpropagation method. That is, the optimal stimulus position calculation server 100 may adjust the weight so that the output of one or more network functions is closer to the learning output data based on the error between the calculation result of the one or more network functions for the learning input data and the learning output data. there is.
  • the optimal magnetic pole position calculation server 100 may determine whether to stop learning using verification data when learning of one or more network functions is performed for a predetermined epoch or longer.
  • the predetermined epochs may be part of an overall learning target epoch.
  • Verification data may consist of at least a part of the labeled training data. That is, the optimal stimulus position calculation server 100 performs learning of the artificial intelligence model through the learning data, and after the learning of the artificial intelligence model is repeated at least a predetermined epoch, the learning effect of the artificial intelligence model is increased using the verification data. It may be determined whether or not the level is equal to or greater than a predetermined level.
  • the optimal stimulus position calculation server 100 performs learning with a target number of learning iterations of 10 using 100 learning data, after performing iterative learning 10 times, which is a predetermined epoch, 10 verification Iterative learning is performed three times using the data, and if the change in the output of the artificial intelligence model during the three iterative learning is less than a predetermined level, it is determined that further learning is meaningless and the learning can be terminated.
  • the verification data can be used to determine the completion of learning based on whether the effect of learning per epoch is greater than or less than a certain level in repeated learning of the artificial intelligence model.
  • the above-described number of learning data, verification data, and number of repetitions are only examples, and the present invention is not limited thereto.
  • the optimal magnetic pole position calculation server 100 may generate an artificial intelligence model by testing performance of one or more network functions using test data and determining whether to activate one or more network functions.
  • the test data may be used to verify the performance of the artificial intelligence model and may be composed of at least a part of training data. For example, 70% of the training data can be used for training of an AI model (i.e., learning to adjust weights to output similar results to labels), and 30% of the training data can be used to verify the performance of an AI model. It can be used as test data for
  • the optimum stimulus position calculation server 100 may determine whether to activate the artificial intelligence model according to whether the artificial intelligence model has a predetermined performance or more by inputting test data to the artificial intelligence model for which learning has been completed and measuring an error.
  • the optimal stimulus position calculation server 100 verifies the performance of the learned artificial intelligence model using test data for the learned artificial intelligence model, and if the performance of the learned artificial intelligence model is above a predetermined standard, the corresponding artificial intelligence model can be activated for use in other applications.
  • the optimal magnetic pole position calculation server 100 may disable and discard the AI model when the performance of the AI model that has been trained is below a predetermined standard.
  • the optimal stimulus position calculation server 100 may determine the performance of the generated artificial intelligence model based on factors such as accuracy, precision, and recall.
  • the performance evaluation criteria described above are only examples and the present invention is not limited thereto.
  • the optimal magnetic pole position calculation server 100 can independently train each artificial intelligence model to generate a plurality of artificial intelligence models, evaluate the performance, and evaluate the performance of the artificial intelligence model above a certain level. can use only However, it is not limited thereto.
  • the data structure may include a neural network.
  • the data structure including the neural network may be stored in a computer readable medium.
  • the data structure including the neural network may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, an activation function associated with each node or layer of the neural network, and a loss function for learning the neural network.
  • a data structure including a neural network may include any of the components described above.
  • the data structure including the neural network includes data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and loss function for training the neural network. It may be configured to include any combination of.
  • the data structure comprising the neural network may include any other information that determines the characteristics of the neural network.
  • the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above.
  • a computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium.
  • a neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons.
  • a neural network includes one or more nodes.
  • the optimal stimulation position calculation server 100 may be connected to the user terminal 200 through the network 400, and a head model associated with a 3D brain map corresponding to an MRI image using an artificial intelligence model. may be generated and provided, and information on an optimal stimulation position for transcranial direct current stimulation (tDCS) treatment on the user's actual head may be provided based on the corresponding head model.
  • the optimal stimulation position calculation server 100 may be a server that provides information about brain stimulation positions in relation to a single channel using two electrodes.
  • the network 400 may refer to a connection structure capable of exchanging information between nodes such as a plurality of terminals and servers.
  • the network 400 includes a local area network (LAN), a wide area network (WAN), a world wide web (WWW), a wired and wireless data communication network, a telephone network, a wired and wireless television communication network, and the like. do.
  • the wireless data communication networks are 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi (Wi-Fi) Fi), Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, A Near-Field Communication (NFC) network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, and the like are included, but are not limited thereto.
  • NFC Near-Field Communication
  • DMB Digital Multimedia Broadcasting
  • the user terminal 200 may be connected to the optimal magnetic pole position calculation server 100 through the network 400, and the user diagnosis information (eg, MRI image) is transmitted to the optimal magnetic pole position calculation server 100.
  • the user diagnosis information eg, MRI image
  • various types of information eg, head model related to a 3D brain map and information on optimal stimulation positions for tDCS procedure, etc.
  • head model related to a 3D brain map and information on optimal stimulation positions for tDCS procedure, etc. may be provided in response to the provided user diagnostic information.
  • the user terminal 200 is a wireless communication device that ensures portability and mobility, and includes navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System) , PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartphone ( It may include all types of handheld-based wireless communication devices such as Smartphone, Smartpad, Tablet PC, etc., but is not limited thereto.
