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Swab Sampling Method for the Detection of Human Norovirus on Surfaces

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Published on: February 6, 2017

Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early

Xucheng Fan¹, Na Xue¹, Zhiguo Han¹

¹Department of Infectious Disease Control, Urumqi Center for Disease Control and Prevention, Urumqi 830026, Xinjiang, China.

Journal of Healthcare Engineering

|September 27, 2021

Summary

Early detection of norovirus in children using AI-powered ultrasound analysis can improve diagnosis and prevention. This study highlights the effectiveness of wavelet transform algorithms for clearer imaging and identifying high-risk age groups and seasons for infection.

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Last Updated: Oct 19, 2025

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Published on: February 6, 2017

VirWaTest, A Point-of-Use Method for the Detection of Viruses in Water Samples

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EPA Method 1615. Measurement of Enterovirus and Norovirus Occurrence in Water by Culture and RT-qPCR. Part III. Virus Detection by RT-qPCR

EPA Method 1615. Measurement of Enterovirus and Norovirus Occurrence in Water by Culture and RT-qPCR. Part III. Virus Detection by RT-qPCR

Published on: January 16, 2016

Area of Science:

Pediatric infectious diseases
Medical imaging analysis
Artificial intelligence in healthcare

Background:

Norovirus infection is a significant cause of diarrhea in children, impacting growth and development.
Current diagnostic and treatment strategies for norovirus often rely on etiological testing and do not require antibiotics.
Prevention is more effective than treatment for norovirus in pediatric populations.

Purpose of the Study:

To develop and validate an artificial intelligence (AI) model for early detection and trend analysis of norovirus infections in children.
To assess the utility of wavelet transform algorithms in processing intestinal ultrasound images for improved norovirus diagnosis.
To identify key epidemiological factors, including age groups and seasonal patterns, associated with norovirus infection in children.

Main Methods:

Collected clinical data from 2133 children with diarrhea.
Constructed an AI model utilizing wavelet transform for data mining and processing of intestinal ultrasound images and stool specimens.
Employed wavelet analysis to establish a norovirus infection trend warning system.

Main Results:

Wavelet transform significantly improved the clarity of intestinal ultrasound images.
Norovirus was detected in 59% of children with clinical diarrhea, with varying degrees of body damage, notably compensatory metabolic acidosis.
Epidemiological analysis revealed infection peaks in children under 2 and over 5 years old, with a seasonal peak in December.

Conclusions:

AI-based wavelet transform effectively reduces noise in intestinal ultrasound imaging, aiding in norovirus diagnosis.
The developed model enables early warning of norovirus infection trends.
Targeted preventive measures for susceptible age groups and seasons are crucial to reduce the clinical infection rate of norovirus.