US 11,928,820 B2
Systems and methods for analyzing electronic images for quality control
Jillian Sue, New York, NY (US); Razik Yousfi, Brooklyn, NY (US); Peter Schueffler, Munich (DE); Thomas Fuchs, New York, NY (US); and Leo Grady, Darien, CT (US)
Assigned to Paige.AI, Inc., New York, NY (US)
Filed by PAIGE.AI, Inc., New York, NY (US)
Filed on Feb. 24, 2023, as Appl. No. 18/174,284.
Application 18/174,284 is a continuation of application No. 17/457,268, filed on Dec. 2, 2021, granted, now 11,615,534.
Application 17/457,268 is a continuation of application No. 17/126,596, filed on Dec. 18, 2020, granted, now 11,222,424, issued on Jan. 11, 2022.
Claims priority of provisional application 62/957,517, filed on Jan. 6, 2020.
Prior Publication US 2023/0222662 A1, Jul. 13, 2023
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01)
CPC G06T 7/0014 (2013.01) [G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30168 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for processing electronic images, the method comprising:
receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;
determining a quality control (QC) machine learning model, the QC machine learning model being configured to predict a quality designation based on a presence or lack of one or more artifacts and/or;
providing the digital image as an input to the QC machine learning model;
receiving the quality designation associated with the digital image as an output from the QC machine learning model;
determining, based on the output from the QC machine learning model, whether the quality designation is either an approved designation or a rejected designation, wherein the quality designation is indicative or artifacts such as errors or imperfections in the digital image, wherein the quality designation includes an approval scale and/or a rejection scale that provides a finer indication of the image quality;
determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers, the QA machine learning model being different than the QC machine learning model;
providing the digital image to the QA machine learning model;
receiving a disease designation for the digital image as an output from the QA machine learning model;
receiving an external designation for one of the digital image or the target specimen and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;
evaluating the external designation by comparing the disease designation to the external designation;
outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation; and
rejecting the external designation based on the evaluation, based on the external designation deviating from the disease designation beyond a predetermined threshold.