WO2023132523A1 - Procédé et dispositif d'assistance au diagnostic de calcification sur la base d'un modèle d'intelligence artificielle - Google Patents

Procédé et dispositif d'assistance au diagnostic de calcification sur la base d'un modèle d'intelligence artificielle Download PDF

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WO2023132523A1
WO2023132523A1 PCT/KR2022/020801 KR2022020801W WO2023132523A1 WO 2023132523 A1 WO2023132523 A1 WO 2023132523A1 KR 2022020801 W KR2022020801 W KR 2022020801W WO 2023132523 A1 WO2023132523 A1 WO 2023132523A1
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data
calcification
target organ
region
information
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PCT/KR2022/020801
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English (en)
Korean (ko)
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하성민
최안네스
맹신희
심학준
홍영택
정현석
이지나
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주식회사 온택트헬스
연세대학교 산학협력단
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Priority claimed from KR1020220177169A external-priority patent/KR20230108213A/ko
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Publication of WO2023132523A1 publication Critical patent/WO2023132523A1/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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • 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

Definitions

  • the present invention relates to a method and apparatus for assisting diagnosis of calcification based on an artificial intelligence (AI) algorithm.
  • AI artificial intelligence
  • a disease refers to a condition that impairs normal functions by causing physical and mental disorders in humans. Accordingly, various social systems and technologies for diagnosing, treating, and even preventing diseases have developed along with human history. In the diagnosis and treatment of diseases, various tools and methods have been developed according to the remarkable development of technology, but it is still a reality that ultimately depends on the judgment of a doctor.
  • AI artificial intelligence
  • the present invention is to provide a method and apparatus for effectively diagnosing calcification in a subject.
  • An object of the present invention is to provide a method and apparatus for determining the degree of calculus detected in a subject.
  • An object of the present invention is to provide a method and apparatus for extracting data on a region of a target organ using at least one of suppression and segmentation in a medical image of a subject.
  • An object of the present invention is to provide a method and apparatus for analyzing data on a region of a target organ using a radiomix technique.
  • a method for assisting in diagnosis of calcification based on an artificial intelligence model includes acquiring medical image data of a subject, and applying the medical image data to a region of a target organ based on the medical image data. extracting data on the target organ, generating feature data of the target organ by analyzing data on the region of the target organ, and diagnosing calcification of the target organ based on the feature data of the target organ. It may include providing information to assist.
  • the medical image data may include at least one of X-ray, ultrasound, computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) image data of the subject. .
  • CT computerized tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • the extracting of the data on the region of the target organ may include suppressing a region other than the target organ and segmenting the region of the target organ.
  • the generating of the characteristic data of the target organ by analyzing the data of the region of the target organ may include generating the data of the region of the target organ using a radiomics technique.
  • the method may include extracting at least one feature, and generating feature data of the target organ based on the at least one feature.
  • the information for assisting in the diagnosis of calcification includes information indicative of whether or not to detect limescale by detecting calcium in the target organ of the subject and the degree of calcification in the detected target organ of the subject. It may include at least one of information indicating the degree of calcification by determining.
  • the target organ may include at least one of an abdominal organ, a thoracic organ, a lumbar spine, a cervical spine, a brain, a liver, a lung, a kidney, an adrenal joint, and a blood vessel.
  • the information to assist in diagnosing the calcification may be generated based on some features selected by logistic regression from among a plurality of features included in the feature data.
  • the logistic regression analysis may include the least absolute shrinkage and selection operator (LASSO) regression analysis.
  • LASSO least absolute shrinkage and selection operator
  • the information to assist diagnosis of the calcification may be generated based on a radiomics score determined by linearly combining the selected features with weights.
  • the information to assist in diagnosing the calcification is generated by a random forest model based on the radiomix score and clinical information, and the clinical information includes: , gender, and body mass index (BMI).
  • BMI body mass index
  • a program stored in a medium according to an embodiment of the present invention may execute the above-described method when operated by a processor.
  • An apparatus for assisting diagnosis of calcification based on an artificial intelligence model includes a transceiver, a storage unit for storing the artificial intelligence model, and at least one connected to the transceiver and the storage unit. and a processor, wherein the at least one processor obtains medical image data of the subject, extracts data about a region of a target organ based on the medical image data, and extracts data about a region of a target organ.
  • the at least one processor obtains medical image data of the subject, extracts data about a region of a target organ based on the medical image data, and extracts data about a region of a target organ.
  • feature data of the target organ may be generated, and information for assisting diagnosis of calcification of the target organ may be provided based on the feature data of the target organ.
  • calcification of a target organ can be effectively diagnosed using a learned artificial intelligence model.
  • FIG. 1 shows a system according to one embodiment of the present invention.
  • FIG 2 shows the structure of a device according to an embodiment of the present invention.
  • FIG 3 shows an example of a perceptron constituting an artificial intelligence model applicable to the present invention.
