WO2022098109A1 - Dispositif et procédé de détection de tumeur du sein - Google Patents

Dispositif et procédé de détection de tumeur du sein Download PDF

Info

Publication number
WO2022098109A1
WO2022098109A1 PCT/KR2021/015863 KR2021015863W WO2022098109A1 WO 2022098109 A1 WO2022098109 A1 WO 2022098109A1 KR 2021015863 W KR2021015863 W KR 2021015863W WO 2022098109 A1 WO2022098109 A1 WO 2022098109A1
Authority
WO
WIPO (PCT)
Prior art keywords
location
heart sound
breast tumor
sound data
breast
Prior art date
Application number
PCT/KR2021/015863
Other languages
English (en)
Korean (ko)
Inventor
문형곤
김희찬
천종호
안지은
Original Assignee
서울대학교병원
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 서울대학교병원 filed Critical 서울대학교병원
Publication of WO2022098109A1 publication Critical patent/WO2022098109A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0041—Detection of breast cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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

  • It relates to an apparatus and method capable of detecting a tumor in the breast based on heart sound data.
  • breast cancer ranks first in the incidence of cancer among women in Korea. This is the result of an increase in the incidence of breast cancer due to westernized eating habits, obesity, reduction in fertility and lactation, delay in early menarche and menopause, increased stress, and exposure to various pollutants.
  • An object of the present invention is to provide an apparatus and method capable of detecting a tumor in the breast based on heart sound data.
  • an apparatus for detecting a breast tumor includes a heart sound acquisition unit configured to acquire heart sound data for each location measured in a breast of a subject; and a processor for determining the presence possibility and location of a breast tumor based on the acquired heart sound data for each location.
  • the heart sound acquisition unit may include one or more microphones, and may acquire heart sound data for each location by measuring heart sound data at each location of the breast using the one or more microphones.
  • the heart sound acquisition unit may receive the heart sound data for each location from an external device, and acquire the heart sound data for each location.
  • the processor may compare the acquired heart sound data for each location with each other and determine the presence possibility and the location of the breast tumor based on the comparison result.
  • the processor extracts a first feature value from the heart sound data for each location, compares the extracted first feature value, and if there is one or more heart sound data for each location in which the heart sound data for each location and the first feature value are dissimilar to each other, , it can be determined that there is a possibility that there is a breast tumor in the corresponding location.
  • the first feature value may include one of a maximum value, a minimum value, an average value, a median value, and a standard deviation.
  • the processor may determine the presence possibility and the position of the breast tumor from the obtained heart sound data for each location using a machine learning-based breast tumor detection model.
  • the processor may extract a second feature value from heart sound data for each location, and determine the presence possibility and location of the breast tumor from the second feature value using the breast tumor detection model.
  • the breast tumor detection model may be generated in advance through machine learning using a second feature value extracted from heart sound data for training and whether or not a breast tumor corresponding thereto is used as learning data.
  • the second feature value is, mean value, median value, standard deviation, standard absolute deviation, 25th percentile (quantile 25), 75th percentile (quantile 75), interquartile range (interquartile range), skewness (skewness), kurtosis (kurtosis) ), degrees of freedom (entropy), S1 peak, S2 peak, S1-S1 interval, S2-S2 interval, S1-S2 interval, S2-S1 interval, S1-S1 interval/S2-S2 interval, It may include at least one of an S1 peak/S2 peak and a Mel-Frequency Cepstral Coefficient (MCFF) 1 to 13 .
  • MCFF Mel-Frequency Cepstral Coefficient
  • a breast tumor detection method includes: acquiring heart sound data for each location measured in the breast of a subject; and determining the presence possibility and location of the breast tumor based on the acquired heart sound data for each location.
  • the obtaining of the heart sound data for each location may include measuring heart sound data at each location of the breast using one or more microphones to obtain the heart sound data for each location.
  • the obtaining of the heart sound data for each location may include receiving the heart sound data for each location from an external device to obtain the heart sound data for each location.
  • the determining of the possibility and the location of the breast tumor may include comparing the obtained heart sound data for each location and determining the possibility and location of the breast tumor based on the comparison result.
  • the determining of the presence possibility and the position of the breast tumor may include: extracting a first feature value from heart sound data for each position; and comparing the extracted first feature values and determining that if there is one or more heart sound data for each location having a first feature value dissimilar to the heart sound data for each location, there is a possibility that a breast tumor is present at the corresponding location.
