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    MEDICAL SCAN INTERFACE FEATURE EVALUATING SYSTEM
    1.
    发明申请
    MEDICAL SCAN INTERFACE FEATURE EVALUATING SYSTEM 审中-公开

    公开(公告)号：US20180341753A1

    公开(公告)日：2018-11-29

    申请号：US15683224

    申请日：2017-08-22

    Applicant: Enlitic, Inc.

    Inventor： Kevin Lyman ,  Devon Bernard ,  Li Yao ,  Alan Liu ,  Brian Basham ,  Ben Covington

    IPC: G06F19/00 ,  G06Q50/22 ,  G06Q10/10

    Abstract: A medical scan interface feature evaluator system is operable to generate an ordered image-to-prompt mapping by selecting a set of user interface features to be displayed with each of an ordered set of medical scans. The set of medical scans and the ordered image-to-prompt mapping are transmitted to a set of client devices. A set of responses are generated by each client device in response to sequentially displaying each of the set of medical scans in conjunction with a mapped user interface feature indicated in the ordered image-to-prompt mapping via a user interface. Response score data is generated by comparing each response to truth annotation data of the corresponding medical scan. Interface feature score data corresponding to each user interface feature is generated based on aggregating the response score data, and is used to generate a ranking of the set of user interface features.

    MEDICAL SCAN ANNOTATOR SYSTEM
    3.
    发明申请
    MEDICAL SCAN ANNOTATOR SYSTEM 审中-公开

    公开(公告)号：US20180341747A1

    公开(公告)日：2018-11-29

    申请号：US15627683

    申请日：2017-06-20

    Applicant: Enlitic, Inc.

    Inventor： Devon Bernard ,  Kevin Lyman ,  Li Yao ,  Brian Basham ,  Ben Covington

    IPC: G06F19/00 ,  G06F3/048 ,  G06T7/00

    Abstract: A medical scan annotator system is operable to select a medical scan for transmission via a network to a first client device and a second client device for display via an interactive interface, and annotation data is received from the first client device and the second client device in response. Annotation similarity data is generated by comparing the first annotation data to the second annotation data, and consensus annotation data is generated based on the first annotation data and the second annotation data in response to the annotation similarity data indicating that the difference between the first annotation data and the second annotation data compares favorably to an annotation discrepancy threshold. The consensus annotation data is mapped to the medical scan in a medical scan database.

    LUNG SCREENING ASSESSMENT SYSTEM
    4.
    发明申请
    LUNG SCREENING ASSESSMENT SYSTEM 审中-公开

    公开(公告)号：US20180338741A1

    公开(公告)日：2018-11-29

    申请号：US15690786

    申请日：2017-08-30

    Applicant: Enlitic, Inc.

    Inventor： Kevin Lyman ,  Devon Bernard ,  Li Yao ,  Ben Covington ,  Diogo Almeida ,  Brian Basham ,  Jeremy Howard ,  Anthony Upton ,  John Zedlewski

    IPC: A61B6/00 ,  A61B6/03

    Abstract: A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

    MEDICAL SCAN NATURAL LANGUAGE ANALYSIS SYSTEM
    8.
    发明申请
    MEDICAL SCAN NATURAL LANGUAGE ANALYSIS SYSTEM 审中-公开

    公开(公告)号：US20180341751A1

    公开(公告)日：2018-11-29

    申请号：US15683113

    申请日：2017-08-22

    Applicant: Enlitic, Inc.

    Inventor： Kevin Lyman ,  Devon Bernard ,  Li Yao ,  Brian Basham ,  Scott McKinney

    IPC: G06F19/00 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  G06F17/27

    Abstract: A medical scan natural language analysis system is operable to generate a medical report natural language model based on a selected set of medical reports of the plurality of medical reports and the at least one medical code mapped to each of the selected set of medical reports. A medical report that is not included in the selected set is received via a network. A medical code is determined by utilizing the medical report natural language model on the first medical report. The medical code is mapped to a medical scan corresponding to the medical report.

