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：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.

公开(公告)号：US11056220B2

公开(公告)日：2021-07-06

申请号：US16360275

申请日：2019-03-21

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: An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing 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：US20210082547A1

公开(公告)日：2021-03-18

申请号：US17022324

申请日：2020-09-16

Applicant: Enlitic, Inc.

Inventor： Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Kevin Lyman ,  Lionel Lints ,  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 multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global 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 global 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. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

公开(公告)号：US10943681B2

公开(公告)日：2021-03-09

申请号：US16299644

申请日：2019-03-12

Applicant: Enlitic, Inc.

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

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 global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global 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 global 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. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device.

公开(公告)号：US20200161005A1

公开(公告)日：2020-05-21

申请号：US16365780

申请日：2019-03-27

Applicant: Enlitic, Inc.

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

IPC: G16H50/70 ,  G16H30/40 ,  G06N5/04

Abstract: A location-based medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans. Location-based subsets of the plurality of medical scans are generated by including ones of the plurality of medical scans with originating locations that compare favorably to location grouping criteria for the each location-based subset. A plurality of location-based models are generated by performing a fine-tuning step on the generic model, utilizing a corresponding one of the plurality of location-based subsets. Inference data is generated for a new medical scan by utilizing one of the location-based models on the new medical scan, where an originating location associated with the new medical scan compares favorably to location grouping criteria for the location-based subset utilized to generate the location-based model. The inference data is transmitted to a client device for display via a display device.

公开(公告)号：US20200160971A1

公开(公告)日：2020-05-21

申请号：US16365772

申请日：2019-03-27

Applicant: Enlitic, Inc.

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

IPC: G16H30/20 ,  G06T7/00 ,  A61B5/00 ,  G06T7/11 ,  G16H10/60

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 can be generated by performing a training step on a corresponding one of the plurality of training sets. A subset of the set of sub-models is selected for a new medical scan. A set of abnormality data is generated by applying a subset of a set of inference functions on the new medical scan, where the subset of the set of inference functions utilize the subset of the set of sub-models. Final abnormality data is generated by performing a final inference function on the set of abnormality data. The final abnormality data can be to a client device for display via a display device.

公开(公告)号：US20200160520A1

公开(公告)日：2020-05-21

申请号：US16365794

申请日：2019-03-27

Applicant: Enlitic, Inc.

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

IPC: G06T7/00 ,  G06K9/62 ,  G16H30/20 ,  G16H30/40

Abstract: A multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models corresponding to one of a plurality of abnormality types can be generated by performing a fine-tuning step on the generic model. Abnormality detection data can be generated for a new medical scan by performing utilizing the generic model. One of the plurality of abnormality types is determined to be detected in the new medical scan based on the abnormality detection data, and a fine-tuned model that corresponds to the abnormality type is selected. Additional abnormality data is generated for the new medical scan by utilizing the selected fine-tuned model. The additional abnormality data can be transmitted to a client device for display via a display device.