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:
Oct. 14, 2025

Filed:

Jan. 17, 2025
Applicants:

Gabriel Fine, Salt Lake City, UT (US);

Nathan Silberman, Brooklyn, NY (US);

Inventors:

Gabriel Fine, Salt Lake City, UT (US);

Nathan Silberman, Brooklyn, NY (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/70 (2017.01); A61B 34/10 (2016.01); A61B 34/20 (2016.01); G06F 18/2413 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 10/82 (2022.01); G06V 30/19 (2022.01); G06V 30/194 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); A61B 90/00 (2016.01); A61F 2/01 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); A61B 34/10 (2016.02); A61B 34/20 (2016.02); G06F 18/2414 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06T 7/70 (2017.01); G06T 7/75 (2017.01); G06V 10/82 (2022.01); G06V 30/19173 (2022.01); G06V 30/194 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); A61B 2034/102 (2016.02); A61B 2034/2051 (2016.02); A61B 2034/2065 (2016.02); A61B 2090/367 (2016.02); A61B 2090/376 (2016.02); A61F 2/01 (2013.01); A61F 2/0105 (2020.05); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10121 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30021 (2013.01);
Abstract

A system, method, and computer program product for image-based detection of an object internal to a patient is disclosed. A model can be trained for an internal object, such as an invasive medical device, the trained model being generated from one or more machine learning algorithms that are trained on annotated images of the object with spatial information of the object. An imaging computer system can receive one or more images of the internal object captured by an imaging device positioned external to the patient. The imaging computer system can further detect, based on applying the trained model to the one or more images of the object, the internal object within the patient. A display can output the one or more images and the identifying information for the object.


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