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:
Jun. 14, 2022

Filed:

May. 12, 2021
Applicant:

Shenzhen Keya Medical Technology Corporation, Shenzhen, CN;

Inventors:

Xin Wang, Seattle, WA (US);

Youbing Yin, Kenmore, WA (US);

Bin Kong, Charlotte, NC (US);

Yi Lu, Seattle, WA (US);

Junjie Bai, Seattle, WA (US);

Zhenghan Fang, Shoreline, WA (US);

Qi Song, Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 6/00 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); G16H 50/20 (2018.01); G16H 10/60 (2018.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G16H 30/40 (2018.01); A61B 6/02 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 6/025 (2013.01); G06T 2207/10076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01);
Abstract

Embodiments of the disclosure provide methods and systems for disease condition prediction from images of a patient. The system may include a communication interface configured to receive a sequence of images acquired of the patient by an image acquisition device. The sequence of images are acquired at a sequence of prior time points during progression of a disease. The system may include a processor, configured to determine regions of interest based on the sequence of images. The processor applies a progressive condition prediction network to the regions of interest to predict a level of disease progression at a future time point during the progression of the disease. The progressive condition prediction network predicts the level of disease progression based on the regions of interest and disease conditions at the sequence of prior time points. The processor further provides a diagnostic output based on the predicted level of disease progression.


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