Deep Learning Diagnosis of Disease Risk Factors in 3D Biomedical Imaging
Summary
The USPTO published patent application US20260100267A1 for deep learning methods and systems that detect biomarkers in volumetric biomedical imaging across optical coherence tomography, ultrasound, magnetic resonance imaging, and computed tomography modalities. The application covers deep neural networks trained to identify clinically useful biomarkers for disease risk factors, filed by Regents of the University of California.
What changed
The USPTO published patent application US20260100267A1 covering deep learning methods and systems for automated diagnosis of disease-related risk factors in 3D biomedical imaging. The application discloses deep neural networks that predict clinically useful biomarkers across multiple imaging modalities including optical coherence tomography, ultrasound, MRI, and CT scans.
AI developers and medical imaging technology companies should monitor this application's progression through examination, as the granted patent will establish IP rights that could affect commercialization pathways for diagnostic AI systems. The university assignee may seek licensing partnerships with healthcare and medical device firms developing computer-aided diagnosis tools.
What to do next
- Review patent claims for competitive analysis
- Assess licensing opportunities for biomarker detection technology
- Monitor for substantive examination and grant status
Archived snapshot
Apr 9, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Systems and Methods for Automated Diagnosis of Disease Related Risk Factors in 3D Biomedical Imaging
Application US20260100267A1 Kind: A1 Apr 09, 2026
Assignee
The Regents of the University of California
Inventors
Oren Avram, Berkin Durmus, Nadav Rakocz, Jeffrey Chiang, Srinivas Sadda, Eran Halperin
Abstract
Deep learning methods and systems for detecting biomarkers within volumetric biomedical imaging dataset using such deep learning methods and systems are provided. Embodiments predict the clinically useful biomarkers in optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images using deep neural networks.
CPC Classifications
G16H 30/40 G06N 3/045 G06N 20/00 G06V 10/774 G16H 50/20 G16H 50/30 G16H 50/70
Filing Date
2024-02-14
Application No.
19156782
Named provisions
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