    MEDICAL SCAN REPORT LABELING SYSTEM
    9.
    发明申请
    MEDICAL SCAN REPORT LABELING SYSTEM 审中-公开

    公开(公告)号：US20180341750A1

    公开(公告)日：2018-11-29

    申请号：US15677630

    申请日：2017-08-15

    Applicant: Enlitic, Inc.

    Inventor： Devon Bernard ,  Kevin Lyman ,  Li Yao ,  Brian Basham ,  Rewon Child

    IPC: G06F19/00 ,  G06F3/16 ,  G06F17/27 ,  G06F17/24

    Abstract: A medical scan report labeling system is operable to transmit a medical report that includes natural language text to a first client device for display. Identified medical condition term data is received from the first client device in response. An alias mapping pair in a medical label alias database is identified by determining that a medical condition term of the alias mapping pair compares favorably to the identified medical condition term data. A medical code that corresponds to the alias mapping pair and a medical scan that corresponds to the medical report are transmitted to a second client device of an expert user for display, and accuracy data is received from the second client device in response. The medical code is mapped to the first medical scan in the medical scan database when the accuracy data indicates that the medical code compares favorably to the medical scan.

    MEDICAL SCAN NATURAL LANGUAGE ANALYSIS SYSTEM, METHODS AND ARTICLES
    10.
    发明申请
    MEDICAL SCAN NATURAL LANGUAGE ANALYSIS SYSTEM, METHODS AND ARTICLES 有权

    公开(公告)号：US20220028530A1

    公开(公告)日：2022-01-27

    申请号：US17450078

    申请日：2021-10-06

    Applicant: Enlitic, Inc.

    Inventor： Kevin Lyman ,  Devon Bernard ,  Li Yao ,  Brian Basham ,  Scott McKinney

    IPC: G16H30/40 ,  G06F3/048 ,  G06T7/00 ,  G06Q50/22 ,  G16H50/20 ,  A61B6/00 ,  A61B8/00 ,  A61B8/08 ,  G16H50/70 ,  G06N3/04 ,  G06F40/30 ,  G06F40/56 ,  G06F40/169 ,  G06F40/197 ,  G06F40/247 ,  G06F40/279 ,  G16H30/20 ,  G06T7/11 ,  G16H40/20 ,  G01T1/24 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  A61B6/03 ,  G06F3/16 ,  G06K9/03 ,  G06K9/62 ,  A61B5/00 ,  G06N3/08 ,  G06Q10/10 ,  H04N5/32 ,  G16H40/63

    Abstract: A medical report natural language model includes an artificial neural network implemented via the processor and is trained based a plurality of medical reports wherein each of the medical reports is mapped to at least one medical code of a plurality of medical codes and further based a plurality of medical condition terms from a plurality of alias mapping pairs, wherein each of the plurality of medical condition terms are unique, wherein each of the plurality of medical condition terms indicates a corresponding medical condition and wherein each of the plurality of alias mapping pairs includes a one of the plurality of medical condition terms and a corresponding single one of the plurality of medical codes that is a deterministic function of the one of the plurality of medical condition terms and wherein the plurality of alias mapping pairs includes two or more alias mapping pairs that map a corresponding two or more of the plurality of medical condition terms to a same one of the plurality of medical codes. A system operates by determining a first medical code of the plurality of medical codes utilizing the medical report natural language model on the first medical report by: recognizing, via the artificial neural network of the medical report natural language model, a first medical condition term of the plurality of medical condition terms in the first medical report; and determining the first medical code of the plurality of medical codes, based on the plurality of alias mapping pairs, wherein the determining the first medical code includes identifying a first alias mapping pair of the plurality of alias mapping pairs that pairs the first medical condition term deterministically to the first medical code; and mapping the first medical code of the plurality of medical codes and the first medical condition term of the plurality of medical condition terms to a first medical scan corresponding to the first medical report.

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