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.
Patent No.:
Date of Patent:
Jun. 30, 2026
Filed:
Aug. 19, 2024
Pathai, Inc., Boston, MA (US);
Fedaa Najdawi, Brookline, MA (US);
Kathleen Sucipto, Brookline, MA (US);
Archit Khosla, New York, NY (US);
Michael Drage, Wellesley, MA (US);
Amaro N. Taylor-Weiner, Brooklyn, NY (US);
Michael C. Montalto, Brielle, NJ (US);
Murray Resnick, Sharon, MA (US);
Maryam Pouryahya, Bethesda, MD (US);
Stephanie Hennek, Medford, MA (US);
Ilan N. Wapinski, Brookline, MA (US);
Andrew H. Beck, Brookline, MA (US);
Christina Jayson, Somerville, MA (US);
Chintan Shah, Chestnut Hill, MA (US);
Waleed Tahir, Dorchester, MA (US);
John Shamshoian, Somerville, MA (US);
Michael Griffin, Georgetown, TX (US);
Lani Clinton, Efland, NC (US);
Zahil Shanis, Claymont, DE (US);
Carlos Gaitán, Kirkland, WA (US);
Jin Li, New York, NY (US);
George Hu, Boston, MA (US);
Andrew Walker, Hopkins, MN (US);
Harshith Padigela, Brookline, MA (US);
Harsha Vardhan Pokkalla, Sudbury, MA (US);
Yibo Zhang, Lynnfield, MA (US);
Emma Krause, Newburyport, MA (US);
Jimish Mehta, Ardmore, PA (US);
PathAI, Inc., Boston, MA (US);
Abstract
In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for training one or more models to predict ulcerative colitis (UC) severity based on human-interpretable image features extracted from a whole-slide image, including acts of accessing a plurality of annotated whole-slide images associated with a plurality of UC patients, wherein each of the plurality of annotated whole-slide images includes at least one annotation describing a cell-type label or a tissue-type segmentation for a portion of the whole-slide image, extracting a plurality of human-interpretable image features based on cell-type labels and tissue-type segmentations associated with the plurality of annotated whole-slide images, training a statistical model based on the plurality of human-interpretable image features to predict the UC severity for a whole-slide image, and storing the trained model on at least one storage device.