WO2022114070A1 - 嚥下評価システムおよび嚥下評価方法 - Google Patents

嚥下評価システムおよび嚥下評価方法 Download PDF

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Publication number
WO2022114070A1
WO2022114070A1 PCT/JP2021/043235 JP2021043235W WO2022114070A1 WO 2022114070 A1 WO2022114070 A1 WO 2022114070A1 JP 2021043235 W JP2021043235 W JP 2021043235W WO 2022114070 A1 WO2022114070 A1 WO 2022114070A1
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Prior art keywords
swallowing
sound
image
subject
evaluation
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English (en)
French (fr)
Japanese (ja)
Inventor
樹彦 苅部
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Fujifilm Corp
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Fujifilm Corp
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Priority to JP2022565412A priority Critical patent/JP7848134B2/ja
Publication of WO2022114070A1 publication Critical patent/WO2022114070A1/ja
Priority to US18/322,393 priority patent/US12419569B2/en
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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
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4205—Evaluating swallowing
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Clinical applications
    • A61B8/0833—Clinical applications involving detecting or locating foreign bodies or organic structures
    • A61B8/085—Clinical applications involving detecting or locating foreign bodies or organic structures for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
    • A61B8/461—Displaying means of special interest
    • A61B8/463—Displaying means of special interest characterised by displaying multiple images or images and diagnostic data on one display
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5292—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves using additional data, e.g. patient information, image labeling, acquisition parameters

Definitions

  • the present invention relates to a swallowing evaluation system and a swallowing evaluation method for evaluating swallowing of a subject using an ultrasonic image.
  • the swallowing ability measurement system of Patent Document 1 acquires an ultrasonic image of the inside of the neck and a swallowing sound of a subject, and synchronizes the acquisition timing of the ultrasonic image and the time change of the frequency of the swallowing sound with each other in time.
  • the swallowing ability of the subject is evaluated by acquiring the moving speed of at least one of the tube wall of the tubular organ of the pharynx of the subject and the food passing through the tubular organ.
  • An object of the present invention is to provide a swallowing evaluation system and a swallowing evaluation method capable of evaluating swallowing of a subject with high accuracy.
  • the swallowing evaluation system includes an ultrasonic probe, an image acquisition unit that acquires an ultrasonic image in the pharynx of a subject by transmitting and receiving an ultrasonic beam using the ultrasonic probe, and swallowing by the subject.
  • An evaluation unit that evaluates the swallowing of a subject by using machine learning that combines the sound acquisition unit that acquires sound, the ultrasonic image acquired by the image acquisition unit, and the swallowing sound acquired by the sound acquisition unit. It is characterized by having and.
  • the evaluation unit evaluates swallowing by inputting an ultrasonic image and a swallowing sound into a neural network. At this time, the evaluation unit inputs an ultrasonic image to the first neural network to calculate the image feature amount, inputs the swallowing sound to the second neural network to calculate the sound feature amount, and calculates the image feature amount. And the sound feature amount can be input to the third neural network to evaluate swallowing.
  • the evaluation unit inputs the swallowing sound to the second neural network, calculates the sound feature amount, and when the sound feature amount exceeds the defined feature amount threshold value, the ultrasonic image and the sound feature amount It is also possible to evaluate swallowing by inputting and into the fourth neural network.
  • the evaluation unit inputs an ultrasonic image to the first neural network to calculate the image feature amount, and when the image feature amount exceeds the defined feature amount threshold value, the swallowing sound and the image feature amount Can also be evaluated for swallowing by inputting to the fifth neural network. Further, the evaluation unit can also evaluate swallowing by inputting both the ultrasonic image and the swallowing sound into the same neural network.
  • the evaluation unit can input the ultrasonic image and the time-series data of the swallowing sound into the neural network.
  • the sound acquisition unit can acquire the swallowing sound during swallowing, and the image acquisition unit can acquire an ultrasonic image after swallowing. Further, the image acquisition unit can also acquire an ultrasonic image at the time of swallowing. In addition, the sound acquisition unit can acquire at least one of the breath sounds before swallowing and the breath sounds after swallowing.
  • the sound acquisition unit can have a microphone built in the ultrasonic probe.
  • the sound acquisition unit may also have a microphone that is independent of the ultrasonic probe and is in contact with the pharynx of the subject.
  • the evaluation unit can output the presence or absence of swallowing residue in the pharynx of the subject as the evaluation result.
  • the evaluation unit can also output the presence or absence of dysphagia of the subject as the evaluation result.
  • the evaluation unit can also output the hardness of the swallowing food suitable for the subject as the evaluation result.
  • the swallowing evaluation system includes a monitor that displays an ultrasonic image acquired by the image acquisition unit and information representing the swallowing sound acquired by the sound acquisition unit.
  • an ultrasonic image in the pharynx of a subject is acquired by transmitting and receiving an ultrasonic beam using an ultrasonic probe, and a swallowing sound by the subject is acquired. It is characterized in that the swallowing of a subject is evaluated by using machine learning in which an ultrasonic image and a swallowing sound are input.