  • FIG 4 shows an example of an artificial neural network constituting an artificial intelligence model applicable to the present invention.
  • FIG. 5 illustrates a concept of calcification diagnosis based on an artificial intelligence model according to an embodiment of the present disclosure.
  • FIG. 6 illustrates an example of a procedure for assisting diagnosis of calcification of a target organ according to an embodiment of the present invention.
  • FIG. 7 illustrates an example of a procedure for extracting data on a region of a target organ according to an embodiment of the present invention.
  • FIG. 8 illustrates an example of a procedure for generating feature data of a target organ by analyzing data for a region of the target organ according to an embodiment of the present invention.
  • FIG. 9 illustrates an example of a procedure for training an artificial intelligence model for calcification prediction according to an embodiment of the present invention.
  • FIG. 10 illustrates a concept of calcification diagnosis based on an artificial intelligence model according to another embodiment of the present disclosure.
  • FIG. 11 illustrates an example of a procedure for assisting diagnosis of calcification of a target organ according to another embodiment of the present disclosure.
  • FIG. 12 illustrates a validation result according to a 10-fold cross-validation method for a calcification diagnosis technique according to an embodiment of the present invention.
  • the present invention is to assist diagnosis of calcification of organs of the human body based on an artificial intelligence model. , It relates to a technology that assists in detecting calcification and determining the degree of calcification in a subject's target organ by analyzing data on the region of the target organ extracted through artificial intelligence.
  • FIG. 1 shows a system according to one embodiment of the present invention.
  • the system includes a service server 110 , a data server 120 , and at least one client device 130 .
  • the service server 110 provides a service based on an artificial intelligence model. That is, the service server 110 performs learning and prediction operations using an artificial intelligence model.
  • the service server 110 may communicate with the data server 120 or at least one client device 130 through a network. For example, the service server 110 may receive training data for training an artificial intelligence model from the data server 120 and perform training.
  • the service server 110 may receive data necessary for learning and prediction operations from at least one client device 130 .
  • the service server 110 may transmit information about a prediction result to at least one client device 130 .
  • the data server 120 provides learning data for training of the artificial intelligence model stored in the service server 110 .
  • the data server 120 may provide public data accessible to anyone or data requiring permission. If necessary, the learning data may be pre-processed by the data server 120 or the service server 120 .
  • the data server 120 may be omitted. In this case, the service server 110 may use an artificial intelligence model trained externally, or training data may be provided to the service server 110 offline.
  • At least one client device 130 transmits and receives data related to the artificial intelligence model operated by the service server 110 to and from the service server 110 .
  • At least one client device 130 is a device used by a user, transmits information input by the user to the service server 110, stores information received from the service server 110, or provides it to the user (e.g. : mark) can be done.
  • a prediction operation may be performed based on data transmitted from one client, and information related to a prediction result may be provided to another client.
  • the at least one client device 130 may be various types of computing devices such as a desktop computer, a laptop computer, a smart phone, a tablet, and a wearable device.
  • the system may further include a management device for managing the service server 110 .
  • the management device is a device used by a subject who manages a service, and monitors a state of the service server 110 or controls settings of the service server 110 .
  • the management device may access the service server 110 through a network or may be directly connected through a cable connection. Under the control of the management device, the service server 110 may set parameters for operation.
  • a service server 110 may be connected through a network and interact with each other.
  • the network may include at least one of a wired network and a wireless network, and may include any one or a combination of two or more of a cellular network, a local area network, and a wide area network.
  • the network is based on at least one of a local area network (LAN), a wireless LAN (WLAN), bluetooth, long term evolution (LTE), LTE-advanced (LTE-A), and 5th generation (5G) can be implemented.
  • LAN local area network
  • WLAN wireless LAN
  • WLAN wireless LAN
  • LTE long term evolution
  • LTE-A LTE-advanced
  • 5G 5th generation
  • FIG. 2 shows the structure of a device according to an embodiment of the present invention.
  • the structure illustrated in FIG. 2 may be understood as a structure of the service server 110 , the data server 120 , and at least one client device 130 of FIG. 1 .
  • the device includes a communication unit 210, a storage unit 220, and a control unit 230.
  • the communication unit 210 performs functions for accessing a network and communicating with other devices.
  • the communication unit 210 may support at least one of wired communication and wireless communication.
  • the communication unit 210 may include at least one of a radio frequency (RF) processing circuit and a digital data processing circuit.
  • RF radio frequency
  • the communication unit 210 may be understood as a component including a terminal for connecting a cable. Since the communication unit 210 is a component for transmitting and receiving data and signals, it may be referred to as a 'transceiver'.
  • the storage unit 220 stores data, programs, microcodes, command sets, applications, and the like necessary for the operation of the device.
  • the storage unit 220 may be implemented as a temporary or non-transitory storage medium.
  • the storage unit 220 may be fixed to the device or implemented in a detachable form.
  • the storage unit 220 may include a compact flash (CF) card, a secure digital (SD) card, a memory stick, a solid-state drive (SSD), and a microSD card. It may be implemented as at least one of a NAND flash memory such as an SD card and a magnetic computer storage device such as a hard disk drive (HDD).
  • CF compact flash
  • SD secure digital
  • HDD hard disk drive
  • the controller 230 controls the overall operation of the device.
  • the controller 230 may include at least one processor or at least one microprocessor.
  • the controller 230 may execute a program stored in the storage 220 and access a network through the communication unit 210 .
  • the controller 230 may perform algorithms according to various embodiments described later and control the device to operate according to embodiments described later.
  • an artificial intelligence algorithm-based service may be provided.
  • an artificial intelligence model made of an artificial neural network may be used to implement an artificial intelligence algorithm.
  • the concept of a perceptron, which is a unit of artificial neural networks, and artificial neural networks are as follows.
  • Perceptron is a model of a living organism's nerve cell, and has a structure that outputs one signal by taking multiple signals as input.
  • 3 shows an example of a perceptron constituting an artificial intelligence model applicable to the present invention.
  • the perceptron sets weights 302-1 to 302- n ( eg, w 1j , w 2j , After multiplying w 3j , ..., w nj ), the weighted input values are summed using a transfer function 304 .
  • a bias value eg, b k
  • the perceptron generates an output value (eg, o j ) by applying an activation function 306 to a net input value (eg, net j ) that is an output of the conversion function 304 .
  • the activation function 306 may operate based on a threshold value (eg, ⁇ j ).
  • the activation function may be defined in various ways. Although the present invention is not limited thereto, for example, as an activation function, a step function, sigmoid, Relu, Tanh, etc. may be used.
  • An artificial neural network can be designed by arranging perceptrons as shown in FIG. 3 and forming layers.
  • 4 shows an example of an artificial neural network constituting an artificial intelligence model applicable to the present invention.
  • each node represented by a circle can be understood as the perceptron of FIG. 3 .
  • the artificial neural network includes an input layer 402, a plurality of hidden layers 404a and 404b, and an output layer 406.
  • Calcification is the formation of plaque by the accumulation of calcium in the inner wall of blood vessels.
  • vascular disease can be detected by detecting calcification of human organs (eg, abdominal organs, thoracic organs, lumbar vertebrae, cervical vertebrae, brain, liver, lungs, kidneys, adrenal joints, blood vessels) and determining the degree of calcification.
  • human organs eg, abdominal organs, thoracic organs, lumbar vertebrae, cervical vertebrae, brain, liver, lungs, kidneys, adrenal joints, blood vessels
  • the present invention proposes a technique capable of detecting vascular disease by analyzing medical image data of a patient and detecting and analyzing calcification of target organs of the patient.
  • 5 illustrates a concept of calcification diagnosis based on an artificial intelligence model according to an embodiment of the present disclosure.
  • 5 illustrates a system 500 for diagnosing cardiac calcification of a subject by extracting heart region data through an artificial intelligence model based on medical image data and analyzing the extracted data using a radiomix technique.
  • the system 500 acquires medical image data of a subject and extracts heart region data from the medical image data.
  • System 500 analyzes the heart region data and diagnoses the subject's cardiac calcifications.
  • the system 500 may acquire medical image data of a subject.
  • the medical image may be an X-ray image 510 . Since the X-ray image 510 is a two-dimensional projection image, several organs may overlap. Therefore, it is necessary to extract only the heart region from the X-ray image 510 as data in order to prevent the detection of calculus based on an unnecessary region other than the heart and to use the medical image for quantitative evaluation.
  • the system 500 extracts heart region data using an artificial intelligence model that divides only the heart region for more accurate prediction of cardiovascular calcification.
  • the system 500 may perform suppression to improve segmentation accuracy of the heart region with respect to the medical image data. Suppression is to leave a specific region to be extracted from image data and to filter out regions other than the specific region. Filtering may include removing an area other than a specific area or blurring the area.
  • the suppression may include bone suppression 520 . This suppression suppresses the bone region in the medical image data.
  • the system 500 may perform bone suppression, that is, bone suppression, which suppresses a bone region, through the trained data.
  • the system 500 may acquire medical image data, compare the obtained medical image data with bone-suppressed data in the medical image image, and learn a comparison result.
  • the system 500 may generate soft tissue data in which bones are suppressed from the medical image data that is the subject of the suppression through the training data, and may also remove bone data.
  • the artificial intelligence model can learn the residual error between the predicted bone suppression image and the actual medical image image.
  • the system 500 may extract heart region data through an artificial intelligence segmentation model in order to analyze only the heart region from the suppressed data. Extracting the heart region data may include generating a heart region segmentation mask.
  • the artificial intelligence segmentation model may segment 530 the heart region to generate a segmentation mask of the heart region.
  • the system 500 may generate heart feature data by performing radiomics 540 analysis on the extracted heart region data.
  • the system 500 analyzes the shape 542, intensity 544, texture 546, and filters 548 from the heart region data of the subject through the radiomix 540 analysis technique. ), etc., heart feature data can be generated.
  • System 500 provides information to assist in diagnosing a subject's cardiac calcifications 560 based on cardiac feature data.
  • the auxiliary information may include at least one of information indicative of whether calcareous matter is detected or not and information indicative of the degree of detected calcal matter based on the cardiac feature data.
  • the information indicating the degree of calcification may indicate a group to which a coronary artery calcification score, which is a continuous value, belongs.
  • the group to which the coronary artery calcification score belongs is a group in which the coronary artery calcification score is classified according to a certain criterion.
  • a coronary artery calcification score group may be classified based on the results of a clinical study conducted on the correlation of the coronary artery calcification score with mortality or ischemic risk prevalence.
  • the system 500 compares the cardiac calcification prediction result with a coronary artery calcification score (CACS) 550 extracted from a computed tomography (CT) image to verify the accuracy of the cardiovascular calcification prediction through medical image data.
  • CACS coronary artery calcification score
  • CT computed tomography
  • the system 500 may perform re-learning for cardiac calcification prediction through artificial intelligence based on the verified data.
  • medical image data may be different even for the same symptom. Therefore, even if the same radiomix technique is applied to heart region data extracted from medical image data of subjects having the same degree of cardiac calcification, generated heart feature data may be different. Accordingly, the system 500 needs to appropriately select features of heart region data and assist diagnosis based on the selected features in order to assist in accurately diagnosing cardiac calcification. Accordingly, the system 500 may additionally build a cardiac calcification prediction model based on at least one of the extracted features.
  • the cardiac calcification prediction model is based on a value obtained by comparing a plurality of calcification results predicted based on at least one of the features included in the cardiac feature data and a coronary artery calcification score (CACS) can be built For example, if the calcification result predicted by considering only the intensity 544 and the texture 546 of the first heart region data is more consistent with CACS than the calcification results predicted by considering other features, then the first heart region data If the second heart region data similar to is extracted, system 500 may consider only intensity 544 and texture 546 to assist in diagnosing cardiac calcification. The system 500 may determine accurate information such as the location and amount of cardiac calcification through a cardiac calcification prediction model.
  • CACS coronary artery calcification score
  • the present invention in order to prevent cardiovascular calcification detection and classification artificial intelligence model from diagnosing cardiac calcification based on unnecessary regions other than the heart, medical images (e.g., : We propose an algorithm optimized for the detection and classification of cardiovascular calcifications in X-ray images).
  • the heart region data extraction model may be referred to as an artificial intelligence segmentation model for heart segmentation, an artificial intelligence segmentation model, or other terms having equivalent technical meaning.
  • 6 illustrates an example of a procedure for assisting diagnosis of calcification of a target organ according to an embodiment of the present invention. 6 illustrates an operating method of a device having computing capability (eg, the service server 110 of FIG. 1 ).
  • the device acquires medical image data.
  • the medical image may be related to at least one of X-rays, ultrasound, computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).
  • CT computerized tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • medical image data may be used by the device to predict cardiac calcification.
  • the device may receive medical image data from a data server (eg, the data server 120 of FIG. 1 ) or a client device (eg, the client device 130 of FIG. 1 ).
  • the device extracts data on the region of the target organ based on the medical image data.
  • Data on the region of the target organ is data extracted by filtering medical image data.
  • the device may extract heart region data based on medical image data.
  • the heart region data is data about a heart region extracted by filtering medical image data.
  • the device may filter data other than the heart region based on the subject's medical image data acquired in step S601 and extract data about the remaining heart region.
  • the extracted cardiac region data can be used to assist in the diagnosis of cardiovascular calcification in a subject.
  • the device generates feature data of the target organ by analyzing the data on the region of the target organ extracted in step S603.
  • the feature data of the target organ may be heart feature data obtained by analyzing heart region data.
  • the device may analyze heart region data using a radio mix technique.
  • Radiomix is a technique for extracting features from medical images using a data-characterization algorithm.
  • the device may generate heart feature data by extracting various features of heart region data through a radiomix technique.
  • the feature may include the shape, intensity, and texture of heart region data.
  • the device may selectively use cardiac characteristic data to predict cardiovascular calcification in a subject.
  • the device provides information to assist diagnosis of calcification based on the characteristic data of the target organ.
  • the device may provide information to assist diagnosis of cardiac calcification based on cardiac feature data.
  • the device may provide information indicative of whether or not to detect calcium by detecting calcium in the cardiovascular system of the subject based on heart feature data.
  • the information indicating whether or not to detect may include a plurality of result values obtained by detecting calcareous matter using at least one of feature information included in the heart region data.
  • the device may provide information indicating the degree of calcification by determining the degree of calcification detected.
  • the information indicating the degree of calcification may include a plurality of result values obtained by determining the degree of calcification using at least one of feature information included in the heart region data.
  • the auxiliary information may include information about a result of comparing CACS with at least one of information indicating detection or not and information indicating the degree of calcification.
  • the apparatus performing the procedure of FIG. 6 acquires medical image data using at least one artificial intelligence model, extracts data on a region of a target organ, generates feature data of the target organ, and diagnoses calcification. Information can be provided to assist.
  • the device may analyze data on a region of a target organ using a radio mix technique using at least one artificial intelligence model, and classify features extracted by the analysis.
  • 7 illustrates an example of a procedure for extracting data on a region of a target organ according to an embodiment of the present invention. 7 illustrates an operating method of a device having computing capability (eg, the service server 110 of FIG. 1 ).
  • the device suppresses a region other than the target organ in the medical image data.
  • Suppression is to leave a specific region to be extracted from image data and to filter out regions other than the specific region.
  • Filtering may include removing an area other than a specific area or blurring the area.
  • the device may suppress an area other than the heart in medical image data.
  • the device may suppress image data of a bone portion from medical image data.
  • the device may suppress image data of organs other than the heart and muscles in medical image data.
  • the device segments the region of the target organ in the image data in which regions other than the target organ are suppressed.
  • the device may segment a heart region in image data in which a region other than the heart is suppressed.
  • the device may segment the heart region from suppressed image data using an artificial intelligence model or a machine learning model learned for segmentation.
  • the present invention proposes a radiomix technique as a method for efficiently diagnosing calcification of a target organ by analyzing heart region data extracted from a medical image for each feature.
  • Radiomics is a compound word of “Radiology” meaning radiology and the suffix “-omics” meaning physiology. This is a method of combining and analyzing biomarker data with genetic data based on image information, and it is a method of developing a non-invasive biomarker that can help diagnose and predict the prognosis of a disease.
  • Various characteristics are extracted from the image and fused with genetic information and clinical data of the patient to find useful biomarkers for the patient group based on this, which can be used for disease prevention, early diagnosis, prognosis prediction, and customized treatment.
  • FIG. 8 illustrates a heart region data analysis procedure using radiomix.
  • 8 illustrates an example of a procedure for generating feature data of a target organ by analyzing data on a region of the target organ according to an embodiment of the present invention. 8 illustrates an operating method of a device having computing capability (eg, the service server 110 of FIG. 1 ).
  • the device extracts at least one feature of data of a region of a target organ using a radio mixing technique.
  • the device may extract at least one feature of heart region data.
  • the device may extract radiomix features by using heart region data divided through an artificial intelligence segmentation model as an input value.
  • Feature extraction through radiomix is a process of quantifying various quantitative/statistical characteristics such as shape, signal intensity, and texture of lesions of heart area data using mathematical/statistical techniques.
  • the features extracted using the radio mix technique may include at least one of the features listed in Table 1 below.
  • radiomics features original shape2D Elongation original shape2D MajorAxisLength original shape2D Sphericity original firstorder InterquartileRange original first-order Median original glcm ClusterShade original glcm Correlation original glcm JointEnergy original glcm imc1 original glszm LargeAreaLowGrayLevelEmphasis original glszm SmallAreaLowGrayLevelEmphasis original glszm ZoneVariance original ngtdm Busyness wavelet-LH firstorder Kurtosis wavelet-LH firstorder Mean wavelet-LH firstorder RootMeanSquared wavelet-LH glcm Correlation wavelet-LH glcm Imc1 wavelet-LH glcm Idmn wavelet-LH glcm InverseVariance wavelet-LH glrlm GrayLevelNonUniformityNorm
  • At least one of the radiomix features listed in [Table 1] can be selectively used for calcification diagnosis according to various embodiments of the present invention.
  • the performance or characteristics of calcification diagnosis may vary depending on the selected radiomix features, and an appropriate subset may be selectively used depending on the intention or purpose of implementing the present invention, the target organ to be diagnosed, etc.
  • [Table 1] For a description of the radiomix features exemplified in ], see [Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017 Nov 1;77(21):e104-e107] and the contents found on January 7, 2022 in the links included in the above references. Therefore, even if the contents described in the links included in the references are changed later, the description of the Radio Mix features follows the contents found in the links included in the references as of January 7, 2022.
  • the device may build a machine learning-based inference model that identifies features of calcification in the heart region in order to detect cardiac calcification and classify the severity of cardiac calcification based on at least one of the features of the extracted image. Additionally, the device may select key features by ranking various features of the extracted image. The device can develop a machine-learning-based inference model that identifies the calcification characteristics of the heart region based on the selected key features.
  • the device generates feature data of the target organ based on at least one feature.
