Diagnosing COVID-19: The Disease and Tools for Detection

By Buddhisha N Udugama, Pranav Kadhiresan, Hannah N. Kozlowski, Ayden Malekjahani, M. Osborne, Vanessa Y. C. Li, Hongmin Chen, S. Mubareka, J. Gubbay, W. Chan. ACS Nano, 2020

1,513 citations recorded by the source corpus.

COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7b4d551e70ca2ddbb9261eb5aca2596ad5c5ce4d

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In Radiology, Nuclear Medicine and Imaging

1. Quoc-Viet Pham
2. Deng-Ping Fan
3. Shanghua Gao
4. Ming-Ming Cheng

Citations in the corpus

• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, methodology): “Currently, the main manual diagnostic tool is the RT-PCR test [22].”
• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, methodology): “Adapted from [124], we propose a mobile phone based framework for COVID-19 detection and surveillance, as shown in Fig.”