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:
Oct. 28, 2025

Filed:

Aug. 09, 2023
Applicant:

Taichung Veterans General Hospital, Taichung, TW;

Inventors:

Lin-Shien Fu, Taichung, TW;

Yueh-Chuan Chang, Taichung, TW;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 7/0012 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30084 (2013.01);
Abstract

A method for interpreting kidney ultrasound images with artificial intelligence includes: using a deep-learning method on abnormal and non-anomalous kidney ultrasound images obtained from classification by experienced nephrological experts and via image pre-processing to obtain prediction modules, and upon or after obtaining the prediction modules, obtaining a rules module establishing specific rules for combination and sequence among the prediction modules, and obtaining an interpretation model from a combination of the prediction modules and the rules module to predict whether a kidney ultrasound image is abnormal; wherein each prediction module is used to determine the probability of predefined abnormal pattern, and the rules module provides the logic for determining the prediction modules. Accordingly, the method for interpreting kidney ultrasound images with artificial intelligence is able to allow non-kidney image interpretation experts to find kidney abnormality early and to use it as the basis for future remote medical care item.


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