  • the first feature value may include one of a maximum value, a minimum value, an average value, a median value, and a standard deviation.
  • the possibility and location of the breast tumor may be determined from the obtained heart sound data for each location using a machine learning-based breast tumor detection model.
  • the determining of the presence possibility and the position of the breast tumor may include: extracting a second feature value from heart sound data for each position; and determining the possibility and location of the breast tumor from the second feature value using the breast tumor detection model.
  • the breast tumor detection model may be generated in advance through machine learning using a second feature value extracted from heart sound data for training and whether or not a breast tumor corresponding thereto is used as learning data.
  • the second feature value is, mean value, median value, standard deviation, standard absolute deviation, 25th percentile (quantile 25), 75th percentile (quantile 75), interquartile range (interquartile range), skewness (skewness), kurtosis (kurtosis) ), degrees of freedom (entropy), S1 peak, S2 peak, S1-S1 interval, S2-S2 interval, S1-S2 interval, S2-S1 interval, S1-S1 interval/S2-S2 interval, It may include at least one of an S1 peak/S2 peak and a Mel-Frequency Cepstral Coefficient (MCFF) 1 to 13 .
  • MCFF Mel-Frequency Cepstral Coefficient
  • FIG. 1 is a diagram illustrating an apparatus for detecting a breast tumor according to an exemplary embodiment.
  • Fig. 2 is a diagram showing an example in which the apparatus for detecting a breast tumor according to an exemplary embodiment is implemented.
  • 3 is a diagram illustrating an example of arrangement of a microphone.
  • FIG. 4 is an exemplary diagram for explaining a relationship between a breast tumor and a first characteristic value of heart sound data for each location.
  • FIG. 5 is a diagram illustrating an apparatus for detecting a breast tumor according to an exemplary embodiment.
  • FIG. 6 is a diagram illustrating a method for detecting a breast tumor according to an exemplary embodiment.
  • each step may occur differently from the specified order unless a specific order is clearly stated in context. That is, each step may be performed in the same order as specified, may be performed substantially simultaneously, or may be performed in a reverse order.
  • each constituent unit is responsible for. That is, two or more components may be combined into one component, or one component may be divided into two or more for each more subdivided function. In addition to the main function in charge of each component, each component may additionally perform some or all of the functions of other components. may be performed.
  • Each component may be implemented as hardware or software, or may be implemented as a combination of hardware and software.
  • FIG. 1 is a diagram illustrating an apparatus for detecting a breast tumor according to an exemplary embodiment.
  • the breast tumor detection apparatus 100 is a device capable of determining the existence possibility and location of a breast tumor by analyzing heart sound data measured in the breast of a subject, and is mounted on an electronic device or wrapped in a housing. and can be formed as a separate device.
  • the electronic device may include a digital stethoscope, a desktop computer, a mobile phone, a smartphone, a tablet, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like;
  • the wearable device may include a bra type, a wrist watch type, a wrist band type, a ring type, a belt type, a necklace type, an ankle band type, a thigh band type, an forearm band type, and the like.
  • the electronic device is not limited to the above-described example, and the wearable device is also not limited to the above-described example.
  • the breast tumor detection apparatus 100 may include a heart sound acquisition unit 110 and a processor 120 .
  • the heart sound acquisition unit 110 may acquire heart sound data for each location measured in the breast of the subject.
  • the location-specific heart sound data is heart sound data measured at each location of the breast, and may include location data and heart sound data measured at the corresponding location.
  • the heart sound acquisition unit 110 may include one or more microphones, and may acquire heart sound data for each location by directly measuring the heart sound data from each location of the breast using the one or more microphones.
  • the location data at which the heart sound data is measured may be input from the user.
  • the heart sound acquisition unit 110 may include an acceleration sensor, and may directly measure the position data from which the heart sound data is measured by using the acceleration sensor.
  • the user may measure heart sound data at a specific location of the breast for the first time, move the heart sound acquisition unit 110 in a desired direction, and measure the heart sound data.
