公开(公告)号：US20210193300A1

公开(公告)日：2021-06-24

申请号：US17107121

申请日：2020-11-30

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：US20250029709A1

公开(公告)日：2025-01-23

申请号：US18908015

申请日：2024-10-07

Applicant: PAIGE.AI, Inc.

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

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

Abstract: Systems and methods are disclosed for processing digital pathology images, prioritizing the digital pathology images, and outputting a sequence of the digital pathology images based on the prioritization. The prioritization may be determined by a machine learning model trained to determine prioritization values based on various criteria. For example, the machine learning may generate biomarker expression information and determine prioritization values based on the generated information.

公开(公告)号：US20220139533A1

公开(公告)日：2022-05-05

申请号：US17511871

申请日：2021-10-27

Applicant: PAIGE.AI, Inc.

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

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

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：US20220138450A1

公开(公告)日：2022-05-05

申请号：US17519847

申请日：2021-11-05

Applicant: PAIGE.AI, Inc.

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

IPC: G06K9/00 ,  G06T7/11 ,  G06N20/00 ,  G06K9/62

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：US20220076416A1

公开(公告)日：2022-03-10

申请号：US17530028

申请日：2021-11-18

Applicant: PAIGE.AI, Inc.

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

IPC: G06T7/00 ,  G06N20/00 ,  G16H50/20

Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing microscopic features extracted from multiple 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, a pathologist review outcome, and an analytic difficulty; 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; and 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：US20210350166A1

公开(公告)日：2021-11-11

申请号：US17313617

申请日：2021-05-06

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Thomas FUCHS ,  Christopher KANAN

IPC: G06K9/62 ,  G06T7/00 ,  G16H30/20 ,  G16H50/20

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：US20210233642A1

公开(公告)日：2021-07-29

申请号：US17160129

申请日：2021-01-27

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Jason LOCKE ,  Peter SCHUEFFLER ,  Christopher KANAN ,  Thomas FUCHS ,  Leo GRADY

IPC: G16H30/20 ,  G06N20/00

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

公开(公告)号：US20210209760A1

公开(公告)日：2021-07-08

申请号：US17126596

申请日：2020-12-18

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Razik Yousfi ,  Peter Schueffler ,  Thomas Fuchs ,  Leo Grady

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

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：US20250037870A1

公开(公告)日：2025-01-30

申请号：US18916458

申请日：2024-10-15

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Thomas FUCHS ,  Christopher KANAN

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

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：US20240112338A1

公开(公告)日：2024-04-04

申请号：US18530844

申请日：2023-12-06

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

CPC classification number: G06T7/0012 ,  G06N20/00 ,  G06T3/40 ,  G06T11/60 ,  G16H10/40 ,  G16H30/40 ,  G16H70/60 ,  G06T2207/20081 ,  G06T2207/30024

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.