公开(公告)号：US20230147471A1

公开(公告)日：2023-05-11

申请号：US18150491

申请日：2023-01-05

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Thomas FUCHS ,  Christopher KANAN

IPC: G16H50/20 ,  G16H30/20 ,  G06T7/00 ,  G06F18/2113 ,  G06F18/214

CPC classification number: G16H50/20 ,  G16H30/20 ,  G06T7/0012 ,  G06F18/2113 ,  G06F18/214 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20104 ,  G06T2207/30024 ,  G06V2201/03

Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.

公开(公告)号：US20220328190A1

公开(公告)日：2022-10-13

申请号：US17809313

申请日：2022-06-28

Applicant: PAIGE.AI, Inc.

Inventor： Ran GODRICH ,  Jillian SUE ,  Leo GRADY ,  Thomas FUCHS

IPC: G16H50/20 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H30/40 ,  G16H70/20 ,  G16B40/20 ,  G06K9/62 ,  G06N20/00 ,  G06T7/00

Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing features extracted from image regions in the target image, the machine learning system being generated by processing a plurality of training images each comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, and a pathologist review outcome; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image.

公开(公告)号：US20220198666A1

公开(公告)日：2022-06-23

申请号：US17552438

申请日：2021-12-16

Applicant: PAIGE.AI, Inc.

Inventor： Danielle GORTON ,  Patricia RACITI ,  Jillian SUE ,  Razik YOUSFI

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/12 ,  G06V10/77

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

公开(公告)号：US20220092782A1

公开(公告)日：2022-03-24

申请号：US17457268

申请日：2021-12-02

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Razik YOUSFI ,  Peter SCHUEFFLER ,  Thomas FUCHS ,  Leo GRADY

IPC: G06T7/00 ,  G16H70/60 ,  G16H50/20 ,  G16H30/40

Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

公开(公告)号：US20210398278A1

公开(公告)日：2021-12-23

申请号：US17350328

申请日：2021-06-17

Applicant: PAIGE.AI, Inc.

Inventor： Jason LOCKE ,  Jillian SUE ,  Christopher KANAN ,  Sese IH

IPC: G06T7/00 ,  G06T11/60 ,  G06T3/40 ,  G16H10/40 ,  G16H30/40 ,  G16H70/60 ,  G06N20/00

Abstract: Systems and methods are disclosed for analyzing an image of a slide corresponding to a specimen, the method including receiving at least one digitized image of a pathology specimen; determining, using the digitized image at an artificial intelligence (Al) system, at least one salient feature, the at least one salient comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type; determining, at the Al system, a salient region overlay for the digitized image, wherein the Al system indicates a value for each pixel; and suppressing, based on the value for each pixel, one or more non-salient regions of the digitized image.

公开(公告)号：US20210193301A1

公开(公告)日：2021-06-24

申请号：US17119885

申请日：2020-12-11

Applicant: PAIGE.AI, Inc.

Inventor： Christopher KANAN ,  Rodrigo CEBALLOS LENTINI ,  Jillian SUE ,  Thomas FUCHS ,  Leo GRADY

IPC: G16H30/40 ,  G16H50/20 ,  G06T7/00

Abstract: Systems and methods are disclosed for determining at least one geographic region of a plurality of geographic regions, at least one data variable, and/or at least one health variable, estimating a current prevalence of a data variable in a geographic region of the plurality of geographic regions, determining a trend in a relationship between the data variable and the geographic region at a current time, determining a second trend in the relationship between the data variable and the geographic region at at least one prior point in time, determining if the trend in the relationship is irregular within a predetermined threshold with respect to the second trend from the at least one prior point in time, and, upon determining that the trend in the relationship is irregular within a predetermined threshold, generating an alert.

公开(公告)号：US20240087124A1

公开(公告)日：2024-03-14

申请号：US18514504

申请日：2023-11-20

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Razik YOUSFI ,  Peter SCHUEFFLER ,  Thomas FUCHS ,  Leo GRADY

IPC: G06T7/00 ,  G16H30/40 ,  G16H50/20 ,  G16H70/60

CPC classification number: G06T7/0014 ,  G16H30/40 ,  G16H50/20 ,  G16H70/60 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30168

Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

公开(公告)号：US20240062372A1

公开(公告)日：2024-02-22

申请号：US18451507

申请日：2023-08-17

Applicant: PAIGE.AI, INC.

Inventor： Jillian SUE ,  Marc GOLDFINGER ,  Brandon ROTHROCK ,  Matthew LEE

IPC: G06T7/00 ,  G06T5/50 ,  G06V10/46 ,  G16H30/20 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T5/50 ,  G06V10/462 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/20221 ,  G06T2207/30024 ,  G06T2207/30068

Abstract: Systems and methods are described herein for processing electronic medical images to predict a biomarker's presence, including receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen associated with a patient. A machine learning system may determine a biomarker expression level prediction for the one or more digital medical images. The biomarker expression level prediction may be based on a determined transcriptomic score and protein expression score for the one or more digital medical images. A slide overlay indicating a region of tissue on the one or more digital medical images that is most likely to contribute to the slide level biomarker expression prediction may be generated.

公开(公告)号：US20230351599A1

公开(公告)日：2023-11-02

申请号：US18329024

申请日：2023-06-05

Applicant: PAIGE.AI, Inc.

Inventor： Danielle GORTON ,  Patricia RACITI ,  Jillian SUE ,  Razik YOUSFI

IPC: G16H15/00 ,  G06T11/60 ,  G06T7/00 ,  G06V10/25 ,  G06V10/77 ,  G16H30/40 ,  G16H80/00 ,  G06V10/12 ,  G16H10/40 ,  G16H50/20

CPC classification number: G06T7/0014 ,  G06T11/60 ,  G06V10/12 ,  G06V10/25 ,  G06V10/7715 ,  G16H10/40 ,  G16H15/00 ,  G16H30/40 ,  G16H50/20 ,  G16H80/00 ,  G06T2207/10004 ,  G06T2207/30004 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

公开(公告)号：US20230222662A1

公开(公告)日：2023-07-13

申请号：US18174284

申请日：2023-02-24

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Razik YOUSFI ,  Peter SCHUEFFLER ,  Thomas FUCHS ,  Leo GRADY

IPC: G06T7/00 ,  G16H50/20 ,  G16H70/60 ,  G16H30/40

CPC classification number: G06T7/0014 ,  G16H50/20 ,  G16H70/60 ,  G16H30/40 ,  G06T2207/30168 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.