WO2019098415A1 - Procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, et dispositif utilisant ledit procédé - Google Patents

Procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, et dispositif utilisant ledit procédé Download PDF

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WO2019098415A1
WO2019098415A1 PCT/KR2017/013015 KR2017013015W WO2019098415A1 WO 2019098415 A1 WO2019098415 A1 WO 2019098415A1 KR 2017013015 W KR2017013015 W KR 2017013015W WO 2019098415 A1 WO2019098415 A1 WO 2019098415A1
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information
cervical cancer
computing device
cervical
analysis information
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PCT/KR2017/013015
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English (en)
Korean (ko)
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정재훈
최성원
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주식회사 버즈폴
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Priority to PCT/KR2017/013015 priority Critical patent/WO2019098415A1/fr
Priority to CN201780004364.9A priority patent/CN110352461A/zh
Priority to KR1020177033520A priority patent/KR20190087681A/ko
Publication of WO2019098415A1 publication Critical patent/WO2019098415A1/fr

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    • 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
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    • 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
    • A—HUMAN NECESSITIES
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    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/43—Detecting, measuring or recording for evaluating the reproductive systems
    • A61B5/4306—Detecting, measuring or recording for evaluating the reproductive systems for evaluating the female reproductive systems, e.g. gynaecological evaluations
    • A61B5/4318—Evaluation of the lower reproductive system
    • A61B5/4331—Evaluation of the lower reproductive system of the cervix
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • the present invention relates to a method for judging whether or not a subject develops cervical cancer and a judgment apparatus using the same.
  • the determination apparatus according to the present invention is a determination apparatus for acquiring an image of the cervix of the subject, and acquiring, from the input of the obtained cervical-radiographic image, And provides analysis information on the incidence of the cervical cancer and provides the generated analysis information so as to allow the user of the determination apparatus or the remote user to read out the incidence of the cervical cancer corresponding to the analysis information Acquires and stores evaluation information on the analysis information as a result of the reading, and outputs the evaluation information.
  • the determination apparatus according to the present invention can re-learn the machine learning model based on the evaluation information.
  • Cervical cancer is the most common cancer ranking among Korean women, because it can be affected by pregnancy and childbirth due to hysterectomy, and it can cause a sense of loss as a woman. According to the statistics for 2013, the number of cervical cancer patients in Korea is 26,207, ranking 4th among female cancer (Ministry of Health and Welfare data). In addition, it is the 7th cancer recommendation in Korea, and it is included in the national cancer screening project in 1999, and the rate of early diagnosis is increasing. In recent years, cervical intraepithelial cancer (precancerous stage) called "0 period" cancer of the cervix is also on the rise, and it is recommended that women who have experience of sexual experience have annual checkups.
  • the proportion of cervical intraepithelial neoplasia in young women is increasing, and the screening target has been lowered from 30 to 20 years from 2016.
  • health insurance benefits apply to 300% of the cost of screening for cervical cytology examinations.
  • it is recommended that screening tests be conducted in parallel with the cervical screening test because the false negative rate (ie, false positive rate) of screening reaches 55%.
  • the market for cervical cancer screening in the world is estimated at 6.86 trillion won Of these, the cervical dilatation test is 30%, reaching about 2 trillion won.
  • FIG. 1 is a conceptual diagram schematically showing a method of examining a cervical cell examination and a cervical dilatation examination, which has been conventionally performed to diagnose cervical cancer. Referring to the bottom of FIG. 1, (For example, the cervical lumen shown in FIG. 1), and analyzing the resultant image and using the result, the misdiagnosis rate of the examination for cervical cancer can be lowered.
  • the medical staff confirms whether the cervical cancer has developed in relation to the image of the cervix in view of education and experience. This method is often repeated and obscure, It can take a long time and the accuracy can drop together.
  • the CDSS Clinical Decision Support System
  • KRW24 trillion up 25% on average
  • CDSS which is specialized for cervical cancer is used as a CDSS method for improving the efficiency of cervical cancer screening and preventing misdiagnosis through a computing device, thereby enabling a medical staff to perform diagnosis of cervical cancer more quickly and accurately. And a judgment device for this purpose is proposed.
  • a method of determining whether a subject has developed cervical cancer comprising: (a) the computing device acquiring an image of the cervix of the subject Supporting another device associated with the computing device to obtain; (b) the computing device generates analysis information on the incidence of the cervical cancer of the subject based on the obtained machine learning model of the cervical cancer from the input of the cervical radiographic image, To generate the generated data; (c) the computing device provides the generated analysis information or provides the other device with information, thereby allowing a user of the computing device or a remote user of the remote location to determine whether the cervical cancer corresponding to the analysis information has occurred Supporting to read; And (d) the computing device is configured to: (i) acquire and store evaluation information for the analysis information as a result of performing the step (c), or support the other device to acquire and store the evaluation information And (ii) performing a process of outputting the evaluation information or supporting the output of the other device.