  • the swallowing evaluation system has an ultrasonic probe, an image acquisition unit that acquires an ultrasonic image in the pharynx of the subject by transmitting and receiving an ultrasonic beam using the ultrasonic probe, and a subject.
  • the swallowing of the subject is evaluated by using machine learning that combines the sound acquisition unit that acquires the swallowing sound by the image acquisition unit, the ultrasonic image acquired by the image acquisition unit, and the swallowing sound acquired by the sound acquisition unit. Since it is provided with an evaluation unit, swallowing of a subject can be evaluated with high accuracy.
  • FIG. 1 shows the configuration of the swallowing evaluation system 1 according to the first embodiment of the present invention.
  • the swallowing evaluation system 1 includes an ultrasonic probe 2, a device body 3, and a microphone 4.
  • the ultrasonic probe 2 and the device main body 3 are connected to each other, and the device main body 3 and the microphone 4 are connected to each other.
  • the ultrasonic probe 2 has an oscillator array 11, and a transmission / reception circuit 12 is connected to the oscillator array 11.
  • the device main body 3 has an image generation unit 13, and the image generation unit 13 is connected to the transmission / reception circuit 12 of the ultrasonic probe 2.
  • An image acquisition unit is configured by the transmission / reception circuit 12 and the image generation unit 13.
  • the display control unit 14 and the monitor 15 are sequentially connected to the image generation unit 13.
  • the image memory 16 is connected to the image generation unit 13.
  • the device main body 3 has a sound processing unit 17, and the sound processing unit 17 is connected to the microphone 4.
  • the sound acquisition unit is composed of the microphone 4 and the sound processing unit 17.
  • a sound memory 18 is connected to the sound processing unit 17.
  • the sound memory 18 is connected to the display control unit 14.
  • the evaluation unit 19 is connected to the image memory 16 and the sound memory 18, and the display control unit 14 is connected to the evaluation unit 19.
  • control unit 20 is connected to the transmission / reception circuit 12, the image generation unit 13, the display control unit 14, the image memory 16, the sound processing unit 17, the sound memory 18, and the evaluation unit 19.
  • input device 21 is connected to the control unit 20.
  • the processor 22 is composed of an image generation unit 13, a display control unit 14, a sound processing unit 17, an evaluation unit 19, and a control unit 20.
  • the oscillator array 11 has a plurality of oscillators arranged one-dimensionally or two-dimensionally. Each of these oscillators transmits an ultrasonic wave according to a drive signal supplied from the transmission / reception circuit 12, receives an ultrasonic echo from a subject, and outputs a signal based on the ultrasonic echo.
  • Each oscillator includes, for example, a piezoelectric ceramic represented by PZT (Lead Zirconate Titanate), a polymer piezoelectric element represented by PVDF (Poly Vinylidene Di Fluoride), and PMN-PT (PMN-PT).
  • Lead Magnesium Niobate-Lead Titanate It is composed by forming electrodes at both ends of a piezoelectric body made of a piezoelectric single crystal represented by (lead magnesium niobate-lead titanate solid solution).
  • the transmission / reception circuit 12 transmits ultrasonic waves from the vibrator array 11 and generates a sound line signal based on the received signal acquired by the vibrator array 11.
  • the transmission / reception circuit 12 includes a pulser 31 connected to the oscillator array 11, an amplification unit 32 sequentially connected in series from the oscillator array 11, an AD (Analog Digital) conversion unit 33, and a beam former.
  • AD Analog Digital
  • the pulsar 31 includes, for example, a plurality of pulse generators, and is transmitted from a plurality of oscillators of the oscillator array 11 based on a transmission delay pattern selected according to a control signal from the control unit 20.
  • Each drive signal is supplied to a plurality of oscillators by adjusting the delay amount so that the sound wave forms an ultrasonic beam.
  • a pulsed or continuous wave voltage is applied to the electrodes of the vibrator of the vibrator array 11
  • the piezoelectric body expands and contracts, and pulsed or continuous wave ultrasonic waves are generated from each vibrator.
  • An ultrasonic beam is formed from the combined waves of those ultrasonic waves.
  • the transmitted ultrasonic beam is reflected by, for example, a target such as a site of a subject, and propagates toward the vibrator array 11 of the ultrasonic probe 2.
  • the ultrasonic echo propagating toward the oscillator array 11 in this way is received by each oscillator constituting the oscillator array 11.
  • each of the vibrators constituting the vibrator array 11 expands and contracts by receiving the propagating ultrasonic echo to generate a received signal which is an electric signal, and these received signals are transmitted to the amplification unit 32. Output.
  • the amplification unit 32 amplifies the signal input from each of the vibrators constituting the vibrator array 11, and transmits the amplified signal to the AD conversion unit 33.
  • the AD conversion unit 33 converts the signal transmitted from the amplification unit 32 into digital reception data, and transmits these reception data to the beam former 34.
