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Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...

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Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection
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Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.

Thi-Thu-Hien Pham1,2, Hoang-Phuoc Nguyen1,2, Thanh-Ngan Luu1,2

  • 1International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam.

Journal of Biomedical Optics
|December 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method combining Mueller matrix imaging and artificial intelligence (AI) for accurate hepatitis B virus detection. The hybrid approach achieved 94.5% accuracy, offering a simple and effective diagnostic tool.

Keywords:
HBsAgMueller matrix imagingconvolutional neural networkhepatitis Bpolarimetry

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Area of Science:

  • Medical imaging
  • Artificial Intelligence
  • Hepatitis B diagnostics

Background:

  • Polarized imaging combined with AI offers objective and precise medical diagnosis.
  • Hepatitis B virus (HBV) detection requires accurate and efficient methods.

Purpose of the Study:

  • To develop and validate a hybrid approach for hepatitis B virus (HBV) detection.
  • To leverage Mueller matrix imaging and deep learning for objective HBV diagnosis.

Main Methods:

  • Mueller matrix imaging polarimetry was used to acquire images of serum samples.
  • Deep learning models (Xception, VGG16, VGG19, ResNet50, ResNet150) were trained using specific Mueller matrix elements.
  • Kernel estimation density was employed to identify discriminatory matrix elements.

Main Results:

  • Mueller matrix elements M44 and M11 demonstrated the highest discriminatory power between HBV-positive and negative samples.
  • The VGG19 deep learning model, utilizing M44 as input, achieved an optimal classification accuracy of 94.5%.
  • The hybrid framework proved effective in distinguishing between HBsAg-containing and HBsAg-free serum samples.

Conclusions:

  • The proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HBV detection.
  • This method offers a promising tool for objective and precise medical diagnosis in the context of infectious diseases.