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WO2024237917A1 - Patch à ultrasons pouvant être porté pour surveiller des sujets en mouvement à l'aide de l'apprentissage automatique et d'un dispositif électronique sans fil - Google Patents

Patch à ultrasons pouvant être porté pour surveiller des sujets en mouvement à l'aide de l'apprentissage automatique et d'un dispositif électronique sans fil Download PDF

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Publication number
WO2024237917A1
WO2024237917A1 PCT/US2023/022674 US2023022674W WO2024237917A1 WO 2024237917 A1 WO2024237917 A1 WO 2024237917A1 US 2023022674 W US2023022674 W US 2023022674W WO 2024237917 A1 WO2024237917 A1 WO 2024237917A1
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acoustic waves
ultrasonic
monitoring
ultrasonic acoustic
analog front
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Sheng Xu
Muyang LIN
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University of California Berkeley
University of California San Diego UCSD
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University of California Berkeley
University of California San Diego UCSD
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    • 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
    • A61B8/42—Details of probe positioning or probe attachment to the patient
    • A61B8/4209—Details of probe positioning or probe attachment to the patient by using holders, e.g. positioning frames
    • A61B8/4236—Details of probe positioning or probe attachment to the patient by using holders, e.g. positioning frames characterised by adhesive patches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4483—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer
    • A61B8/4488—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer the transducer being a phased array
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
    • G01S15/8909—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration
    • G01S15/8915—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration using a transducer array
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
    • G01S15/8909—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration
    • G01S15/8929—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration using a three-dimensional transducer configuration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52079—Constructional features
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/02—Measuring pulse or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/04—Measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/06—Measuring blood flow
    • A61B8/065—Measuring blood flow to determine blood output from the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4444—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to the probe
    • A61B8/4472—Wireless probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/56—Details of data transmission or power supply
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52079—Constructional features
    • G01S7/5208—Constructional features with integration of processing functions inside probe or scanhead
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N39/00—Integrated devices, or assemblies of multiple devices, comprising at least one piezoelectric, electrostrictive or magnetostrictive element covered by groups H10N30/00 – H10N35/00

Definitions

  • the artificial intelligence techniques employ models that are generalizable to allow physiological monitoring to be performed on different subjects.
  • the selected monitoring channel received by the digital circuit is dynamically selected in real-time by the back-end computing environment to accommodate motion of tissue relative to the conformal ultrasonic transducer array.
  • the physiological parameter being monitored is selected from the group including blood pressure, heart rate, pulse wave velocity, stroke volume, cardiac output, augmentation index, and expiratory volume.
  • the digital circuit includes a wireless communication circuit for communicating with the back-end computing environment.
  • the backend computing environment is configured to measure a shift, the shift in the time domain, in a detected peak of the received reflected acoustic wave, the shift due to movement of an organ or tissue, and wherein the displayed indication of the monitored physiologic parameter is based on the measured shift.
  • the analog front end is further configured to steer or direct the generated ultrasonic acoustic waves toward an organ, tissue, or location of interest, the steering or directing by beamforming.
  • step (c) is also performed by components within the integrated conformable wearable device.
  • step (c) is performed by a back-end computing environment located external to the integrated conformable wearable device and the method further comprises: transmitting data concerning the received reflected/transmitted ultrasonic waves from the conformable integrated wearable device to the back-end computing device; and receiving from the back-end computing device at least an identifier of the selected monitoring channel.
  • the selected monitoring channel is dynamically selected in real-time at least in part using artificial intelligence techniques.
  • the artificial intelligence techniques identify sensing channels that cause reflected ultrasonic acoustic waves to be reflected from specified tissue that is to be monitored. [029] In accordance with another aspect of the subject matter disclosed herein, the artificial intelligence techniques identify sensing channels that cause ultrasonic acoustic waves to be transmitted to specified tissue that is to be monitored. [030] In accordance with another aspect of the subject matter disclosed herein, the artificial intelligence techniques employ models that are generalizable to allow physiological monitoring to be performed on different subjects. [031] In accordance with another aspect of the subject matter disclosed herein, the selected monitoring channel is dynamically selected in real-time to accommodate motion of tissue relative to the conformal ultrasonic transducer array.
  • the physiological parameter being monitored is selected from the group including blood pressure, heart rate, stroke volume, cardiac output, augmentation index, and expiratory volume.
  • the integrated conformable wearable device includes a wireless communication circuit for communicating with the back-end computing environment.
  • the wireless communication circuit is a Wi-Fi communication circuit.
  • the displaying includes transmitting an indication of the reflected ultrasonic acoustic waves arising from use of the selected monitoring channel to an external computing environment for display thereon.
