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. 27, 2022

Filed:

Jun. 14, 2019
Applicant:

Kheiron Medical Technologies Ltd, London, GB;

Inventors:

Peter Kecskemethy, London, GB;

Tobias Rijken, London, GB;

Edith Karpati, Budapest, HU;

Michael O'Neill, London, GB;

Andreas Heindl, London, GB;

Joseph Elliot Yearsley, London, GB;

Dimitrios Korkinof, London, GB;

Galvin Khara, London, GB;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G16H 30/40 (2018.01); A61B 6/00 (2006.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G06T 7/143 (2017.01); G16H 40/67 (2018.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 30/19 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 6/502 (2013.01); A61B 6/5205 (2013.01); A61B 6/5217 (2013.01); A61B 6/5235 (2013.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/70 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 30/19147 (2022.01); G06V 30/19173 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01);
Abstract

The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.


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