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公开(公告)号:US20210082547A1
公开(公告)日:2021-03-18
申请号:US17022324
申请日:2020-09-16
申请人: Enlitic, Inc.
发明人: Li Yao , Jordan Prosky , Eric C. Poblenz , Kevin Lyman , Lionel Lints , Ben Covington , Anthony Upton
IPC分类号: 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
摘要: 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.
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公开(公告)号:US10902940B2
公开(公告)日:2021-01-26
申请号:US16356134
申请日:2019-03-18
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Anthony Upton , Li Yao , Ben Covington
IPC分类号: G06K9/00 , 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
摘要: 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.
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公开(公告)号:US10783990B2
公开(公告)日:2020-09-22
申请号:US16353952
申请日:2019-03-14
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Keith Lui , Anthony Upton , Li Yao , Ben Covington
IPC分类号: G06K9/00 , 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
摘要: 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.
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公开(公告)号:US20200161005A1
公开(公告)日:2020-05-21
申请号:US16365780
申请日:2019-03-27
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Li Yao , Eric C. Poblenz , Jordan Prosky , Ben Covington , Anthony Upton
摘要: 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.
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公开(公告)号:US20200160971A1
公开(公告)日:2020-05-21
申请号:US16365772
申请日:2019-03-27
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Li Yao , Eric C. Poblenz , Jordan Prosky , Ben Covington , Anthony Upton
摘要: 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.
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公开(公告)号:US20200160954A1
公开(公告)日:2020-05-21
申请号:US16359258
申请日:2019-03-20
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Anthony Upton , Li Yao , Ben Covington
摘要: A peer-review flagging system is operable to receive a medical scan and a medical report written by a medical professional in conjunction with review of the medical scan. Automated assessment data is generated by performing an inference function on the medical scan by utilizing a computer vision model trained on a plurality of medical scans. Human assessment data is generated by performing an extraction function on the medical report. Consensus data is generated by comparing the automated assessment data to the first human assessment data. A peer-review notification is transmitted to a client device for display. The peer-review notification indicates the medical scan is flagged for peer-review in response to determining the consensus data indicates the automated assessment data compares unfavorably to the human assessment data.
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公开(公告)号:US20200160520A1
公开(公告)日:2020-05-21
申请号:US16365794
申请日:2019-03-27
申请人: Enlitic, Inc.
发明人: Jordan Prosky , Li Yao , Eric C. Poblenz , Kevin Lyman , Ben Covington , Anthony Upton
摘要: 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.
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公开(公告)号:US20200160122A1
公开(公告)日:2020-05-21
申请号:US16299779
申请日:2019-03-12
申请人: Enlitic, Inc.
发明人: Lionel Lints , Li Yao , Kevin Lyman , Chris Croswhite , Ben Covington , Anthony Upton
摘要: A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes. An interactive interface that displays image data of the medical scan and at least one of the set of heat maps is generated for display on a display device associated with the multi-label heat map display system. User input to a client device is received, and an updated interactive interface that includes a change to the display of the at least one of the set of heat maps by the second portion of the interactive interface in response to the user input is displayed.
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公开(公告)号:US10541050B2
公开(公告)日:2020-01-21
申请号:US16168866
申请日:2018-10-24
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Devon Bernard , Li Yao , Diogo Almeida , Ben Covington , Anthony Upton
IPC分类号: 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
摘要: A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.
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公开(公告)号:US20190066835A1
公开(公告)日:2019-02-28
申请号:US16168866
申请日:2018-10-24
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Devon Bernard , Li Yao , Diogo Almeida , Ben Covington , Anthony Upton
摘要: A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.
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