Patent number: 12670595
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.
Type:
Grant
Filed:
August 19, 2024
Date of Patent:
June 30, 2026
Assignee:
PathAI, Inc.
Inventors:
Fedaa Najdawi, Kathleen Sucipto, Archit Khosla, Michael Drage, Amaro N. Taylor-Weiner, Michael C. Montalto, Murray Resnick, Maryam Pouryahya, Stephanie Hennek, Ilan N. Wapinski, Andrew H. Beck, Christina Jayson, Chintan Shah, Waleed Tahir, John Shamshoian, Michael Griffin, Lani Clinton, Zahil Shanis, Carlos Gaitán, Jin Li, George Hu, Andrew Walker, Harshith Padigela, Harsha Vardhan Pokkalla, Yibo Zhang, Emma Krause, Jimish Mehta