  • the beam former 34 has a sound velocity or a sound velocity distribution set based on a reception delay pattern selected according to a control signal from the control unit 20, and has a respective reception data converted by the AD conversion unit 33. By giving a delay and adding, so-called reception focus processing is performed. By this reception focus processing, each received data converted by the AD conversion unit 33 is phase-adjusted and added, and a sound line signal in which the focus of the ultrasonic echo is narrowed down is acquired. This sound line signal is sent to the image generation unit 13.
  • the image generation unit 13 has a configuration in which a signal processing unit 35, a DSC (Digital Scan Converter) 36, and an image processing unit 37 are sequentially connected in series.
  • the signal processing unit 35 corrects the attenuation of the sound line signal transmitted from the transmission / reception circuit 12 by the distance according to the depth of the reflection position of the ultrasonic wave, and then performs the envelope detection process to perform the subject.
  • Generates a B-mode image signal which is tomographic image information about the tissue inside.
  • the DSC 36 converts the B-mode image signal generated by the signal processing unit 35 into an image signal according to a normal television signal scanning method (raster conversion).
  • the image processing unit 37 performs various necessary image processing such as gradation processing on the B mode image signal input from the DSC 36, and then displays the B mode image signal in response to a command from the control unit 20. And send to the image memory 16.
  • the B-mode image signal that has been image-processed by the image processing unit 37 is simply referred to as an ultrasonic image.
  • the image memory 16 is a memory for storing and reading an ultrasonic image generated by the image generation unit 13 under the control of the control unit 20.
  • the ultrasonic image stored in the image memory 16 is read out under the control of the control unit 20 and sent to the evaluation unit 19.
  • Examples of the image memory 16 include a flash memory, an HDD (Hard Disc Drive), an SSD (Solid State Drive), an FD (Flexible Disc), and an MO disk (Magneto-Optical disc: optical disc). Magnetic disc), MT (Magnetic Tape), RAM (Random Access Memory), CD (Compact Disc: compact disc), DVD (Digital Versatile Disc), SD card (Secure Digital card) : Secure digital card) or recording media such as USB memory (Universal Serial Bus memory) can be used.
  • a flash memory an HDD (Hard Disc Drive), an SSD (Solid State Drive), an FD (Flexible Disc), and an MO disk (Magneto-Optical disc: optical disc). Magnetic disc), MT (Magnetic Tape), RAM (Random Access Memory), CD (Compact Disc: compact disc), DVD (Digital Versatile Disc), SD card (Secure Digital card) : Secure digital card) or recording media such as USB memory (Universal Serial Bus memory) can be
  • the microphone 4 is arranged independently of the ultrasonic probe 2 and in the vicinity of the throat of the subject, and is for acquiring the swallowing sound of the subject as analog data.
  • the swallowing sound of the subject acquired by the microphone 4 is sent to the sound processing unit 17.
  • the microphone 4 can be touched to the pharynx of the subject by the user's hand, for example, and can be attached to the pharynx of the subject.
  • the microphone 4 has, for example, a mounting tool (not shown) having a shape such as a frame shape for mounting on the neck of the subject, and by mounting this mounting tool on the neck of the subject, the microphone 4 Can also be placed near the pharynx of the subject.
  • the sound processing unit 17 converts the analog data of the swallowing sound acquired by the microphone 4 into digital data, and sends the obtained digital data to the sound memory 18. Further, the sound processing unit 17 generates information representing the swallowing sound such as a waveform graph showing the time change of the amplitude of the swallowing sound based on the digital data of the swallowing sound, and sends this information to the sound memory 18.
  • the sound memory 18 is a memory for storing and reading digital data of the swallowing sound transmitted from the sound processing unit 17 and information representing the swallowing sound such as a waveform graph under the control of the control unit 20. be.
  • the swallowing sound data stored in the sound memory 18 is read out under the control of the control unit 20 and sent to the evaluation unit 19. Further, the information representing the swallowing sound stored in the sound memory 18 is read out under the control of the control unit 20, sent to the display control unit 14, and displayed on the monitor 15.
  • the evaluation unit 19 uses machine learning (multimodal learning) that combines the ultrasonic image transmitted from the image memory 16 and the swallowing sound data of the subject transmitted from the sound memory 18 to obtain the subject. Evaluate swallowing.
  • machine learning multimodal learning
  • the evaluation unit 19 has an image analysis unit 38, a sound analysis unit 39, and an evaluation result output unit 40.
  • the evaluation result output unit 40 is connected to the image analysis unit 38 and the sound analysis unit 39, and the display control unit 14 is connected to the evaluation result output unit 40.
  • the image analysis unit 38 receives an ultrasonic image of the pharynx of the subject from the image memory 16 and inputs the ultrasonic image to the trained first neural network to calculate the image feature amount.
  • the image feature amount is an abnormality in swallowing of the subject, such as the probability that food remains in the pharynx of the subject or the probability that the subject has dysphagia, which is calculated based on the ultrasonic image. It is an index showing the degree of occurrence. It can be determined that the larger the image feature amount is, the higher the probability that the swallowing of the subject is abnormal, and the smaller the image feature amount is, the lower the probability that the swallowing of the subject is abnormal.