  • a method for monitoring a physiologic parameter includes: (a) determining a location of interest, the location associated with the physiologic parameter to be monitored; (b) transmitting ultrasonic acoustic waves toward the location of interest and receiving resulting ultrasonic acoustic waves transmitted through the location of interest using a plurality of sensing channels; (c) dynamically selecting a monitoring channel in real- time from among the plurality of sensing channels; (d) monitoring the physiological parameter in real-time by transmitting ultrasound acoustic waves toward the location of interest and receiving resulting ultrasonic acoustic waves transmitted through the location of interest using the selected monitoring channel; (e) outputting data reflective of the monitored physiological parameter; and (f) wherein at least steps (b) and (d) are performed by components within the conformable
  • Figs.1a-1d provide an overview of the fully integrated ultrasonic-system-on- patch (USoP).
  • Figs.2a-2e illustrate the monitoring and analysis of tissue interface motions using the USoP.
  • Figs.3a-3f illustrate the autonomous and continuous blood pressure recording in a moving subject.
  • Figs.4a-4f illustrate the continuous monitoring process during exercise.
  • Figs.5a-5d present data characterizing bandwidth, axial resolution, and penetration of the stretchable ultrasonic probes.
  • Figs.6a-6d shows schematics and the control sequence for ultrasonic sensing.
  • Figs.7a-7d illustrate deformation of the packaged USoP.
  • Figs.8a-8g illustrate the skin integration of the conformal USoP device.
  • Figs.9a-9d show pulse wave propagation paths and pulse wave velocity (PWV) measurements.
  • Figs.10a-10e show the validation metrics of four models on ideal and compromised image datasets.
  • Figs.11a-11c illustrate the continuous monitoring process during high- intensity interval training (HIIT).
  • Figs.12a-12d show ultrasonic devices for wearable or point-of-care applications.
  • Figs.13a-13b show probe layout designs for reducing noise coupling.
  • Fig.14 demonstrates the improved axial resolution that arises with a backing layer.
  • Figs.15a-15b show radiofrequency signals collected from the carotid artery with and without gel.
  • Figs.16a-16b show results of a durability test of the soft probe.
  • Figs.17a-17e show layout and beam profile designs of three soft probes.
  • Figs.18a-18d show characterization data for the detachable ACF connection.
  • Figs.19a-19c show layout designs of the fPCB circuit.
  • Fig.20 shows schematic connections of the analog front-end and wireless data acquisition module.
  • Figs.21a-21d illustrate the foldability of the fPCB.
  • Figs.22a-22c show designs of the mold for the elastomeric package.
  • Figs.23a-23e show mechanical simulations of the fPCB and the elastomeric package.
  • Fig.24 compares the raw signal frequency and circuit sampling rate of representative wearable physiological monitors.
  • Figs.25a-25c illustrate the wireless transmission of the ultrasonic signals via Wi-Fi.
  • Figs.26a-26b illustrate the power consumption and battery life of the USoP.
  • Figs.27a-27d illustrate multi-mode sensing with wearable ultrasonic probes.
  • Figs.28a-28g illustrate the lateral and elevational resolution of the soft probes.
  • Figs.29a-29e illustrate the transmission beam patterns with elevational deformation.
  • Figs.30a-30b show simulated B-mode images of point sources with azimuthal bending.
  • Figs.31a-31c illustrate tissue interfacial motion detection using the auto- correlation method.
  • Figs.32a-32g show probe positions and acoustic views of different bio- interface measurements.
  • Figs.33a-33b show fractional shortening measurements using a commercial ultrasonic system.
  • Figs.34a-34b show calculations of expiratory volumes.
  • Figs.35a-35b illustrate model training and validation with modified datasets
  • Figs.36a-36e illustrate the process of classifying carotid artery images by the image processing and logistic model.
  • Fig.37 illustrates the statistical validation of the prediction of the best channel for carotid artery sensing against the ground truth.
  • Figs.38a-38c illustrate the process of recording head rotation.
  • Figs.45a-45b show representative pressure waveforms recorded during cycling and HIIT.
  • Figs.46a-46b show measurements of the AIx.
  • Figs.47a-48b show measurements of the arterial stiffness index ( ⁇ ) before, during, and after exercise.
  • Figs.48a-48b show muscle recruitments and corresponding AIx during cycling and HIIT.
  • Fig.49 shows an estimation of the stroke volume by the pulse contour method.
  • Figs.50a-50d illustrate acquisition errors in conventional ultrasonography.
  • Fig.51 is a flowchart illustrating one example of a method for monitoring a physiologic parameter that may be performed by various embodiments of the USoP described herein.
  • Described herein is a fully integrated autonomous ultrasonic-system-on-patch (USoP).
