Cirrhotic portal hypertension non-invasive evaluation method based on 3D multi-channel convolutional neural network

A technology of convolutional neural network and portal hypertension, which is applied in the fields of radiological diagnostic instruments, medical science, diagnosis, etc., can solve problems affecting accuracy, difficult operation, high risk, etc., and achieve high sensitivity and accuracy , Improve the quality of life and reduce the financial burden

Pending Publication Date: 2020-02-07
WEST CHINA HOSPITAL SICHUAN UNIV
View PDF4 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to propose a non-invasive evaluation method for cirrhosis and portal hypertension based on 3D multi-channel convolutional neural network, which solves the p

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cirrhotic portal hypertension non-invasive evaluation method based on 3D multi-channel convolutional neural network
  • Cirrhotic portal hypertension non-invasive evaluation method based on 3D multi-channel convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The present invention aims to propose a non-invasive assessment method for cirrhosis and portal hypertension based on 3D multi-channel convolutional neural network, which provides a new way for non-invasive assessment of patients with portal hypertension; A multi-channel 3D convolutional neural network structure system composed of three convolutional layers, three pooling layers and one fully connected layer, using convolutional neural network to extract 3D CT image features of liver, spleen and portal system, through Softmax function With the expected portal hypertension grading results as the output, the output probability is calculated, and the output weight of each neuron is adjusted through repeated learning and training, and an artificial intelligence evaluation system for cirrhosis portal hypertension risk classification based on CTA images is constructed. The individualized precise diagnosis and treatment of portal hypertension provides non-invasive auxiliary dec...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to the field of non-invasive measurement, discloses a cirrhotic portal hypertension non-invasive evaluation method based on a 3D multi-channel convolutional neural network, and solves the problems that in the prior art, an invasive measurement method is high in risk, high in cost and high in operation difficulty, and the accuracy of a current clinical non-invasive predictionmodel is still affected by various interference factors. The method comprises the following steps: S1, acquiring a CTA layer sequence image of a patient with liver cirrhosis and portal hypertension ina portal period; S2, preprocessing the CTA layer sequence image; S3, performing segmentation based on the preprocessed CTA layer sequence image to obtain three-dimensional images of the liver, the spleen and the portal vein system; S4, extracting the characteristics of the three-dimensional images of the liver, spleen and portal system by adopting a multi-channel 3D convolutional neural network,and training by taking a measured HVPG grading value of the patient with liver cirrhosis portal hypertension as output to obtain a stable portal hypertension prediction model; and S5, evaluating the portal pressure of the patient through the stable portal high-pressure prediction model.

Description

technical field [0001] The invention relates to the field of non-invasive measurement, in particular to a non-invasive evaluation method for liver cirrhosis and portal hypertension based on a 3D multi-channel convolutional neural network. Background technique [0002] Liver cirrhosis is the end-stage manifestation of various chronic liver diseases, and portal hypertension is one of the important manifestations of liver cirrhosis. Accurate assessment of portal hypertension is of great significance for risk stratification and individualized treatment of liver cirrhosis. Hepatic Venous Pressure Gradient (HVPG) is the gold standard for risk stratification of portal hypertension. However, as an invasive measurement method, HVPG suffers from low patient acceptance and high examination costs when there are no serious complications in the early stage of portal hypertension, and its clinical application in my country and other developing countries is greatly limited. Therefore, fin...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): A61B6/03
CPCA61B6/504A61B6/032A61B6/481A61B6/5217
Inventor 晏玉玲杨丽
Owner WEST CHINA HOSPITAL SICHUAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap