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:
Jan. 02, 2024

Filed:

Nov. 19, 2018
Applicants:

Mayo Foundation for Medical Education and Research, Rochester, MN (US);

Arizona Board of Regents on Behalf of Arizona State University, Scottsdale, AZ (US);

Inventors:

Leland S. Hu, Phoenix, AZ (US);

Jing Li, Tempe, AZ (US);

Kristin R. Swanson, Phoenix, AZ (US);

Teresa Wu, Gilbert, AZ (US);

Nathan Gaw, Tempe, AZ (US);

Hyunsoo Yoon, Tempe, AZ (US);

Andrea Hawkins-Daarud, Houston, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06N 20/10 (2019.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G06T 7/00 (2017.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06N 20/10 (2019.01); G06F 18/2155 (2023.01); G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.


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