WO2023113272A1 - Procédé pour fournir des informations concernant un traitement endodontique et dispositif pour fournir des informations concernant un traitement endodontique à l'aide de celui-ci - Google Patents

Procédé pour fournir des informations concernant un traitement endodontique et dispositif pour fournir des informations concernant un traitement endodontique à l'aide de celui-ci Download PDF

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WO2023113272A1
WO2023113272A1 PCT/KR2022/018393 KR2022018393W WO2023113272A1 WO 2023113272 A1 WO2023113272 A1 WO 2023113272A1 KR 2022018393 W KR2022018393 W KR 2022018393W WO 2023113272 A1 WO2023113272 A1 WO 2023113272A1
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image
dental medical
heat map
root canal
medical image
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PCT/KR2022/018393
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English (en)
Korean (ko)
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김의성
이정훈
서현석
이예슬
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연세대학교 산학협력단
한국과학기술연구원
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Publication of WO2023113272A1 publication Critical patent/WO2023113272A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/51—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for dentistry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • 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
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30036—Dental; Teeth

Definitions

  • the present invention relates to a method for providing information on root canal treatment and a device for providing information on root canal treatment using the same.
  • root canal treatment In general, if the dental pulp inside the tooth is infected by bacteria or inflamed by harmful stimuli, completely remove the contaminated tissue and pulp in the root canal, wash it, and fill it with a filling material to prevent bacteria from entering the root canal, thereby preventing the bacteria from entering the root canal. to keep teeth healthy.
  • This treatment method is referred to as root canal treatment or root canal treatment.
  • the information providing system predicts (segments) a tooth area within an input dental medical image by utilizing an artificial neural network-based network, or a treatment area within a tooth (eg, gutta percha, prosthesis, etc.), furthermore, a tooth My disease can be predicted.
  • the researchers further applied image preprocessing procedures to the system so that the artificial neural network-based network can predict a specific area (eg, tooth area, treatment area, or lesion area) with higher accuracy.
  • a specific area eg, tooth area, treatment area, or lesion area
  • Such an image segmentation-based information providing system may have excellent segmentation performance for a specific region within an image as preprocessed dental medical images are used for region segmentation.
  • the inventors of the present invention use the original dental medical image together with the preprocessed dental medical image to improve not only the accuracy of prediction of a specific region but also the accuracy of prediction of the prognosis of endodontic treatment. I was aware that it could be.
  • CAM Class-activation map
  • the inventors of the present invention apply CAM images to predict the prognosis of root canal treatment, just as specialists consider clinical features such as temporary restoration materials, absence of adjacent teeth, and fistulas in the treatment planning stage for actual root canal treatment. It could be recognized that clinical features can reinforce the predictions of artificial neural networks.
  • the inventors of the present invention could expect that the prognosis prediction performance of endodontic treatment can be improved even when learning data is insufficient in learning an artificial neural network by applying CAM images.
  • the inventors of the present invention could expect that the new endodontic treatment information provision system would enable specialists to establish accurate diagnosis and treatment plans by referring to the diagnosis probability determined by the predictive model regardless of skill level.
  • an object to be solved by the present invention is to provide a method for providing information on root canal treatment configured to obtain a CAM image from a received dental medical image and determine whether or not the root canal treatment is successful for an individual based on the obtained CAM image, and a device using the same.
  • the method of providing information is a method of providing information about endodontic treatment implemented by a processor, comprising the steps of receiving a dental medical image of an object, class-activation map (CAM) image based on clinical features of the dental medical image Generating a CAM image based on a dental medical image by using a CAM generation model configured to generate a dental medical image, using a predictive model configured to predict a prognosis of endodontic treatment using the dental medical image and the CAM image as inputs, and determining whether endodontic treatment of the subject is successful based on the image and the CAM image.
  • CAM class-activation map
  • the information providing method may further include, after the receiving, pre-processing of the dental medical image to obtain a pre-processed image.
  • the step of generating the CAM image includes the step of generating the CAM image based on the preprocessed image and the dental medical image using a CAM generation model, and the step of determining whether root canal treatment is successful uses a predictive model.
  • determining whether or not root canal treatment of the object was successful based on the CAM image, the preprocessing image, and the dental medical image may be included.
  • performing pre-processing may include performing intensity-based clustering on dental medical images to obtain a pre-processed image.
