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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.

Patent No.:

US 11923070 B1

PDFFull Text
Date of Patent:
Mar. 05, 2024

Filed:

Nov. 27, 2020

Automated visual reporting technique for medical imaging processing system

Applicant:

Braid Health Inc., San Francisco, CA (US);

Inventors:

Kevin Quennesson, San Francisco, CA (US);

Alessandro Sabatelli, San Francisco, CA (US);

Assignee:

Braid Health Inc., San Francisco, CA (US);

Attorney:

Fenwick & West LLP

Primary Examiner:

John Villecco

Assistant Examiner:

Courtney Joan Nelson

Int. Cl.
CPC ...
G16H 30/40 (2018.01); G06F 21/60 (2013.01); G06F 40/40 (2020.01); G06N 20/00 (2019.01); G06T 3/40 (2006.01); G06T 7/00 (2017.01); G06T 15/04 (2011.01); G06T 15/08 (2011.01); G06T 17/20 (2006.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); H04L 9/32 (2006.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06F 21/602 (2013.01); G06F 40/40 (2020.01); G06N 20/00 (2019.01); G06T 3/40 (2013.01); G06T 7/0012 (2013.01); G06T 15/04 (2013.01); G06T 15/08 (2013.01); G06T 17/20 (2013.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 50/50 (2018.01); H04L 9/3242 (2013.01); G06T 2207/20081 (2013.01); G06T 2210/08 (2013.01); G06T 2210/41 (2013.01); G16H 50/20 (2018.01);
Abstract

A medical imaging communication system generates an automated report for a medical image corresponding to a patient. The system receives one or more medical images for a patient. The system applies a model to identify an abnormality in the received one or more medical images. The model is configured to determine a likelihood score for the presence of the abnormality in the one or more medical images. If the likelihood score is above a threshold value, the system generates an automated report indicating the type of abnormality identified in the received one or more medical images. The automated report is then provided to a viewing user (e.g., a medical professional viewing the one or more medical images).

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