公开(公告)号：US10553311B2

公开(公告)日：2020-02-04

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: G16H30/40 ,  G06F19/00 ,  G06F3/048 ,  G06T7/00 ,  G06Q50/22 ,  G16H50/20 ,  G06F17/22 ,  G06F17/27 ,  G06F17/28 ,  A61B6/00 ,  A61B8/00 ,  A61B8/08 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  A61B6/03 ,  G06F3/16 ,  G06F17/24 ,  G06K9/03 ,  G06K9/62 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  G16H40/20 ,  G06Q10/10 ,  G06T7/11 ,  G01T1/24 ,  H04N5/32 ,  G16H40/63 ,  G16H50/50 ,  H04L29/08 ,  H04L29/06 ,  G06F3/0484 ,  G06F3/0485 ,  G06T11/00

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.

公开(公告)号：US10360999B2

公开(公告)日：2019-07-23

申请号：US15690808

申请日：2017-08-30

Applicant: Enlitic, Inc.

Inventor： Devon Bernard ,  Kevin Lyman ,  Li Yao ,  Anthony Upton ,  Ben Covington ,  Jeremy Howard

IPC: G16H30/40 ,  G06F19/00 ,  G06F3/048 ,  G06T7/00 ,  G06Q50/22 ,  G16H50/20 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  A61B6/00 ,  G06F3/16 ,  G06F17/24 ,  G06F17/27 ,  G06K9/62 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  G16H40/20 ,  G06Q10/10 ,  G06T7/11 ,  G01T1/24 ,  H04N5/32 ,  G16H40/63 ,  G06F17/22 ,  G06F17/28 ,  A61B8/00 ,  A61B8/08 ,  G16H50/50 ,  A61B6/03 ,  H04L29/08 ,  H04L29/06 ,  G06K9/03 ,  G06F3/0484 ,  G06F3/0485 ,  G06T11/00

Abstract: A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.

公开(公告)号：US20190057769A1

公开(公告)日：2019-02-21

申请号：US16169231

申请日：2018-10-24

Applicant: Enlitic, Inc.

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

IPC: G16H30/40 ,  G06F3/048 ,  G06T7/00 ,  G16H50/20 ,  G06Q50/22

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.

公开(公告)号：US11681962B2

公开(公告)日：2023-06-20

申请号：US17453082

申请日：2021-11-01

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Li Yao ,  Ben Covington

IPC: G16H15/00 ,  G16H30/20 ,  G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A peer-review flagging system is operable to train a computer vision model and to generate automated assessment data by performing an inference function on a first medical scan by utilizing the computer vision model. Human assessment data is generated based on a first medical report written by a medical professional in conjunction with review of the first medical scan. First consensus data is generated based on the automated assessment data, the human assessment data, and a first threshold, and the first medical scan is determined to be flagged based on the first consensus data. A second threshold is selected use in generating second consensus data for a second medical scan and a second medical report written by the medical professional in conjunction with review of the second medical scan, and is selected to be stricter than the first threshold based on determining to flag the first medical scan.

公开(公告)号：US11631175B2

公开(公告)日：2023-04-18

申请号：US17666659

申请日：2022-02-08

Applicant: Enlitic, Inc.

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

IPC: G06T7/00 ,  H04W4/021 ,  A61B6/00

Abstract: 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.

公开(公告)号：US20220223243A1

公开(公告)日：2022-07-14

申请号：US17656925

申请日：2022-03-29

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton

IPC: G16H10/60 ,  H04L67/01 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  G06V10/22

Abstract: A multi-model medical scan analysis system is operable to generate a plurality of training sets from a plurality of medical scans. Each of a set of sub-models is generated by performing a training step on a corresponding one of the plurality of training sets of the plurality of medical scans. A set of abnormality data is generated by applying a subset of a set of inference functions on a new medical scan. The subset of the set of inference functions utilize the subset of the set of sub-models, and each of the set of abnormality data is generated as output of performing one of the subset of the set of inference functions. The multi-model medical scan analysis system is further operable to generate final abnormality data that includes a global probability indicating a probability that any abnormality is present based on the set of abnormality data.