  • the method further comprises the step of (e) re-learning the machine learning model or allowing the other device to re-learn the machine learning model based on the evaluation information .
  • a computer program recorded on a machine-readable non-volatile storage medium, comprising instructions embodied to perform the above-described method.
  • a computing device for determining whether a subject is affected by cervical cancer, the computing device comprising: a communication unit for obtaining an image of the cervix of the subject; And generating analysis information on the onset of the cervical cancer of the subject based on the obtained machine learning model of the cervical cancer from the input of the cervical radiographic image,
  • the processor is configured to provide the analysis information or to provide the other apparatus with information on whether the cervical cancer corresponding to the analysis information is present or not, (I) acquiring and storing evaluation information on the analysis information as a result of the reading, or (ii) supporting the other device to acquire and store the evaluation information, and (ii) And outputs the evaluation information or causes the other device to output
  • the processor of the computing device re-learns the machine learning model based on the evaluation information.
  • the present invention compared to the conventional method in which a medical staff directly observes a cervical image obtained through a cervical loupe and confirms the state of the cervix individually based on education and experience, it is possible to quickly and accurately determine the onset of cervical cancer There is an effect that can be done.
  • the present invention it is possible to facilitate the division of labor in the medical field by making it possible to read out the cervical cancer even in a remote place away from the photographing site of the cervical image.
  • the present invention has the effect of continuously improving the determination performance through re-learning according to the method of the present invention.
  • FIG. 1 is a conceptual diagram schematically showing a method of cervical cytology examination and cervical dilatation examination which were conventionally performed to diagnose cervical cancer.
  • FIG. 2 is a diagram showing a main concept for explaining a CNN (convolutional neural network) which is one of the machine learning models used in the present invention.
  • FIG. 3 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a method for determining whether a subject develops cervical cancer according to the present invention.
  • FIG. 4 is a flowchart illustrating an exemplary method for determining the incidence of cervical cancer according to the present invention.
  • 5A to 5E are diagrams illustrating exemplary user interfaces (UIs) provided at respective steps of the method for determining the incidence of cervical cancer according to the present invention.
  • UIs user interfaces
  • 'learning' is a term referring to performing machine learning in accordance with a procedure, and is not intended to refer to a mental function such as a human educational activity. Can be understood.
  • FIG. 2 is a diagram showing a main concept for explaining a CNN (convolutional neural network) which is one of the machine learning models used in the present invention.
  • a CNN (convolutional neural network) model can be briefly described as an artificial neural network stacked in multiple layers. That is, this is referred to as a deep neural network in the sense of a network of deep structure.
  • a deep neural network in the sense of a network of deep structure.
  • CNN by learning a large amount of data in a structure of a multi-layer network, It is a form that learns the network by learning the feature automatically and minimizing the error of the objective function through it. It is also expressed as a connection between the nerve cells of the human brain, and is thus a representative of artificial intelligence.
  • CNN is a model suitable for classification of a two-dimensional image such as an image.
  • the CNN is a composite layer for creating feature maps using a plurality of filters (eg, points, lines, and surfaces) by repeating a pooling layer (a sub-sampling layer) that reduces the size of the feature map and extracts features that are invariant to changes in position or rotation It is possible to extract various levels of features from complicated and meaningful high-level features. Finally, if the feature extracted through the fully-connected layer is used as the input value of the existing model, Can be constructed.
  • filters eg, points, lines, and surfaces
  • FIG. 3 schematically shows an exemplary configuration of a computing device that performs a method of determining whether a subject has a cervical cancer incidence according to the present invention (hereinafter referred to as " a method of determining whether or not a cervical cancer has occurred) It is a conceptual diagram.
  • a computing device 300 includes a communication unit 310 and a processor 320.
  • the communication unit 310 communicates with an external computing device (not shown) Communication is possible.
  • the computing device 300 may include a variety of devices, such as routers, switches, and the like, which may include conventional computer hardware (e.g., a computer processor, memory, storage, input and output devices, Electronic communication devices, electronic information storage systems such as Network Attached Storage (NAS) and Storage Area Networks (SAN)) and computer software (i. E., Instructions that cause a computing device to function in a particular manner) System performance.
  • conventional computer hardware e.g., a computer processor, memory, storage, input and output devices, Electronic communication devices, electronic information storage systems such as Network Attached Storage (NAS) and Storage Area Networks (SAN)
  • computer software i. E., Instructions that cause a computing device to function in a particular manner
  • the communication unit 310 of such a computing device can send and receive requests and responses to and from other interworking computing devices.
  • requests and responses can be made by the same TCP session, For example, as a UDP datagram.
  • the communication unit 310 may include a keyboard, a mouse, and other external input devices for receiving commands or instructions.