  • PCS Personal Communication System
  • GSM Global System for Mobile communications
  • PDC Personal Digital Cellular
  • PHS Personal Handyphone System
  • PDA Personal Digital Assistant
  • IMT International Mobile Telecommunication
  • CDMA Code Division Multiple Access
  • W-CDMA Wide-Code Division Multiple Access
  • Wibro Wireless Broadband Internet
  • smartphone It may include all types of handheld-based wireless communication devices such as Smartphone, Smartpad, Tablet PC, etc.,
  • the user terminal 200 may be a terminal associated with a user who accesses the server 100 and seeks to obtain an optimal magnetic pole position based on user diagnostic information (eg, MRI image).
  • the user terminal 200 may transmit an MRI image taken or stored by the user terminal 200 to the server 100 .
  • Such a user terminal 200 may be, for example, a terminal associated with a specialist providing transcranial direct current stimulation to a patient based on MRI information.
  • the optimal stimulation position information received from the server 100 can be used as medical assistance information for clearly determining the position of the stimulation.
  • the user terminal 200 has a display, so it can receive a user's input and provide an arbitrary type of output to the user.
  • the user of the user terminal 200 is a medical expert, and may mean a doctor, nurse, clinical pathologist, medical imaging expert, etc., and may be a technician who repairs a medical device, but is not limited thereto.
  • the external server 300 may be connected to the optimal magnetic pole position calculation server 100 through the network 400, and a method in which the server 100 calculates the optimal magnetic pole position using an artificial intelligence model It is possible to provide various information/data required to perform the operation, or to receive, store, and manage result data derived from performing a method for calculating the optimal position of a magnetic pole using an artificial intelligence model.
  • the external server 100 may be a storage server provided separately outside the server 100, but is not limited thereto. Referring to FIG. 2, the hardware configuration of the server 100 that performs the optimal magnetic pole position calculation method using an artificial intelligence model will be described.
  • FIG. 2 is a hardware configuration diagram of an optimal magnetic pole position calculation server using an artificial intelligence model according to another embodiment of the present invention.
  • an optimal magnetic pole position calculation server 100 (hereinafter referred to as “server 100”) according to another embodiment of the present invention includes one or more processors 110 and a computer executed by the processor 110. It may include a memory 120 for loading the program 151 , a bus 130 , a communication interface 140 and a storage 150 for storing the computer program 151 .
  • server 100 includes one or more processors 110 and a computer executed by the processor 110. It may include a memory 120 for loading the program 151 , a bus 130 , a communication interface 140 and a storage 150 for storing the computer program 151 .
  • FIG. 2 only components related to the embodiment of the present invention are shown. Therefore, those skilled in the art to which the present invention pertains can know that other general-purpose components may be further included in addition to the components shown in FIG. 2 .
  • the processor 110 controls the overall operation of each component of the server 100.
  • the processor 110 includes a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art of the present invention. It can be.
  • CPU Central Processing Unit
  • MPU Micro Processor Unit
  • MCU Micro Controller Unit
  • GPU Graphic Processing Unit
  • the processor 110 may read a computer program stored in the memory 120 and process data for an artificial intelligence model according to an embodiment of the present invention. According to an embodiment of the present invention, the processor 110 may perform an operation for learning a neural network.
  • the processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed.
  • DL deep learning
  • a CPU, a GPGPU, and a TPU may process learning of a network function.
  • the CPU and GPGPU can process learning of network functions and data classification using network functions.
  • the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together.
  • a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU or TPU executable program.
  • network functions may be used interchangeably with artificial neural networks and neural networks.
  • a network function may include one or more neural networks, and in this case, an output of the network function may be an ensemble of outputs of one or more neural networks.
  • the processor 110 may read the computer program stored in the memory 120 and provide an image region classification model according to an embodiment of the present invention. According to an embodiment of the present disclosure, the processor 110 may classify the video image into one or more brain region images. According to an embodiment of the present invention, the processor 110 may perform calculations for training an image region classification model.
  • the processor 110 may normally process the overall operation of the server 100 .
  • the processor 110 provides or processes appropriate information or functions to a user or user terminal by processing signals, data, information, etc. input or output through the components described above or by running an application program stored in the memory 120. can do.
  • the processor 110 may perform an operation for at least one application or program for executing a method according to embodiments of the present invention
  • the server 100 may include one or more processors.
  • the processor 110 may temporarily and/or permanently store signals (or data) processed in the processor 110 (RAM: Random Access Memory, not shown) and ROM (ROM: Read -Only Memory, not shown) may be further included.
  • the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphics processing unit, RAM, and ROM.
  • SoC system on chip
  • Memory 120 stores various data, commands and/or information. Memory 120 may load computer program 151 from storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151.
  • the memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.
  • the bus 130 provides a communication function between components of the server 100 .
  • the bus 130 may be implemented in various types of buses such as an address bus, a data bus, and a control bus.
  • the communication interface 140 supports wired and wireless Internet communication of the server 100 . Also, the communication interface 140 may support various communication methods other than Internet communication. To this end, the communication interface 140 may include a communication module well known in the art. In some embodiments, communication interface 140 may be omitted.
  • the storage 150 may non-temporarily store the computer program 151 .
  • the storage 150 may store various information necessary to provide the optimal magnetic pole position calculating process using the artificial intelligence model. there is.
  • the storage 150 may be a non-volatile memory such as read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.
  • ROM read only memory
  • EPROM erasable programmable ROM
  • EEPROM electrically erasable programmable ROM
  • flash memory or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.
  • Computer program 151 may include one or more instructions that when loaded into memory 120 cause processor 110 to perform methods/operations in accordance with various embodiments of the invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present disclosure by executing the one or more instructions.