  • the first neural network used by the image analysis unit 38 imaged the throats of a plurality of subjects including, for example, the case where food remained in the pyriform sinus and the case where no food remained in the pyriform sinus. Based on the ultrasonic images, the features such as the structure drawn in those ultrasonic images are learned, and the learned features are compared with the features in the input ultrasonic image to output the image feature amount. do.
  • the sound analysis unit 39 receives the swallowing sound data of the subject from the sound memory 18, and inputs the swallowing sound data into the trained second neural network to calculate the sound feature amount.
  • the sound feature amount is the probability that food remains in the pharynx of the subject or the probability that the subject has dysphagia, which is calculated based on the data of the swallowing sound of the subject. It is an index showing the degree of abnormality in swallowing. It can be determined that the larger the sound feature amount is, the higher the probability that the subject's swallowing is abnormal, and the smaller the sound feature amount is, the lower the probability that the subject's swallowing abnormality is occurring.
  • an abnormal noise sound is included and a sound having an abnormal frequency is included as compared with the case where the subject swallows normally. Often. Therefore, when the swallowing sound contains an abnormal noise sound, or when the frequency analysis is performed on the swallowing sound and the peak value of the amplitude is detected in the abnormal frequency band as compared with the normal swallowing sound. It can be determined that there is a high possibility that an abnormality has occurred in the swallowing of the subject.
  • the second neural network used by the sound analysis unit 39 includes, for example, a typical time variation and a typical frequency distribution of the amplitude of a normal swallowing sound learned based on the swallowing sound data of a plurality of subjects.
  • the sound feature amount is output by comparing the time change of the amplitude of the input swallowing sound and the frequency distribution, respectively.
  • the evaluation result output unit 40 inputs the image feature amount calculated by the image analysis unit 38 and the sound feature amount calculated by the sound analysis unit 39 into the trained third neural network, and swallows the subject.
  • the evaluation result such as whether or not a failure has occurred is output.
  • the third neural network learns the relationship between the combination of the image feature value and the sound feature value and the evaluation result regarding the swallowing of the subject based on the evaluation for a plurality of subjects, and the image feature is learned. When the amount and sound feature amount are input, the evaluation result of swallowing is output based on the learning result.
  • the control unit 20 controls each unit of the swallowing evaluation system 1 according to a program or the like recorded in advance. Under the control of the control unit 20, the display control unit 14 performs predetermined processing on the ultrasonic image, the time change of the amplitude of the swallowing sound of the subject, the evaluation result of swallowing, and the like, and displays the image on the monitor 15. ..
  • the monitor 15 performs various displays under the control of the display control unit 14.
  • the monitor 15 includes, for example, a display device such as an LCD (Liquid Crystal Display) and an organic EL display (Organic Electroluminescence Display).
  • the input device 21 is for the user to perform an input operation.
  • the input device 21 is composed of, for example, a keyboard, a mouse, a trackball, a touch pad, a touch panel, and other devices for the user to perform an input operation.
  • the processor 22 having an image generation unit 13, a display control unit 14, a sound processing unit 17, an evaluation unit 19, and a control unit 20 performs various processing on the CPU (Central Processing Unit) and the CPU. It consists of a control program for making it, but FPGA (Field Programmable Gate Array: Feed Programmable Gate Array), DSP (Digital Signal Processor: Digital Signal Processor), ASIC (Application Specific Integrated Circuit: Application Specific Integrated Circuit), GPU It may be configured by using (Graphics Processing Unit) or another IC (Integrated Circuit), or may be configured by combining them.
  • FPGA Field Programmable Gate Array: Feed Programmable Gate Array
  • DSP Digital Signal Processor: Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • GPU GPU It may be configured by using (Graphics Processing Unit) or another IC (Integrated Circuit), or may be configured by combining them.
  • the image generation unit 13, the display control unit 14, the sound processing unit 17, the evaluation unit 19, and the control unit 20 of the processor 22 can be partially or wholly integrated into one CPU or the like. ..
  • step S1 the control unit 20 receives an instruction to start acquiring the swallowing sound of the subject for evaluating the swallowing sound of the subject.
  • the control unit 20 determines that the instruction has been accepted, for example, when the user has input an instruction to start acquiring the swallowing sound via the input device 21.
  • step S1 the user calls the subject to swallow food, and the swallowing of the subject is started.
  • the food that the subject swallows for example, jelly food that is generally distributed for the subject having a dysphagia or the like is used.
  • the swallowing sound when the subject is swallowed is acquired by the microphone 4.
  • the analog data of the swallowing sound acquired by the microphone 4 is converted into digital data by the sound processing unit 17 and stored in the sound memory 18.
  • the sound processing unit 17 generates information representing the swallowing sound such as a waveform graph showing the time change of the amplitude of the swallowing sound, and this information is displayed on the monitor 15 as shown in FIG. 6, for example.
  • the waveform graph W showing the time change of the amplitude of the swallowing sound is displayed as the information representing the swallowing sound.