  • the USoP integrates the ultrasonic probe and miniaturized wireless control electronics in a soft, wearable format, which overcomes the above-mentioned limitations.
  • Multiple channels of deep tissue signals acquired from the subject are conditioned and preprocessed on-board, then wirelessly transferred to a backend receiver, where they are analyzed by a customized machine learning algorithm.
  • the algorithm classifies the data and selects the best channel in real time, yielding a continuous data stream from the target tissue. Therefore, this technology allows continuous monitoring of deep tissue signals during human motion.
  • the fully integrated autonomous USoP eliminates the operator dependency of conventional ultrasonography, standardizes the data interpretation process, and therefore expands the accessibility of this powerful diagnostic tool in both inpatient and outpatient settings.
  • the back-end receiver analyzes the channels using machine learning algorithms, more generally any suitable artificial intelligence algorithms and techniques may be employed.
  • the USoP hardware consists of an ultrasound probe and control electronics which are fabricated in a miniaturized, soft format (Fig.1a).
  • the ultrasonic probe is made of piezoelectric transducers, backing materials, serpentine interconnects, and contact pads, similar to our reported structures, illustrative examples of which are shown in the following references which are hereby incorporated by reference in their entirety: Wang, C. et al.
  • the probes of center frequencies from 2 MHz to 6 MHz to achieve the desired bandwidth, axial resolution, and penetration.
  • the 2 MHz transducers achieve a depth of ⁇ 164 mm with an axial resolution of ⁇ 600 ⁇ m for targeting visceral organs (e.g., heart and diaphragm).
  • the 4 MHz transducers achieve a depth of ⁇ 78 mm with an axial resolution of ⁇ 330 ⁇ m for targeting major arteries (e.g., aorta, carotid, and femoral arteries).
  • the 6 MHz transducers achieve a depth of ⁇ 9 mm and an axial resolution of ⁇ 230 ⁇ m for targeting smaller peripheral arteries (e.g., radial and brachial arteries) (Fig.5).
  • the control electronics are designed as a flexible printed circuit board (Fig.19 and Table 2) for ultrasonic sensing and wireless communication.
  • the circuitry includes an analog front-end (AFE) and a data acquisition (DAQ) module (Fig.1b and Fig.20).
  • AFE analog front-end
  • DAQ data acquisition
  • the AFE achieves ultrasonic sensing through coordinated sequence control of multiple components (see Fig.6 and the section below entitled Sequence control of the ultrasonic sensing).
  • the sequencer initiates sensing by sending trigger signals to the pulse generator and multiplexer.
  • the pulse generator reads the trigger signals and outputs high-voltage impulses to activate the ultrasound transducers. Meanwhile, the multiplexer drives the arrayed transducers to generate ultrasound and receive echoes. Finally, the echoes are collected by the transmit/receive switch, and then amplified and filtered by the receiver circuit. After the AFE completes the ultrasonic sensing process, the analog echoes are relayed to the DAQ module.
  • the microcontroller unit samples the echoes with a built-in analog-to-digital converter, and then the Wi-Fi module wirelessly transmits the digitalized echoes to a terminal device (e.g., a smartphone or a computer), where an online machine learning algorithm and an application program can process and display the signals autonomously (Fig.1c).
  • a terminal device e.g., a smartphone or a computer
  • an online machine learning algorithm and an application program can process and display the signals autonomously
  • Fig.1c a terminal device
  • the AFE and the DAQ modules are interconnected by serpentine wires that allow for folding to minimize their footprint (Fig.21).
  • An elastomeric encapsulation mitigates strain concentrations and protects the circuit from irreversible deformations (Figs.22-23 and Methods).
  • the fully integrated system can be bent, stretched, and twisted (Fig.7) and be conformally laminated on the human body (Fig.1a, Fig.8).
  • the ultrasonic probes have MHz-level bandwidth, significantly higher than other common sensors (Fig.24). Therefore, achieving high sensing bandwidths and sampling rates is critical for the circuitry design.
  • the DAQ has a sampling rate of 12 Msps corresponding to a sensing bandwidth of 6 MHz.
  • the Wi-Fi module can transmit such wide-band signals at a distance of ⁇ 10 m and a speed of 3.4 Mbps with zero data loss (Fig.25).
  • the USoP system has a power consumption of ⁇ 614 mW.
  • a standard 3.7 V commercial lithium-polymer battery can enable continuous operation for up to 12 hours (Fig.26).
  • the USoP can perform tissue sensing in multiple modalities, including amplitude mode (A-mode), brightness mode (B-mode), and motion mode (M-mode), to reveal the tissue structures and interface movements (see Fig.27, the section below entitled Multi-mode ultrasonic sensing, Fig.1d).
