Research on Diagnosis and Prescription System of Coronary Heart Disease with Syndrome Elements Based on Improved Transformer Algorithm / 中国实验方剂学杂志

ObjectiveTo construct a traditional Chinese medicine （TCM） syndrome diagnosis and prescription model for coronary heart disease with the improved Transformer algorithm. MethodTaking the syndrome elements of coronary heart disease as key links， the model was constructed based on the clinical diagnosis and treatment principle of "symptoms-syndrome elements-syndrome-treatment method-prescription-medicine （dose）". The basic logic of improved Transformer algorithm was constructed with multi-head attention mechanism， compound term vector and dropout， in order to form the model with functions of TCM syndrome elements judgment， syndrome diagnosis， prescription recommendation. After the model was constructed， it was trained by 8 030 cases. And 100 cases with TCM prescriptions were randomly selected for testing， and the model output prescriptions were compared with those of clinicians for qualitative evaluation of the model. ResultThe improved Transformer with multi-head attention improved the accuracy of the model. The model was consistent with clinicians in the judgment of main syndromes and the selection of prescriptions. Whereas there was a certain room for improvement in the addition and subtraction of medicines. ConclusionThe improved Transformer model can improve the accuracy and stability of output， which is an embodiment of the intelligent development of TCM.

Research progress on the application of deep learning in cephalometric analysis / 口腔疾病防治

@#In orthodontic and orthognathic practice, cephalometric analysis is an integral tool throughout the clinical process. However, as landmark identification is still unautomated, both the conventional and semiautomated approaches are open to considerable subjectivity and could be time-consuming for inexperienced clinicians. Deep learning (DL), a state-of-the-art artificial intelligence (AI) technique, is highly effective in image recognition. In recent years, many studies have focused on the application of DL in cephalometric analysis, including automated landmark detection, automated diagnosis, cervical vertebral maturation stage determination, adenoid hypertrophy analysis and upper airway identification. Studies show that DL can effectively improve the efficiency of cephalometric analysis. In most studies, the accuracy of DL can reach more than 80%, and its difference from the gold standard is clinically acceptable, demonstrating good potential for future applications. However, most studies are limited to landmark detection, and the broadness and richness of the training dataset are limited. Future studies should broaden the research scope, improve the algorithm, elevate the richness of the datasets, and combine DL with other AI algorithms to improve its accuracy, stability and generalizability.

Análisis de sentimiento de tweets sobre la vacuna contra el COVID-19 en países iberoamericanos hispanohablantes / Sentiment analysis of tweets about the COVID-19 vaccine in Spanish-speaking Ibero-American countries

Resumen Introducción: En este estudio se evalúa la emocionalidad asociada a la vacunación contra el COVID-19 a partir de la técnica de análisis de sentimientos de los tweets en países iberoamericanos hispanohablantes. Método: En enero de 2021 se realizó un estudio mixto observacional transversal de 41023 tweets procedentes de nueve países iberoamericanos hispanohablantes (Chile, El Salvador, Venezuela, Ecuador, Argentina, México, Panamá, Perú y España) con una fase cuantitativa y técnicas de análisis de sentimientos mediante algoritmos de inteligencia artificial y una fase cualitativa donde se realizó un análisis del discurso de los tweets cuya emocionalidad era en extremo positiva y negativa. Resultados: A partir del análisis de sentimiento de los tweets, se observó que los países presentan una emocionalidad negativa asociada a la vacunación contra el COVID-19, que se podría atribuir a la desconfianza hacia las autoridades y a la eficacia o seguridad de las vacunas, según el análisis del discurso en los tweets de emocionalidad en extremo negativa. Conclusiones: Las técnicas de análisis de sentimientos en combinación con el análisis del discurso de la emocionalidad extrema posibilitaron la monitorización de las opiniones negativas y sus posibles factores asociados en la vacunación contra el COVID-19 en los países iberoamericanos estudiados.

Abstract Introduction: This study evaluates the emotionality associated with vaccination against COVID-19 using the sentiment analysis technique of tweets in Spanish-speaking Ibero-American countries. Method: In January 2021 a mixed cross-sectional observational study of 41023 tweets from nine Spanish-speaking Ibero-American countries (Chile, El Salvador, Venezuela, Ecuador, Argentina, Mexico, Panama, Peru and Spain) was carried out with a quantitative phase and analysis techniques of feelings based on artificial intelligence algorithms and a qualitative phase where an analysis of the discourse of the tweets whose emotionality was extremely positive and negative was carried out. Results: From the sentiment analysis of the tweets, it was observed that the countries present a negative emotionality associated with the vaccination against COVID-19, which could be attributed to mistrust towards the authorities and the efficacy or safety of the vaccines, according to the analysis of the discourse in the extremely negative emotionality tweets. Conclusions: Sentiment analysis techniques in combination with extreme emotionality discourse analysis made it possible to monitor negative opinions and their possible associated factors in vaccination against COVID-19 in the Ibero-American countries studied.