  • the performing of preprocessing may include converting a grayscale value of a dental medical image to a predetermined level so as to obtain a preprocessed image.
  • performing preprocessing may include performing CLAHE (Contrast Limited Adaptive Histogram Equalization) on a dental medical image to obtain a preprocessed image.
  • CLAHE Contrast Limited Adaptive Histogram Equalization
  • the CAM generation model is composed of at least a part of the predictive model
  • the acquiring of the CAM image includes processing for predicting the prognosis of endodontic treatment for the dental medical image and the preprocessed image using the predictive model Among them, a step of acquiring a CAM image as a feature based on a clinical feature may be included.
  • clinical features include full coverage restoration (FV), absence of proximal teeth (PrX), coronal defect (CoD), and residual root (Root). Rest; RR), Temporary restoration (TM), Canal visibility (CV), Previous filling (PF), Periapical radiolucency (PAR), Sinus tract (ST) and a root post (PS).
  • FV full coverage restoration
  • PrX absence of proximal teeth
  • CoD coronal defect
  • Root residual root
  • Rest RR
  • Temporary restoration Temporary restoration
  • TM Canal visibility
  • CV Previous filling
  • PF Periapical radiolucency
  • ST Sinus tract
  • PS root post
  • the clinical features may include a combination of at least two of a full length prosthesis, proximal absence of adjacent teeth, root canal visibility of a crown defect, an existing filling, an apical radiographic image, and a fistula.
  • the predictive model includes a self-attention layer and a plurality of feature extraction layers, and the self-attention layer may exist between the plurality of feature extraction layers.
  • the information providing method includes, after the step of generating the CAM image, generating a feature emphasis image including a background region and a feature region based on the activity of the CAM image,
  • the determining whether treatment is successful may further include determining whether root canal treatment is successful for the object based on the dental medical image and the feature-enhanced image by using a predictive model.
  • the dental medical image includes a target tooth region
  • the information providing method crops the target tooth region in the dental medical image to obtain the target tooth image after the receiving step.
  • the step of generating the CAM image may include generating the CAM image based on the image of the target tooth by using the CAM generation model.
  • the predictive model may be configured to output a success or failure of endodontic treatment within a predetermined period by taking dental medical images, preprocessing images, and CAM images as inputs.
  • the device includes a communication unit configured to receive a dental medical image of an object, and a processor connected to communicate with the communication unit.
  • the processor generates a CAM image based on the dental medical image using a CAM generation model configured to generate a class-activation map (CAM) image based on clinical features of the dental medical image, and
  • CAM class-activation map
  • a predictive model configured to predict a prognosis of endodontic treatment using the medical image and the CAM image as inputs is configured to determine whether or not the object's endodontic treatment is successful based on the dental medical image and the CAM image.
  • a processor performs pre-processing on a dental medical image to obtain a pre-processed image, generates a CAM image based on the pre-processed image and the dental medical image using a CAM generation model, and generates a predictive model Using , it may be configured to determine whether the root canal treatment is successful for the object based on the CAM image, the preprocessing image, and the dental medical image.
  • the processor may be further configured to perform intensity-based clustering on dental medical images to obtain a pre-processed image.
  • the processor may be further configured to convert a grayscale value for a dental medical image to a predetermined level, so as to obtain a preprocessed image.
  • the processor may be further configured to perform Contrast Limited Adaptive Histogram Equalization (CLAHE) on a dental medical image, so as to obtain a preprocessed image.
  • CLAHE Contrast Limited Adaptive Histogram Equalization
  • the CAM generation model is composed of at least a part of the predictive model, and the processor, using the predictive model, during processing for predicting the endodontic treatment prognosis for the dental medical image and the preprocessed image, clinical features It may be further configured to acquire a CAM image as a feature on the basis of.
  • the processor generates a feature-enhanced image including a background region and a feature region based on the activity of the CAM image, and uses a predictive model to determine the root canal of the object based on the dental medical image and the feature-enhanced image. It may be further configured to determine the success of the treatment.
  • the processor crops a target tooth region in a dental medical image to obtain a target tooth image, and generates a CAM image based on the target tooth image using a CAM generation model. More can be configured.
  • the present invention provides a system for providing information on root canal treatment using a predictive model configured to predict the success of root canal treatment using dental medical images, CAM images, and furthermore, preprocessed images, thereby providing information for analyzing root canal treatment of a subject. There is an effect that can provide.
