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:
Apr. 30, 2024

Filed:

Mar. 04, 2019
Applicants:

The Medical College of Wisconsin, Inc., Milwaukee, WI (US);

New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery, New York, NY (US);

Inventors:

Kevin M. Koch, Wauwatosa, WI (US);

Andrew S. Nencka, Greendale, WI (US);

Robin A. Karr, Wauwatosa, WI (US);

Bradley J. Swearingen, Waukesha, WI (US);

Hollis Potter, Greenwich, CT (US);

Matthew F. Koff, Livingston, NJ (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06F 18/213 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/055 (2013.01); A61B 5/4504 (2013.01); A61B 5/4851 (2013.01); G06F 18/213 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 2576/02 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01);
Abstract

Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.


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