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. 26, 2025

Filed:

Jun. 30, 2022
Applicant:

The University of Hong Kong, Hong Kong, CN;

Inventors:

Xihe Kuang, Hong Kong, CN;

Jason Py Cheung, Hong Kong, CN;

Teng Zhang, Hong Kong, CN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/70 (2022.01); G06T 3/4007 (2024.01); G06T 7/00 (2017.01); G06T 7/60 (2017.01); G06T 7/70 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/28 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 3/4007 (2013.01); G06T 7/60 (2013.01); G06T 7/70 (2017.01); G06V 10/25 (2022.01); G06V 10/267 (2022.01); G06V 10/28 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30012 (2013.01); G06V 2201/033 (2022.01);
Abstract

A system for automated comprehensive assessment of clinical lumbar MRIs includes a MRI standardization component that reads MRI data from raw lumbar MRI files, uses an artificial intelligence (AI) model to convert the raw MRI data into a standardized format. A core assessment component automatically generates MRI assessment results, including multi-tissue anatomical annotation, multi-pathology detection and multi-pathology progression prediction based on the structured MRI data package. The core assessment component contains a semantic segmentation module that utilizes a deep learning artificial intelligence (AI) model to generate an MRI assessment results that contains multi-tissue anatomical annotation, a pathology detection module to generate multi-pathology detection, and a pathology progression prediction module to generate multi-pathology progression prediction. A model optimization component archives clinical MRI data and MRI assessment results based on comments provided by a specialist, and periodically optimizes the AI deep learning model of the core assessment component.


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