  • the heart sound acquisition unit 110 measures the moving direction and the moving distance of the heart sound acquiring unit 110 through the acceleration sensor, and based on the initial position where the heart sound data was measured, and the moving direction and the moving distance measured through the acceleration sensor. Position data in which each heart sound data is measured may be obtained.
  • the heart sound acquisition unit 110 may acquire the heart sound data for each location by receiving the heart sound data for each location of the subject from an external device that measures and/or stores the heart sound data for each location.
  • the heart sound acquisition unit 110 may use a wired/wireless communication technology.
  • the wireless communication technology is Bluetooth (bluetooth) communication, BLE (Bluetooth Low Energy) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared (Infrared Data Association, IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication, 5G communication, etc. may include, but are not limited thereto.
  • the processor 120 may control the overall operation of the breast tumor detection apparatus 100 .
  • the processor 120 may obtain heart sound data for each location by controlling the heart sound acquisition unit 110 according to a user's request.
  • the processor 120 may generate guide information for inducing a user's motion in order to acquire heart sound data for each location through the heart sound acquisition unit 110 and output the guide information through an output means. For example, when the heart sound acquisition unit 110 measures heart sound data from each location of the breast using one or more microphones, the processor 120 measures heart sound data for a set time at each location to obtain sufficient heart sound data In order to do this, guide information for inducing a user's motion may be generated and outputted through an output means.
  • the processor 120 may determine the presence possibility and the location of the breast tumor based on the obtained heart sound data for each location.
  • the processor 120 may compare the heart sound data for each location with each other and determine the presence possibility and the location of the breast tumor based on the comparison result. For example, the processor 120 may extract a first feature value from heart sound data for each location. In this case, the first feature value may include a maximum value, a minimum value, an average value, a median value, a standard deviation, and the like. In addition, the processor 120 compares the extracted first feature values and determines that there is a possibility that a breast tumor is present at the corresponding location if there is one or more heart sound data for each location having a similar first feature value to the heart sound data for each location. can In this case, the processor 120 may use a clustering algorithm.
  • the processor 120 may determine the existence possibility and the position of the breast tumor from heart sound data for each location using the breast tumor detection model. For example, the processor 120 may extract a second feature value from heart sound data for each location.
  • the second feature value is the mean value, median value, standard deviation, standard absolute deviation, 25th percentile (quantile 25), 75th percentile (quantile 75), interquartile range, skewness, and kurtosis.
  • S1 peak may be a sound produced when the bicuspid valve and tricuspid valve are closed
  • S2 may be a sound produced when the aortic valve and pulmonary valve are closed
  • the processor 120 may determine the presence possibility and the position of the breast tumor from the second feature value using the breast tumor detection model.
  • the breast tumor detection model represents the relationship between the second feature value extracted from the heart sound data and whether there is a breast tumor, and may be generated in advance through machine learning.
  • the breast tumor detection model may be generated in advance through machine learning by using the second feature value extracted from heart sound data for training and whether or not a breast tumor corresponding thereto is used as learning data.
  • the machine learning algorithm is an Artificial Neural Network, Decision Tree, Genetic Algorithm, Genetic Programming, K-Nearest Neighbor, Radial Basis Function Network ( Radial Basis Function Network), random forest (Random Forest), support vector machine (Support Vector Machine), and may include deep-learning (deep-learning), and the like.
  • FIG. 2 is a diagram illustrating an example in which a breast tumor detection apparatus according to an exemplary embodiment is implemented
  • FIG. 3 is a diagram illustrating an example of arrangement of a microphone.
  • the breast tumor detection apparatus 100 may include a housing 210 , a heart sound acquisition unit 110 , and a processor 120 .
  • a heart sound acquisition unit 110 may include a processor 120 .
  • the processor 120 may include a processor 120 .
  • the heart sound acquisition unit 110 and the processor 120 are the same as those described above with reference to FIG. 1 , detailed descriptions thereof will be omitted in the overlapping range.
  • the housing 210 may be formed in a cylindrical shape having a length (l) of 12 cm and a diameter (d) of 2.6 cm, but this is only an exemplary embodiment and is not limited thereto. That is, the shape and size of the housing 210 may be variously changed.
