Overcoming the Bottleneck in COVID-19 Detection: A Machine-Learning Approach to Improve Accuracy of Electrochemical Impedance Spectroscopy (EIS) Detection Sensitivity: OAJBS Publishers

Overcoming the Bottleneck in COVID-19 Detection: A Machine-Learning Approach to Improve Accuracy of Electrochemical Impedance Spectroscopy (EIS) Detection Sensitivity by Mohammadreza Nemati* in Open Access Journal of Biomedical Science (OAJBS)


In late 2019, coronavirus disease (COVID-19), emerged in Wuhan city, Hubei, China and claimed a large number of lives and affected billions all around the world. Accurate and scalable devices are essential for screening, diagnosis and monitoring of COVID-19 patients. In this Article, we first investigate the Electrochemical Impedance Spectroscopy (EIS) method and its limitations in detecting the positive COVID-19 cases. Afterwards, we demonstrate a machine learning model to determine the optimal parameters for designing an EIS method

https://biomedscis.com/fulltext/overcoming-the-bottleneck-in-covid-19-detection-a-machine-learning-approach-to-improve-accuracy-of-electrochemical.ID.000229.php

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