  • A-mode amplitude mode
  • B-mode brightness mode
  • M-mode motion mode
  • A-mode and M-mode the elevational and lateral resolutions show a degrading trend when the sensing depth increases (see Fig.28 and the section below entitled Multi-mode ultrasonic sensing).
  • the augmentation index increases with exercise and recovers with resting; when the exercise is sufficiently long, as in the case of cycling, the augmentation index stabilizes (Fig.4e).
  • the increase in the augmentation index during exercise may have two causes: vessel stiffening and vasodilation.
  • vessel stiffening and vasodilation We measure the change in the arterial stiffness index before, during, and after exercise (Fig.47 and Methods). The results suggest a negligible change ( ⁇ 0.34%) in the stiffness index. Additionally, such a negligible change in the stiffness index leads to a central blood pressure error ⁇ 1.58 mmHg after calibration, which proves the reliability of the blood pressure recordings during exercise (see Fig.47 and the section below entitled Changes in arterial stiffness index and errors in blood pressure calibration during exercise).
  • the measured cardiac output increases as the exercise intensifies, and the heart rate increases together with the cardiac output.
  • the stroke volume increases before plateauing as end-systolic volume approaches the mechanical limits of the heart and the increase of end-diastolic volume begins to be limited by the shorter filling times at higher heart rates.
  • the stroke volume plateaus, and the increase in cardiac output is mainly attributed to the increase in heart rate.
  • HIIT produces a greater increase in stroke volume and a higher maximum cardiac output, indicating that HIIT may be a more effective training modality for enhancing cardiac functions.
  • the microcontroller sent trigger signals to allow the pulse generator to output a high-voltage impulse, and the receiver circuit then received the echo signals from the transducer (Fig.6c).
  • the sensing-enable voltage was set to be logical low for 680 ⁇ s.
  • the sequencer sent a series of digital signals to the multiplexer, including the clock (CLK), reset (RES), digital input (D in ), and latch enable ( L E ). These digital signals functionalized the shift register and latch in the multiplexer for transducer selection.
  • An example channel selection sequence was shown in Fig.6d.
  • An ultrasound beam was generated to penetrate the tissue layers, and then the beam was reflected by tissue interfaces of mismatched acoustic impedances.
  • the tissue impedance information was then encoded in the amplitudes of the ultrasonic reflections, while the depth information was encoded in the acoustic time-of-flight.
  • An example of A-mode sensing is shown by the arterial diameter measurement using a 4 MHz probe (Fig.27a left).
  • the posterior and anterior wall reflections were captured as the local maximums in the echo amplitude.
  • the arterial diameter could be calculated from the acoustic time-of-flight and acoustic speed in tissues (7 Fig.27a right).
  • Such a sensing mode can be used for spatial detection of target arteries or guiding catheterization.
  • the arterial pulse amplitudes and the mapped location of the brachial artery are shown in Fig.27c.
  • the lateral and elevational resolutions of the arrayed probes could be defined by the transmission beam patterns in A-mode and M-mode.
  • a single transducer would transmit a narrow beam.
  • the real beam would spread laterally and elevationally. With such a spread beam pattern, two adjacent objects with a spacing smaller than the beam width cannot be differentiated by the transducer.
  • the auto-correlation decoding is based on envelope shifting, thus it is not sensitive to the transducer bandwidth or ringing in the radiofrequency signals as long as the envelope can roughly maintain its profile during shifting.
  • the tissue interfaces in this study such as arterial pulsation, cardiac contraction, and diaphragmic movement, were of varying depths and excursion amplitudes, as summarized in Table 2. [0139] Therefore, a proper selection of ultrasonic probes was needed to fit the specific sensing depths and resolutions.
  • the waveforms in Fig.2a were collected from a healthy 25-year-old participant.
  • a 6 MHz 2D probe was used for arterial pulsations in shallow arteries with minimum excursions ( ⁇ 0.05 mm), such as the radial (2 mm deep) and brachial arteries (4 mm deep).
  • a 4 MHz linear array probe was used for deeper arteries with medium excursions ( ⁇ 0.5 mm), such as the carotid artery (14 mm deep), femoral arteries (17 mm deep), and abdominal aorta (60 mm deep).
  • a 2 MHz disc probe was used for central organs with large excursions (>8 mm), such as the heart (70 mm deep) and diaphragm (120 mm deep).
  • the measured pulse intensity effectively represents the arterial diameter change, which is a function of two variables: blood pressure and arterial stiffness.
  • the blood pressure tends to expand the cross-section of the artery, while the arterial wall stiffness resists this expansion.