  • the computer program 151 may include generating a head model based on user diagnosis information, generating a spherical model based on the head model, and a plurality of spherical coordinates corresponding to each of a plurality of spherical coordinate data related to the spherical model.
  • Performing an optimal stimulation position calculation method using an artificial intelligence model including the step of identifying transcriptional coordinate data of and processing a plurality of transcriptional coordinate data as inputs of an optimal positioning model to obtain optimal stimulation position information It may contain one or more instructions to do so.
  • Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof.
  • a software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.
  • Components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium.
  • Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler).
  • Functional aspects may be implemented in an algorithm running on one or more processors.
  • FIGS. 3 to 11 a method of calculating an optimal magnetic pole position using an artificial intelligence model performed by the server 100 will be described.
  • FIG. 3 is a flowchart exemplarily illustrating a method for calculating an optimal magnetic pole position using an artificial intelligence model according to another embodiment of the present invention.
  • server 100 may obtain user diagnostic information.
  • the user diagnostic information is information that is a basis for generating the head model 330, and may include medical image information about the user's brain or head.
  • the user diagnosis information may be a magnetic resonance image (ie, an MRI image) obtained by capturing the head portion including the brain of the object. That is, the user diagnosis information in the present invention may mean an MRI image including not only the brain but also the skull and scalp of the object.
  • Acquisition of user diagnostic information may be receiving or loading user diagnostic information stored in the memory 120 . Also, obtaining user diagnostic information may be receiving or loading data from another storage medium, another server, or a separate processing module in the same server based on wired/wireless communication means.
  • the server 100 may generate a head model 330 based on user diagnosis information.
  • the head model may refer to a 3D brain map model related to the brain or head image of an actual user.
  • the server 100 may generate the head model 330 related to the 3D brain map model of the user based on the MRI image including the user's brain, skull, and scalp.
  • the server 100 may obtain one or more brain region images 320 by processing the user diagnostic information as an input of an image region classification model.
  • the image region classification model may be a CNN-based neural network model learned through learning data including learning input data related to a plurality of user diagnostic information and learning output data related to brain region classification information corresponding to each user diagnostic information. .
  • the server 100 takes the MRI image 310 as learning input data, and uses one or more brain region (ie, correct answer) images included in the corresponding MRI image 310 as learning output data, and Learning data may be generated by labeling the learning input data and the learning output data.
  • the server 100 may output learning result data by processing the learning input data as an input of one or more network functions.
  • result data may be information about one or more brain region images.
  • the server 100 may derive an error between learning result data output from one or more network functions and learning output data labeled in the learning input data.
  • the server 100 may perform learning on one or more network functions by backpropagating the corresponding error and adjusting the weight of the neural network so that the learning result data becomes similar to the learning output data.
  • an image region classification model may be generated. That is, the image region classification model may classify one or more brain region images in the user's MRI image through the above-described learning.
  • each of the one or more brain region images may be associated with various physical characteristics (eg, white matter, gray matter, cerebrospinal fluid, skull, scalp, etc.) of the brain.
  • the image region classification model may include one or more batch normalization layers, activation layers, and convolution layers, but is not limited thereto.
  • the image region classification model includes a horizontal pipeline composed of a plurality of blocks for extracting high-level features from low-level features of an MRI image, and a vertical pipe for performing segmentation by collecting features extracted from the horizontal pipeline. It may also be configured to perform segmentation of an MRI image of relatively low image quality including lines.
  • the server 100 may perform pre-processing on one or more brain region images 320 .
  • the preprocessing performed by the server 100 may mean preprocessing of removing noise from each brain region image.
  • the server 100 may perform pre-processing for noise removal and pre-processing for hole removal based on maximum chunks.
  • the server 100 may perform noise removal preprocessing based on the maximum chunk. Specifically, the server 100 may perform preprocessing of removing the remaining chunks except for the largest chunk (ie, the largest connected component) in each brain region image. For example, as a result of segmentation of an image region classification model, when several components exist in each image, it may mean that classification accuracy is low. When these low-accuracy classification images are used as a basis for generating a head model that is a three-dimensional mesh model, there is a concern that the accuracy of the generated head model may be somewhat lacking. That is, the server 100 may improve the accuracy of segmentation classification by performing preprocessing to remove the remaining small components except for the largest component on the image through preprocessing to remove noise based on the maximum chunk.
  • the server 100 may improve the accuracy of segmentation classification by performing preprocessing to remove the remaining small components except for the largest component on the image through preprocessing to remove noise based on the maximum chunk.
  • the server 100 may perform preprocessing related to hole removal. Specifically, the server 100 may perform preprocessing to remove holes in each brain region image, that is, to fill in the hole portion. For example, a hole present on each brain region image may be an error generated in a CNN-based segmentation process. Accordingly, the server 100 may improve the accuracy of segmentation classification by performing preprocessing to remove a hole, which is a type of segmentation error. has a contributing effect.
  • the server 100 may generate the head model 330 based on the preprocessed one or more brain region images 320 .
  • the server 100 may generate a 3D brain map based on properties of each of the brain region images classified through the image region classification model. That is, the server 100 may generate a 3D head model 330 composed of a plurality of grids based on each brain region image.
  • the server 100 may obtain information about physical characteristics related to each brain region.
  • the physical characteristics related to each brain region may include at least one of isotropic electrical conductivity and anisotropic electrical conductivity.