  • the present invention can overcome the limitations of the conventional information providing system capable of only predicting a specific region of a tooth by using only preprocessed medical images, and can determine the success of root canal treatment with higher reliability. can provide information.
  • the present invention can provide a predictive model with excellent performance in predicting the prognosis of endodontic treatment even when learning data is insufficient in learning an artificial neural network.
  • the present invention provides a system for providing information on endodontic treatment based on a predictive model, so that a specialist can provide information to establish an accurate diagnosis and treatment plan by referring to the diagnosis probability determined by the predictive model regardless of skill level.
  • the present invention provides a system for providing information on root canal treatment based on various predictive models, thereby overcoming errors in diagnosis according to the proficiency of medical staff and low reliability of diagnosis results, and providing an accurate root canal treatment for a subject. It may be possible to establish a treatment plan prior to treatment.
  • FIG. 1 illustrates a system for providing information on root canal treatment using a device for providing information on root canal treatment according to an embodiment of the present invention.
  • FIG. 2A illustrates a configuration of a device for providing information on root canal treatment according to an embodiment of the present invention.
  • FIG. 2B illustratively illustrates the configuration of a medical staff device receiving information on root canal treatment according to an embodiment of the present invention.
  • FIG 3 illustrates a procedure of a method for providing information on root canal treatment according to an embodiment of the present invention.
  • 4A and 4B exemplarily illustrate procedures of a method for providing information on root canal treatment according to an embodiment of the present invention.
  • FIG. 5 illustratively illustrates the structure of a predictive model used in a method for providing information on root canal treatment according to an embodiment of the present invention.
  • 6A to 6C illustrate CAM images of clinical features used to predict success or failure of root canal treatment in the method for providing information on root canal treatment according to an embodiment of the present invention.
  • 7A to 7D illustrate evaluation results for prediction of endodontic treatment according to a combination of clinical characteristics in a method for providing information on endodontic treatment according to an embodiment of the present invention.
  • 9a and 9b illustrate evaluation results of a predictive model including a self-attention layer in a method for providing information on root canal treatment according to an embodiment of the present invention.
  • the term "dental medical image” may refer to an oral cavity image of a subject received from a medical imaging diagnosis apparatus.
  • the dental medical image disclosed herein may be a dental radiograph image, but is not limited thereto.
  • the dental medical image may be a 2D image, a 3D image, a still image of one cut, or a video composed of a plurality of cuts.
  • the success or failure of root canal treatment for each of the plurality of dental medical images can be predicted according to the method for providing information on root canal treatment according to an embodiment of the present invention. can
  • the dental medical image includes a tooth region of the upper jaw teeth for incisors, canines, premolars, and molars, and/or a target tooth region for at least one of the incisors, canines, molars, and molars of the lower jaw teeth. can do.
  • preprocessed image refers to image parameters such as background noise deletion, grayscale conversion, and image contrast control for a dental medical image, particularly a target tooth image in which a target tooth region is cropped. It may mean an image on which transformation has been performed.
  • the term “explainable heatmap image” may refer to an image in which activity is visually distinguishable.
  • the heat map image may be a class-activation map (CAM) image.
  • the "CAM (Class-activation map) image” is based on the weight determined in the calculation process of convolution and global average pooling in the classification process of the CNN (Convolutional Neural Network). It may be an image generated by At this time, it may be possible to check which part the predictive model focuses on checking through the CAM image.
  • a CAM image may be an image generated based on at least one clinical feature.
  • clinical characteristics may refer to dental clinical characteristics associated with prediction of endodontic treatment.
  • the clinical characteristics are full coverage restoration (FV), absence of proximal teeth (PrX), coronal defect (CoD), root rest (RR), temporary restoration ( Temporary restoration (TM), canal visibility (CV), previous filling (PF), periapical radiolucency (PAR), sinus tract (ST) and root post (Post; PS) It may include at least one or a combination of two or more selected from among them.
  • feature-enhanced image may refer to an image in which a CAM image is converted to have a background region or a feature region based on the activity of the CAM image.
  • a feature-enhanced image is an image converted to be distinguished as 1 (feature region) when the activity is 100 or more and 0 (background region) when the activity is 100 or less based on a predetermined threshold value of 100 for a CAM image.
  • the feature-enhanced image may be a masking image of a clinical feature region in a dental medical image.
  • heat map generation model may be a model learned to generate a heat map image based on clinical features of a dental medical image.
