Biases associated with database structure for COVID-19 detection in X-ray images.

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Towards the development of an epitope-focused vaccine for SARS-CoV-2.

The rapid spread of COVID-19 on all continents and the mortality induced by SARS-CoV-2 virus, the cause of the pandemic coronavirus disease 2019 (COVID-19) has motivated an unprecedented effort for vaccine development. Inactivated viruses as well as vaccines focused on the partial or total sequence of the Spike protein using different novel platforms such us RNA, DNA, proteins, and non-replicating viral vectors have been developed. The high global need for vaccines, now and in the future, and the emergence of new variants of concern still requires development of accessible vaccines that can be adapted according to the most prevalent variants in the respective regions. Here, we describe the immunogenic properties of a group of theoretically predicted RBD peptides to be used as the first step towards the development of an effective, safe and low-cost epitope-focused vaccine. One of the tested peptides named P5, proved to be safe and immunogenic. Subcutaneous administration of the peptide, formulated with alumina, induced high levels of specific IgG antibodies in mice and hamsters, as well as an increase of IFN-γ expression by CD8+ T cells in C57 and BALB/c mice upon in vitro stimulation with P5. Neutralizing titers of anti-P5 antibodies, however, were disappointingly low, a deficiency that we will attempt to resolve by the inclusion of additional immunogenic epitopes to P5. The safety and immunogenicity data reported in this study support the use of this peptide as a starting point for the design of an epitope restricted vaccine.

COVID-19 detection in X-ray images using convolutional neural networks.

COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases' spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accessible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers' approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.