  • the heart sound acquisition unit 110 may be disposed at one end of the housing 210 formed in a cylindrical shape. One surface of the heart sound acquisition unit 110 may be exposed to the outside of the housing 210 to obtain heart sound data by contacting the breast of the subject.
  • the heart sound acquisition unit 110 includes seven microphones 111 , and the seven microphones 111 are spaced apart from each other by a predetermined interval (a) to the heart sound acquisition unit 110 . ) may be disposed on the exposed surface.
  • the predetermined distance a may be 1 cm, but this is only an embodiment and is not limited thereto.
  • the number and arrangement of the microphones 111 may also be variously changed in consideration of the size of the housing and the like.
  • the breast tumor detection apparatus 100 may further include a terminal unit 220 .
  • the terminal unit 220 includes a Universal Serial Bus (USB) terminal and the like, and may be used as an input/output terminal and/or a power terminal.
  • USB Universal Serial Bus
  • FIG. 4 is an exemplary diagram for explaining a relationship between a breast tumor and a first characteristic value of heart sound data for each location.
  • the x-axis and the y-axis may represent positions at which each heart sound data is measured, and the z-axis may represent a first feature value of each heart sound data expressed in an arbitrary unit.
  • the first characteristic value 410 of heart sound data measured at a location where a breast tumor does not exist may have similar values.
  • the first feature value 420 of the heart sound data measured at a location where a breast tumor exists may have a value similar to the first feature value 410 of the heart sound data measured at a location where a breast tumor does not exist. there is. That is, the first characteristic value of the heart sound data may show a different pattern depending on the presence or absence of a breast tumor.
  • the breast tumor detection apparatus 100 may determine the presence possibility and the presence position of the breast tumor by analyzing the heart sound data measured at each position of the breast.
  • FIG. 5 is a diagram illustrating an apparatus for detecting a breast tumor according to an exemplary embodiment.
  • the breast tumor detection apparatus 500 is a device capable of determining the presence possibility and location of a breast tumor by analyzing heart sound data measured in the breast of a subject, and is mounted on an electronic device or wrapped in a housing. and can be formed as a separate device.
  • the electronic device may include a digital stethoscope, a desktop computer, a mobile phone, a smartphone, a tablet, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like;
  • the wearable device may include a bra type, a wrist watch type, a wrist band type, a ring type, a belt type, a necklace type, an ankle band type, a thigh band type, an forearm band type, and the like.
  • the electronic device is not limited to the above-described example, and the wearable device is also not limited to the above-described example.
  • the breast tumor detection apparatus 500 may include a heart sound acquisition unit 110 , a processor 120 , an input unit 510 , a storage unit 520 , a communication unit 530 , and an output unit 540 .
  • a heart sound acquisition unit 110 and the processor 120 are the same as those described above with reference to FIGS. 1 to 4 , a detailed description thereof will be omitted.
  • the input unit 510 may receive various manipulation signals and information from the user.
  • the input unit 510 includes a key pad, a dome switch, a touch pad, a jog wheel, a jog switch, and a H/W button. and the like.
  • the touch pad forms a layer structure with the display, it may be referred to as a touch screen.
  • the storage unit 520 may store programs or commands for the operation of the breast tumor detection apparatus 500 , and may store data input to the breast tumor detection apparatus 500 and processed data. Also, the storage unit 520 may store the acquired heart sound data for each location, a breast tumor detection model, and a result of determining the presence of a breast tumor.
  • the storage unit 520 includes a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, 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), magnetic memory, magnetic disk, optical disk and at least one type of storage medium.
  • the breast tumor detection apparatus 500 may operate an external storage medium such as a web storage that performs a storage function of the storage unit 520 on the Internet.
  • the communication unit 530 may communicate with an external device.
  • the communication unit 530 may transmit data input to the breast tumor detection apparatus 500, stored data, processed data, etc. to an external device, or receive various data for determining the presence of a breast tumor from the external device. .
  • the external device may be a medical device that uses data input to the breast tumor detection device 500 , stored data, processed data, and the like, and a printer or display device for outputting a result.
  • the external device may be a digital TV, a desktop computer, a mobile phone, a smart phone, a tablet, a notebook computer, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a navigation device, an MP3 player, a digital camera, a wearable device, etc. not limited
