USPTO Patent Grant: AI for Brain Injury Progression

ChangeBridge: Patent Grants - Pharma (A61K)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent to The University of Chicago for a method using AI and machine learning to identify the progression of hypoxic-ischemic brain injury from medical images. The patent, US12582345B2, details a system that extracts features from 3D medical scans to generate a score indicating the presence or progression of the injury.

View original document View source feed page

What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12582345B2 to The University of Chicago. This patent covers systems and methods for identifying the presence or progression of hypoxic-ischemic brain injury (HIBI) using artificial intelligence. The technology involves inputting subsets of 3D medical images into a machine-learning model to extract features, construct vectors, and pool them into a scan-level vector. This vector is then used to generate a score indicating HIBI presence or progression, potentially via a pre-trained classifier.

This patent grant is primarily informational for the healthcare and technology sectors. While it does not impose new regulatory obligations, it signifies innovation in AI-driven medical diagnostics. Companies involved in developing AI for medical imaging or diagnostic tools, particularly for neurological conditions, should be aware of this patented technology. The filing date for this patent was February 1, 2022, with the grant effective March 24, 2026.

Source document (simplified)

← USPTO Patent Grants

Systems and methods for identifying progression of hypoxic-ischemic brain injury

Grant US12582345B2 Kind: B2 Mar 24, 2026

Assignee

The University of Chicago

Inventors

Jordan D. Fuhrman, Ali Mansour, Maryellen L. Giger, Fernando D. Goldenberg

Abstract

A method for identifying the presence or progression of hypoxic ischemic brain injury includes, for each subset of one or more subsets of a three-dimensional medical image of a head of a patient: (i) inputting said each subset into a machine-learning model, (ii) extracting one or more features or feature maps from the machine-learning model, and (iii) constructing, based on the one or more features or feature maps, one of a sequence of vectors. The sequence of vectors is then pooled to obtain a scan-level vector that is used to obtain a score indicating HIBI presence or progression in the patient. For example, the scan-level vector can be inputted into a pre-trained classifier that generates the score based on the scan-level vector. The machine-learning model may be a pre-trained conventional neural network or support vector machine.