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    Heat map generating system and methods for use therewith
    7.
    发明授权
    Heat map generating system and methods for use therewith 有权

    公开(公告)号：US11282198B2

    公开(公告)日：2022-03-22

    申请号：US16939495

    申请日：2020-07-27

    申请人： Enlitic, Inc.

    发明人： Kevin Lyman ,  Eric C. Poblenz ,  Li Yao ,  Ben Covington ,  Anthony Upton

    IPC分类号： G06K9/62 ,  G06T7/00 ,  H04W4/021 ,  A61B6/00

    摘要： A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.

    RISK ASSESSMENT SYSTEM AND METHODS FOR USE THEREWITH
    9.
    发明申请
    RISK ASSESSMENT SYSTEM AND METHODS FOR USE THEREWITH 有权

    公开(公告)号：US20220061746A1

    公开(公告)日：2022-03-03

    申请号：US17007924

    申请日：2020-08-31

    申请人： Enlitic, Inc.

    发明人： Kevin Lyman ,  Ben Covington, JR. ,  Anthony Upton

    IPC分类号： A61B5/00 ,  G16H50/30 ,  G16H70/20 ,  G16H50/70 ,  G16H40/20 ,  G16H70/60 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H15/00

    摘要： A risk assessment system is configured to receive patient history data for a patient. A set of risk assessment scores corresponding to the patient are generated for a set of risk assessment categories based on applying at least one risk assessment function to the patient history data. One of the set of risk assessment categories is identified as high risk for the patient based on a corresponding one of the set of risk assessment scores. A high risk protocol corresponding to the one of the set of risk assessment categories is identified, and performance of the high risk protocol is facilitated for the patient based on identification of the one of the set of risk assessment categories as high risk for the patient.

    AI-BASED LABEL GENERATING SYSTEM AND METHODS FOR USE THEREWITH
    10.
    发明申请
    AI-BASED LABEL GENERATING SYSTEM AND METHODS FOR USE THEREWITH 有权

    公开(公告)号：US20210398283A1

    公开(公告)日：2021-12-23

    申请号：US17446863

    申请日：2021-09-03

    申请人： Enlitic, Inc.

    发明人： Li Yao ,  Kevin Lyman ,  Ashwin Jadhav ,  Eric C. Poblenz ,  Anthony Upton

    IPC分类号： G06T7/00 ,  G06K9/32 ,  G06N7/00 ,  G06K9/62 ,  G06T7/187

    摘要： A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.