MEDICAL IMAGE ANALYSIS METHOD
    41.
    发明申请

    公开(公告)号:US20240412358A1

    公开(公告)日:2024-12-12

    申请号:US18702737

    申请日:2022-10-19

    Applicant: VUNO Inc.

    Abstract: Disclosed is a method for analyzing a medical image performed by a user terminal performed by a computing device. The method may include: determining a region of interest of a medical image based on a first user input on a user interface; transmitting identification information and contour information of the region of interest to a server, and receiving position information and staining information of a stained cell present in the region of interest from the server; distinguishing the cell present in the region of interest as positive or negative based on a threshold which is adjustable according to a second user input on the user interface by using the position information and the staining information of the stained cell present in the region of interest; and outputting analysis information generated based on the cell distinguished as positive or negative through the user interface.

    PORTABLE ECG MEASURING DEVICE
    42.
    发明公开

    公开(公告)号:US20240252091A1

    公开(公告)日:2024-08-01

    申请号:US18632087

    申请日:2024-04-10

    Applicant: VUNO Inc.

    CPC classification number: A61B5/332 A61B5/0006 A61B5/28 A61B5/7221

    Abstract: Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.

    Display panel with graphical user interface

    公开(公告)号:USD1025115S1

    公开(公告)日:2024-04-30

    申请号:US35516798

    申请日:2022-04-05

    Applicant: VUNO Inc.

    Abstract: The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
    1. Display panel with graphical user interface
    1.0 : Front
    The outermost broken line illustrates a display panel and forms no part of the claimed design. The dot-dash broken lines in the reproduction represent boundaries of the claimed design and form no part thereof. All other broken lines in the reproduction depict portions of the graphical user interface that form no part of the claimed design.

    METHOD TO PREDICT HEART AGE
    46.
    发明公开

    公开(公告)号:US20240062911A1

    公开(公告)日:2024-02-22

    申请号:US18355246

    申请日:2023-07-19

    Applicant: VUNO Inc.

    CPC classification number: G16H50/30 G06N3/09 G06N3/096 G06N3/045

    Abstract: An exemplary embodiment of the present disclosure discloses a method of estimating a heart age by using a pre-trained artificial neural network model. In particular, according to the present disclosure, a computing device obtains vital sign data of a user. The computing device estimates a heart age of the user based on the vital sign data of the user by using a pre-trained artificial neural network model. The pre-trained artificial neural network model corresponds to an artificial neural network model pre-trained based on information related to a heart disease.

    METHOD FOR DETECTING SERIAL SECTION OF MEDICAL IMAGE

    公开(公告)号:US20220237780A1

    公开(公告)日:2022-07-28

    申请号:US17586286

    申请日:2022-01-27

    Applicant: VUNO Inc.

    Abstract: Disclosed is a method for detecting a serial section of a medical image, which is performed by a computing device. The method may include: detecting segments included in at least one tissue which exists in the medical image; estimating a number of tissue sections corresponding to the serial section and a distance between the segments based on the segments; and distinguishing tissue sections corresponding to the serial section based on the estimated number of tissue sections corresponding to a serial section and the distance between the segments.

    METHOD AND APPARATUS FOR PREDICTING MEDICAL EVENT FROM ELECTRONIC MEDICAL RECORD USING PRE_TRAINED ARTFICIAL NEURAL NETWORK

    公开(公告)号:US20220084681A1

    公开(公告)日:2022-03-17

    申请号:US17474059

    申请日:2021-09-14

    Applicant: VUNO INC.

    Abstract: A method of predicting a medical event based on a pre-trained artificial neural network by a computing apparatus, and an apparatus therefor are disclosed. The method includes receiving an electronic medical record vector including a plurality of vital sign components, and outputting the medical event corresponding to the electronic medical record vector using the acritical neural network. The artificial neural network is pre-trained based on learning data, and the learning data includes augmentation electronic medical record vectors which are reconstructed using original electronic medical record vectors pre-acquired at an earlier time point than a first time point based on a mask vector for losing at least one of the plurality of vital sign components of the first time point.

    DEEP NEURAL NETWORK PRE-TRAINING METHOD FOR CLASSIFYING ELECTROCARDIOGRAM (ECG) DATA

    公开(公告)号:US20220084679A1

    公开(公告)日:2022-03-17

    申请号:US17464685

    申请日:2021-09-02

    Applicant: VUNO INC.

    Abstract: A deep neural network pre-training method for classifying electrocardiogram (ECG) data and a device for the same are disclosed. A method for training an ECG feature extraction model may include receiving a ECG signal, extracting one or more first features related to the ECG signal by inputting the ECG signal to a rule-based feature extractor or a neural network model, extracting at least one second feature corresponding to the at least one first feature by inputting the ECG signal to an encoder, and pre-training the ECG feature extraction model by inputting the at least one second feature into at least one of a regression function and a classification function to calculate at least one output value. The pre-training of the ECG feature extraction model may include training the encoder to minimize a loss function that is determined based on the at least one output value and the at least one first feature.

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