  • the processor 320 of the computing device may include a hardware configuration such as a micro processing unit (MPU) or a central processing unit (CPU), a cache memory, a data bus, and the like. It may further include a software configuration of an operating system and an application that performs a specific purpose.
  • MPU micro processing unit
  • CPU central processing unit
  • cache memory a cache memory
  • data bus a data bus
  • FIG. 4 is a flowchart illustrating an exemplary method for determining the incidence of cervical cancer according to the present invention.
  • the method for determining the incidence of cervical cancer according to the present invention is characterized in that the communication unit 310 of the computing device 300 acquires an image of the cervix of the subject, (Step S410).
  • the photographed image may be acquired by a predetermined photographing module linked to the computing device 300.
  • the captured image may be captured and obtained by another apparatus located far away from the place where the cervical cancer incidence determination method according to the present invention is performed by the computing apparatus 300, 300 can acquire it.
  • 5A to 5E are diagrams illustrating exemplary user interfaces (UIs) provided at respective steps of the method for determining the incidence of cervical cancer according to the present invention.
  • UIs user interfaces
  • the photographed image 514 obtained by the other device is illustrated as an example in step S410, for example, when the 'Request' button 512 is detected to be pressed
  • the captured image 514 may be transmitted to the computing device 300 so that the computing device 300 acquires the captured image 514.
  • the subject corresponding to the photographed image 514 that is, the subject information as the patient information
  • information on the input time point, which is the acquired time point can also be obtained together.
  • the information on the subject and the input time point can be obtained from the captured image 514 and the captured image 514, May be communicated to the computing device 300 together.
  • the method for determining the incidence of cervical cancer according to the present invention is characterized in that an analysis module (not shown) implemented by the processor 320 of the computing device 300 detects the cervical cancer (S420) generating analysis information on the onset of the cervical cancer of the subject based on a machine learning model of the cervical cancer from the input of the image or supporting the other apparatus to generate the analysis information.
  • an analysis module (not shown) implemented by the processor 320 of the computing device 300 detects the cervical cancer (S420) generating analysis information on the onset of the cervical cancer of the subject based on a machine learning model of the cervical cancer from the input of the image or supporting the other apparatus to generate the analysis information.
  • the machine learning model includes a plurality of previously entered training information, that is, (i) data of a plurality of cervical tomography images, (ii) whether or not there are lesions of cervical cancer in the plurality of cervical- Data, and (iii) if there is a lesion, the processor 320 learns the machine learning model using information including data indicating in which part of the image the lesion is present.
  • the machine learning model may be a CNN (convolutional neural network) model, or a combination of CNN and a support vector machine (SVM).
  • learning can be performed by applying a gradient descent and a backpropagation algorithm based on an image of input training information.
  • the analysis information may include classification information on negative, atypical, positive, and malignant characteristics of the cervical cancer.
  • classification information may include probability information indicating how accurate the classification is.
  • the analytical information may include negative judgment information, such as information on whether the subject is negative or whether the risk is positive or low (low cancer risk vs. high cancer risk).
  • negative judgment information such as information on whether the subject is negative or whether the risk is positive or low (low cancer risk vs. high cancer risk).
  • Acetowhite Epithelium, Mosaic Morphological information such as Erosion or ulceration, Irregular surface contour, Punctation, Atypical Vessels, and Discolaration.
  • the analysis information may be cataloged to correspond to a plurality of photographed images 514 and may be provided to the subject information 520 and the input time point information 522 as exemplarily shown in FIG. 5B.
  • (Denoted as 'suspicious') 524 may be provided depending on the classification information and probability information calculated by the machine learning model and whether or not the onset of the cervical cancer is suspected.
  • 5B illustrates an example in which the 'Evaluation' buttons 526 corresponding to a specific shot image are displayed in the example (FIG. 5B) so that the user can continue to perform subsequent steps following step S420 with respect to the selected specific shot image, Respectively.
  • the method for determining the incidence of cervical cancer may include a preprocessing module implemented by the processor 320 of the computing device 300, (Not shown) may perform the pre-processing on the cervical image or support the other device to perform the preprocessing (S415).
  • the preprocessing may include at least one of image quality enhancement through blurring, histogram smoothing, etc., blurring and noise processing to perform robustness to the illuminance and noise of the photographed image. It will be understood by those of ordinary skill in the art.
  • a method for determining the incidence of cervical cancer is then performed by a read support module (not shown) implemented by the processor 320 of the computing device 300, (S430) supporting the user of the computing device or another user at the remote location to read whether or not the cervical cancer corresponding to the analysis information is caused by providing information or providing the other device with the information .
  • information transmission / reception between the computing device and the other device may be performed by encryption and decryption, for example, AES 128 bit encryption and / Decoding may be applied.
  • all or part of the photographed image may be provided on the user interface for image reading of the other user of the user or the remote site, as exemplarily shown in Fig. 5C, (For example, a rectangle, an arrow, a text input, and the like) while judging whether or not there is an unusual area.
