Micro and Nanoscale Technologies for Diagnosis of Viral Infections.

Viral infection is one of the leading causes of mortality worldwide. The growth of globalization significantly increases the risk of virus spreading, making it a global threat to future public health. In particular, the ongoing coronavirus disease 2019 (COVID-19) pandemic outbreak emphasizes the importance of devices and methods for rapid, sensitive, and cost-effective diagnosis of viral infections in the early stages by which their quick and global spread can be controlled. Micro and nanoscale technologies have attracted tremendous attention in recent years for a variety of medical and biological applications, especially in developing diagnostic platforms for rapid and accurate detection of viral diseases. This review addresses advances of microneedles, microchip-based integrated platforms, and nano- and microparticles for sampling, sample processing, enrichment, amplification, and detection of viral particles and antigens related to the diagnosis of viral diseases. Additionally, methods for the fabrication of microchip-based devices and commercially used devices are described. Finally, challenges and prospects on the development of micro and nanotechnologies for the early diagnosis of viral diseases are highlighted.

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics.

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.