  • isotropic electrical conductivity can be obtained by assigning a known electrical conductivity through an experiment to each segmented region.
  • the server 100 may perform stimulation application simulation for applying stimulation to the head model 330 related to the 3D brain map.
  • the stimulation application simulation simulates transcranial direct current stimulation in which an electrode model 430 is placed on one side of the user's head, and electrical stimulation is applied to the user through the corresponding electrode model 430 to stimulate and treat a specific part of the brain. It may be for For example, when a specific electrical stimulus is applied to a point on the head, a propagation state of the specific electrical stimulus in the brain of the subject may be simulated.
  • the server 100 may use the head model 330 and the physical characteristics constituting the head model 330 to simulate a state in which electrical stimulation propagates in the user's brain.
  • electrical stimulation that may be applied to the user's head may include at least one of a magnetic field, an electric field, and a current.
  • a magnetic field is applied to the user's head, a current induced by the magnetic field may propagate to the user's brain.
  • the server 100 may obtain an optimal position for applying electrical stimulation based on the head model 330 of the user. For example, the server 100 identifies several pathways through which electrical stimulation is transmitted from the user's scalp to the stimulation target point through the head model 330, and selects the optimal location for applying electrical stimulation to the head among the corresponding pathways. can explore
  • the optimal position to apply the stimulation may be a point on the scalp where the electrical stimulation applied to the stimulation target point through the electrode model 430 is maximized.
  • the server 100 may perform stimulation application simulation while changing the coordinates little by little using one position as a reference point in order to search for an optimal position to apply the stimulation. As a result of the simulation at various positions, a point on the scalp where electrical stimulation applied to the stimulation target point is maximized can be determined as the optimal stimulation position.
  • the head model 330 is an unstandardized surface (ie, it is necessary to generate the head model 330). If the head of a specific user is an irregular surface), it may be difficult to continuously move the correct coordinates. Specifically, it may be difficult to move the user's head model on the scalp through changes based on the variables of the three-dimensional coordinates, that is, the x, y, and z axes, and thus, performing stimulation application simulation while changing the coordinates little by little Errors may occur in the process.
  • the corresponding position may not be a region on the scalp but a region inside the skull.
  • This may cause an error in determining the optimal stimulation position through stimulation application simulation, and the error may consequently reduce the efficiency of brain disease treatment through transcranial direct current stimulation.
  • the server 100 of the present invention can generate a spherical model 340 based on the user's 3D head model 330 so that the coordinates are always located on the scalp even if the coordinates are continuously changed or moved. . That is, the server 100 creates a spherical model 340 corresponding to the user's head model 330 and sequentially moves the test position based on spherical coordinate data that can be expressed in the spherical model 340, thereby stimulating It is possible to search for the optimal position to apply. In other words, the server 100 may enable continuous coordinate transformation in a stimulus application simulation process for searching for an optimal position of the magnetic pole through generation of the spherical model 340 .
  • the server 100 may generate the spherical model 340 based on the head model 330.
  • the spherical model 340 may be implemented through at least a part of a 3D sphere model having a specific radius.
  • the spherical model 340 may be a model that can be expressed through a plurality of spherical coordinate data.
  • the plurality of spherical coordinate data may be data related to spherical coordinates for theta and pi that can be expressed in a spherical model.
  • the server 100 may develop one or more spheres based on the central point of the head model 330 . Expanding one or more spheres may mean expanding spheres having different radii based on an arbitrary center point. Also, the server 100 may identify a first sphere having the smallest distance between the surface of the sphere and the surface of the head model 330 and the largest radius among one or more deployed spheres. In other words, the server 100 develops spheres having various radii based on an arbitrary center point of the head model 330, and among the developed spheres, the distance from the surface of the head model 330 is minimized, and the radius The first phrase, which is the largest, can be identified.
  • minimizing the distance between the surface of the sphere and the surface of the head model 330 means that the average of the distance difference between all points on the surface of the head model 330 and all points on the surface of the sphere corresponding thereto is minimized.
  • the first sphere has a minimum Root Mean Square Error (RMSE) difference between a point and a point related to the surface of the head model 330 (eg, a point corresponding to the upper hemisphere or a point corresponding to the entire area). At the same time as , it may mean a sphere having a maximum radius.
  • the server 100 may generate the spherical model 340 based on the identified first sphere.
  • the server 100 may identify a sphere having a radius that contacts the first point of the head model 330 inwardly. It may be characterized as a point on the head model 330 at which the radius is maximized based on the virtual center point.
  • the server 100 may generate a sphere model 340 based on the identified sphere. That is, the server 100 may contact a part of the surface of the head model 330 and generate the spherical model 340 based on a sphere having a maximum radius.
  • the server 100 identifies a sphere capable of enclosing the head model 330 and generates a sphere model 340 whose coordinates are always located on the scalp even when the coordinates are continuously changed or moved based on the radius of the sphere.
  • the server 100 may generate the spherical model 340 based on the user's head model 330 through a sphere having the smallest RMS error with the user's head.
  • the spherical model 340 may be implemented through at least a part of a sphere most closely fitted to the user's head.
  • the server 100 may obtain one or more brain region images 320 based on the corresponding MRI image 310.
  • a head model 330 may be generated based on the one or more brain region images 320, and a spherical model 340 may be generated based on the generated head model 330.
  • the spherical model 340 generated through the above process is implemented based on the 3D head model 330 generated in correspondence with the user's MRI image, and is implemented to surround the head model 330.