  • step S3 the control unit 20 determines whether or not to end the acquisition of the swallowing sound. For example, the control unit 20 determines that the acquisition of the swallowing sound is terminated when the user inputs an instruction to end the acquisition of the swallowing sound via the input device 21, and the control unit 20 determines that the acquisition of the swallowing sound is terminated by the user via the input device 21. It is determined that the acquisition of the swallowing sound is continued when the instruction to end the acquisition is not input.
  • step S3 If it is determined in step S3 that the acquisition of the swallowing sound is continued, the process returns to step S2 and the acquisition of the swallowing sound is newly performed. In this way, the processes of steps S2 and S3 are repeated until it is determined in step S3 that the acquisition of the swallowing sound is completed. As a result, the swallowing sound of the subject acquired by repeating steps S2 and S3 during swallowing is stored in the sound memory 18.
  • step S3 when the user determines that the swallowing of the subject has been completed and inputs an instruction to end the acquisition of the swallowing sound via the input device 21, it is determined that the acquisition of the swallowing sound is completed.
  • step S4 an ultrasonic image U of the pharynx after swallowing of the subject is acquired.
  • the ultrasonic image U that visualizes the food remaining in the pyriform sinus can be obtained. Often obtained.
  • the transmission / reception circuit 12 When the ultrasonic image U is captured, the transmission / reception circuit 12 performs reception focus processing using preset sound velocity values under the control of the control unit 20 to generate a sound line signal.
  • the sound line signal generated by the transmission / reception circuit 12 in this way is transmitted to the image generation unit 13.
  • the image generation unit 13 generates an ultrasonic image using the sound line signal transmitted from the transmission / reception circuit 12. For example, as shown in FIG. 6, the ultrasonic image U generated in this way is sent to the display control unit 14 and displayed on the monitor 15.
  • step S5 the control unit 20 determines whether or not to save the ultrasonic image U acquired in step S4 in the image memory 16. For example, when the user inputs an instruction to save the ultrasonic image U via the input device 21, the control unit 20 determines that the ultrasonic image U acquired in step S4 is to be saved, and the ultrasonic image U is determined to be saved. Is stored in the image memory 16, and when the user does not input an instruction to save the ultrasonic image U via the input device 21, it is determined that the ultrasonic image U acquired in step S4 is not saved.
  • the control unit 20 displays the freeze save button B on the monitor 15, and when the freeze save button B is selected by the user via the input device 21, it is displayed on the monitor 15.
  • the ultrasonic image U can be frozen and displayed, and the ultrasonic image U can be saved in the image memory 16, and it can be determined that the ultrasonic image U is not saved when the freeze save button B is not selected.
  • step S5 If it is determined in step S5 that the ultrasonic image U is not to be saved, the process returns to step S4, and the ultrasonic image U is newly acquired. Therefore, the processes of steps S4 and S5 are repeated until it is determined in step S5 that the ultrasonic image U is to be saved. If it is determined in step S5 that the ultrasonic image U is to be saved and the ultrasonic image U is saved in the image memory 16, the process proceeds to step S6.
  • step S6 the evaluation unit 19 describes the swallowing sound data of the subject stored in the sound memory 18 by repeating steps S2 and S3, and the ultrasonic image U stored in the image memory 16 in step S5.
  • the swallowing of the subject is evaluated by using machine learning in combination with.
  • the process of step S6 will be described in detail with reference to the flowchart shown in FIG.
  • step S8 the image analysis unit 38 of the evaluation unit 19 inputs the ultrasonic image U stored in the image memory 16 to the first neural network in step S5, so that an abnormality occurs in swallowing the subject.
  • the image feature amount indicating the degree is calculated.
  • the first neural network is an ultrasonic image of the throats of a plurality of subjects including, for example, a case where food remains in the pyriform sinus and a case where no food remains in the pyriform sinus.
  • step S9 the sound analysis unit 39 of the evaluation unit 19 inputs the data of the swallowing sound at the time of swallowing of the subject acquired by repeating steps S2 and S3 into the second neural network.
  • the sound feature amount indicating the degree of abnormality in swallowing of the subject is calculated.
  • the second neural network was input, for example, with a typical time variation and a typical frequency distribution of the amplitude of the normal swallowing sound learned based on the swallowing sound data of a plurality of subjects.
  • the sound feature amount is output by comparing the time change of the amplitude of the swallowing sound and the frequency distribution, respectively. For example, if the amplitude of the noise sound is detected at an abnormal timing compared to the typical time variation of the amplitude of the normal swallowing sound, or compared to the typical frequency distribution of the normal swallowing sound. When a peak value of amplitude is detected in an abnormal frequency band, a sound feature amount having a large value can be obtained.
  • the evaluation result output unit 40 of the evaluation unit 19 inputs the image feature amount calculated in step S8 and the sound feature amount calculated in step S9 into the third neural network, whereby the subject is subjected to. Output the evaluation result of swallowing.
  • the evaluation result of swallowing for example, the presence or absence of residual food in the pyriform sinus, the presence or absence of suspicion of dysphagia in the subject, and the like are output.