  • the exponential relationship between the diameter and arterial stiffness is independent of the blood pressure at the time of measurement within the physiological range (63-200 mmHg).
  • ⁇ ( ⁇ ) is the time-dependent blood pressure and ⁇ ( ⁇ ) is the time-dependent arterial diameter
  • ⁇ ⁇ and ⁇ ⁇ are the systolic and diastolic arterial diameters, respectively, derived from the measured pulse intensity
  • ⁇ ⁇ and ⁇ ⁇ are the reference systolic and diastolic pressures, respectively, measured using a commercial blood pressure cuff
  • ⁇ is the stiffness index.
  • ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ , and ⁇ ⁇ at the brachial artery of the subject were measured to obtain ⁇ , with the subject sitting upright in a chair with the measured arm relaxed on a table.
  • ⁇ ⁇ and ⁇ ⁇ were measured using a commercial cuff as calibration.
  • the arterial diameter was then measured at the same location using the USoP to derive ⁇ ⁇ and ⁇ ⁇ .
  • ⁇ ( ⁇ ) was determined based on the corresponding ⁇ ( ⁇ ) measured by the USoP.
  • Measurement of ⁇ ( ⁇ ) using the USoP is highly stable with little need for recalibration.
  • the initial calibration using the commercial cuff only needs to be performed once at the beginning of this process, as ⁇ ⁇ remains relatively stable from beat to beat.
  • the measurement of blood pressure using the USoP at the brachial artery is applicable to other arterial sites as well because ⁇ and ⁇ ⁇ do not change significantly along the major branches of the arterial tree. This allows us to equate brachial blood pressure measurements to the carotid blood pressure in healthy adults. Note that ⁇ and ⁇ ⁇ may change substantially on younger subjects and patients with vascular diseases, such as carotid atherosclerosis. In these populations, we may need to acquire accurate local carotid stiffness index and carotid blood pressure using catheterization to minimize the calibration error. In addition, the body habitus of the subject may also influence the calibration accuracy.
  • the height of subject may influence vascular resistance and further influence blood pressure calibration.
  • the vascular resistance could be estimated using nomograms or demographic databases, and then the stiffness index for blood pressure calibration could be corrected for better accuracy.
  • Pulse Wave Velocity Measurements [0144] The pulse wave velocity is defined as the propagation distance divided by the pulse transit time. Following a standard procedure, the propagation distances were measured on the body surface of the participants using a tape measure. Example tape measurements from a healthy participant illustrate the path lengths (Fig.9a). Then, a pair of USoPs were deployed to measure the pulse propagation delay between myocardium contraction waveforms and the arterial pulse waveforms (Fig.9b).
  • the two USoPs were synchronized by encoding time stamps in each cycle of pulse-echo transceiving.
  • the pulse transit time was calculated based on the foot-to-foot method, where the pulse transit time was defined as the mechanical propagation delay between the diastolic phase of myocardial contraction and arterial pulsation waveforms (Fig.9b).
  • the results of the USoP were compared with those of the tonometer. The comparison suggests a mean difference of ⁇ 1 ms, showing high consistency between the two devices (Fig. 9c).
  • FIG.9d A systemic stiffness mapping across different arterial segments was performed to show the variation of pulse transit time and, therefore, regional pulse wave velocity (Fig.9d).
  • Fig.9d a systemic stiffness mapping across different arterial segments was performed to show the variation of pulse transit time and, therefore, regional pulse wave velocity (Fig.9d).
  • heart-proximal e.g., heart-aorta, heart-carotid artery, and heart-femoral artery
  • Fig.9e heart-distal branches
  • a cold pressor test was performed sequentially.
  • a lower limit of normal (LLN) was used as the diagnostic threshold.
  • the LLN was set as each parameter’s value of the lower fifth percentile of a large healthy reference group. The LLN depends on the age, height, ethnicity, and other health conditions of the subject, so its value varies in different individuals.
  • the LLN values for a specific subject were calculated using the NHANES III database provided by the Centers of Disease Control and Prevention.
  • the respiratory function was evaluated based on the following criteria: If FEV 1 /FVC ratio ⁇ LLN, the patient is considered to have an obstructive issue. If FEV 1 /FVC ratio ⁇ LLN while FVC ⁇ LLN, the patient is considered to have a restrictive issue.
  • FEV 1 /FVC ⁇ LLN and FVC ⁇ LLN the patient is considered healthy.
  • the FVC and FEV 1 were derived from the USoP measured diaphragm excursion (Fig.34a).
  • a four-quadrant plot shows the measurement results (Fig.34b).
  • Data points in the top-right, top-left, bottom-right, and bottom-left suggest that the patient has healthy, obstructed, restricted, and combined obstructed and restricted conditions, respectively. For a health subject without respiratory issues, these values could be used to quantify expiratory performance.
