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:
Sep. 30, 2025

Filed:

Aug. 07, 2019
Applicant:

Yale University, New Haven, CT (US);

Inventors:

James S. Duncan, Madison, CT (US);

Brian Letzen, Orange, CT (US);

Julius Chapiro, New Haven, CT (US);

Clinton Wang, New Haven, CT (US);

Assignee:

Yale University, New Haven, CT (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06F 18/2413 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 5/77 (2024.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06F 18/24147 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 5/77 (2024.01); G06T 7/0012 (2013.01); G06T 11/00 (2013.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01);
Abstract

One aspect of the invention provides a computer-implemented method of identifying one or more clinical factors associated with an artificial intelligence prediction. The computer implemented method includes: applying a previously trained deep neural network to one or more images for a subject to produce a prediction, the previously trained deep neural network comprising a plurality of nodes; comparing outputs of the nodes to previously identified patterns of node outputs for a plurality of individual clinical factors; and identifying which of the plurality of individual clinical factors are most correlated with the outputs of the nodes.


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