Procesado de imágenes para el diagnóstico de la COVID-19

Introducción: El diagnóstico positivo de COVID-19 en pacientes paucisintomáticos constituye una prioridad para minimizar la propagación de la enfermedad. La no existencia de manifestaciones respiratorias dificulta el diagnóstico y facilita la propagación. Ante esta situación es necesario adoptar soluciones técnicas que permitan el diagnóstico en este tipo de pacientes. Objetivo: Describir el software de procesado de imágenes reumatológicas y dermatológicas en el diagnóstico de pacientes paucisintomáticos con COVID-19. Métodos: Se diseñó y elaboró un software basado en un algoritmo para el diagnóstico de COVID-19 en pacientes paucisintomáticos. El procedimiento constó de tres etapas; la primera de ellas se relacionó con el procesado de imágenes y todos sus elementos relacionados; la segunda etapa se orientó hacia la identificación de preguntas a modo de anamnesis médica. La tercera etapa se centró en la identificación y análisis de resultados de exámenes de laboratorio y la definición de las recomendaciones finales en base al resultado final. Resultados: Se diseñó un software basado en un algoritmo que incluye tres etapas y se basa en porcentajes de coincidencia, orientando al usuario en la conducta a seguir en dependencia del porcentaje de coincidencia. Inicia con la captura de una imagen y se siguen aspectos clínicos, epidemiológicos y de laboratorio de la COVID-19. Conclusiones: El algoritmo de aproximación diagnóstica de la COVID-19 tiene facilidades de uso, bajo costo de utilización y comodidades para su implementación, convirtiéndolo en una herramienta tecnológica al servicio de la salud humana para frenar la propagación de la COVID-19.

Introduction: The positive diagnosis of COVID-19 in paucisymptomatic patients is a priority to minimize the spread of the disease. The absence of respiratory manifestations makes diagnosis difficult and facilitates the spread. Given this situation, it is necessary to adopt technical solutions that allow diagnosis in this type of patient. Objective: Describe rheumatological and dermatological image processing software in the diagnosis of paucisymptomatic patients with COVID-19. Methods: A software based on the algorithm for the diagnostic approach of COVID-19 in paucisymptomatic patients was designed and developed. The procedure consisted of three stages; the first one was related to image processing and all its related elements; the second stage was oriented towards the identification of questions as a medical anamnesis. The third stage focused on the identification and analysis of laboratory test results and the definition of final recommendations based on the final result. Results: A software was designed based on an algorithm that includes three stages and is based on coincidence percentages, guiding the user in the behavior to follow depending on the coincidence percentage. It begins with the capture of an image and is followed by clinical, epidemiological and laboratory aspects of COVID-19. Conclusions: The algorithm for the diagnostic approach to COVID-19 is easy to use, low cost of use, and easy to implement, making it a technological tool at the service of human health to stop the spread of COVID-19.

Compression of images of dermatological manifestations of paucisymptomatic COVID-19 patients through periodic principal components / Compresión de imágenes de manifestaciones dermatológicas de pacientes paucisintomáticos de COVID-19 mediante componentes principales periódicos

Introduction: The management of medical images has been gaining followers based on the advantages it offers for the diagnosis of diseases, which, like COVID-19, present with clinical manifestations that can be captured in the form of images. Objective: Take advantage of the quasi-periodicity of the principal components (PCs) in the decomposition into PCs of medical images, which represent dermatological manifestations in paucisymptomatic patients of COVID-19. Methods: Here, a set of photos was taken of one of the most frequent patterns in COVID-19, the maculopapular pattern, characterized by an erythmatopapular rash, and compression of one of the medical images was performed. Said compression was carried out in different ways: (1) using two PCs, (2) using both a periodic PC and a non-periodic PC, (3) using two periodic PCs, (4) using a single PC, and (5) using a single periodic PC. Result: The results of this research proved that it is possible to work with acceptable reconstructions of compressed images in the field of dermatology, without losing the quality and characteristics that allow to reach a correct diagnosis. In addition, this achievement permits to correctly classify many diseases without fear of being wrong. Conclusion: With the method presented, the use of a robust medical image compression technique that could be very useful in the field of health is proposed. The images allow the diagnosis of diseases such as COVID-19 in paucisymptomatic patients, understanding them allows minimizing their weight without losing quality, which facilitates their use and storage.