  • the analysis information may be processed in a manner easy for the user to understand, such as reading, and provided through a predetermined display device (display).
  • a predetermined display device display
  • the classification information on the lesion location or lesion included in the analysis information may be displayed in a predetermined format.
  • the user of the computing device 300 by using the analysis information provided through a predetermined display device, the user of the computing device 300, for example, Evaluation information on the classification information may be generated, and evaluation information on the occurrence and classification information of the classification information may be determined by the reading of the remote location.
  • the evaluation information includes information on whether the provided analysis information is correct, that is, whether or not the onset included in the analysis information is correct, and if the classification information included in the analysis information is correct or not, May be included.
  • the computing device 300 may acquire the evaluation information through the communication unit 310.
  • the evaluation information may include information on the quality of the photographed image, for example, information on a technical defect of the photographed image.
  • a technical defect is that it is difficult to accurately determine the photographed image due to excessive mucus or blood in the photographed image, or it is difficult to confirm the incidence of the cervical cancer due to the angle of the photographed image or the position of the photographed region It may also be an image problem with an acetic acid reaction, insufficient acetic acid reaction, or out of focus, overexposure, or underexpression, even though there is an acetic acid reaction.
  • step S430 as exemplarily shown in Fig. 5D, all or part of the photographed image 540, history information 541 of another image previously photographed with respect to the same subject, A subject information exposure area 542 indicating the subject information as inputted through the subject information input area 510, a sound positive determination information input area 543 to which sound positive determination information can be inputted, A technical defect information input area 545 in which information on technical defects of the morphological feature information input area 544 and the shot image 540 to which information can be inputted can be inputted, An artificial intelligence analysis information output area 546 representing information and a user opinion input area 547 by which the user (reader) can input the findings based on the photographed image can be provided on the user interface , Thereby making it easy for the user of the computing apparatus or the remote user to read whether or not the onset of the cervical cancer corresponding to the analysis information is read.
  • a method for determining the incidence of cervical cancer includes: (i) acquiring and storing evaluation information on the analysis information as a result of performing step (S430); or And (ii) performing a process of outputting the evaluation information or supporting the output of the other device (S440).
  • the evaluation information may be processed and provided in the form of a medical result report, for example, a user interface provided for this purpose is shown in FIG. 5E, and the medical result report 550 may include information on the onset of cervical cancer, And the like.
  • the method for determining the incidence of cervical cancer according to the present invention can determine whether the cervical cancer has an onset based on a previously learned machine learning model, As an example of the method for determining the incidence of cervical cancer according to the present invention for taking advantage of this advantage, there is an advantage that the machine learning model can perform more accurate reading, A re-learning module (not shown) implemented by the processor 320 of the computing device 300 re-learns the machine learning model (S450) based on the evaluation information .
  • the present invention in comparison with the conventional method in which the medical staff directly examines the cervical image obtained through the cervical loupe and confirms the state of the cervix individually based on education and experience, There is an effect of quickly and accurately determining the incidence of cervical cancer.
  • An advantage of the techniques described hereinabove with respect to the above embodiments is that it is possible to prevent mistakes, that is, misdiagnosis, of the medical personnel who must accurately determine the cervical cancer despite a large number of diagnoses.
  • the use of machine learning technology allows the physician to analyze and learn the characteristics and forms of lesions of the cervical cancer, which are known only through education and experience of many years, by the computing device itself, It is possible to assist the judgment in cases where it is difficult to determine the incidence of cervical cancer.
  • Objects of the technical solution of the present invention or portions contributing to the prior art can be recorded in a machine-readable recording medium implemented in the form of program instructions that can be executed through various computer components.
  • the machine-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination.
  • the program instructions recorded on the machine-readable recording medium may be those specially designed and constructed for the present invention or may be those known to those of ordinary skill in the computer software arts.
  • machine-readable recording medium examples include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.
  • program instructions include machine language code such as those generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.
  • the hardware device may be configured to operate as one or more software modules for performing the processing according to the present invention, and vice versa.
  • the hardware device may include a processor, such as a CPU or a GPU, coupled to a memory, such as ROM / RAM, for storing program instructions, and configured to execute instructions stored in the memory, And a communication unit.
  • the hardware device may include a keyboard, a mouse, and other external input devices for receiving commands generated by the developers.

