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. 09, 2024

Filed:

Mar. 10, 2023
Applicant:

Johnson & Johnson Enterprise Innovation Inc., New Brunswick, NJ (US);

Inventors:

George R. Washko, Jr., West Roxbury, MA (US);

Christopher Scott Stevenson, West Sussex, GB;

Samuel Yoffe Ash, Newton, MA (US);

Raul San Jose Estepar, Wellesley, MA (US);

Matthew David Mailman, New Hope, PA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06V 10/40 (2022.01); G16H 50/50 (2018.01); G16H 50/30 (2018.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); A61B 6/5217 (2013.01); G06T 7/0014 (2013.01); G06V 10/40 (2022.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/032 (2022.01);
Abstract

Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.


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