  • a longitudinal study was performed to record the FVC and FEV 1 of a participant.
  • the pulsating feature also existed in the surrounding tissue.
  • the mechanical cave would propagate in surrounding tissues and generate tissue pulses, although the tissue pulses had smaller amplitudes due to energy loss in propagation. Therefore, the tissue texture (Fig.35a, the third panel from left) could also serve as a differentiable but weak feature.
  • Fig.35a the third panel from left
  • the images were labeled with CA and nCA regardless of their true identity (Fig.35a, the rightmost panel). After training, the model learned chaotic correlations and had a poor performance that the precision, recall, and accuracy are close to 50% (Fig.35b).
  • Fig. 36b shows an example of CA image, where the edge curve was extracted from a carotid artery image. After fast Fourier transforming, the frequency response suggested a peak at ⁇ 1 Hz representing a heart rate of 60 bpm. In an nCA case, the extracted edge curve would be non-periodic, therefore its frequency response would show no notable peaks within the heart rate range.
  • the critical parameters used in the logistic model are subject-dependent. Manual iterations and tedious optimizations would be required before the model could accept a new subject.
  • the deep learning model could transfer the model to new subjects via a minimal entropy correlation alignment model without manually tuning parameters. [0163] With these results presented, we could conclude three advantages of the deep learning model over logistic models and justify the use of deep learning models in our task. First, it offered better classification accuracy. Second, it is more dependable to handle “corner cases” than the logistic models. Third, it offers labor-free generalization opportunities while the logistic models rely on manual optimizations.
  • Deep learning networks produce a posterior probability for the presence of the carotid artery in each of the 32 channels. Ideally, this should follow a bell-shaped profile, with the peak of this profile representing the arterial center. However, the probabilities produced by the network may have random noise due to possible acquisition of compromised M-mode images. This could lead to misjudging the position of the arterial center. [0165] To decrease the possibility of such failure, we convolved the raw prediction profile with a one-dimensional Gaussian kernel function. In our experiments, this was sufficient to produce a bell-shaped curve that reliably determines the position of the arterial center.
  • the Limit Of Motion Tolerance And Pulse Waveform Continuity [0166] The speed of head motion is a critical factor that can compromise model prediction and waveform recording of the carotid artery. For very high motion speeds, attempted measurement of the carotid artery risks the signal passing through the sensing channels without even generating a full pulse cycle.
  • the rapid motion might possibly result in a lack of features for the model to recognize.
  • the head yawing rate was quantified using a pair of inertia measurement units (Fig.38). When the head yawing rate was increased from 0°/s to 80°/s, the recorded pulse periods decreased from 2.8 s to 0.3 s (Fig.41).
  • the former period contained at least two cycles of arterial pulsation at a resting heart rate (i.e., 60 ⁇ 80 bpm), while the latter period contained less than 1/3 of a pulse cycle.
  • the machine learning model was unable to recognize the carotid artery.
  • the threshold of a recognizable pulse cycle is ⁇ 1 s, corresponding to ⁇ 1 pulse cycle and a head yawing rate of ⁇ 60°/s, to ensure the true positive (true carotid artery image) rate is high enough for a successful prediction.
  • each sensing channel can collect a long period of arterial pulses containing several cardiac cycles.
  • the classification model reliably recognized the M-mode images containing the carotid artery pulses.
  • the pulse waveforms experienced no distortion under the re- selection of scanning channels.
  • the artery crossed over sensing channels resulting in a significantly decreased pulse period in M-mode images and thus a low true positive rate.
  • the waveform recording experienced distortion.
  • Training classifiers require data labeling, which requires some effort by human annotators. Domain adaptation is used to transfer a classifier trained with labeled data from a single subject to other subjects for whom labels are not available. We define the training set as the source domain data, ⁇ ⁇ containing pairs of images ⁇ ⁇ ⁇ ⁇ and labels ⁇ ⁇ ⁇ ⁇ .
  • the goal of domain adaptation is to learn a transfer function ⁇ that aligns features extracted from images from the source ( ⁇ ⁇ ) and target ( ⁇ ⁇ ) domain.
  • MECA as our domain adaptation model because it provides a systematical way to adjust the weight of the domain discrepancy and the cross-entropy in the loss function. It is crucial to minimize the human effort in hyper-parameter fine-tuning for applications in this work because there will be multiple subjects.
  • ⁇ G( ⁇ ⁇ ) and ⁇ G( ⁇ ⁇ ) are the covariance matrices of the feature vectors generated by the domain transferer G for source and target data, respectively; ⁇ is the dimension of these feature vectors; ⁇ and ⁇ are the eigenvector matrices of the eigendecomposition of ⁇ G( ⁇ ⁇ ) ⁇ and ⁇ are the corresponding eigenvalues; and ⁇ represents the Frobenius norm.
