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:
May. 05, 2026

Filed:

Dec. 07, 2021
Applicant:

Koninklijke Philips N.v., Eindhoven, NL;

Inventors:

Man M Nguyen, Melrose, MA (US);

Jochen Kruecker, Andover, MA (US);

Raghavendra Srinivasa Naidu, Auburndale, MA (US);

Haibo Wang, Melrose, MA (US);

Assignee:

KONINKLIJKE PHILIPS N.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G06T 7/00 (2017.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
G16H 50/50 (2018.01); G06T 7/0012 (2013.01); G16H 50/20 (2018.01); G06T 2200/24 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30101 (2013.01);
Abstract

The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and/or descriptors of disease features that may be likely to develop over time.


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