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:
Apr. 25, 2023

Filed:

Apr. 17, 2018
Applicants:

Bgi Shenzhen, Guangdong, CN;

Eye, Ear, Nose, and Throat Hospital of Fudan University, Shanghai, CN;

Inventors:

Xiaoqing Liu, Shenzhen, CN;

Jiaxu Hong, Shanghai, CN;

Yong Ni, Shenzhen, CN;

Shuangshuang Li, Shenzhen, CN;

Lili Wang, Shenzhen, CN;

Wei He, Shenzhen, CN;

Youwen Guo, Shenzhen, CN;

Yuxuan Liu, Shenzhen, CN;

Yong Liu, Shenzhen, CN;

Wei Wang, Shenzhen, CN;

Ruiqi Xu, Shenzhen, CN;

Jingyi Cheng, Shanghai, CN;

Lijia Tian, Shanghai, CN;

Wenbin Chen, Shenzhen, CN;

Xun Xu, Shenzhen, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G16H 50/20 (2018.01); G16H 10/20 (2018.01); A61B 3/12 (2006.01); A61B 3/14 (2006.01); G06T 7/00 (2017.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); A61B 3/12 (2013.01); A61B 3/14 (2013.01); G06F 18/214 (2023.01); G06F 18/2178 (2023.01); G06T 7/0012 (2013.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/698 (2022.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

The present disclosure proposes a modeling method and apparatus for diagnosing an ophthalmic disease based on artificial intelligence, and a storage medium. The modeling method includes: establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires; training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model; training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires; and merging the first classification model and the second classification model to obtain a target classification network model, in which, a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease.


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