COVID-19 classification using thermal images.

SIGNIFICANCE: There is a scarcity of published research on the potential role of thermal imaging in the remote detection of respiratory issues due to coronavirus disease-19 (COVID-19). This is a comprehensive study that explores the potential of this imaging technology resulting from its convenient aspects that make it highly accessible: it is contactless, noninvasive, and devoid of harmful radiation effects, and it does not require a complicated installation process. AIM: We aim to investigate the role of thermal imaging, specifically thermal video, for the identification of SARS-CoV-2-infected people using infrared technology and to explore the role of breathing patterns in different parts of the thorax for the identification of possible COVID-19 infection. APPROACH: We used signal moment, signal texture, and shape moment features extracted from five different body regions of interest (whole upper body, chest, face, back, and side) of images obtained from thermal video clips in which optical flow and super-resolution were used. These features were classified into positive and negative COVID-19 using machine learning strategies. RESULTS: COVID-19 detection for male models [receiver operating characteristic (ROC) area under the ROC curve (AUC) = 0.605 95% confidence intervals (CI) 0.58 to 0.64] is more reliable than for female models (ROC AUC = 0.577 95% CI 0.55 to 0.61). Overall, thermal imaging is not very sensitive nor specific in detecting COVID-19; the metrics were below 60% except for the chest view from males. CONCLUSIONS: We conclude that, although it may be possible to remotely identify some individuals affected by COVID-19, at this time, the diagnostic performance of current methods for body thermal imaging is not good enough to be used as a mass screening tool.

Impact of NGS in the medical sciences: Genetic syndromes with an increased risk of developing cancer as an example of the use of new technologies.

The increased speed and decreasing cost of sequencing, along with an understanding of the clinical relevance of emerging information for patient management, has led to an explosion of potential applications in healthcare. Currently, SNP arrays and Next-Generation Sequencing (NGS) technologies are relatively new techniques used to scan genomes for gains and losses, losses of heterozygosity (LOH), SNPs, and indel variants as well as to perform complete sequencing of a panel of candidate genes, the entire exome (whole exome sequencing) or even the whole genome. As a result, these new high-throughput technologies have facilitated progress in the understanding and diagnosis of genetic syndromes and cancers, two disorders traditionally considered to be separate diseases but that can share causal genetic alterations in a group of developmental disorders associated with congenital malformations and cancer risk. The purpose of this work is to review these syndromes as an example of a group of disorders that has been included in a panel of genes for NGS analysis. We also highlight the relationship between development and cancer and underline the connections between these syndromes.