Point-wise ex vivo electrical impedance spectroscopy measurements were conducted on excised hepatic tissue from human patients with metastatic colorectal cancer using a linear four-electrode impedance probe. This study of 132 measurements from 10 colorectal cancer patients, the largest to date, reports that the equivalent electrical conductivity for tumor tissue is significantly higher than normal tissue (p < 0.01), ranging from 2-5 times greater over the measured frequency range of 100 Hz-1 MHz. Difference in tissue electrical permittivity is also found to be statistically significant across most frequencies. Furthermore, the complex impedance is also reported for both normal and tumor tissue. Consistent with trends for tissue electrical conductivity, normal tissue has a significantly higher impedance than tumor tissue (p < 0.01), as well as a higher net capacitive phase shift (33° for normal liver tissue in contrast to 10° for tumor tissue).

This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system. The adaptive visual diagnostics utilize few-shot learning (FSL) to generate encoded disease state models that are stored and classified using a dictionary of knowns. The novel vocabulary based feature processing of the pipeline adapts the knowledge of a pretrained deep neural network to compress the ultrasound images into discrimative descriptions. The computational efficiency of the FSL approach enables high diagnostic deep learning performance in PoC settings, where training data is limited and the annotation process is not strictly controlled. The algorithm performance is evaluated on the open source COVID-19 POCUS Dataset to validate the system's ability to distinguish COVID-19, pneumonia, and healthy disease states. The results of the empirical analyses demonstrate the appropriate efficiency and accuracy for scalable PoC use. The code for this work will be made publicly available on GitHub upon acceptance.

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