公开(公告)号：US11348669B2

公开(公告)日：2022-05-31

申请号：US16997237

申请日：2020-08-19

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Keith Lui ,  Anthony Upton ,  Li Yao ,  Ben Covington

IPC: G06K9/00 ,  G16H10/60 ,  H04L67/01 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L67/12 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  G06K9/66 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295

Abstract: A clinical trial re-evaluation system is operable to perform at least one assessment function on a set of medical scans for each of a first subset of a set of patients of a failed clinical trial to generate automated assessment data for each of the first subset of the set of patients. The first subset of the set of patients corresponds to a subset of human assessment data determined to have failed to meet criteria of the clinical trial. Patient re-evaluation data is generated for each of the first subset of the set of patients by comparing the automated assessment data to the criteria. The patient re-evaluation data for a second subset of the first subset of the set of patients indicates the automated assessment data passes the criteria. Trial re-evaluation data is generated based on the patient re-evaluation data for transmission to a computing device for display.

公开(公告)号：US11295840B2

公开(公告)日：2022-04-05

申请号：US16363289

申请日：2019-03-25

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Chris Croswhite ,  Ben Covington

IPC: G16H30/20 ,  G16H10/60 ,  H04L67/01 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06V30/194

Abstract: A medical scan header standardization system is operable to determine a set of standard DICOM headers based on determining a standard set of fields and based on further determining a standard set of entries for each of the standard set of fields. A DICOM image is received via a network, and a header of the DICOM image is determined to be incorrect. A selected one of the set of standard DICOM headers to replace the header of the DICOM image is determined. The selected one of the set of standard DICOM headers is transmitted, via the network, to a medical scan database for storage in conjunction with the DICOM image.

公开(公告)号：US11257575B2

公开(公告)日：2022-02-22

申请号：US16365787

申请日：2019-03-27

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton ,  Lionel Lints

IPC: G16H50/20 ,  G16H10/60 ,  H04L67/01 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L67/12 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  G06K9/66 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295

Abstract: A model-assisted annotating system is operable to receive a first set of annotation data for a first set of medical scans from a set of client devices. A computer vision model is trained by utilizing first set of medical scans and the first set of annotation data. A second set of annotation data for a second set of medical scans is generated by utilizing the computer vision model. The second set of medical scans and the second set of annotation data is transmitted to the set of client devices, and a set of additional annotation data is received in response. An updated computer vision model is generated by utilizing the set of additional annotation data. A third set of annotation data is generated for a third set of medical scans by utilizing the updated computer vision model for transmission to the set of client devices for display.

公开(公告)号：US11222717B2

公开(公告)日：2022-01-11

申请号：US16365776

申请日：2019-03-27

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Li Yao ,  Eric C. Poblenz ,  Jordan Prosky ,  Ben Covington ,  Anthony Upton

IPC: G16H10/60 ,  H04L29/06 ,  G16H30/40 ,  G16H15/00 ,  G06K9/62 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G06Q10/06 ,  G16H10/20 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  G06K9/20 ,  H04L29/08 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  G06K9/66 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295

Abstract: A medical scan triaging system is operable to generate a global abnormality probability for each of a plurality of medical scans by utilizing a computer vision model trained on a training set of medical scans. A triage probability threshold is determined based on user input to a client device. A first subset of the plurality of medical scans, designated for human review, is determined by identifying medical scans with a corresponding global abnormality probability that compares favorably to the triage probability threshold. A second subset of the plurality of medical scans, designated as normal, is determined by identifying ones of the plurality of medical scans with a corresponding global abnormality probability that compares unfavorably to the triage probability threshold. Transmission of the first subset of the plurality of medical scans to a plurality of client devices associated with a plurality of users is facilitated.