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    ULTRASOUND IMAGING SYSTEM AND METHOD
    14.
    发明申请
    ULTRASOUND IMAGING SYSTEM AND METHOD 有权

    公开(公告)号：US20240407764A1

    公开(公告)日：2024-12-12

    申请号：US18689179

    申请日：2022-09-09

    Applicant: REGENTS OF THE UNIVERSITY OF MINNESOTA

    Inventor： Emad S. Ebbini ,  Nayef Abdulrahman Alshamlan ,  Abhishek Sahoo

    IPC: A61B8/08 ,  G06T7/00

    Abstract: Embodiments are directed to a method for use in ultrasound imaging of matter in a region comprising providing wave energy into the region and transducing wave energy returned from the region to form a set of receive signals defining received signal data. The method comprises separating linear and nonlinear components of the received signal data based on a second order Volterra model. The method also comprises computing an estimate of a tissue acoustic nonlinearity parameter, B/A, using a ratio of the nonlinear component to the linear component, and identifying tissue characteristics of the matter in the region using the B/A.

    ANALYZING AND SELECTING PREDICTIVE ELECTROCARDIOGRAM FEATURES
    16.
    发明申请
    ANALYZING AND SELECTING PREDICTIVE ELECTROCARDIOGRAM FEATURES 有权

    公开(公告)号：US20240378437A1

    公开(公告)日：2024-11-14

    申请号：US18563316

    申请日：2022-05-23

    Applicant: Mayo Foundation for Medical Education and Research ,  Regents of the University of Minnesota

    Inventor： Paul A. Friedman ,  Itzhak Zachi Attia ,  Gilad Lerman

    IPC: G06N3/08 ,  A61B5/319 ,  A61B5/35

    Abstract: In some aspects, values of features obtained from training first and second machine-learning models are analyzed to correlate at least a subset of features from the first machine-learning model with at least a subset of features from the second machine-learning model. The correlated features are then applied to update the first or second machine-learning model, or to train a third machine-learning model. In other aspects, a generator machine-learning model processes a seed and a target characteristic indicator to generate a synthetic ECG signal. The synthetic ECG signal is biased according to a target physiological characteristic represented by the target characteristic indicator. The generator machine-learning model can be trained using an expert machine-learning model and in an adversarial process with a discriminator machine-learning model.

    Artificial Calculus
    20.
    发明公开
    Artificial Calculus 审中-公开

    公开(公告)号：US20240355227A1

    公开(公告)日：2024-10-24

    申请号：US18643653

    申请日：2024-04-23

    Applicant: Regents of the University of Minnesota

    Inventor： Alex Siu Lun Fok ,  Anqi Zhang ,  Ruoqiong Chen ,  Tamer Abdelrehim ,  Bruno Pierre Lima ,  Stephanie Porter ,  Hooi Pin Chew ,  Dina Elsherbini ,  Saba Tohidkhah

    IPC: G09B23/28

    CPC classification number: G09B23/283

    Abstract: Example artificial dental calculus models are described. In one example, an in vitro artificial dental calculus includes a dental substrate and a mineralized biofilm comprising an extracellular support material and at least one bacterial species. In one example, a method for creating an in vitro dental calculus model includes applying saliva to a dental substrate in a production environment, applying a mixture of an extracellular support material and at least one bacterial species to the dental substrate, applying a solution comprising calcium and phosphate to the mixture on the dental substrate, and removing the dental substrate from the production environment after a period of time during which a mineralized biofilm comprising the extracellular support material and the at least one bacterial species forms on the dental substrate.

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