Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.
The worldwide outbreak of COVID-19 disease was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2). The existence of spike proteins, which allow these viruses to infect host cells, is one of the distinctive biological traits of various prior viruses. As a result, the process by which these viruses infect people is largely dependent on spike proteins. The density of SARS-CoV-2 spike proteins must be estimated to better understand and develop diagnostics and vaccines against the COVID-19 pandemic. CT scans and X-rays have three issues: frosted glass, consolidation, and strange roadway layouts. Each of these issues can be graded separately or together. Although CT scan is sensitive to COVID-19, it is not very specific. Therefore, patients who obtain these results should have more comprehensive clinical and laboratory tests to rule out other probable reasons. This work collected 586 SARS-CoV 2 transmission electron microscopy (TEM) images from open source for density estimation of virus spike proteins through a segmentation approach based on the superpixel technique. As a result, the spike density means of SARS-CoV2 and SARS-CoV were 21,97 nm and 22,45 nm, respectively. Furthermore, in the future, we aim to include this model in an intelligent system to enhance the accuracy of viral detection and classification. Moreover, we can remotely connect hospitals and public sites to conduct environmental hazard assessments and data collection.
The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.