Multiclass Classification of Nonalcoholic Steatohepatitis Mouse Models Using Dielectric Properties as Disease Biomarker

Samaddar, Poulami; Gopalakrishnan, Keerthy; Anvekar, Priyanka; Samadder, Poushali; E, Ivone Cristina Igreja; Bayer, Rachel L; Gaddam, Sunil; Mitra, Dipankar; Roy, Sayan; Hirsova, Petra; Arunachalam, Shivaram P.

doi:10.1109/bibm55620.2022.9995712

Cited by 2 publications

(1 citation statement)

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“…The dataset was classified using six machine-learning models. Among all k-nearest neighbors and random forests, the classifier performed the best [151,152]. These studies must be expanded in the future on human subjects, as this can potentially eliminate the highly invasive diagnostic method such as biopsy for NASH and fibrosis.…”

Section: Ai-assisted Dielectric Spectroscopymentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

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Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

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Section: Ai-assisted Dielectric Spectroscopymentioning

confidence: 99%