Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method and system for predicting treatment effects of shingles neuralgia based on structural magnetic resonance

A technology of herpes zoster and prediction method, applied in the field of medical image pattern recognition, can solve the problem of not being able to help doctors predict the efficacy of drugs in patients, and achieve the effect of improving work efficiency

Active Publication Date: 2019-07-19
SHENZHEN UNIV +1
View PDF3 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method is highly subjective and cannot help doctors predict the patient's drug efficacy

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
  • Method and system for predicting treatment effects of shingles neuralgia based on structural magnetic resonance
  • Method and system for predicting treatment effects of shingles neuralgia based on structural magnetic resonance
  • Method and system for predicting treatment effects of shingles neuralgia based on structural magnetic resonance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] At first the nouns and terms involved in the present invention are explained:

[0055] sMRI: Structural Magnetic Resonance Imaging, structural magnetic resonance imaging;

[0056] SVM: Support Vector Machine, support vector machine, a kind of machine learning method;

[0057] RF: Random Forests, random forest, a kind of machine learning classification method;

[0058] Logistic Regression: Logistic regression, a type of machine learning classification method;

[0059] MSP: Medication-sensitive Patients, most patients show positive response to drug treatment, defined as drug-sensitive patients;

[0060] MRP: Medication-resistant Patients, some patients, especially those who are elderly and immunocompromised, have poor drug treatment effect and are more susceptible to drug side effects, defined as drug-resistant patients.

[0061] ROC: Receiver Operating Characteristic Curve, receiver operating characteristic curve.

[0062] The present invention will be further explaine...

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 discloses a method and system for predicting treatment effects of shingles neuralgia based on structural magnetic resonance. The method comprises the following steps: obtaining a structural magnetic resonance image of a patient suffering from the shingles neuralgia; performing pretreatment on the obtained structural magnetic resonance image so as to obtain gray matter volume, whereinthe pretreatment comprises steps of rectification, separation and modulation; and according to the gray matter volume and a machine learning method, predicting the treatment effects of the shingles neuralgia. According to the method disclosed by the invention, the structural resonance imaging and machine learning are combined to predict the treatment effects of the shingles neuralgia, through themachine learning method, the gray matter volume as a nerve image mark is found, the medicine treatment effects of patients can be objectively predicted and judged, doctors can more reasonably formulate a medical care scheme for the patients, and the working efficiency of the doctors can be improved. The method disclosed by the invention can be widely applied to the field of medical image patternrecognition.

Description

technical field [0001] The invention relates to the field of medical image pattern recognition, in particular to a method and system for predicting the curative effect of herpetic neuralgia based on structural magnetic resonance. Background technique [0002] Herpes zoster is a clinically common acute herpetic skin disease caused by the varicella-zoster virus. This virus invades the body through respiratory infection and hides in the nerve cells of the dorsal root ganglia of the spinal nerve or other diseased sites. When the immune system of this virus is weakened (such as: trauma, fatigue, cold, immune system disease, etc.), the latent virus will multiply in large numbers, causing inflammation and necrosis of the ganglion, causing pain to the patient, and at the same time, the virus descends along the nerve pathway. Transmission to the area innervated by this innervation causes herpes segmentalis. Clinically, herpes zoster often presents several clustered herpes clusters,...

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
Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055A61B5/00
CPCA61B5/055A61B5/0042A61B5/004A61B5/4848
Inventor 黄炳升吴松雄曾萍黄佳彬肖礼祖陈富勇陶蔚钱程瑞孙武平廖宇良张家宁杨泽帆钟少楠
Owner SHENZHEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com