  • the device may generate heart feature data based on at least one feature.
  • the heart feature data may include information about features extracted from heart region data using a radiomix technique. Cardiac characteristic data is used by the device to provide information to assist in diagnosis of cardiac calcification.
  • the present invention proceeds with machine learning-based coronary artery calcification prediction by using the coronary artery calcification score extracted through coronary artery CT angiography as GT based on the core features extracted from the heart region data of the subject, and the artificial intelligence model can train
  • 9 illustrates an example of a procedure for training an artificial intelligence model for calcification prediction according to an embodiment of the present invention. 9 illustrates an operating method of a device having computing capability (eg, the service server 110 of FIG. 1 ).
  • the device acquires heart region analysis data and coronary artery calcification data for learning.
  • the heart region analysis data includes data obtained by analyzing heart region data extracted from medical images using a radiomix technique.
  • the heart region analysis data to be used as learning may include information about a heart region analysis result of at least one patient with calcification.
  • the heart region analysis data to be used as learning may further include information about a heart region analysis result of a non-calcified patient.
  • Information about the heart region analysis result may include information about the shape, intensity, and texture of the heart region analyzed through the radiomix technique.
  • Coronary artery calcification data includes data on coronary artery calcification scores analyzed in CT images.
  • Data on the coronary artery calcification score may include information on a total coronary artery score, a left main (LM) score, a left anterior descending (LAD) score, a left circumflex (LCX) score, and a right coronary artery (RCA) score.
  • LM left main
  • LAD left anterior descending
  • LCX left circumflex
  • RCA right coronary artery
  • step S903 the device preprocesses the heart analysis data and the coronary artery calcification data and adds labels to generate training data. That is, the device processes cardiac analysis data and coronary artery calcification data into a format usable by an artificial intelligence model, and adds a label.
  • the device performs training using the learning data. That is, the device updates at least one weight by inputting training data to the artificial intelligence model and performing backpropagation based on the prediction result and the label.
  • the device may perform machine learning-based cardiac calcification prediction by using a coronary artery calcification score extracted through coronary artery CT angiography as a GT based on core features extracted from heart region data of the subject. In this case, the device compares the preprocessed heart region analysis data and the coronary artery calcification data to determine the accuracy of the heart region analysis data, and may use the determined result as learning data.
  • 10 illustrates a concept of calcification diagnosis based on an artificial intelligence model according to another embodiment of the present disclosure.
  • 10 illustrates a system for diagnosing cardiac calcification of a subject by extracting heart region data through an artificial intelligence model based on medical image data and analyzing the extracted data using a radiomix technique.
  • the system acquires medical image data of a subject and extracts heart region data from the medical image data.
  • the system analyzes the cardiac region data and diagnoses the subject's cardiac calcifications.
  • the system may obtain medical image data of a subject. Since the image 1010 is a two-dimensional projection image, several organs may be overlapped. Therefore, it is necessary to extract only the heart region from the image 1010 as data in order to prevent calcification from being detected based on an unnecessary region other than the heart and to use the medical image for quantitative evaluation. Therefore, the system extracts heart region data using an artificial intelligence model that divides only the heart region for more accurate prediction of cardiovascular calcification. For example, the system may perform suppression to improve segmentation accuracy of the heart region on the medical image data.
  • Suppression is to leave a specific region to be extracted from image data and to filter out regions other than the specific region.
  • Filtering may include removing an area other than a specific area or blurring the area.
  • the suppression may include the present suppression 1020 . This suppression suppresses the bone region in the medical image data.
  • the system may perform bone suppression, that is, bone suppression, which suppresses a bone region, through trained data.
  • the system may obtain medical image data, compare the obtained medical image data with bone suppressed data in the medical image image, and learn a comparison result.
  • the system may generate soft tissue data in which bones are suppressed from the medical image data that is the object of the suppression through the learning data, and may also remove bone data.
  • the artificial intelligence model can learn the residual error between the predicted bone suppression image and the actual medical image image.
  • the system can extract heart region data through an artificial intelligence segmentation model in order to analyze only the heart region from the suppressed data. Extracting the heart region data may include generating the heart region segmentation mask 1040 .
  • the artificial intelligence segmentation model may segment 1030 the heart region to generate a segmentation mask 1040 of the heart region.
  • the system may perform feature extraction 1050 to generate heart feature data for the extracted heart region data.
  • the system may generate feature data by performing radiomics analysis.
  • the system can generate heart feature data by extracting features such as shape, intensity, texture, and filters from heart region data of a subject through a radiomix analysis technique.
  • the radio mix analysis technique is an example, and feature data may be generated by other analysis techniques according to various embodiments.
  • the system After feature data extraction, the system performs feature selection (1060). For example, the system may select at least some features using the least absolute shrinkage and selection operator (LASSO) regression technique, which is a type of logistic regression.