  • the communication unit 530 may communicate with an external device using a wired/wireless communication technology.
  • the wireless communication technology is Bluetooth (bluetooth) communication, BLE (Bluetooth Low Energy) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared (Infrared Data Association, IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication, 5G communication, etc. may include, but this is only an example, and , but is not limited thereto.
  • the output unit 540 may output data input to the breast tumor detection apparatus 500 , stored data, processed data, and the like. According to an embodiment, the output unit 540 may output the obtained heart sound data for each location and the result of determining the presence of a breast tumor using at least one of an auditory method, a visual method, and a tactile method. To this end, the output unit 540 may include a display, a speaker, a vibrator, and the like.
  • FIG. 6 is a diagram illustrating a method for detecting a breast tumor according to an exemplary embodiment.
  • the breast tumor detection method of FIG. 6 may be performed by the breast tumor detection apparatus 100 or 500 of FIG. 1 or 5 .
  • the apparatus for detecting a breast tumor may acquire heart sound data for each location measured in the breast of the subject ( 610 ).
  • the location-specific heart sound data is heart sound data measured at each location of the breast, and may include location data and heart sound data measured at the corresponding location.
  • the breast tumor detection apparatus may acquire heart sound data for each location by directly measuring heart sound data from each location of the breast using one or more microphones.
  • the breast tumor detection apparatus may acquire heart sound data for each location by receiving heart sound data for each location of the subject from an external device that measures and/or stores heart sound data for each location.
  • the breast tumor detection apparatus may use a wired/wireless communication technology.
  • the wireless communication technology is Bluetooth (bluetooth) communication, BLE (Bluetooth Low Energy) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared (Infrared Data Association, IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication, 5G communication, etc. may include, but are not limited thereto.
  • the breast tumor detection apparatus may determine the presence possibility and the presence location of the breast tumor based on the acquired heart sound data for each location ( 620 ).
  • the apparatus for detecting a breast tumor may mutually compare heart sound data for each location and determine the presence possibility and location of the breast tumor based on the comparison result.
  • the breast tumor detection apparatus may extract a first feature value from heart sound data for each location.
  • the first feature value may include a maximum value, a minimum value, an average value, a median value, a standard deviation, and the like.
  • the breast tumor detection apparatus compares the extracted first feature value, and if there is one or more heart sound data for each location having a similar first feature value to the heart sound data for each location, it is determined that there is a possibility that a breast tumor is present at the corresponding location.
  • the apparatus for detecting a breast tumor may determine the presence possibility and location of a breast tumor from heart sound data for each location using a breast tumor detection model.
  • the breast tumor detection apparatus may extract the second feature value from heart sound data for each location.
  • the second feature value is the mean value, median value, standard deviation, standard absolute deviation, 25th percentile (quantile 25), 75th percentile (quantile 75), interquartile range, skewness, and kurtosis.
  • the breast tumor detection apparatus may determine the presence possibility and the presence location of the breast tumor from the second feature value using the breast tumor detection model.
  • the breast tumor detection model represents the relationship between the second feature value extracted from the heart sound data and whether there is a breast tumor, and may be generated in advance through machine learning.
  • the breast tumor detection model may be generated in advance through machine learning by using the second feature value extracted from heart sound data for training and whether or not a breast tumor corresponding thereto is used as learning data.
  • the above-described embodiments may be implemented as computer-readable codes on a computer-readable recording medium.
  • the computer-readable recording medium may include any type of recording device in which data readable by a computer system is stored. Examples of the computer-readable recording medium may include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, and the like.
  • the computer-readable recording medium may be distributed in network-connected computer systems, and may be written and executed as computer-readable codes in a distributed manner.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Pathology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Biophysics (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Oncology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Physiology (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