  • the “CAM image generation model” may be a model trained to generate a CAM image based on clinical features of dental medical images.
  • the CAM image generation model may be a model learned to determine (presence or absence) by learning clinical features associated with prediction of endodontic treatment with respect to dental medical images (and/or pre-processed images).
  • the CAM image generation model may include two input channels that take a dental medical image and a preprocessed image as inputs, and two output channels that output whether or not a clinical feature exists.
  • the CAM image may be acquired in the process of predicting the presence or absence of clinical features in the dental medical image by the CAM image generation model.
  • the CAM image generation model is full coverage restoration (FV), absence of proximal teeth (PrX), coronal defect (CoD), root rest (RR), temporary Temporary restoration (TM), canal visibility (CV), previous filling (PF), periapical radiolucency (PAR), sinus tract (ST) and root post (Post; PS) It may be a plurality of models learned to output whether or not each clinical feature is present.
  • the CAM image generation model may be a model obtained by learning a combination of two or more clinical features as one mask.
  • the CAM image generation model may consist of a partial network structure of a predictive model to be described later.
  • prediction model may be a model learned to output success or failure of endodontic treatment by taking a dental medical image, a CAM image, and optionally a preprocessing image as inputs.
  • the prediction model includes three input channels of an original dental medical image (or a cropped target tooth image), a preprocessing image, and a CAM image (particularly, a feature-enhanced image in which clinical features are masked) and a predetermined period of time. It can be configured to have two output channels of endodontic treatment success within or root canal treatment failure within a predetermined period of time.
  • the "predetermined period” may be 1 month to 10 years after root canal treatment, but is not limited thereto.
  • the predictive model may be configured to have two input channels of a dental medical image and a CAM image, and two output channels of success or failure of endodontic treatment.
  • the predictive model may be a network equipped with a self-attention layer.
  • prediction model may be used interchangeably with “network according to an embodiment of the present invention”.
  • the term "self attention layer” may be a layer designed to re-receive an original image, that is, a dental medical image, in a prediction process.
  • the self-attention layer may be present between a plurality of feature extraction layers in which calculations for feature extraction are performed, and thus, prediction based on a main region may be performed in a process of predicting success or failure of root canal treatment.
  • FIG. 1 illustrates a system for providing information on root canal treatment using a device for providing information on root canal treatment according to an embodiment of the present invention.
  • FIG. 2A illustrates a configuration of a device for providing information on root canal treatment according to an embodiment of the present invention.
  • FIG. 2B illustratively illustrates the configuration of a medical staff device receiving information on root canal treatment according to an embodiment of the present invention.
  • an information providing system 1000 may be a system configured to provide information related to root canal treatment based on a dental medical image of an object. At this time, the information providing system 1000 generates a CAM image based on the received dental medical image, and determines the success or failure of root canal treatment within a predetermined period therefrom. It can be composed of a medical device 200 that receives information related to the success of treatment and a dental medical imaging device 300 that provides dental medical images.
  • the information providing device 100 performs various calculations for determining success or failure of root canal treatment within a predetermined period based on a dental medical image provided from a dental medical imaging device 300 such as a radiographic device.
  • a dental medical imaging device 300 such as a radiographic device.
  • the medical staff device 200 may be a device for accessing a web server providing a web page or a mobile web server providing a mobile web site, but is not limited thereto.
  • the device for providing information 100 receives a dental medical image from the dental medical imaging device 300, obtains a preprocessed image and/or a CAM image from the received dental medical image, and obtains a preprocessed image and/or a CAM image therefrom within a predetermined period of time. It can be provided by determining the success of root canal treatment. At this time, it is possible to obtain a CAM projection from a dental medical image using the device 100 for providing information and a predictive model, and predict whether root canal treatment is successful or not.
  • the device 100 for providing information may provide a result of predicting success or failure of endodontic treatment to the medical device 200 .
  • the information provided from the device 100 for providing information in this way may be provided as a web page through a web browser installed in the medical device 200, or may be provided in the form of an application or program. In various embodiments, this data may be provided in a form incorporated into the platform in a client-server environment.
  • the medical device 200 is an electronic device that provides a user interface for indicating success or failure of root canal treatment within a predetermined period of time, and includes at least one of a smart phone, a tablet PC (Personal Computer), a laptop computer, and/or a PC. may contain one.