  • the evaluation result output unit 40 determines, for example, the amount of food remaining in the pyriform sinus as "no residue", "a little residue", or "a large amount remains”. It is also possible to output in multiple stages such as "very much remains".
  • the evaluation result output unit 40 can output the hardness of the swallowing food suitable for the evaluated subject as the evaluation result of swallowing.
  • the hardness of the swallowing food for example, the hardness of the swallowing food described in "Swallowing Adjustment Society Classification 2013, Journal of the Japanese Society for Swallowing and Swallowing Rehabilitation 17 (3): 255-267, 2013" can be used. ..
  • step S10 it is calculated based on the image feature amount indicating the degree of abnormality in swallowing of the subject calculated based on the analysis of the ultrasonic image U and the analysis of the swallowing sound of the subject.
  • the final evaluation result of the swallowing of the subject is output based on both of the sound feature amounts indicating the degree of abnormality in the swallowing of the subject. Therefore, for example, the evaluation of swallowing of the subject is evaluated by using only one of the image feature amount calculated based on the analysis of the ultrasonic image U and the sound feature amount calculated based on the analysis of the swallowing sound of the subject. It is possible to obtain more accurate evaluation results than this is done.
  • step S6 By performing the processes of steps S8 to S10 in this way, the process of step S6 is performed.
  • step S6 the process proceeds to step S7.
  • step S7 the information representing the evaluation result of swallowing of the subject output in step S6 is displayed on the monitor 15. As a result, the operation of the swallowing evaluation system 1 according to the first embodiment is completed.
  • the swallowing of the subject is performed by machine learning that combines both the ultrasonic image U of the pharynx of the subject and the swallowing sound of the subject. Since the evaluation is performed, the swallowing of the subject can be evaluated with high accuracy.
  • the transmission / reception circuit 12 is provided in the ultrasonic probe 2, but may be provided in the apparatus main body 3 instead of being provided in the ultrasonic probe 2.
  • the image generation unit 13 is provided in the device main body 3, it may be provided in the ultrasonic probe 2 instead of being provided in the device main body 3.
  • the image generation unit 13 includes a signal processing unit 35, a DSC 36, and an image processing unit 37, of which the signal processing unit 35 can be included in the ultrasonic probe 2.
  • connection method between the ultrasonic probe 2 and the apparatus main body 3 is not particularly limited, and may be a wired connection or a wireless connection. Further, the connection method between the device main body 3 and the microphone 4 is not particularly limited, and a wired connection or a wireless connection may be used. Further, the device main body 3 may be a so-called handheld type that can be easily carried by the user, or may be a so-called stationary type.
  • the microphone 4 is independent of the ultrasonic probe 2, it may be attached to the ultrasonic probe 2 such as being built in the ultrasonic probe 2. In this case, it is not necessary to independently place the microphone 4 in the vicinity of the pharynx of the subject, and the ultrasonic probe 2 is brought into contact with the pharynx of the subject in order to capture the ultrasonic image U. The swallowing sound of the subject can also be obtained.
  • the sound processing unit 17 of the apparatus main body 3 and the microphone 4 form a sound acquisition unit for acquiring the swallowing sound of the subject, but the swallowing evaluation system 1 includes the microphone 4 and the sound acquisition unit 1. It is also possible to have a sound processing unit 17 and a sound acquisition unit independent of the device main body 3. In this case, the swallowing sound data of the subject acquired by the sound acquisition unit is input to the apparatus main body 3.
  • steps S2 to S5 after the swallowing sound of the subject is acquired and stored, the ultrasonic image U after the subject is swallowed is acquired and stored.
  • the ultrasonic image U at the time of swallowing may also be acquired and stored.
  • the image feature amount is calculated in step S8, it is possible to analyze not only the ultrasonic image U after swallowing but also the ultrasonic image U during swallowing, so that the accuracy of the image feature amount is accurate. Can be improved.
  • the image feature amount can be calculated by analyzing the time-series data of the ultrasonic image U acquired within a certain time in step S8.
  • the movement of the structure in the pharynx can be analyzed based on the ultrasonic image U of a plurality of frames, so that the accuracy is higher than that of calculating the image feature amount based on the ultrasonic image U of one frame, for example.
  • the image feature amount can be calculated.
  • the breathing sound contains an abnormal noise sound and a different breathing sound is acquired as compared with the case where the swallowing is performed normally.
  • the sound acquisition unit having the microphone 4 and the sound processing unit 17 acquires the data of the swallowing sound at the time of swallowing, and also the data of the breathing sound before swallowing and the data of the breathing sound after swallowing.
  • the acquired and data of these breathing sounds can also be stored in the sound memory 18.
  • the sound feature amount is calculated by analyzing the swallowing sound data and the respiratory sound data of the subject. Thereby, for example, the sound feature amount can be calculated with higher accuracy than the sound feature amount is calculated based on the swallowing sound of the subject.
  • step S9 is performed after step S8, step S8 may be performed after step S9, and steps S8 and S9 may be performed at the same time.
  • the method of notifying the user of the output result of the evaluation result is not limited to the method of displaying the evaluation result on the monitor 15.