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Abstract

L'invention concerne un procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, ainsi qu'un dispositif utilisant ledit procédé. En particulier, un dispositif de détermination selon l'invention : acquiert une image capturée du col de l'utérus d'un sujet; génère des informations d'analyse indiquant si le sujet a développé un cancer du col de l'utérus d'après un modèle d'apprentissage automatique pour le cancer du col de l'utérus et à partir d'une entrée de l'image capturée du col de l'utérus qui a été acquise; et fournit les informations d'analyse générées pour aider un utilisateur du dispositif de détermination ou un autre utilisateur situé à distance à déterminer si un cancer du col de l'utérus s'est développé, qui correspond aux informations d'analyse, puis d'acquérir, de stocker et de générer les informations d'évaluation des informations d'analyse à la suite de la lecture.
PCT/KR2017/013015 2017-11-16 2017-11-16 Procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, et dispositif utilisant ledit procédé WO2019098415A1 (fr)

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PCT/KR2017/013015 WO2019098415A1 (fr) 2017-11-16 2017-11-16 Procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, et dispositif utilisant ledit procédé
CN201780004364.9A CN110352461A (zh) 2017-11-16 2017-11-16 用于确定受试者中是否发生宫颈癌的方法和设备
KR1020177033520A KR20190087681A (ko) 2017-11-16 2017-11-16 자궁경부암에 대한 피검체의 발병 여부를 판정하는 방법

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PCT/KR2017/013015 WO2019098415A1 (fr) 2017-11-16 2017-11-16 Procédé permettant de déterminer si un sujet a développé un cancer du col de l'utérus, et dispositif utilisant ledit procédé

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KR20200139606A (ko) * 2019-06-04 2020-12-14 주식회사 아이도트 자궁경부암 자동 진단 시스템
CN112200253A (zh) * 2020-10-16 2021-01-08 武汉呵尔医疗科技发展有限公司 基于senet的宫颈细胞图像分类方法
EP4042928A4 (fr) * 2019-10-28 2023-11-01 Aidot Inc. Appareil d'acquisition d'images du col de l'utérus
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KR20230099995A (ko) * 2021-12-28 2023-07-05 가천대학교 산학협력단 자궁 경부암의 진단에 대한 정보 제공 방법 및 이를 이용한 자궁 경부암의 진단에 대한 정보 제공용 디바이스

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EP4042928A4 (fr) * 2019-10-28 2023-11-01 Aidot Inc. Appareil d'acquisition d'images du col de l'utérus
CN112200253A (zh) * 2020-10-16 2021-01-08 武汉呵尔医疗科技发展有限公司 基于senet的宫颈细胞图像分类方法

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