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. 03, 2023

Filed:

Jul. 26, 2021
Applicant:

GE Precision Healthcare Llc, Wauwatosa, WI (US);

Inventors:

Khaled Salem Younis, Parma Heights, OH (US);

Ravi Soni, San Ramon, CA (US);

Katelyn Rose Nye, Glendale, WI (US);

Gireesha Chinthamani Rao, Pewaukee, WI (US);

John Michael Sabol, Sussex, WI (US);

Yash N. Shah, Sunderland, MA (US);

Assignee:

GE Precision Healthcare LLC, Wauwatosa, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/70 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/2431 (2023.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01);
U.S. Cl.
CPC ...
G06T 7/70 (2017.01); G06F 18/217 (2023.01); G06F 18/2155 (2023.01); G06F 18/2413 (2023.01); G06F 18/2431 (2023.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06V 10/764 (2022.01); G06V 10/7784 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 2201/03 (2022.01);
Abstract

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.


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