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:
Mar. 15, 2022

Filed:

Jul. 23, 2021
Applicant:

Qure.ai Technologies Private Limited, Mumbai, IN;

Inventors:

Preetham Putha, Guntur, IN;

Manoj Tadepalli, Gudivada, IN;

Bhargava Reddy, Mumbai, IN;

Tarun Raj, Vishakapatnam, IN;

Ammar Jagirdar, Mumbai, IN;

Pooja Rao, Pune, IN;

Prashant Warier, Mumbai, IN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 6/00 (2006.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06N 3/08 (2006.01); G06T 7/11 (2017.01); G16H 50/70 (2018.01); G16H 10/60 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); A61B 6/50 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30064 (2013.01); G16H 10/60 (2018.01); G16H 70/60 (2018.01);
Abstract

A system and method for predicting a lung cancer risk based on a chest X-ray in which a nodule is detected in a chest of a patient based on an analysis of the chest X-ray using an image processing technique. A region of interest associated with the nodule is identified using the image processing technique. The region of interest is further analyzed using deep learning to determine a plurality of characteristics associated with the nodule. The plurality of characteristics comprises a size of the nodule, a calcification in the nodule, a homogeneity of the nodule and a spiculation of the nodule. Further, the plurality of characteristics is compared with a trained data model using deep learning. Based on the comparison, a risk score associated with the nodule is generated. Further, the lung cancer risk is predicted when the risk score exceeds a predefined threshold value.


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