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Department of Biological and Medical ... 1 Department of Computer Science Univer... 1 Department of Electrical Engineering ... 1 Department of Electrical and Computer... 1 Department of Medicine Mayo Clinic Ro... 1 Department of Radiology Mayo Clinic R... 1 GIH Artificial Intelligence Laborator... 1 Gastroenterology Research Division of... 1 Microwave Engineering and Imaging Lab... 1
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Department of Biological and Medical ... 1 Department of Computer Science Univer... 1 Department of Electrical Engineering ... 1 Department of Electrical and Computer... 1 Department of Medicine Mayo Clinic Ro... 1 Department of Radiology Mayo Clinic R... 1 GIH Artificial Intelligence Laborator... 1 Gastroenterology Research Division of... 1 Microwave Engineering and Imaging Lab... 1
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- Samaddar, Poulami
- Mishra, Anup Kumar
- Gaddam, Sunil
- Singh, Mansunderbir
- Modi, Vaishnavi K
- Gopalakrishnan, Keerthy
- Bayer, Rachel L
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Igreja Sa, Ivone Cristina
Autor Igreja Sa, Ivone Cristina ORCID Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA Department of Biological and Medical Sciences, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovskeho 1203, 500 05 Hradec Kralove, Czech Republic
- Khanal, Shalil
- Hirsova, Petra
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Directory of Open Access Journals
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PubMed Central
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Europe PubMed Central
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PubMed
36560303
DOI
10.3390/s22249919
Knihovny.cz E-zdroje
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.
- 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
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- zvířata MeSH
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