Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and life-threatening medical complications elevated mortality rates, especially among older adults people with existing morbidity. Current evidence suggests that spreads primarily through respiratory droplets emitted infected persons when breathing, coughing, sneezing, or speaking. These can reach another person their mouth, nose, eyes, resulting in infection. The “gold standard” for clinical diagnosis is laboratory-based nucleic acid amplification test, which includes reverse transcription-polymerase chain reaction (RT-PCR) test nasopharyngeal swab samples. main concerns this type are relatively high cost, long processing time, considerable false-positive false-negative results. Alternative approaches have been suggested to detect so those they contact be quickly isolated break chains hopefully, control pandemic. alternative include electrochemical biosensing deep learning. In review, we discuss current state-of-the-art technology used both fields public surveillance present comparison methods terms sampling, timing, accuracy, instrument complexity, accessibility, feasibility, adaptability mutations. Finally, issues potential future research detecting utilizing