  • LASSO regression technique is a technique for creating an interpretable model by setting the regression coefficients of unnecessary variables to 0 among various regression techniques.
  • the LASSO regression technique imposes a constraint condition such that the sum of the absolute values of the weights is minimized in addition to the existing linear regression technique that minimizes the mean square error.
  • [Equation 1] below shows an example of the existing linear regression technique
  • [Equation 2] below shows an example of a result of applying constraint conditions.
  • n is the total number of datasets
  • y i is the correct answer value
  • w is a weight
  • b is a bias value
  • ⁇ is a hyperparameter controlling the effect of the penalty
  • m is the number of weights
  • w is the weight
  • n is the total number of datasets
  • y i is the correct answer value
  • the LASSO regression technique finds appropriate weights and biases like a general linear regression technique, while minimizing the sum of the absolute values of the weights (eg, making the weights close to or zero) to reduce the weights of features that are unnecessary for prediction. By setting to 0, it is possible to select features effectively. For example, 11 features among 455 features may be selected, and coefficients for each feature may be determined through the LASSO technique. A weight is assigned to each feature using coefficients, and a radiomics score 1070 for each image may be extracted by a linear combination of the weighted features.
  • the system uses the predictive model 1080 to determine a calcification score.
  • the system may determine a calcification score using clinical information and a radiomix score.
  • predictive model 1080 can be based on a random forest. That is, finally, random forest modeling is performed based on a dataset including age information, gender information, body mass index (BMI) information, and a radiomix score.
  • BMI body mass index
  • effectiveness evaluation may be further performed in addition to the procedure described with reference to FIG. 10 .
  • Validity evaluation is an operation of determining how valid the features selected from the whole are.
  • the validity evaluation is an operation of evaluating each of the selected features using prediction accuracy determined based on a statistical model.
  • the accuracy of prediction means the accuracy of calcification judgment. For example, if a prediction result with higher accuracy is shown compared to a prediction result of a model using only radiomix information and/or a prediction result of a model using only clinical information, the corresponding feature may be evaluated as valid.
  • validity evaluation may be performed after feature selection (1060).
  • a prediction result of a first model using only clinical information and a prediction result of a second model using clinical information and radiomix information corresponding to the selected feature are compared, and if the prediction accuracy of the second model is higher, the selected feature can be evaluated as valid.
  • 11 illustrates an example of a procedure for assisting diagnosis of calcification of a target organ according to another embodiment of the present disclosure. 11 illustrates an operating method of a device having computing capability (eg, the service server 110 of FIG. 1 ).
  • the device acquires medical image data.
  • the medical image may relate to at least one of X-rays, ultrasound, CT, MRI, and PET.
  • medical image data may be used by the device to predict cardiac calcification.
  • the device may receive medical image data from a data server (eg, the data server 120 of FIG. 1 ) or a client device (eg, the client device 130 of FIG. 1 ).
  • the device suppresses a region other than the target organ in the medical image data.
  • Suppression is to leave a specific region to be extracted from image data and to filter out regions other than the specific region.
  • Filtering may include at least one of removing an area other than a specific area and blurring the area.
  • the device may suppress a region other than the heart in the medical image data.
  • the device may suppress a part of image data corresponding to a bone portion excluding the heart from the medical image data.
  • the device may suppress a part of image data corresponding to an organ other than the heart and a muscle part in the medical image data.
  • the device segments the target organ region in the medical image data.
  • the device segments the region of the target organ in the image data in which regions other than the target organ are suppressed.
  • the device may segment the heart region in image data in which regions other than the heart are suppressed.
  • the device may segment the heart region from suppressed image data using an artificial intelligence model or a machine learning model learned for segmentation.
  • the device extracts features.
  • the device extracts specific information including a plurality of features from the segmented target organ region.
  • the device extracts at least one feature of data of a region of a target organ using a radio mix technique.
  • the device may extract at least one feature of heart region data.
  • the device may extract radiomix features by using heart region data divided through an artificial intelligence segmentation model as an input value.
  • Feature extraction through radiomix is a process of quantifying various quantitative/statistical characteristics such as shape, signal intensity, and texture of lesions of heart area data using mathematical/statistical techniques.
  • the features extracted using the radio mix technique may include at least one of the features listed in Table 1 below.
  • the device determines a radio mix score. Specifically, the device may select some of the features extracted in step S1107 and determine the Radiomix score by linearly combining the selected features with weights. In this case, weights for each feature may be determined based on the LASSO technique. According to an embodiment, feature selection and weight determination using the LASSO technique may be performed in advance. In this case, the device may check some features according to a predefined rule and perform linear combination of the checked features with weights. there is.
  • the device may determine auxiliary diagnosis information using a random forest model.
  • the device may use the subject's clinical information and Radiomix score.
  • diagnostic auxiliary information may include a calcification score determined based on clinical information and a radiomix score.
  • the device may obtain auxiliary diagnosis information for determining whether or not to detect cardiovascular calcium in the subject based on the heart feature data.