  • the medical staff device 200 may receive a predictive result of success or failure of endodontic treatment for an object within a predetermined period from the information providing device 100 and display the received result through a display unit to be described later.
  • the information providing device 100 includes a storage unit 110, a communication unit 120 and a processor 130.
  • the storage unit 110 may store various data generated in the process of determining success or failure of root canal treatment within a predetermined period from dental medical images and CAM images.
  • the storage unit 110 stores dental medical images received from the dental medical imaging device 300 through the communication unit 120 to be described later, further along with CAM images and pre-processed images together with various data in the prediction process of the predictive model. It may be configured to store products.
  • the storage unit 110 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, magnetic memory , a magnetic disk, and an optical disk may include at least one type of storage medium.
  • the communication unit 120 connects the information providing device 100 to enable communication with an external device.
  • the communication unit 120 is connected to the medical staff device 200 and further to the dental medical imaging device 300 using wired/wireless communication to transmit/receive various data.
  • the communication unit 120 may receive a dental medical image of an object from the dental medical imaging device 300 .
  • the communication unit 120 may transmit a prediction result and a CAM image based on clinical features to the medical device 200 .
  • the processor 130 is operatively connected to the storage unit 110 and the communication unit 120, and can perform various commands for analyzing a dental medical image of an object.
  • the processor 130 may be configured to acquire a CAM image and/or a pre-processed image from a dental medical image received through the communication unit 120, and finally determine whether root canal treatment is successful within a predetermined period of time. .
  • the processor 130 may be based on a CAM generation model for generating a CAM image based on a dental medical image, and a predictive model for determining success or failure of root canal treatment within a predetermined period from a plurality of images.
  • processor 130 may be based on a self-attention layer-based network according to an embodiment of the present invention.
  • the processor 130 includes SegNet, VGG-16, DCNN (Deep Convolutional Neural Network) and ResNet DNN (Deep Neural Network), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), SSD (Single Shot Detector) model, or U-net based prediction model.
  • the device 100 for providing information is not limited to being designed in terms of hardware.
  • the processor 130 of the device 100 for providing information may be implemented as software.
  • a display unit (not shown) connected to the dental medical imaging device 300 to which software is applied may be able to provide information related to root canal treatment for an object.
  • the medical device 200 includes a communication unit 210 , a display unit 220 , a storage unit 230 and a processor 240 .
  • the communication unit 210 may be configured to allow the medical device 200 to communicate with an external device.
  • the communication unit 210 may be connected to the information providing device 100 using wired/wireless communication to transmit whether root canal treatment has been successful within a predetermined period.
  • the display unit 220 may display various interface screens to indicate success or failure of root canal treatment within a predetermined period of time.
  • the display unit 220 may include a touch screen, and for example, a touch using an electronic pen or a part of the user's body, a gesture, a proximity, a drag, or a swipe A swipe or hovering input may be received.
  • the storage unit 230 may store various data used to provide a user interface for displaying result data.
  • the storage unit 230 may be a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (for example, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) , a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium.
  • the processor 240 is operatively connected to the communication unit 210, the display unit 220, and the storage unit 230, and executes various commands for providing a user interface for displaying success or failure of root canal treatment within a predetermined period of time. can be done
  • FIGS. 3 and 4A and 4B illustrate a procedure of a method for providing information on root canal treatment according to an embodiment of the present invention.
  • 3 illustrates a procedure of a method for providing information on root canal treatment according to an embodiment of the present invention.
  • 4A and 4B exemplarily illustrate procedures of a method for providing information on root canal treatment according to an embodiment of the present invention.
  • an information providing procedure is as follows. First, a dental medical image of an object is received (S310). Then, preprocessing is performed on the dental medical image (S320). Then, a CAM image is generated by the CAM generation model (S330). Finally, the success of root canal treatment is determined using the predictive model (S340).
  • a dental radiographic image including a target tooth may be received.
  • a tooth radiographic image in DICOM form may be received in step S310 of receiving a dental medical image.
  • a preprocessing step (S320) may be optionally performed.
  • various image preprocessing techniques are performed so that a preprocessed image 314 is obtained with respect to the dental medical image 312 received in the preprocessing step (S320).
  • intensity-based clustering may be performed on the dental medical image in the pre-processing step (S320).
  • K-means clustering is performed twice to obtain a preprocessed image in which background noise is removed and a soft tissue region corresponding to the gum is maintained.
