Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity
Poulami Samaddar,
Anup Kumar Mishra,
Sunil Gaddam,
Mansunderbir Singh,
Vaishnavi K. Modi,
Keerthy Gopalakrishnan,
Rachel L. Bayer,
Ivone Cristina Igreja Sa,
Shalil Khanal,
Petra Hirsova,
Enis Kostallari,
Shuvashis Dey,
Dipankar Mitra,
Sayan Roy,
Shivaram P. Arunachalam
Affiliations
Poulami Samaddar
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Anup Kumar Mishra
GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Sunil Gaddam
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Mansunderbir Singh
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
Vaishnavi K. Modi
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Keerthy Gopalakrishnan
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Rachel L. Bayer
Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
Ivone Cristina Igreja Sa
Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
Shalil Khanal
Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
Petra Hirsova
Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
Enis Kostallari
Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
Shuvashis Dey
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Dipankar Mitra
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Sayan Roy
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Shivaram P. Arunachalam
Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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.