Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review
Article
Sadak, Omer, Sadak, Ferhat, Yildirim, Ozal, Iverson, Nicole M., Qureshi, Rizwan, Talo, Muhammed, Ooi, Chui Ping, Acharya, U. Rajendra, Gunasekaran, Sundaram and Alam, Tanvir. 2022. "Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review." IEEE Access. 10, pp. 98633-98648. https://doi.org/10.1109/ACCESS.2022.3207207
Article Title | Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Sadak, Omer, Sadak, Ferhat, Yildirim, Ozal, Iverson, Nicole M., Qureshi, Rizwan, Talo, Muhammed, Ooi, Chui Ping, Acharya, U. Rajendra, Gunasekaran, Sundaram and Alam, Tanvir |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 98633-98648 |
Number of Pages | 16 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3207207 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9893825 |
Abstract | COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The “gold standard” for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning. |
Keywords | COVID-19; SARS-CoV-2; PCR; deep learning; electrochemical biosensor |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Export Date: 9 October 2023 |
Byline Affiliations | Ardahan University, Turkiye |
University of Nebraska-Lincoln, United States | |
Bartin University, Turkey | |
Firat University, Turkey | |
Hamad Bin Khalifa University, Qatar | |
Singapore University of Social Sciences (SUSS), Singapore | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Kumamoto University, Japan | |
University of Wisconsin-Madison, United States |
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License: CC BY 4.0 | ||
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