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:
Dec. 06, 2022

Filed:

Dec. 03, 2021
Applicant:

Qure.ai Technologies Private Limited, Mumbai, IN;

Inventors:

Prashant Warier, Mumbai, IN;

Ankit Modi, Koderma, IN;

Preetham Putha, Guntur, IN;

Prakash Vanapalli, Vishakapatnam, IN;

Vikash Challa, Vizianagaram, IN;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/62 (2017.01); G06T 5/00 (2006.01); G06T 7/11 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/82 (2022.01); G06V 10/75 (2022.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/20 (2018.01); G06T 7/20 (2017.01); G06T 7/40 (2017.01);
U.S. Cl.
CPC ...
G06T 7/0016 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); A61B 6/5217 (2013.01); A61B 6/5223 (2013.01); A61B 6/5258 (2013.01); G06T 5/002 (2013.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); G06T 7/62 (2017.01); G06V 10/25 (2022.01); G06V 10/273 (2022.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30064 (2013.01);
Abstract

Disclosed is a system and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.


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