Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.

Supporting Clinical COVID-19 Diagnosis with Routine Blood Tests Using Tree-Based Entropy Structured Self-Organizing Maps

Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases.

Inter-observer variability with five computed tomography severity scales for COVID-19 pneumonia assessment.

INTRODUCTION: By the end 2019 there was an outbreak of pneumonia caused by a new coronavirus, a disease that was called coronavirus disease 2019 (COVID-19). Computed tomography (CT) has played an important role in the diagnosis of COVID-19 patients. OBJECTIVE: To demonstrate inter-observer variability with five scales proposed for measuring the extent of COVID-19 pneumonia on tomography. METHODS: Thirty five initial chest CT scans of patients who attended respiratory triage for suspected COVID-19 pneumonia were analyzed. Three radiologists classified the tomographic images according to the severity scales proposed by Yang (1), Yuan (2), Chun (3), Wang (4) and Instituto Nacional de Enfermedades Respiratorias-Chung-Pan (5). The percentage of agreement between the evaluators for each scale was calculated using the intra-class correlation index. RESULTS: In most patients were five pulmonary lobes compromised (77.1% of the patients). Scales 1, 2, 4 and 5 showed an intra-class correlation > 0.91 (p < 0.0001), with agreement thus being almost perfect. CONCLUSIONS: Scale 4 (proposed by Wang) showed the best inter-observer agreement, with a coefficient of 0.964 (p = 0.001).

INTRODUCCIÓN: A finales de 2019 se presentó un brote de neumonía causada por un nuevo coronavirus, enfermedad a la que se denominó COVID-19. La tomografía computarizada ha desempeñado un papel importante en el diagnóstico de los pacientes con COVID-19. OBJETIVO: Demostrar la variabilidad interobservador con cinco escalas propuestas para la medición de la extensión de la neumonía ocasionada por COVID-19 mediante tomografía. MÉTODOS: Se analizaron 35 tomografías de tórax iniciales de pacientes que asistieron al triaje respiratorio por sospecha de neumonía por COVID-19. Tres radiólogos realizaron la clasificación de las imágenes tomográficas de acuerdo con las escalas de severidad propuestas por Yang (1), Yuan (2), Chun (3), Wang (4) e INER-Chung-Pan (5). Se calculó el porcentaje de concordancia entre los evaluadores para cada escala con el índice de correlación intraclase. RESULTADOS: La mayoría de los pacientes presentó afección de cinco lóbulos pulmonares (77.1 % de los pacientes). Las escalas 1, 2, 4 y 5 mostraron una correlación intraclase > 0.91, con p < 0.0001, por lo que la concordancia fue casi perfecta. CONCLUSIONES: La escala 4 (de Wang) mostró la mejor concordancia interobservador, con un coeficiente de 0.964 (p = 0.001).

A Multimodal Real-Time Feedback Platform Based on Spoken Interactions for Remote Active Learning Support.

While technology has helped improve process efficiency in several domains, it still has an outstanding debt to education. In this article, we introduce NAIRA, a Multimodal Learning Analytics platform that provides Real-Time Feedback to foster collaborative learning activities' efficiency. NAIRA provides real-time visualizations for students' verbal interactions when working in groups, allowing teachers to perform precise interventions to ensure learning activities' correct execution. We present a case study with 24 undergraduate subjects performing a remote collaborative learning activity based on the Jigsaw learning technique within the COVID-19 pandemic context. The main goals of the study are (1) to qualitatively describe how the teacher used NAIRA's visualizations to perform interventions and (2) to identify quantitative differences in the number and time between students' spoken interactions among two different stages of the activity, one of them supported by NAIRA's visualizations. The case study showed that NAIRA allowed the teacher to monitor and facilitate the learning activity's supervised stage execution, even in a remote learning context, with students working in separate virtual classrooms with their video cameras off. The quantitative comparison of spoken interactions suggests the existence of differences in the distribution between the monitored and unmonitored stages of the activity, with a more homogeneous speaking time distribution in the NAIRA supported stage.

Nanotechnology as an Alternative to Reduce the Spread of COVID-19

The current emerging COVID-19 pandemic has caused a global impact on every major aspect of our societies. It is known that SARS-Cov-2 can endure harsh environmental conditions for up to 72 h, which may contribute to its rapid spread. Therefore, effective containment strategies, such as sanitizing, are critical. Nanotechnology can represent an alternative to reduce the COVID-19 spread, particularly in critical areas, such as healthcare facilities and public places. Nanotechnology-based products are effective at inhibiting different pathogens, including viruses, regardless of their drug-resistant profile, biological structure, or physiology. Although there are several approved nanotechnology-based antiviral products, this work aims to highlight the use of nanomaterials as sanitizers for the prevention of the spread of mainly SARS-Cov-2. It has been widely demonstrated that nanomaterials are an alternative for sanitizing surfaces to inactivate the virus. Also, antimicrobial nanomaterials can reduce the risk of secondary microbial infections on COVID-19 patients, as they inhibit the bacteria and fungi that can contaminate healthcare-related facilities. Finally, cost-effective, easy-to-synthesize antiviral nanomaterials could reduce the burden of the COVID-19 on challenging environments and in developing countries.