公开(公告)号：US11829914B2

公开(公告)日：2023-11-28

申请号：US17680493

申请日：2022-02-25

Applicant: Enlitic, Inc.

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

IPC: G16H30/20 ,  G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  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 medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model.

公开(公告)号：US11790297B2

公开(公告)日：2023-10-17

申请号：US17573184

申请日：2022-01-11

Applicant: Enlitic, Inc.

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

IPC: G16H50/20 ,  G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  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 ,  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 model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.

公开(公告)号：US11664114B2

公开(公告)日：2023-05-30

申请号：US15627644

申请日：2017-06-20

Applicant: Enlitic, Inc.

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

IPC: G16H30/40 ,  G06F3/048 ,  G06T7/00 ,  G06Q50/22 ,  G16H50/20 ,  A61B6/00 ,  A61B8/00 ,  A61B8/08 ,  G16H50/70 ,  G06F40/30 ,  G06F40/56 ,  G06F40/169 ,  G06F40/197 ,  G06F40/247 ,  G06F40/279 ,  G16H30/20 ,  G06T7/11 ,  G16H40/20 ,  G01T1/24 ,  G06V10/98 ,  G06F18/22 ,  G06F18/24 ,  G06N3/045 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  A61B6/03 ,  G06F3/16 ,  A61B5/00 ,  G06N3/04 ,  G06N3/084 ,  G06Q10/10 ,  H04N5/32 ,  G16H40/63 ,  G06T11/60 ,  G06N20/10 ,  G16H40/67 ,  G06V10/75 ,  H04L67/01 ,  G06F18/214 ,  G06N7/01 ,  G16H50/50 ,  H04L67/12 ,  G06F3/04842 ,  G06F3/0485 ,  G06T11/00

CPC classification number: G16H30/40 ,  A61B5/002 ,  A61B5/0022 ,  A61B6/032 ,  A61B6/4233 ,  A61B6/463 ,  A61B6/468 ,  A61B6/50 ,  A61B6/503 ,  A61B6/5217 ,  A61B6/5288 ,  A61B6/5294 ,  A61B6/563 ,  A61B8/468 ,  A61B8/5223 ,  A61B8/565 ,  G01T1/247 ,  G06F3/048 ,  G06F3/167 ,  G06F18/22 ,  G06F18/24 ,  G06F40/169 ,  G06F40/197 ,  G06F40/247 ,  G06F40/279 ,  G06F40/30 ,  G06F40/56 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084 ,  G06Q10/10 ,  G06Q10/103 ,  G06Q50/22 ,  G06T7/0012 ,  G06T7/0016 ,  G06T7/11 ,  G06V10/98 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H40/20 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  H04N5/32 ,  A61B6/505 ,  G06F3/0485 ,  G06F3/04842 ,  G06F18/214 ,  G06N7/01 ,  G06N20/10 ,  G06T11/003 ,  G06T11/60 ,  G06T2200/24 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06T2207/30061 ,  G06T2207/30068 ,  G06V10/751 ,  G06V2201/03 ,  G16H40/67 ,  G16H50/50 ,  H04L67/01 ,  H04L67/12

Abstract: A medical scan assisted review system is operable to receive, via a network, a medical scan for review. Abnormality data is generated by identifying a plurality of abnormalities in the medical scan by utilizing a computer vision model that is trained on a plurality of training medical scans. The abnormality data includes location data and classification data for each of the plurality of abnormalities. Text describing each of the plurality of abnormalities is generated based on the abnormality data. The abnormality data and the text is transmitted to a client device. A display device associated with the client device displays the abnormality data in conjunction with the medical scan via an interactive interface, and the display device further displays the text via the interactive interface.

公开(公告)号：US11626195B2

公开(公告)日：2023-04-11

申请号：US17447708

申请日：2021-09-15

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Lionel Lints ,  Ben Covington

IPC: G16H10/60 ,  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 ,  G06Q10/0631 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  G06V10/22 ,  H04L67/01 ,  G06V10/82 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06V30/194

Abstract: A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.

公开(公告)号：US11462308B2

公开(公告)日：2022-10-04

申请号：US17109431

申请日：2020-12-02

Applicant: Enlitic, Inc.

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

IPC: G06K9/00 ,  G16H10/60 ,  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 ,  H04L67/01 ,  G06V10/82 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06V30/194

Abstract: A triage routing system is operable to receive a medical scan via a receiver. Inference data for the medical scan is generated by performing an inference function, where the inference function utilizes a computer-vision model trained on a plurality of medical scans. One of a plurality of medical professionals is selected to review the medical scan based on the inference data. Triage routing data that indicates the medical scan and the one of the plurality of medical professionals is generated. The medical scan is transmitted to a client device associated with the one of the plurality of medical professionals for display via a display device in accordance with the triage routing data.

公开(公告)号：US11282198B2

公开(公告)日：2022-03-22

申请号：US16939495

申请日：2020-07-27

Applicant: Enlitic, Inc.

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

IPC: G06K9/62 ,  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：US20220084642A1

公开(公告)日：2022-03-17

申请号：US17457050

申请日：2021-12-01

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

Abstract: A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

公开(公告)号：US11087872B2

公开(公告)日：2021-08-10

申请号：US16710431

申请日：2019-12-11

Applicant: Enlitic, Inc.

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

IPC: G06F3/048 ,  G16H30/40 ,  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 ,  G06T11/60 ,  G06N20/10 ,  G06N7/00 ,  G16H40/67 ,  G16H50/50 ,  H04L29/08 ,  H04L29/06 ,  G06F3/0484 ,  G06F3/0485 ,  G06T11/00

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.

公开(公告)号：US20210074394A1

公开(公告)日：2021-03-11

申请号：US17100059

申请日：2020-11-20

Applicant: Enlitic, Inc.

Inventor： Jordan Prosky ,  Li Yao ,  Eric C. Poblenz ,  Kevin Lyman ,  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

Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.

公开(公告)号：US20200160979A1

公开(公告)日：2020-05-21

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: G16H30/40 ,  G16H50/70 ,  G06K9/62 ,  G06T7/00

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.