  • the corresponding head model 330 it is possible to identify movable coordinate data on the user's scalp, that is, on the scalp of the head model 330 .
  • the spherical model 340 as shown in FIG. 8 is generated, continuous coordinate transformation on the user's scalp is possible, and simulation for calculating the optimal position of the magnetic pole can be performed.
  • the spherical model 340 is used, as the variables of the existing coordinates are converted from three (eg, x, y, z) to two ( ⁇ , ⁇ ), continuously converting the coordinates for simulation Convenience can be improved in the process.
  • the server 100 may identify a plurality of transcriptional coordinate data corresponding to each of a plurality of spherical coordinate data related to the spherical model 340 .
  • the plurality of transferred coordinate data may be coordinates on the actual head model 330 transferred at each of a plurality of spherical coordinates. That is, the plurality of transcribed coordinate data may be data related to orthogonal coordinates that can be expressed on the head model 330 .
  • the server 100 identifies spherical coordinate data that can be moved on the scalp by the user even through continuous coordinate transformation through the spherical model 340, and relates to the coordinates transferred on the head model 330 based on the spherical coordinate data. Transfer coordinate data can be identified.
  • the server 100 may obtain optimal magnetic pole position information by processing a plurality of transcriptional coordinate data as an input of an optimal positioning model.
  • the server 100 identifies spherical coordinate data that can be moved on the scalp by the user even through continuous coordinate transformation through the spherical model 340, and optimizes the optimum based on the plurality of transcription data corresponding to the identified spherical coordinate data.
  • Stimulus position information can be obtained. That is, in the present invention, the point where the stimulation position is maximum based on continuous coordinate transformation in the head model 330 is not determined as the optimal stimulation position, but in the spherical model 340 corresponding to the head model 330.
  • Optimum stimulation position information may be information about an optimal position to apply electrical stimulation from the user's scalp to the stimulation target point, as a result of the simulation.
  • the optimal position for applying the electrical stimulation may be a point on the scalp where the electrical stimulation applied to the stimulation target point through the electrode model 430 is maximized.
  • the server 100 may obtain optimal magnetic pole position information based on a plurality of transcribed coordinate data corresponding to a plurality of spherical coordinate data identified through the spherical model 340 .
  • the server 100 may obtain optimal magnetic pole position information by processing a plurality of transcriptional coordinate data as an input of an optimal positioning model.
  • the optimal position determination model may be a model designed to perform an optimization algorithm that outputs optimal magnetic pole position information by taking a plurality of transcribed coordinate data as inputs.
  • the optimal positioning model of the present invention derives brain activity prediction information corresponding to each of a plurality of transcriptional coordinate data, and the difference between each of the derived brain activity prediction information and each reference brain activity corresponding to each of a plurality of transcriptional coordinate data
  • Optimal transfer coordinate data having a loss function related to a predetermined reference value or less may be identified, and optimal stimulation position information may be output based on the identified optimal transfer coordinate data. More specifically, an optimization algorithm performed by the optimal positioning model may be as shown in FIG. 5 .
  • the optimal positioning model may determine an electrode position based on at least one of a plurality of transfer coordinate data (S141).
  • electrode position determination is a region on the scalp for disposing the electrode model 430, and may be related to an initial electrode position.
  • the optimal positioning model may proceed with simulation with the determined electrode position. Specifically, the optimal positioning model may perform stimulation application simulation for applying stimulation to the head model 330 related to the 3D brain map based on the determined initial electrode position (S142). For example, when electrical stimulation is applied to a corresponding electrode location, a state in which a specific electrical stimulation propagates in the brain may be simulated.
  • the optimal positioning model may derive brain activity prediction information according to the simulation results described above (S143).
  • the brain activity prediction information is a value derived from a simulation result of applying electrical stimulation to the determined electrode position, and may include, for example, a value related to an electric field. That is, the brain activity prediction information may be prediction information related to an electric field value generated at a specific point in the brain when electrical stimulation is applied at the determined electrode position. For example, the greater the value of brain activity prediction information generated in response to electrical stimulation, the closer the optimal stimulation position may be.
  • the optimal positioning model may calculate a loss function related to a difference between brain activity prediction information and reference brain activity information.
  • the reference brain activity information is related to an electric field value at a desired point, and may be, for example, a value arbitrarily set by a user.
  • the optimal positioning model may determine whether an end condition is satisfied based on a loss function related to a difference between the derived brain activity prediction information corresponding to the determined point and the reference brain activity information (S145).
  • the optimal positioning model may determine whether an end condition is satisfied based on whether the loss function is equal to or less than a predetermined reference value.
  • the optimal positioning model determines that the end condition is not satisfied and determines a new electrode position as the difference between the brain activity prediction information and the reference brain activity information is large.
  • the spherical coordinate data on the spherical model 340 is moved, and the new electrode position can be determined based on the transferred coordinate data corresponding to the moved spherical coordinate data.
  • the optimal positioning model determines that the end condition is satisfied and determines the position as the optimal electrode position (S146).
  • optimal stimulus position information related to a position where the optimal stimulus is expected to be applied among a plurality of transcriptional coordinate data is obtained. It can be. Accordingly, since optimal stimulation position information with improved accuracy can be obtained through stimulation application simulation, when using transcranial direct current stimulation for the treatment of brain diseases, it is possible to provide high-quality information corresponding to each head for various users. This creates an effect of providing a customized guide, such as assisting in medical procedures, and as a result, it can improve the efficiency of brain disease treatment.