  • dilated arteries are of lower impedance, which have weaker reflections and slower backpropagation speeds, and thus, lead to a late and mild reflection peak in the pulse waveform.
  • the AIx is defined as the difference between the systolic peak and the reflection peak/inflection point divided by the systolic peak.
  • Example waveforms recorded before and after exercise indicate an increase in the AIx due to dilated arteries and decreased impedance of pulse wave propagation post-exercise (Fig.46b lower panel).
  • the AIx can be calculated in a beat-to-beat manner from the blood pressure waveforms.
  • the carotid artery diameter during strenuous exercises increased up to 19.91% from baseline. Accordingly, the maximum blood pressure error is calculated to be 1.58 mmHg between the two ⁇ values from the resting carotid artery diameter (3.92 mm) to the high intensity exercise-induced carotid artery diameter (4.70 mm) (Fig.47b). This blood pressure error was lower than the recommended maximum mean difference of 5 mmHg by the Association for the Advancement of Medical Instrumentation. Thus, there is no need to adjust ⁇ when measuring blood pressure during exercise.
  • the blood pressure waveform can be used to monitor fluid flow throughout the circulatory system, such as flow velocity, distensibility, pressure, and volume, which allows relating the pulse contour waveform to the stroke volume.
  • the main differential equation describing the system is written as: or where ⁇ is the volume of liquid flowing in per unit time; ⁇ is time; and ⁇ is the constant ⁇ ⁇ 4 from Poiseuille’s law.
  • the area under the systolic portion is proportionally related to the stroke volume, by a factor representing the characteristic impedance of the circulatory system, ⁇ : Stroke where ⁇ ⁇ is the end of the ejection period; P(t) is the real-time blood pressure; and ⁇ ⁇ is the diastolic pressure.
  • vascular response to exercise can be characterized by pulse waveform analysis.
  • the AIx reveals pulse wave reflection and arterial stiffness.
  • a low AIx is desirable, as high arterial stiffness is strongly associated with cardiovascular diseases.
  • cardiac function such as stroke volume and resulting cardiac output which represents the heart’s capacity to deliver blood throughout the body, can be derived using the pulse contour method. All cells in the body require oxygen and nutrients delivered via the blood for their metabolism. The inability of the heart to deliver sufficient blood to support the body’s metabolic needs, such as abnormally low stroke volume and cardiac output at rest or early plateaus of cardiac output during exercise, is a hallmark of heart failure.
  • central diastolic blood pressure is one of the main elements driving coronary perfusion. Therefore, continuously monitoring the central diastolic blood pressure may provide an early warning signal for acute cardiac ischemia.
  • Clinical Need For Continuous Tissue Monitoring In High-Risk Populations The USoP can monitor the cardiovascular and respiratory systems autonomously, using similar image-based machine learning algorithms to those for arteries. Continuous monitoring of these vital systems can be critical for certain high- risk populations, yielding better patient management and clinical outcomes.
  • senior populations are at high risk for developing coronary heart disease. However, the development of such diseases is chronic and often ignored before acute symptoms are detected (e.g., cardiogenic shock due to myocardial infarction).
  • Fig.51 is a flowchart illustrating one example of a method for monitoring a physiologic parameter that may be performed by various embodiments of the USoP described herein.
  • step 205 a location of interest associated with the physiologic parameter to be monitored is determined.
  • step 210 ultrasonic acoustic waves are transmitted by a conformable integrated wearable device toward the location of interest and reflected ultrasonic acoustic waves are received from the location of interest using a plurality of sensing channels.
  • Data concerning the reflected ultrasonic acoustic waves are transmitted by the conformable integrated wearable device to a back-end computing environment in step 215.
  • step 220 a monitoring channel is dynamically selected in real-time from among the plurality of sensing channels by the back-end computing environment.
  • the conformable integrated wearable device receives at least an identifier of the monitoring channel from the back-end computing environment in step 225.
  • the conformable integrated wearable device monitors the physiological parameter in real-time by transmitting ultrasound acoustic waves toward the location of interest and receiving reflected ultrasonic acoustic waves using the selected monitoring channel. Data reflective of the monitored physiological parameter is output in step 235.
  • the monitoring channel is dynamically selected by a back-end computing environment that is located external to the conformable integrated wearable device. In other embodiments, however, the dynamic selection of the monitoring channel may be performed on the conformable integrated wearable device itself, thus avoiding the need to respectively transmit and receive data to and from the back-end computing environment in steps 215 and 225.