  • the grayscale value of the dental medical image may be converted to a predetermined level.
  • a tooth radiographic image in DICOM format having a gray scale of 12 to 16 bits may be replaced with 8 bits. Due to such preprocessing, computing power for predicting the success of endodontic treatment may be reduced.
  • CLAHE Contrast Limited Adaptive Histogram Equalization
  • the target tooth region may be cropped in the dental medical image so that the target tooth image is obtained before the preprocessing step (S320) is performed.
  • preprocessing may be performed on the target tooth image.
  • a CAM image may be generated by a CAM generation model based on clinical features of a dental medical image.
  • a dental medical image 312 and/or a preprocessed image 314 may be input to the CAM image generation model 340. Then, during the processing of the CAM image generation model 340, a CAM image 342 with high activity for a region of high interest, particularly a clinical feature region, may be obtained as a feature.
  • the CAM image generation model 340 may be a model learned to determine whether (existence or absence) exists by learning clinical features associated with prediction of endodontic treatment with respect to dental medical images (and/or pre-processed images).
  • the CAM image generation model 340 is used to generate a full coverage restoration (FV) and the absence of adjacent teeth (absence of proximal teeth) in a dental medical image. teeth; PrX), Coronal Defect (CoD), Root Rest (RR), Temporary restoration (TM), Canal visibility (CV), Previous filling (PF),
  • a CAM image may be acquired in the process of predicting whether or not at least two clinical features selected from among a periapical radiolucency (PAR), a sinus tract (ST), and a root post (PS) are present.
  • a step of generating a feature-enhanced image including a background region and a feature region based on the activity of the CAM image may be further performed after the step of generating the CAM image (S330).
  • the CAM image 342 has an activity of 100 or more based on a predetermined threshold value of 100, and the activity is 1 (feature region), and the activity is 100. If it is less than or equal to 0 (background area), it can be converted to be distinguished.
  • the CAM image is characterized based on clinical characteristics during processing for predicting the endodontic treatment prognosis for the dental medical image and the preprocessed image using the predictive model can be obtained as
  • the CAM generation model may be composed of at least a part of the predictive model.
  • the success of root canal treatment is finally determined based on a plurality of images (S340).
  • a predictive model learned to output success or failure of root canal treatment using a dental medical image, a CAM image, and optionally a preprocessed image as inputs is used.
  • a dental medical image 312, a preprocessing image 314, and a feature-enhanced image 316 form a predictive model 314.
  • a predetermined period e.g., within 3 years
  • success or failure 332 may be probabilistically output.
  • the medical staff may be provided with information on whether or not the subject's root canal treatment was successful according to the information providing method according to various embodiments of the present invention, and thus may make a decision with a high probability of success and establish a treatment plan.
  • FIG. 5 illustratively illustrates the structure of a predictive model used in a method for providing information on root canal treatment according to an embodiment of the present invention.
  • the predictive model 320 largely includes an 'input layer' receiving dental medical images, CAM images, and/or preprocessing images, a plurality of 'feature extraction layers 3202' for feature extraction, and an original image in the prediction process. It can be composed of a 'self-attention layer 3204' designed to receive re-input and an 'output layer' that finally outputs the success or failure of root canal treatment.
  • the feature extraction layer 3202 may include a convolution layer for extracting features from an input image, a batch normalization layer and a max pooling layer, and a ReLu layer of an activation function.
  • Various operations for feature extraction can be performed in the plurality of feature extraction layers 3202 .
  • a self-attention layer 3204 may be provided between the plurality of feature extraction layers 3202 . That is, as the image is re-input through the self-attention layer, prediction centered on the main region may be performed in the process of predicting success or failure of root canal treatment.
  • Prediction models used in various embodiments of the present invention can predict the success of root canal treatment with high accuracy in a dental medical image according to the above structural characteristics.
  • the predictive model 320 may have a network structure of an embodiment of the present invention equipped with a self-attention layer, but is not limited thereto, and SegNet, VGG-16, DCNN (Deep Convolutional Neural Network), and ResNet DNN (Deep Neural Network) Network), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), SSD (Single Shot Detector) model, or U-net based network.
  • SegNet VGG-16, DCNN (Deep Convolutional Neural Network), and ResNet DNN (Deep Neural Network) Network), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), SSD (Single Shot Detector) model, or U-net based network.