  • the server 100 may determine whether at least two or more spherical coordinate data among a plurality of spherical coordinate data overlap and indicate a point of the head model 330 .
  • the spherical coordinate data may be coordinate data that can be expressed in the spherical model 340 corresponding to the head model 330 so that it can be moved on the user's head even with continuous coordinate transformation. That is, the spherical coordinate data may be spherical coordinate data including the spherical model 340 formed to wrap the inside of the head model 330, and may be displayed based on the spherical coordinate system.
  • each spherical coordinate on the user's head Points indicated by the data may overlap with each other. In other words, they may be different points on the spherical coordinate system, but the transferred positions on the scalp may overlap.
  • efficiency may be reduced in the process of searching for the optimal stimulation position. For example, it is possible to determine whether the end condition is satisfied based on the loss function at the first position, and if the end condition is not satisfied, the second position, which is another point (or position) on the head, is searched for, and the corresponding A stimulation application simulation may be performed at the second location.
  • the first position and the second position may be different points on the spherical coordinate system, but the positions transferred on the scalp may overlap.
  • the efficiency of simulation for obtaining an optimal stimulation position may be reduced.
  • the server 100 determines whether at least two or more spherical coordinate data among a plurality of coordinate data indicate overlapping points of the head model 330, and overlaps the points. At least some of the spherical coordinate data determined to be indicated may be corrected by doing so.
  • the server 100 may determine whether or not at least two or more spherical coordinate data among a plurality of spherical coordinate data indicate overlapping points of the head model 330 based on a plurality of transcriptional coordinate data.
  • the plurality of transferred coordinate data may be coordinates on the actual head model 330 transferred at each of a plurality of spherical coordinates.
  • the plurality of transfer coordinate data may be data related to orthogonal coordinates that can be expressed on the head model 330 . That is, when at least one of the plurality of transferred coordinate data transferred to the head model 330 is matched with two or more spherical coordinate data based on each of the plurality of spherical coordinate data, the server 100 may perform at least two or more transfer coordinate data. It can be determined that the spherical coordinate data points to the same point of the head model 330 .
  • the server 100 indicates that at least two or more spherical coordinate data overlap one point of the head model 330. can be identified as
  • some of the at least two or more spherical coordinate data may be modified.
  • the server 100 may remove at least one spherical coordinate data among at least two or more spherical coordinate data indicating overlapping points on the head model 330 .
  • the server 100 may remove the first spherical coordinate data.
  • the specific description of the above-described transfer coordinate data and spherical coordinate data is only an example, and the present disclosure is not limited thereto.
  • the server 100 removes at least one of the spherical coordinate data corresponding to the same point on the head model 330, thereby performing simulation corresponding to only one spherical coordinate data in the simulation process of acquiring the optimal magnetic pole position. As a result, the overall simulation efficiency can be maximized.
  • the server 100 may correct the coordinates of at least one spherical coordinate data among at least two or more spherical coordinate data indicating overlapping points on the head model 330 .
  • the server 100 adjusts theta or pi of the first spherical coordinate data to obtain the spherical coordinate data.
  • the server 100 adjusts at least one of the spherical coordinate data corresponding to the same point on the head model 330 so as to correct the coordinates so that no overlapping position occurs during the simulation process for obtaining the optimal position of the magnetic pole. Overall simulation efficiency can be maximized.
  • the server 100 may perform electrode attachment simulation in which the electrode model 430 is attached to the head model 330 .
  • the electrode attachment simulation may provide information about a process in which the electrode model 430 contacts or attaches to the head model 330 .
  • visualization information related to how to attach an electrode patch on the user's actual scalp may be provided through the corresponding electrode attachment simulation.
  • the electrode model 430 of the present invention is a three-dimensional model of an electrode patch attached to the scalp of an actual user, and includes a first surface 432 contactable with the head model 330 and a corresponding A second surface 431 corresponding to the first surface 432 may be included.
  • coordinates forming each of the first surface 432 and the second surface 431 may have directivity in the same direction. That is, when the first surface 432 of the electrode model 430 is moved in one direction during the simulation process, the second surface 431 may also be moved in the one direction.
  • the server 100 may arrange the electrode model at the first position corresponding to the optimal stimulation position information.
  • the first position may be a position in the direction of the normal vector (or normal vector) 410 of the optimal magnetic pole position information. That is, the server 100 may place the electrode model 430 at a first position in the direction of the normal vector 410 outside the head from a point on the scalp predicted to apply the optimal stimulus.
  • the electrode model 430 may be positioned at a point (ie, a first position) away from a target point (ie, an optimal stimulation position) in a normal vector direction.
  • the server 100 may gradually move the electrode model 430 located at the first position to a position corresponding to the optimal stimulation position information to perform electrode attachment simulation on the head model 330.
  • the server 100 may stop the movement of the electrode model 430 when the first surface 432 of the electrode model 430 and the head model 330 come into contact.
  • the first surface 432 of the electrode model 430 comes into contact with a point (eg, an optimal stimulation position) of the head model 330, the movement of the electrode model 430 may be stopped.
  • the server 100 may calculate a movement distance of each of a plurality of first coordinates related to the first surface 432 of the electrode model 430 in which movement is stopped. For example, as the electrode model 430 is moved from the first position to a point on the head model, a plurality of first coordinates constituting the first surface (ie, bottom surface) of the corresponding electrode model 430 may be changed. . The server 100 may calculate the movement distance of these first coordinates.
