A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices

Abstract

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.

Keywords: Asymptomatic; COVID-19; Machine learning; Screening; Wearable systems.

Fig. 1

Transmission of coronavirus by crossing the species barrier [26].

Fig. 2

CRISPR-based diagnostic system using fluorescence for the detection of SARS-CoV-2. (a) Schematic chart of a CRISPR-FDS assay to detect SARS-CoV-2 RNA; (b) SARS-CoV-2 genomic map of COVID-19 CRISPR-FDS targeted sequences [54].

Fig. 3

Lateral flow assays. (a) Structure and detection process of the LFIA strip [68]. (c) Design of developed assay by Chen et al. [69].

Fig. 4

Different physiological changes due to COVID-19 infection. (a) Deviation in the ratio of heart rate and steps before the onset of the COVID-19 symptoms (marked by the red dotted vertical line). The violet dotted vertical line denotes the COVID-19 diagnosis date and the red marker and star on the graph indicate the anomalous data and the first instance of the anomaly, respectively [99]. (b) Correlation of different symptoms with COVID-19 infection among 3811 untested, 54 COVID-19-positive, and 279 COVID-19-negative subjects. The asterisk denotes significant differences in symptoms (p-value < 0.05) [102].

Fig. 5

AI-based COVID-19 detection pipeline using a combination of radiology and symptom data [110].

Fig. 6

AI-based COVID-19 detection pipeline using CT images using multiple encoder-decoder networks [111].

Fig. 7

Schematic representation of the COVID-19 detection and infection quantification method [121].

Fig. 8

Score-CAM visualization for properly classified COVID-19 X-ray images using the different enhancement techniques: CXR (top row), Score-CAM heat map on original CXR (middle row), and segmented lungs CXR (bottom row). Note: Gamma correction technique was the best performing image pre-processing technique reported in Ref. [124].

Fig. 9

Grad-CAM heat-map for Network visualization for the model interpretation [125].