  • the diagnostic auxiliary information may include information about a result of comparing CACS with at least one of information indicating detection or not and information indicating the degree of calcification.
  • radiomics features original firstorder InterquartileRange original firstorder Skewness original gldm DependenceVariance original gldm LargeDependenceLowGrayLevelEmphasis original gldm SmallDependenceLowGrayLevelEmphasis wavelet-LH glcm Idn wavelet-LHglcm MaximumProbability wavelet-HL firstorder 10Percentile wavelet-HL glcm ClusterShade wavelet-HH gldm DependenceVariation wavelet-LL glcm Imc2
  • Radiomix score For the features selected as shown in [Table 2], weights determined according to the LASSO regression technique are assigned, and when linear combination is performed, a Radiomix score is determined. For example, an example of a Radio Mix score is as shown in [Equation 3] below.
  • the performance of the calcification diagnosis technique as shown in FIG. 10 is as follows. As a comparative technique to confirm performance, a diagnostic model using only clinical information was used. Performance comparison is shown in [Table 3] and FIG. 12 below.
  • FIG. 12 illustrates a validation result according to a 10-fold cross-validation method for a calcification diagnosis technique according to an embodiment of the present invention.
  • the CF-RS model which is the proposed technology, shows a higher area under the curve (AUC) than the CF model using only clinical information.
  • the system may provide information to assist in diagnosing a subject's calcification using an artificial intelligence model.
  • the system may provide information indicating whether or not to detect limescale of the target person and information indicating the degree of the detected limescale.
  • Artificial intelligence models to assist diagnosis of calcifications can be designed in various ways.
  • Exemplary methods of the present invention are presented as a series of operations for clarity of explanation, but this is not intended to limit the order in which steps are performed, and each step may be performed concurrently or in a different order, if desired.
  • other steps may be included in addition to the exemplified steps, other steps may be included except for some steps, or additional other steps may be included except for some steps.
  • various embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • It may be implemented by a processor (general processor), controller, microcontroller, microprocessor, or the like.
  • the scope of the present invention is software or machine-executable instructions (eg, operating systems, applications, firmware, programs, etc.) that cause operations according to methods of various embodiments to be executed on a device or computer, and such software or It includes a non-transitory computer-readable medium in which instructions and the like are stored and executable on a device or computer.

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Abstract

L'objectif de la présente invention est d'assister le diagnostic de la calcification sur la base d'un modèle d'intelligence artificielle et un procédé d'assistance au diagnostic de calcification peut comprendre les étapes consistant : à obtenir des données d'image médicale d'un sujet ; à extraire des données sur une région d'un organe cible sur la base des données d'image médicale ; à générer des données de caractéristiques de l'organe cible en analysant les données de la région de l'organe cible ; et à fournir des informations pour assister le diagnostic de calcification de l'organe cible sur la base des données de caractéristiques de l'organe cible.
PCT/KR2022/020801 2022-01-10 2022-12-20 Procédé et dispositif d'assistance au diagnostic de calcification sur la base d'un modèle d'intelligence artificielle WO2023132523A1 (fr)

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KR20190106403A (ko) * 2018-03-09 2019-09-18 연세대학교 산학협력단 질환 예측 방법 및 이를 이용한 질환 예측 디바이스
KR20200056105A (ko) * 2018-11-14 2020-05-22 울산대학교 산학협력단 관상동맥석회화점수 산정 방법 및 장치
WO2020154649A1 (fr) * 2019-01-25 2020-07-30 Cleerly, Inc. Systèmes et procédé de caractérisation de plaques à risque élevé
KR20210069059A (ko) * 2018-09-18 2021-06-10 옥스포드 유니버시티 이노베이션 리미티드 혈관 주위 영역의 레디오믹 시그니처
KR20210073282A (ko) * 2019-12-10 2021-06-18 한국전자통신연구원 영상 처리 장치 및 이를 포함하는 석회화 분석 시스템

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Publication number Priority date Publication date Assignee Title
KR20190106403A (ko) * 2018-03-09 2019-09-18 연세대학교 산학협력단 질환 예측 방법 및 이를 이용한 질환 예측 디바이스
KR20210069059A (ko) * 2018-09-18 2021-06-10 옥스포드 유니버시티 이노베이션 리미티드 혈관 주위 영역의 레디오믹 시그니처
KR20200056105A (ko) * 2018-11-14 2020-05-22 울산대학교 산학협력단 관상동맥석회화점수 산정 방법 및 장치
WO2020154649A1 (fr) * 2019-01-25 2020-07-30 Cleerly, Inc. Systèmes et procédé de caractérisation de plaques à risque élevé
KR20210073282A (ko) * 2019-12-10 2021-06-18 한국전자통신연구원 영상 처리 장치 및 이를 포함하는 석회화 분석 시스템

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