  • the server 100 may move each of the plurality of second coordinates related to the second surface 431 based on the movement distance of each of the plurality of first coordinates. For example, as the electrode model 430 moves from the first position to a point on the head model 330, the plurality of first coordinates constituting the first surface 432 of the corresponding electrode model 430 move in one direction. When moved by '3', the plurality of second coordinates constituting the second surface 431 may also be moved by '3' in the corresponding one direction.
  • the specific numerical description of the movement distance of each surface in the above example is only an example to help understanding of the present disclosure, and the present disclosure is not limited thereto.
  • the server 100 may match each of a plurality of first coordinates with each of a plurality of second coordinates. For example, each of a plurality of first coordinates constituting the first surface 432 and each of a plurality of second coordinates constituting the second surface 431 may be matched to each other based on their relative positions. For a more specific example, the server 100 may match each of a plurality of first coordinates with each of a plurality of second coordinates parallel to each coordinate. That is, coordinates matched with each other may be coordinates located at the shortest distance on a straight line. The server 100 may identify a movement distance of each of the plurality of first coordinates. In addition, the server 100 may move each of the plurality of second coordinates matched to each of the plurality of first coordinates based on the movement distance of each of the plurality of first coordinates.
  • the server 100 may move the second surface 431 by moving each of the second coordinates matched to the first coordinates based on the movement distance of each of the plurality of first coordinates constituting the first surface. there is.
  • the first surface 432 and the second surface 431 may be equally moved.
  • the second coordinates corresponding to the first coordinates move by the distance that the first coordinates constituting the first surface 432 of the electrode model 430 move
  • the second surface 431 moves along the first surface 431. It moves as much as the face 432 is moved.
  • the server 100 may perform an electrode attachment simulation for a process in which the electrode model 430 contacts or attaches to the head model 330 .
  • Visualization information related to how to attach an electrode patch to a region on the user's actual scalp ie, an optimal stimulation position
  • the user may acquire visualization information related to medical assistance through electrode attachment simulation.
  • the server 100 may perform a candidate electrode attachment simulation in which the candidate electrode model 430 is attached based on the electrode model 430 in contact with the head model 330 .
  • the candidate electrode model 430 may be characterized in that it is attached to the head model 330 within a predetermined separation distance from the electrode model 430 .
  • additional electrode patches ie, candidate electrodes
  • the additional electrode patches should be arranged to face the same direction as the electrode patch attached to the head within a limited radius.
  • each electrode patch may be difficult to arrange in the same direction on the scalp, which is an irregular surface.
  • it is easy to define 0 degree as a direction parallel to a specific axis but in the case of three-dimensional coordinates related to x, y, z, 0 degree is defined based on a specific axis It can be difficult to do.
  • the scalp surface is uneven, it may be difficult to attach the additional electrode patch on the scalp surface in a direction consistent with the electrode patch, since the direction may be distorted with a slight change in position.
  • the server 100 may locate the candidate electrode model 430 based on the attachment position of the electrode model 430 . That is, the server 100 may position the candidate electrode model 430 in relation to the attachment position of the electrode model 430 on the head model 330 . For example, the server 100 may locate the candidate electrode model 430 within a predetermined distance from the electrode model 430 .
  • the server 100 may determine the attachment direction of the candidate electrode model 430 based on the attachment shape of the electrode model 430 .
  • the electrode model 430 may be implemented as a hexahedron having a thin thickness.
  • the server 100 may determine the attachment direction of the candidate electrode model 430 based on the direction in which the longest side of the electrode model 430 is formed in the electrode model 430 attached to the head model 330 .
  • the attachment direction of the candidate electrode model 430 may be determined so that the longest side of the electrode model 430 and the longest side of the candidate electrode model 430 are close to parallel.
  • the case in which the first and second surfaces are rectangular is specifically described as an example, but the present disclosure is not limited thereto.
  • the server 100 identifies the attachment direction of the electrode model 430 on the head model 330 and attaches the candidate electrode model 430 based on the identified attachment direction, so that the candidate electrode model is the electrode model 430. ) and can be arranged in a uniform direction.
  • the server 100 may sequentially change the orientation angle of the candidate electrode model 430 and obtain a plurality of direction vectors according to the orientation angle change.
  • the orientation angle may be related to rotation about a point of the head model 330 that is in contact with the candidate electrode model 430 as a central axis when the candidate electrode model 430 contacts the surface of the head model 330.
  • the server 100 may change the orientation angle of the candidate electrode model 430 as shown in FIG. 11 .
  • the candidate electrode model rotates (430a-) on the surface of the head model 330, as shown in FIG. 11(b). 1) It can be.
  • the server 100 may sequentially change the orientation angle of the candidate electrode model 430 from 0 degree to 360 degrees, and may obtain each of a plurality of direction vectors according to each angle change. For example, when the orientation angle of the candidate electrode model 430 is changed to 90 degrees, the server 100 may obtain a direction vector -1 related thereto, and when the orientation angle of the candidate electrode model 430 is changed to 180 degrees. , the server 100 may obtain direction vector-2 related thereto. In this case, direction vector-1 and direction vector-2 may be different from each other.
  • the server 100 may determine the attachment direction of the candidate electrode model 430 on the head model 330 based on a comparison between the obtained plurality of direction vectors and the first direction vector of the electrode model 430 . Specifically, the server 100 may sequentially change the orientation angle from 0 degrees to 360 degrees, obtain each of a plurality of direction vectors according to each angle change, and obtain a plurality of direction vectors of the electrode model 430 among the plurality of direction vectors. A vector most similar to the first direction vector may be identified. In addition, the server 100 may determine the attachment direction of the candidate electrode model 430 based on an orientation angle corresponding to a vector most similar to the first direction vector of the electrode model 430 .
