公开(公告)号：US20230410986A1

公开(公告)日：2023-12-21

申请号：US18458532

申请日：2023-08-30

Applicant: PAIGE.AI, Inc.

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

IPC: G16H30/40 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H50/20 ,  G16H70/20 ,  G16B40/20 ,  G06N20/00 ,  G06T7/00 ,  G06F18/214

CPC classification number: G16H30/40 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H50/20 ,  G16H70/20 ,  G16B40/20 ,  G06N20/00 ,  G06T7/0012 ,  G06F18/214 ,  G06V2201/03 ,  G06V2201/04 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30204

Abstract: Systems and methods are disclosed for processing images including, for example, receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; determining a quality control metric for the target image via a first trained machine learning model having been trained to predict the quality control metric based on the target image, wherein the quality control metric signifies a quality control issue; and outputting, via a user interface, a sequence of a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the quality control metric.

公开(公告)号：US20230268059A1

公开(公告)日：2023-08-24

申请号：US18310801

申请日：2023-05-02

Applicant: PAIGE.AI, Inc.

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

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

CPC classification number: G16H30/40 ,  G06T7/0012 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/20084

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：US20230115448A1

公开(公告)日：2023-04-13

申请号：US17954490

申请日：2022-09-28

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Matthew LEE ,  Christopher KANAN

IPC: G06T5/50 ,  G06T7/10 ,  G06T7/70 ,  G06T7/00

Abstract: A method for processing electronic medical images may include receiving an initial whole slide image of a pathology specimen, receiving information about slide quality aspects to modify, and generating a synthetic whole slide image by applying a machine learning model to modify the received initial whole slide image according to the received information. The pathology specimen may be associated with a patient. The synthetic whole slide image may have a reduced quality as compared to the initial whole slide image.

公开(公告)号：US20210073984A1

公开(公告)日：2021-03-11

申请号：US17014532

申请日：2020-09-08

Applicant: PAIGE.AI, Inc.

Inventor： Jason LOCKE ,  Jillian SUE ,  Peter SCHUEFFLER ,  Jose Sebastian IZURIETA-HERRERA

IPC: G06T7/00 ,  G06N20/00 ,  G06K9/32 ,  G06T11/00 ,  G06T3/40

Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.

公开(公告)号：US20250166397A1

公开(公告)日：2025-05-22

申请号：US19028824

申请日：2025-01-17

Applicant: PAIGE.AI, Inc.

Inventor： Brandon ROTHROCK ,  Jillian SUE ,  Matthew HOULISTON ,  Patricia RACITI ,  Leo GRADY

IPC: G06V20/69 ,  G06F18/2431 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/194 ,  G16H30/40

Abstract: A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

公开(公告)号：US20250045922A1

公开(公告)日：2025-02-06

申请号：US18920046

申请日：2024-10-18

Applicant: PAIGE.AI, Inc.

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

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

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 (AI) 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 AI system, a salient region overlay for the digitized image, wherein the AI 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：US20230360414A1

公开(公告)日：2023-11-09

申请号：US18346391

申请日：2023-07-03

Applicant: PAIGE.AI, Inc.

Inventor： Brandon ROTHROCK ,  Jillian SUE ,  Matthew HOULISTON ,  Patricia RACITI ,  Leo GRADY

IPC: G06V20/69 ,  G06T7/11 ,  G06N20/00 ,  G06T7/194 ,  G16H30/40 ,  G06T7/00 ,  G06F18/2431

CPC classification number: G06V20/695 ,  G06T7/11 ,  G06N20/00 ,  G06V20/698 ,  G06T7/194 ,  G16H30/40 ,  G06T7/0012 ,  G06F18/2431 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

公开(公告)号：US20230196562A1

公开(公告)日：2023-06-22

申请号：US17936626

申请日：2022-09-29

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Sam SEYMOUR

IPC: G06T7/00 ,  G06K9/62 ,  G06V10/764 ,  G06V10/25 ,  G16H10/40

CPC classification number: G06T7/0012 ,  G06K9/6235 ,  G06V10/765 ,  G06V10/25 ,  G16H10/40 ,  G06K2009/6237 ,  G06T2207/30168 ,  G06T2207/30024

Abstract: Systems and methods are described herein for processing electronic medical images to optimize a review order of pathology cases. For example, a plurality of variables and one or more constraints may be received along with a plurality of pathology cases. Each case of the plurality of pathology cases may include one or more medical images of at least one pathology specimen associated with a patient. The medical images from each case, the plurality of variables, and the one or more constraints may be provided as input to a trained system. A sequential order for user review of the plurality of cases to optimize one or more of the plurality of variables based on the one or more constraints may be received as output of the trained system. Each case of the plurality of cases may be automatically provided to a user for review according to the sequential order.

公开(公告)号：US20230095896A1

公开(公告)日：2023-03-30

申请号：US18061837

申请日：2022-12-05

Applicant: PAIGE.AI, Inc.

Inventor： Jason LOCKE ,  Jillian SUE ,  Peter SCHUEFFLER ,  Jose Sebastian IZURIETA-HERRERA

IPC: G06T7/00 ,  G06N20/00 ,  G06T3/40 ,  G06T11/00 ,  G06V10/25

Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.

公开(公告)号：US20220199255A1

公开(公告)日：2022-06-23

申请号：US17565681

申请日：2021-12-30

Applicant: PAIGE.AI, Inc.

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

IPC: G16H50/20 ,  G16H30/40 ,  G06T7/00 ,  G16H15/00 ,  G16H10/40 ,  G16H80/00 ,  G06T11/60

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.