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Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity
P. Samaddar, AK. Mishra, S. Gaddam, M. Singh, VK. Modi, K. Gopalakrishnan, RL. Bayer, IC. Igreja Sa, S. Khanal, P. Hirsova, E. Kostallari, S. Dey, D. Mitra, S. Roy, SP. Arunachalam
Jazyk angličtina Země Švýcarsko
Typ dokumentu časopisecké články
Grantová podpora
R01DK130884
NIDDK NIH HHS - United States
NLK
Directory of Open Access Journals
od 2001
PubMed Central
od 2003
Europe PubMed Central
od 2003
ProQuest Central
od 2001-01-01
Open Access Digital Library
od 2001-01-01
Open Access Digital Library
od 2003-01-01
Health & Medicine (ProQuest)
od 2001-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2001
PubMed
36560303
DOI
10.3390/s22249919
Knihovny.cz E-zdroje
- MeSH
- jaterní cirhóza MeSH
- játra patologie MeSH
- myši inbrední C57BL MeSH
- myši MeSH
- nealkoholová steatóza jater * diagnóza patologie MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.
Department of Computer Science University of Wisconsin La Crosse WI 54601 USA
Department of Electrical and Computer Engineering North Dakota State University Fargo ND 58105 USA
Department of Electrical Engineering and Computer Science South Dakota Mines Rapid City SD 57701 USA
Department of Medicine Mayo Clinic Rochester MN 55905 USA
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- $a The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.
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