  • the server 100 may obtain a plurality of direction vectors related to the candidate electrode model 430 through a change in orientation angle, and a direction most similar to the first direction vector of the electrode model 430 among the plurality of direction vectors.
  • An attachment direction of the candidate electrode model 430 may be determined based on an orientation angle angle having a vector.
  • the candidate electrode model 430 may be disposed in a uniform direction with the electrode model 430 .
  • the server 100 may arrange the electrode models 430 and the candidate electrode models 430 at uniform intervals and in a uniform direction on the uneven scalp surface.
  • the optimal magnetic pole position information may include one or more optimal magnetic pole position sub information.
  • one or more optimal magnetic pole position sub-information may be output through an optimal position determination model. That is, according to embodiments, there may be several locations on the scalp where electrical stimulation applied to the stimulation target point is maximized. In other words, when one or more stimuli are attached to each of the one or more optimal stimulation position sub-information to apply the stimuli, the efficiency of stimulation transmission can be further improved.
  • the server 100 may perform one or more electrode attachment simulations on the head model 330 in correspondence with each of one or more optimal stimulation position sub-information.
  • Each electrode attachment simulation may be intended to provide information about a process in which each electrode model is contacted or attached to the head model 330 in correspondence with each optimal stimulation position sub-information. For example, visualization information related to how to attach each electrode patch to various regions of the user's actual scalp may be provided through each electrode attachment simulation.
  • the server 100 may identify whether at least one electrode model 430 overlaps on the head model 330 as a result of one or more electrode attachment simulations. For example, one or more electrode models may be overlapped on the head model 330 according to the arrangement size of each electrode model or one or more optimal stimulation position sub-information. Also, the server 100 may modify at least one piece of optimal stimulation position sub-information based on the identified overlapping electrode model. In one embodiment, the server 100 may modify the shape of at least one electrode model based on the identified overlapping electrode model.
  • electrode models overlapping each other may occur in a process of simulating one or more electrode attachments due to the effect of the arrangement size of each electrode model or the distance between optimal positions.
  • the server 100 may modify the optimal stimulation position sub-information so that at least one electrode model among the overlapped electrode models is disposed at a different position.
  • the server 100 may change the shape of at least one electrode model among the overlapping electrode models (eg, reduce the size or change the shape so that they do not overlap each other).
  • the server 100 may perform one or more electrode attachment simulations related to the process of attaching one or more electrode models to the head model 330 .
  • Visualization information related to how to attach each of the electrode patches to various regions on the user's actual scalp ie, each of the optimal stimulation positions
  • the server 100 may predict the possibility of overlapping between electrode models, and when overlapping is expected, provide information on different optimal stimulation positions or change the shape of the electrode models so that interference between electrodes does not occur. there is. Accordingly, the user may obtain information related to a countermeasure for an interference situation that may occur in a process of attaching a plurality of electrode patches.
  • the optimal magnetic pole position calculation method using the above-described artificial intelligence model has been described with reference to the flowchart shown in the drawing.
  • the optimal magnetic pole position calculation method using an artificial intelligence model has been illustrated and described as a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are shown and described herein. It may be performed in a different order or concurrently.
  • new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed.

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Abstract

L'invention concerne un procédé de calcul d'une position optimale de stimulation à l'aide d'un modèle d'intelligence artificielle, un serveur, et un programme informatique. Un procédé de calcul d'une position optimale de stimulation à l'aide d'un modèle d'intelligence artificielle selon divers modes de réalisation de la présente invention est réalisé par un dispositif informatique, et peut comporter les étapes consistant à: générer un modèle de forme de tête d'après des informations de diagnostic d'utilisateur; générer un modèle sphérique d'après le modèle de forme de tête; identifier une pluralité d'éléments de données de coordonnées de transfert correspondant respectivement à une pluralité d'éléments de données de coordonnées sphériques liées au modèle sphérique; et acquérir des informations de position optimale de stimulation en traitant la pluralité d'éléments de données de coordonnées de transfert comme des entrées dans un modèle de détermination de position optimale, la pluralité d'éléments de données de coordonnées de transfert étant des données liées à des coordonnées cartésiennes qui peuvent être représentées sur le modèle de forme de tête.
PCT/KR2021/018145 2021-07-07 2021-12-02 Procédé de calcul de position optimale de stimulation à l'aide d'un modèle d'intelligence artificielle, procédé de simulation de modèle d'électrode, serveur, et programme informatique WO2023282405A1 (fr)

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Application Number Priority Date Filing Date Title
KR10-2021-0089051 2021-07-07
KR1020210089051A KR102373759B1 (ko) 2021-07-07 2021-07-07 전극 모델 시뮬레이션 방법, 서버 및 컴퓨터프로그램
KR1020210089050A KR102373758B1 (ko) 2021-07-07 2021-07-07 인공지능 모델을 활용한 최적의 자극 위치 산출 방법, 서버 및 컴퓨터프로그램
KR10-2021-0089050 2021-07-07

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CN115830105A (zh) * 2023-02-14 2023-03-21 华科精准（北京）医疗科技有限公司 一种电极片的电极定位装置及脑电监测导航系统

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