  • a lamp (not shown) may be provided in the swallowing evaluation system 1 to notify the user of the presence or absence of dysphagia of the subject by the emission color and blinking pattern of the lamp.
  • the lamp may be arranged, for example, on the ultrasonic probe 2, the device main body 3, or may be arranged independently of the ultrasonic probe 2 and the device main body 3.
  • the evaluation unit 19 has an image analysis unit 38, a sound analysis unit 39, and an evaluation result output unit 40, and has three neural networks, a first neural network, a second neural network, and a third neural network.
  • the swallowing of the subject is evaluated using a neural network, but the evaluation method of the swallowing of the subject is not limited to this.
  • the evaluation unit 19 evaluates the swallowing of the subject using only the ultrasonic image U of the subject, the swallowing sound of the subject, and the same neural network that has learned the relationship between the combination thereof and the evaluation result. You can also do it.
  • the swallowing evaluation system 1 outputs the swallowing evaluation result using only one neural network, the subject is similar to the case where the three neural networks are used as in the first embodiment. Since the swallowing of the subject is evaluated by machine learning that combines both the ultrasonic image U of the pharynx and the swallowing sound of the subject, the swallowing of the subject can be evaluated with high accuracy.
  • Embodiment 3 For example, it is also possible to output an evaluation of swallowing of a subject by using two neural networks.
  • FIG. 8 shows the configuration of the evaluation unit 19A in the third embodiment.
  • the evaluation unit 19A has a sound analysis unit 39 and an evaluation result output unit 40A, and the evaluation result output unit 40A is connected to the sound analysis unit 39. Further, the display control unit 14 is connected to the evaluation result output unit 40A.
  • the sound analysis unit 39 receives the data of the swallowing sound of the subject, and inputs the data of the swallowing sound to the second neural network to obtain the sound feature amount. calculate. The calculated sound feature amount is output to the evaluation result output unit 40A.
  • the evaluation result output unit 40A has a sound feature amount threshold value determined for the sound feature amount, and the sound feature amount calculated by the sound analysis unit 39 exceeds the sound feature amount threshold value. Judge whether or not. Further, when the sound feature amount calculated by the sound analysis unit 39 exceeds the sound feature amount threshold, the evaluation result output unit 40A has the sound feature amount and the ultrasonic image generated by the image generation unit 13. By inputting U to the fourth neural network, the evaluation result of swallowing of the subject is output.
  • the fourth neural network used by the evaluation result output unit 40A analyzes the input ultrasonic image U in the same manner as the first neural network used by the image analysis unit 38 in the first embodiment.
  • the calculated image feature amount and the sound feature amount transmitted from the sound analysis unit 39 are calculated in the same manner as in the third neural network used by the evaluation result output unit 40 in the first embodiment.
  • the evaluation result of swallowing of the subject is output based on.
  • step S11 the sound analysis unit 39 calculates the sound feature amount by inputting the data of the swallowing sound of the subject into the second neural network.
  • step S12 the evaluation result output unit 40A determines whether or not the sound feature amount calculated in step S11 exceeds the sound feature amount threshold value. When it is determined that the sound feature amount calculated in step S11 exceeds the sound feature amount threshold value, the evaluation result output unit 40A considers that there is a high possibility that some abnormality has occurred in the swallowing of the subject. After determining, the process of step S13 is performed.
  • step S13 the evaluation result output unit 40A inputs the sound feature amount calculated in step S11 and the ultrasonic image U obtained by photographing the pharynx of the subject into the fourth neural network, whereby the ultrasonic image U is obtained.
  • the evaluation result of swallowing including the analysis result is output.
  • step S12 when it is determined in step S12 that the sound feature amount is equal to or less than the sound feature amount threshold value, the evaluation result output unit 40A determines that it is unlikely that an abnormality has occurred in swallowing the subject. Then, the process of step S14 is performed.
  • step S14 the evaluation result output unit 40A calculates the swallowing evaluation result based on the analysis result of the swallowing sound in step S11.
  • the evaluation result output unit 40A does not use the fourth neural network, and the result that the sound feature amount is equal to or less than the sound feature amount threshold value in step S12 causes an abnormality in swallowing of the subject. Judging that the possibility is low, the evaluation result that no abnormality has occurred in swallowing of the subject is output. In this case, the evaluation result output unit 40A can output the evaluation result of swallowing of the subject by using only the second neural network. In this way, the operation of swallowing evaluation in the third embodiment is completed.
  • the two neural networks of the second neural network and the fourth neural network are used, and the sound feature amount is increased. Since only the second neural network is used when the sound feature amount is equal to or less than the threshold value, the calculation load of the processor 22 when using the neural network can be reduced as compared with the first embodiment. .. Therefore, in the third embodiment, it is possible to reduce the processing time and power required for the evaluation of swallowing of the subject due to the computational load of the processor 22.
