US 12,456,286 B2
Method for interpreting kidney ultrasound images with artificial intelligence
Lin-Shien Fu, Taichung (TW); and Yueh-Chuan Chang, Taichung (TW)
Assigned to TAICHUNG VETERANS GENERAL HOSPITAL, Taichung (TW)
Filed by Taichung Veterans General Hospital, Taichung (TW)
Filed on Aug. 9, 2023, as Appl. No. 18/446,968.
Claims priority of application No. 111130534 (TW), filed on Aug. 15, 2022.
Prior Publication US 2024/0054763 A1, Feb. 15, 2024
Int. Cl. G06V 10/764 (2022.01); G06T 7/00 (2017.01)
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)] 7 Claims
OG exemplary drawing
 
1. A method for interpreting kidney ultrasound images with artificial intelligence, comprising the following steps:
Step a: using a computer to obtain a plurality of pre-processed kidney ultrasound images from a database in order to establish a plurality of first training image sets and a second training image set; wherein the plurality of first training image sets correspond to different classifications of abnormal patterns respectively, and each one of the plurality of training image sets comprises a plurality of kidney ultrasound images belonging to one of the abnormal patterns classified, and the second training image set comprises a plurality of non-anomalous children kidney ultrasound images classified;
Step b: using at least one of the kidney ultrasound images in the first training image sets and at least one of the kidney ultrasound images in the second training set as a training data, using a deep-learning training model to perform classification and computation in order to obtain an interpretation model, wherein:
the interpretation model comprises a plurality of prediction modules corresponding to the abnormal patterns respectively and a rules module; wherein the rules module is a model analysis logic obtained from a result of abnormal probability and normal probability obtained from comprehensive analysis of the prediction modules, in order to select all or a portion of the prediction modules and to form at least two sets of different interpretation sequence combinations;
Step c: using the computer to perform analysis on a kidney ultrasound image to be analyzed pre-processed by the computer according to the rules module and based on the selected interpretation sequence via all or a portion of the prediction modules, in order to obtain a prediction classification result of the kidney ultrasound image to be analyzed; wherein the prediction classification result is used to understand the kidney ultrasound image to be analyzed belongs to an abnormal classification or a non-anomalous classification;
wherein the pre-process undergone by the kidney ultrasound images and the children kidney ultrasound image to be analyzed further comprises cleaning of an auxiliary information and capturing of a region of interest; the auxiliary information comprises a text, an auxiliary pattern, a symbol or a combination of at least two thereof; at least a portion of the region of interest comprises an image corresponding to a kidney or further comprises an image of a portion of other human tissue adjacent to the kidney.