  • Figs.1a-1d provide an overview of the fully integrated USoP. a, A photograph of the encapsulated USoP laminated on the chest for measuring cardiac activity via the parasternal window. The inset shows the folded USoP. b, Design of the USoP, including a stretchable ultrasonic probe, a flexible control circuit, and a battery.
  • the ultrasonic probe consists of a piezoelectric transducer array, serpentine interconnects, and an anisotropic conductive film (ACF) (upper left).
  • ACF anisotropic conductive film
  • the exploded view of the circuit shows two parts: (1) an AFE, including a 32-channel multiplexer (Mux), a transmit/receive switch (T/R SW), a receiver with a variable gain amplifier (VGA) and a filter, a pulse generator with a transmit controller (Tx ctrl) and a booster, and a sequencer; and (2) a DAQ module including a microcontroller unit (MCU) with a built-in analog-to-digital converter (ADC) and a Wi-Fi chip.
  • Mux 32-channel multiplexer
  • T/R SW transmit/receive switch
  • VGA variable gain amplifier
  • Tx ctrl pulse generator with a transmit controller
  • ADC built-in analog-to-digital converter
  • the two modules are connected by serpentine electrodes, which allow the entire circuit to be folded for a smaller footprint.
  • the circuit is powered by a commercial lithium-polymer battery.
  • a smartphone application is designed to display the data stream from the USoP. From the ultrasonic data, M-mode images and physiological signals can be derived and displayed in real time. The smartphone can also communicate with a cloud server for further data analysis (lower right).
  • c Block diagram of the USoP showing the flow of analog impulse, analog echo, and digital signals.
  • the AFE senses pulse-echo to generate ultrasonic signals, and the DAQ samples signals and wirelessly transmits the data to a terminal device for processing and display.
  • Figs.2a-2e illustrate the monitoring and analysis of tissue interface motions using the USoP.
  • a Schematics and measurement results of seven representative dynamic tissue interfaces.
  • b Deriving physiological parameters from myocardial contraction. From the M-mode waveforms of the septum and left ventricular wall, the left ventricular internal diameter at end-diastole (LVIDd) and end-systole (LVIDs) can be used to derive the fractional shortening (left).

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Abstract

Un système de patch à ultrasons (USoP) autonome entièrement intégré pouvant être porté comprend un circuit de commande souple miniaturisé qui est conçu pour s'interfacer avec un réseau de transducteurs à ultrasons pour le pré-conditionnement de signal et la communication de données sans fil. L'intelligence artificielle (par exemple, l'apprentissage automatique) peut être utilisée pour suivre les cibles tissulaires en mouvement et faciliter l'interprétation des données. Dans un mode de réalisation, l'USoP permet un suivi continu de signaux physiologiques provenant de tissus situés à une profondeur pouvant atteindre 164 mm. Sur des sujets mobiles, l'USoP peut surveiller en continu les signaux physiologiques, y compris la pression artérielle centrale, la fréquence cardiaque et le débit cardiaque, pendant une durée pouvant aller jusqu'à, par exemple, douze heures. Ce résultat permet une surveillance autonome et continue des signaux des tissus profonds.
PCT/US2023/022674 2023-05-18 2023-05-18 Patch à ultrasons pouvant être porté pour surveiller des sujets en mouvement à l'aide de l'apprentissage automatique et d'un dispositif électronique sans fil Pending WO2024237917A1 (fr)

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US20190201038A1 (en) * 2017-12-28 2019-07-04 Ethicon Llc Determining tissue composition via an ultrasonic system
US20220133269A1 (en) * 2019-02-28 2022-05-05 The Regents Of The University Of California Integrated wearable ultrasonic phased arrays for monitoring
US11349925B2 (en) * 2012-01-03 2022-05-31 May Patents Ltd. System and method for server based control
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US11349925B2 (en) * 2012-01-03 2022-05-31 May Patents Ltd. System and method for server based control
US20190201038A1 (en) * 2017-12-28 2019-07-04 Ethicon Llc Determining tissue composition via an ultrasonic system
US11540855B2 (en) * 2017-12-28 2023-01-03 Cilag Gmbh International Controlling activation of an ultrasonic surgical instrument according to the presence of tissue
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US20220133269A1 (en) * 2019-02-28 2022-05-05 The Regents Of The University Of California Integrated wearable ultrasonic phased arrays for monitoring

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US12475896B1 (en) * 2025-03-24 2025-11-18 Zhejiang Gongshang University Method for cross-domain speech deepfake detection
CN120807372A (zh) * 2025-09-11 2025-10-17 北京大学国际医院 基于人工智能的麻醉科气道插管图像识别方法

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