  • the device main body 3 when the device main body 3 is a so-called handheld type or portable type, it is preferable that power is supplied to each part of the device main body 3 by a battery (not shown) that is easy to carry. Further, when the device main body 3 is a handheld type or a portable type, the size of the device main body 3 is smaller than that of the so-called stationary type, so that it is difficult to mount the processor 22 having a large size and high computing power. .. Therefore, the embodiment of the third embodiment is particularly useful when the device main body 3 is a handheld type or a portable type.
  • the swallowing sound can be acquired with relatively high sensitivity, it is better to make a judgment based on the swallowing sound than to judge whether or not an abnormality has occurred in the swallowing of the subject by the ultrasonic image U. , A more accurate judgment is possible.
  • the sound feature amount is calculated, and when the calculated sound feature amount exceeds the sound feature amount threshold value, the swallowing sound is evaluated by adding the analysis result of the ultrasonic image U. Therefore, the swallowing of the subject can be evaluated with high accuracy.
  • FIG. 10 shows the configuration of the evaluation unit 19B in the fourth embodiment.
  • the evaluation unit 19B has an image analysis unit 38 and an evaluation result output unit 40B, and the evaluation result output unit 40B is connected to the image analysis unit 38. Further, the display control unit 14 is connected to the evaluation result output unit 40B.
  • the image analysis unit 38 receives the ultrasonic image U obtained by photographing the pharynx of the subject, and inputs the ultrasonic image U to the first neural network. Calculate the image feature amount. The calculated image feature amount is output to the evaluation result output unit 40B.
  • the evaluation result output unit 40B has an image feature amount threshold value determined for the image feature amount, and the image feature amount calculated by the image analysis unit 38 exceeds the image feature amount threshold value. Judge whether or not. Further, the evaluation result output unit 40B has the image feature amount and the sound having the microphone 4 and the sound processing unit 17 when the image feature amount calculated by the image analysis unit 38 exceeds the sound feature amount threshold value.
  • the fifth neural network used by the evaluation result output unit 40B analyzes the input swallowing sound data in the same manner as the second neural network used by the sound analysis unit 39 in the first embodiment.
  • the calculated sound feature amount and the image feature amount transmitted from the image analysis unit 38 are calculated in the same manner as in the third neural network used by the evaluation result output unit 40 in the first embodiment.
  • the evaluation result of swallowing of the subject is output based on.
  • step S21 the image analysis unit 38 calculates the image feature amount by inputting the ultrasonic image U obtained by photographing the pharynx of the subject into the first neural network.
  • step S22 the evaluation result output unit 40B determines whether or not the image feature amount calculated in step S21 exceeds the image feature amount threshold value. In step S22, when it is determined that the image feature amount calculated in step S21 exceeds the image feature amount threshold value, the evaluation result output unit 40B may have some abnormality in swallowing the subject. It is determined that the property is high, and the process of step S23 is performed.
  • step S23 the evaluation result output unit 40B inputs the image feature amount calculated in step S21 and the swallowing sound data of the subject into the fifth neural network, and swallows in consideration of the analysis result of the swallowing sound.
  • the evaluation result of is output.
  • step S22 when it is determined that the image feature amount is equal to or less than the image feature amount threshold value, the evaluation result output unit 40B determines that it is unlikely that an abnormality has occurred in swallowing the subject. Then, the process of step S24 is performed.
  • step S24 the evaluation result output unit 40B calculates the evaluation result of swallowing based on the analysis result of the swallowing sound in step S21.
  • the evaluation result output unit 40B does not use the fifth neural network, and the result that the image feature amount is equal to or less than the image feature amount threshold value in step S22 causes an abnormality in swallowing of the subject. Judging that the possibility is low, the evaluation result that no abnormality has occurred in the swallowing of the subject is output.
  • the evaluation result output unit 40B can output the evaluation result of swallowing of the subject using only the first neural network. In this way, the operation of swallowing evaluation in the fourth embodiment is completed.
  • the two neural networks, the first neural network and the fifth neural network are used only when the image feature amount exceeds the image feature amount threshold, and the image feature amount is increased. Since only the first neural network is used when the image feature amount is equal to or less than the threshold value, the calculation load of the processor 22 when using the neural network can be reduced as compared with the first embodiment. .. Therefore, in the fourth embodiment, similarly to the third embodiment, the processing time and power required for the evaluation of swallowing of the subject due to the computational load of the processor 22 can be reduced.
  • the embodiment of the fourth embodiment is particularly useful when the device main body 3 is a handheld type or a portable type, similarly to the embodiment of the third embodiment.
  • 1 swallowing evaluation system 2 ultrasonic probe, 3 device body, 4 microphone, 11 oscillator array, 12 transmission / reception circuit, 13 image generation unit, 14 display control unit, 15 monitor, 16 image memory, 17 sound processing unit, 18 sound Memory, 19, 19A, 19B evaluation unit, 20 control unit, 21 input device, 22 processor, 31 pulser, 32 amplification unit, 33 AD conversion unit, 34 beam former, 35 signal processing unit, 36 DSC, 37 image processing unit, 38 image analysis unit, 39 sound analysis unit, 40, 40A, 40B evaluation result output unit, B freeze save button, U ultrasonic image, W waveform graph.

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