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    AUTOMATION-ASSISTED VENOUS CONGESTION ASSESSMENT IN POINT OF CARE ULTRASOUND
    2.
    发明公开
    AUTOMATION-ASSISTED VENOUS CONGESTION ASSESSMENT IN POINT OF CARE ULTRASOUND 审中-公开

    公开(公告)号：US20240023937A1

    公开(公告)日：2024-01-25

    申请号：US17868577

    申请日：2022-07-19

    Applicant: EchoNous, Inc.

    Inventor： Eric Wong ,  Babajide Ayinde ,  Philippe Rola

    IPC: A61B8/00 ,  A61B8/06 ,  A61B8/08

    CPC classification number: A61B8/46 ,  A61B8/06 ,  A61B8/52 ,  A61B8/463

    Abstract: A diagnostic facility is described. The facility accesses a set of trained machine learning models. For each of a plurality of stages of a diagnostic ultrasound protocol for blood vessels, the facility causes an ultrasound device to capture from the person an ultrasound artifact of a type specified for the stage that features a blood vessel specified for the stage; applies one of the trained machine learning models to the captured ultrasound artifact to produce a prediction; and determines a score for the stage based at least in part on the produced prediction. The facility combines the determined scores to produce a diagnosis grade for the person.

    Gating machine learning predictions on medical ultrasound images via risk and uncertainty quantification
    3.
    发明授权
    Gating machine learning predictions on medical ultrasound images via risk and uncertainty quantification 有权

    公开(公告)号：US11532084B2

    公开(公告)日：2022-12-20

    申请号：US17088390

    申请日：2020-11-03

    Applicant: EchoNous, Inc.

    Inventor： Babajide Ayinde ,  Eric Wong ,  Allen Lu

    IPC: G06T7/13 ,  G06T7/00 ,  G06K9/62 ,  G06N20/20 ,  G06V10/25 ,  G06V10/75

    Abstract: A facility for processing a medical imaging image is described. The facility applies each of a number of constituent models making up an ensemble machine learning models to the image to produce a constituent model result that predicts a value for each pixel of the image. The facility aggregates the results produced by the constituent models of the plurality to determine a result of the ensemble machine learning model. For each of the pixels of the accessed image, the facility determines a measure of variation among the values predicted for the pixel among the constituent models. Facility determines a confidence measure for the ensemble machine learning model result based at least in part on for how many of the pixels of the accessed image a variation measure is determined that exceeds a variation threshold.

    Automatic depth selection for ultrasound imaging
    4.
    发明授权
    Automatic depth selection for ultrasound imaging 有权

    公开(公告)号：US12144686B2

    公开(公告)日：2024-11-19

    申请号：US17509987

    申请日：2021-10-25

    Applicant: EchoNous, Inc.

    Inventor： Matthew Cook ,  Babajide Ayinde

    IPC: A61B8/08 ,  A61B8/00 ,  G06N3/08

    Abstract: A facility for assessing an ultrasound image captured from a patient with a particular depth setting is described. The facility subjects the received ultrasound image to at least one neural network to produce, for each neural network, an inference. On the basis of the produced inferences, the facility determines whether the depth setting at which the ultrasound image was captured was optimal.

    SYSTEMS AND METHODS FOR AUTOMATED PHYSIOLOGICAL PARAMETER ESTIMATION FROM ULTRASOUND IMAGE SEQUENCES
    6.
    发明申请
    SYSTEMS AND METHODS FOR AUTOMATED PHYSIOLOGICAL PARAMETER ESTIMATION FROM ULTRASOUND IMAGE SEQUENCES 有权

    公开(公告)号：US20210330285A1

    公开(公告)日：2021-10-28

    申请号：US17242064

    申请日：2021-04-27

    Applicant: EchoNous, Inc.

    Inventor： Allen Lu ,  Babajide Ayinde

    IPC: A61B8/08 ,  G06T7/00

    Abstract: Systems and methods for automated physiological parameter estimation from ultrasound image sequences are provided. An ultrasound system includes an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient. An anatomical structure recognition module includes processing circuitry configured to receive the acquired sequence of ultrasound images from the ultrasound imaging device, and automatically recognize an anatomical structure in the received sequence of ultrasound images. A physiological parameters estimation module includes processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

    Automation-assisted venous congestion assessment in point of care ultrasound
    7.
    发明授权
    Automation-assisted venous congestion assessment in point of care ultrasound 有权

    公开(公告)号：US12144682B2

    公开(公告)日：2024-11-19

    申请号：US17868577

    申请日：2022-07-19

    Applicant: EchoNous, Inc.

    Inventor： Eric Wong ,  Babajide Ayinde ,  Philippe Rola

    IPC: A61B8/00 ,  A61B8/06 ,  A61B8/08

    Abstract: A diagnostic facility is described. The facility accesses a set of trained machine learning models. For each of a plurality of stages of a diagnostic ultrasound protocol for blood vessels, the facility causes an ultrasound device to capture from the person an ultrasound artifact of a type specified for the stage that features a blood vessel specified for the stage; applies one of the trained machine learning models to the captured ultrasound artifact to produce a prediction; and determines a score for the stage based at least in part on the produced prediction. The facility combines the determined scores to produce a diagnosis grade for the person.

    AUTOMATIC DEPTH SELECTION FOR ULTRASOUND IMAGING
    9.
    发明申请
    AUTOMATIC DEPTH SELECTION FOR ULTRASOUND IMAGING 有权

    公开(公告)号：US20230125779A1

    公开(公告)日：2023-04-27

    申请号：US17509987

    申请日：2021-10-25

    Applicant: EchoNous, Inc.

    Inventor： Matthew Cook ,  Babajide Ayinde

    IPC: A61B8/08 ,  A61B8/00 ,  G06N3/08

    Abstract: A facility for assessing an ultrasound image captured from a patient with a particular depth setting is described. The facility subjects the received ultrasound image to at least one neural network to produce, for each neural network, an inference. On the basis of the produced inferences, the facility determines whether the depth setting at which the ultrasound image was captured was optimal.

    Robust segmentation through high-level image understanding
    10.
    发明授权
    Robust segmentation through high-level image understanding 有权

    公开(公告)号：US11636593B2

    公开(公告)日：2023-04-25

    申请号：US17091263

    申请日：2020-11-06

    Applicant: EchoNous, Inc.

    Inventor： Babajide Ayinde ,  Fan Zhang

    IPC: G06T7/00 ,  A61B8/14 ,  A61B8/00 ,  A61B8/08 ,  G06N7/00 ,  G06N3/02

    Abstract: A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

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