Introducción: El empleo de imágenes médicas en el diagnóstico de enfermedades ha ido ganando adeptos. Un ejemplo es la COVID-19 que cursa con manifestaciones clínicas dermatológicas. Objetivo: Aprovechar la cuasi-periodicidad de los componentes principales de la descomposición en imágenes médicas, que representan manifestaciones dermatológicas en pacientes paucisintomáticos de COVID-19. Métodos: Se tomó un conjunto de fotografías de uno de los patrones más frecuentes en la COVID-19 (el patrón maculopapular), caracterizado por un exantema eritematopapular, y se realizó la compresión de una de las imágenes médicas. Dicha compresión se realizó de diferentes formas: (1) usando dos componentes principales, (2) usando tanto un componente principal periódico como no periódico, (3) dos componentes principales periódicos, (4) un único componente principal, y (5) un solo componente principal periódico. Resultados: Es posible trabajar con reconstrucciones aceptables de imágenes comprimidas en el campo de la dermatología, sin perder la calidad y características que permitan llegar a un diagnóstico correcto. Además, este logro permite clasificar correctamente muchas enfermedades sin miedo a equivocarse. Conclusiones: Con el método presentado se propone el uso de una técnica robusta de compresión de imágenes médicas que podría ser de gran utilidad en el campo de la salud. Las imágenes permiten el diagnóstico de enfermedades como la COVID-19 en pacientes paucisintomáticos, con cuya compresión se minimiza su peso sin perder la calidad, lo que facilita su uso y almacenamiento.

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020.

In this paper, a comparative analysis between the PM2.5 concentration in downtown Quito, Ecuador, during the COVID-19 pandemic in 2020 and the previous five years (from 2015 to 2019) was carried out. Here, in order to fill in the missing data and achieve homogeneity, eight datasets were constructed, and 35 different estimates were used together with six interpolation methods to put in the estimated value of the missing data. Additionally, the quality of the estimations was verified by using the sum of squared residuals and the following correlation coefficients: Pearson's r, Kendall's τ, and Spearman's ρ. Next, feature vectors were constructed from the data under study using the wavelet transform, and the differences between feature vectors were studied by using principal component analysis and multidimensional scaling. Finally, a robust method to impute missing data in time series and characterize objects is presented. This method was used to support the hypothesis that there were significant differences between the PM2.5 concentration in downtown Quito in 2020 and 2015-2019.

Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador.

Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.

Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area.

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques.

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Robust Estimation of Carbon Monoxide Measurements.

This paper presents a robust analysis of carbon monoxide (CO) concentration measurements conducted at the Belisario air-quality monitoring station (Quito, Ecuador). For the analysis, the data collected from 1 January 2008 to 31 December 2019 were considered. Additionally, each of the twelve years analyzed was considered as a random variable, and robust location and scale estimators were used to estimate the central tendency and dispersion of the data. Furthermore, classic, nonparametric, bootstrap, and robust confidence intervals were used to group the variables into categories. Then, differences between categories were quantified using confidence intervals and it was shown that the trend of CO concentration at the Belisario station in the last twelve years is downward. The latter was proven with the precision provided by both nonparametric and robust statistical methods. The results of the research work robustly proved that the CO concentration at Belisario station in the last twelve years is not considered a health risk, according to the criteria established by the Quito Air Quality Index.

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 µm) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a-trimmed mean and Winsorized standard error of order a, location and scale estimators based on the Andrew's wave, biweight location and scale estimators, and estimators based on the bootstrap-t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: α-trimmed mean, trimean, and median estimators; and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine.

Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets.

Power Performance Verification of a Wind Farm Using the Friedman's Test.

In this paper, a method of verification of the power performance of a wind farm is presented. This method is based on the Friedman's test, which is a nonparametric statistical inference technique, and it uses the information that is collected by the SCADA system from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm. Here, the guaranteed power curve of the wind turbines is used as one more wind turbine of the wind farm under assessment, and a multiple comparison method is used to investigate differences between pairs of wind turbines with respect to their power performance. The proposed method says whether the power performance of the specific wind farm under assessment differs significantly from what would be expected, and it also allows wind farm owners to know whether their wind farm has either a perfect power performance or an acceptable power performance. Finally, the power performance verification of an actual wind farm is carried out. The results of the application of the proposed method showed that the power performance of the specific wind farm under assessment was acceptable.

Remote sensor for spatial measurements by using optical scanning.

In this paper, we propose a low-cost contact-free measurement system for both 3-D data acquisition and fast surface parameter registration by digitized points. Despite the fact that during the last decade several approaches for both contact-free measurement techniques aimed at carrying out object surface recognition and 3-D object recognition have been proposed, they often still require complex and expensive equipment. Therefore, alternative low cost solutions are in great demand. Here, two low-cost solutions to the above-mentioned problem are presented. These are two examples of practical applications of the novel passive optical scanning system presented in this paper.