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.
Patent No.:
Date of Patent:
Jul. 18, 2023
Filed:
Jan. 05, 2021
Applicant:
Ping an Technology (Shenzhen) Co., Ltd., Shenzhen, CN;
Inventors:
Kang Zheng, Bethesda, MD (US);
Yirui Wang, Bethesda, MD (US);
Shun Miao, Bethesda, MD (US);
Changfu Kuo, Taiwan, CN;
Chen-I Hsieh, Taiwan, CN;
Assignee:
PING AN TECHNOLOGY (SHENZHEN) CO., LTD., Shenzhen, CN;
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06N 3/08 (2023.01); A61B 6/00 (2006.01); G06T 7/11 (2017.01); G06N 3/04 (2023.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G06F 18/213 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 6/469 (2013.01); A61B 6/505 (2013.01); A61B 6/5217 (2013.01); G06F 18/213 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 7/73 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G06T 2207/10116 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30012 (2013.01); G06T 2207/30052 (2013.01); G06T 2207/30168 (2013.01); G06V 2201/033 (2022.01);
Abstract
The present disclosure provides a computer-implemented method, a device, and a computer program product for radiographic bone mineral density (BMD) estimation. The method includes receiving a plain radiograph, detecting landmarks for a bone structure included in the plain radiograph, extracting an ROI from the plain radiograph based on the detected landmarks, estimating the BMD for the ROI extracted from the plain radiograph by using a deep neural network.