Un dispositif de détection de tumeur du sein selon un aspect comprend : une unité d'acquisition de son cardiaque pour acquérir des données de son cardiaque pour chaque emplacement mesuré dans le sein d'un sujet ; et un processeur pour déterminer la possibilité et l'emplacement d'une tumeur du sein sur la base des données de son cardiaque acquises pour chaque emplacement.
PCT/KR2021/015863 2020-11-06 2021-11-04 Dispositif et procédé de détection de tumeur du sein WO2022098109A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020200147827A KR20220061634A (ko) 2020-11-06 2020-11-06 유방 종양 검출 장치 및 방법
KR10-2020-0147827 2020-11-06

Publications (1)

Publication Number Publication Date
WO2022098109A1 true WO2022098109A1 (fr) 2022-05-12

Family

ID=81458127

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2021/015863 WO2022098109A1 (fr) 2020-11-06 2021-11-04 Dispositif et procédé de détection de tumeur du sein

Country Status (2)

Country Link
KR (1) KR20220061634A (fr)
WO (1) WO2022098109A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070038060A1 (en) * 2005-06-09 2007-02-15 Cerwin Stephen A Identifying and treating bodily tissues using electromagnetically induced, time-reversed, acoustic signals
JP2007524461A (ja) * 2003-06-25 2007-08-30 シーメンス　メディカル　ソリューションズ　ユーエスエー　インコーポレイテッド 乳房撮像の自動診断及び決定支援システム及び方法
US20100049093A1 (en) * 2003-12-30 2010-02-25 Galkin Benjamin M Acoustic monitoring of a breast and sound databases for improved detection of breast cancer
US20190142367A1 (en) * 2007-05-04 2019-05-16 Delphinus Medical Technologies, Inc. Patient interface system
JP2020022722A (ja) * 2018-07-27 2020-02-13 公立大学法人公立諏訪東京理科大学 癌発生疑い部位特定装置

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101307162B1 (ko) 2011-11-03 2013-09-11 인제대학교 산학협력단 전자파 유방영상에서 유방종양 분석을 위한 보조 진단 시스템 및 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007524461A (ja) * 2003-06-25 2007-08-30 シーメンス　メディカル　ソリューションズ　ユーエスエー　インコーポレイテッド 乳房撮像の自動診断及び決定支援システム及び方法
US20100049093A1 (en) * 2003-12-30 2010-02-25 Galkin Benjamin M Acoustic monitoring of a breast and sound databases for improved detection of breast cancer
US20070038060A1 (en) * 2005-06-09 2007-02-15 Cerwin Stephen A Identifying and treating bodily tissues using electromagnetically induced, time-reversed, acoustic signals
US20190142367A1 (en) * 2007-05-04 2019-05-16 Delphinus Medical Technologies, Inc. Patient interface system
JP2020022722A (ja) * 2018-07-27 2020-02-13 公立大学法人公立諏訪東京理科大学 癌発生疑い部位特定装置

Also Published As

Publication number Publication date
KR20220061634A (ko) 2022-05-13

Similar Documents

Publication Publication Date Title
Shah et al. Human activity recognition: Preliminary results for dataset portability using FMCW radar
WO2016080669A1 (fr) Montre intelligente ayant une fonction de reconnaissance de source de son biologique
WO2015080342A1 (fr) Dispositif portable et son procédé de commande
WO2017175997A1 (fr) Appareil électronique et son procédé de commande
CN104414686B (zh) 超声诊断设备和操作超声诊断设备的方法
WO2014204092A1 (fr) Dispositif vestimentaire et procédé de communication utilisant le dispositif vestimentaire
WO2016175501A1 (fr) Système de reconnaissance de pas et procédé associé, et support d'informations dans lequel est enregistré un programme de traitement dudit procédé
WO2017018828A1 (fr) Procédé et appareil pour fournir des informations sur des aliments
CN114167984B (zh) 设备控制方法、装置、存储介质及电子设备
WO2020149664A1 (fr) Procédé de fourniture de service sur la base d'informations génétiques portant sur un groupe d'utilisateurs, et dispositif électronique associé
WO2014185753A1 (fr) Procédé pour apparier de multiples dispositifs, et dispositif et système de serveur pour permettre l'appariement
CN109003662A (zh) 医师信息的提供方法、装置、设备及存储介质
WO2022098109A1 (fr) Dispositif et procédé de détection de tumeur du sein
WO2016064115A1 (fr) Procédé de commande de dispositif et dispositif associé
WO2016036197A1 (fr) Dispositif et procédé de reconnaissance de mouvement de la main
CN112204501A (zh) 可穿戴式姿势识别装置及相关的操作方法和系统
WO2016129773A1 (fr) Procédé, dispositif et système pour fournir une rétroaction, et support d'enregistrement lisible par ordinateur non-transitoire
CN114359953A (zh) 指示听诊位置的方法及设备
WO2014133258A1 (fr) Appareil de saisie avec un stylet et procédé de fonctionnement associé
WO2013165195A1 (fr) Appareil pour mesurer une information biologique et procédé pour communiquer des données à partir d'un appareil pour mesurer une information biologique
WO2023013959A1 (fr) Appareil et procédé de prédiction de l'accumulation de bêta-amyloïdes
WO2019225875A1 (fr) Procédé et appareil de suivi d'inventaire
KR102061787B1 (ko) 이미지를 촬영하는 전자 장치 및 이미지 표시 방법
Strohmayer et al. Wifi csi-based long-range through-wall human activity recognition with the esp32
WO2021112391A1 (fr) Dispositif électronique et son procédé de commande

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21889574

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21889574

Country of ref document: EP

Kind code of ref document: A1