The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 12, 2025

Filed:

May. 03, 2021
Applicant:

Pathai, Inc., Boston, MA (US);

Inventors:

Amaro N. Taylor-Weiner, Brooklyn, NY (US);

Harsha Vardhan Pokkalla, Sudbury, MA (US);

Hunter L. Elliott, Boston, MA (US);

Benjamin P. Glass, Boston, MA (US);

Ilan N. Wapinski, Brookline, MA (US);

Aditya Khosla, Lexington, MA (US);

Murray Resnick, Sharon, MA (US);

Michael C. Montalto, Brielle, NJ (US);

Andrew H. Beck, Brookline, MA (US);

Zahil Shanis, Claymont, DE (US);

Aryan Pedawi, Austin, TX (US);

Quang Huy Le, Malden, MA (US);

Jason K. Wang, Los Angeles, CA (US);

Maryam Pouryahya, Bethesda, MD (US);

Kenneth Knute Leidal, Cambridge, MA (US);

Oscar M. Carrasco-Zevallos, Somerville, MA (US);

Dinkar Juyal, Boston, MA (US);

Charles Biddle-Snead, New York, NY (US);

Katy Wack, Pittsburgh, PA (US);

Assignee:

PathAI, Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); G06T 7/00 (2017.01); G16H 50/30 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
A61B 5/4244 (2013.01); A61B 5/7267 (2013.01); G06T 7/0012 (2013.01); G16H 50/30 (2018.01); G16H 70/60 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30056 (2013.01);
Abstract

In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.


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