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. 26, 2022

Filed:

Aug. 02, 2018
Applicants:

Shanghai Sixth People's Hospital, Shanghai, CN;

Shanghai Jiao Tong University, Shanghai, CN;

Inventors:

Weiping Jia, Shanghai, CN;

Bin Sheng, Shanghai, CN;

Yaxin Shen, Shanghai, CN;

Huating Li, Shanghai, CN;

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A method of fundus oculi image analysis includes acquiring a target fundus oculi image; analyzing the target fundus oculi image by a fundus oculi image analysis model determined by training to acquire an image analysis result of the target fundus oculi image; and the fundus oculi image analysis model includes at least one of an image overall grade prediction sub-model and an image quality factor sub-model. The method performs quality analysis on the target fundus oculi image by the fundus oculi image analysis model, and when the model includes the overall grade prediction sub-model, a prediction result of whether the target fundus oculi image as a whole is gradable can be acquired; when the model includes the image quality factor sub-model, the analysis result of the fundus oculi image quality factor can be acquired and the image analysis model is determined by extensive image training, and the reliability of the result of whether the image is gradable determined based on the above model is high.


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