  • 6A to 6C illustrate CAM images of clinical features used to predict success or failure of root canal treatment in the method for providing information on root canal treatment according to an embodiment of the present invention.
  • the CAM image generation model appears to have different activities in dental medical images depending on the presence of a full-length prosthesis, the absence of adjacent teeth, and crown defects.
  • Full length prostheses, absence of adjacent teeth, and crown defects can be clinical features associated with root canal treatment. Therefore, CAM images according to the presence of full length prostheses, absence of adjacent teeth, and crown defects can be used to predict the success of root canal treatment using a predictive model. can be used
  • Root Rest RR
  • Temporary filled TM
  • CV Canal visibility
  • CAM images according to visibility are shown. More specifically, the CAM image generation model appears to have different activities in dental medical images depending on the presence of residual root, temporary restorative material, and root canal visibility.
  • residual root, temporary restoration material, and root canal visibility may be clinical features related to root canal treatment, so CAM images according to residual root, temporary restorative material, and root canal visibility can be used to predict the success of root canal treatment using a predictive model. .
  • the CAM image generation model appears to have different activities within dental medical images depending on the presence of existing fillings, apical radiographic images, and fistulas.
  • existing filling, apical radiographic image and fistula may be clinical features related to root canal treatment, so CAM images according to existing filling, apical radiographic image and fistula can be used to predict the success of root canal treatment using a predictive model. there is.
  • the prediction accuracy was 66.00%, similar to the accuracy (68.00%) when all nine features were used.
  • the predictive model can have improved performance while maintaining the accuracy of predicting the success of endodontic treatment.
  • full veneer FV
  • absence of proximal teeth PrX
  • coronal defect CoD
  • root post PS
  • canal visibility CV
  • previous filling PF
  • periapical radiolucency PAR
  • fistula fistula
  • the predictive model when using 7 features excluding the clinical features of the full length prosthesis, the predictive model can have more improved performance by improving the predictive accuracy of the success of endodontic treatment.
  • proximal teeth PrX
  • Coronal Defect CoD
  • Root post PS
  • Canal visibility CV
  • Previous filling PF
  • CAM images considering at least one of 7 clinical features of periapical radiolucency (PAR) and sinus tract (ST), or a combination thereof, can be applied to learning of a predictive model to predict endodontic treatment.
  • Evaluation 2 Performance evaluation of predictive model including self-attention layer
  • 9a and 9b illustrate evaluation results of a predictive model including a self-attention layer in a method for providing information on root canal treatment according to an embodiment of the present invention.
  • the prediction model based on the network (AtteNet17') according to various embodiments of the present invention has an accuracy of 71.00%, a sensitivity of 60.0%, a specificity of 75.7%, and a precision of endodontic treatment prognosis prediction. has a 51.4% F1 score of 55.4, showing better prediction performance than a model based on a network (ResNET-18') that does not include a self-attention layer.
  • AUC of the prediction model based on the network (AtteNet17) according to various embodiments of the present invention is 0.667 and p is 0.008.
  • the AUC of the network (AtteNet17)-based prediction model according to various embodiments of the present invention is higher than that of the conventional network (ResNET)-based model having a similar number of layers without the self-attention layer. appears as
  • the prediction model according to various embodiments of the present invention may have excellent prediction performance because prediction of .

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Abstract

La présente invention concerne un procédé, mis en œuvre par un processeur, pour fournir des informations sur un traitement endodontique, et un dispositif l'utilisant, le procédé comprenant les étapes consistant à : recevoir une image médicale dentaire d'un sujet ; générer une image de carte thermique sur la base de l'image médicale dentaire au moyen d'un modèle de génération de carte thermique conçu pour générer une image de carte thermique explicitée sur la base de caractéristiques cliniques de l'image médicale dentaire ; et déterminer si oui ou non le traitement endodontique a réussi chez le sujet sur la base de l'image médicale dentaire et de l'image de carte thermique à l'aide d'un modèle prédictif conçu pour pronostiquer un traitement endodontique, l'image médicale dentaire et l'image de carte thermique étant entrées.
PCT/KR2022/018393 2021-12-15 2022-11-21 Procédé pour fournir des informations concernant un traitement endodontique et dispositif pour fournir des informations concernant un traitement endodontique à l'aide de celui-ci WO2023113272A1 (fr)

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KR20200102961A (ko) * 2019-02-22 2020-09-01 가천대학교 산학협력단 병변 진단 시스템 및 방법
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