Use of artificial intelligence in ophthalmology: a narrative review

ABSTRACT BACKGROUND: Artificial intelligence (AI) deals with development of algorithms that seek to perceive one's environment and perform actions that maximize one's chance of successfully reaching one's predetermined goals. OBJECTIVE: To provide an overview of the basic principles of AI and its main studies in the fields of glaucoma, retinopathy of prematurity, age-related macular degeneration and diabetic retinopathy. From this perspective, the limitations and potential challenges that have accompanied the implementation and development of this new technology within ophthalmology are presented. DESIGN AND SETTING: Narrative review developed by a research group at the Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil. METHODS: We searched the literature on the main applications of AI within ophthalmology, using the keywords "artificial intelligence", "diabetic retinopathy", "macular degeneration age-related", "glaucoma" and "retinopathy of prematurity," covering the period from January 1, 2007, to May 3, 2021. We used the MEDLINE database (via PubMed) and the LILACS database (via Virtual Health Library) to identify relevant articles. RESULTS: We retrieved 457 references, of which 47 were considered eligible for intensive review and critical analysis. CONCLUSION: Use of technology, as embodied in AI algorithms, is a way of providing an increasingly accurate service and enhancing scientific research. This forms a source of complement and innovation in relation to the daily skills of ophthalmologists. Thus, AI adds technology to human expertise.

Ideologia gerencialista e plataformas de treinamentos de dados para Inteligência Artificial (IA): condições de trabalho e saúde dos trabalhadores no Brasil / Managerial ideology and Artificial Intelligence (AI) data training platform: working conditions and worker's health in Brazil / Ideología managerial y plataformas de anotación de datos para Inteligencia Artificial (IA): condiciones de trabajo y salud de los trabajadores en Brasi

O objetivo deste estudo é analisar as condições de trabalho e os seus impactos na saúde dos trabalhadores no mercado de microtarefas de treinamento de dados para a produção de Inteligência Artificial (IA), em especial no que diz respeito a suas relações com a ideologia gerencialista. Os dados são provenientes de uma netnografia realizada entre os anos de 2020 e 2021, de análises dos websites das plataformas e de entrevistas realizadas com 15 trabalhadores. A partir da análise de quatro instâncias mediadoras (econômica, política, ideológica e psicológica), argumentamos que a ideologia gerencialista, consubstanciada a ideologia californiana, se caracteriza como um operador central na gestão do trabalho, que tem por finalidade garantir a adesão dos trabalhadores às plataformas e ocultar os conflitos do trabalho, direcionando-os para o nível individual e produzindo um cenário de individualização do sofrimento.

The objective of this study is to analyze working conditions and their impacts on worker's health in the Artificial Intelligence (AI) data annotation microtask market, especially to highlight their relationship with managerial ideology. The data comes from a netnography carried out between the years 2020 and 2021, from analysis on the platform's websites, and from interviews with 15 workers. Drawing from the analysis of four different mediation systems (economic, political, ideological, and psychological), we argue that the managerial ideology, overlaid with the Californian ideology, is characterized as a central element in the management of labor, which aims to guarantee the adherence of workers to platforms and hide the labor conflicts, directing them to the individual level and producing a scenario of individualization of suffering.

El objetivo de esta investigación es analizar las condiciones de trabajo y sus impactos en la salud de los tra-bajadores en el mercado de microtareas de anotación de datos para la producción de Inteligencia Artificial (IA), en particular en lo que concierne a su relación con la ideología managerial. Los datos provienen de una netnografía realizada entre los años 2020 y 2021, de análisis en los sitios web de las plataformas y de entrevistas con 15 trabajadores. A partir del análisis de cuatro instancias mediadoras (económica, política, ideológica y psicológica), argumentamos que la ideología gerencial, superpuesta en la ideología californi-ana, se caracteriza como un elemento central en la gestión del trabajo, que pretende garantizar la adhesión de los trabajadores a las plataformas y ocultar los conflictos del trabajo, dirigiéndolos al plano individual y produciendo un escenario de individualización del sufrimiento.

Sistema inteligente para la medición del comportamiento humano en relación al cumplimiento del protocolo de bioseguridad Covid-19 implementando tecnología de IoB / Smart system for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing IoB technology

Este proyecto de investigación 2021 desarrollado por la Escuela de Ingeniería en Computación de ITCA-FEPADE, tuvo como objetivo usar las tecnologías para ayudar a mejorar el comportamiento de la comunidad educativa en pandemia Covid-19. Es un sistema inteligente para la medición del comportamiento humano con relación al cumplimiento del protocolo de bioseguridad Covid-19, implementando tecnologías de Internet del Comportamiento IoB, Internet de las Cosas IoT, Business Intelligence, Big Data y reconocimiento facial. La primera fase consistió en la toma de requerimientos y el estudio de investigaciones previas. Posteriormente se diseñó la interfaz del aplicativo que interpreta los datos colectados y la estructura de un dispensador inteligente de alcohol gel para ser impreso en 3D. Finalmente se realizó la programación del sistema y del circuito que conforman el dispositivo. Como resultado se construyó un dispositivo inteligente que mide y alerta la temperatura, dispensa alcohol gel y toma de fotografía para reconocimiento facial en la portación correcta de mascarilla. Incorpora un sistema informático que procesa los datos colectados que son utilizados por la aplicación de Inteligencia de Negocios para analizar el comportamiento de las personas ante el cumplimiento del protocolo de bioseguridad para Covid-19. El resultado del proyecto es un dispositivo inteligente y automatizado, que dotará a la institución de una herramienta innovadora de bajo costo para medir el comportamiento de la población que hace uso de las instalaciones de ITCA-FEPADE Sede Central y contribuirá a prevenir contagios por Covid-19, dando mayor seguridad a un retorno presencial al campus.

This research project was carried out in 2021 by the Escuela de Ingeniería en Computación of ITCA-FEPADE and aimed to use technologies to improve the behavior of the educational community in the context of Covid-19 pandemic. A smart system was development for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing Internet of Behavior (IoB), Internet of Things (IoT), Business Intelligence, Big Data and facial recognition technologies. The first phase consisted on the identification of requirements and previous investigations. Subsequently, the application interface that interprets the collected data and the structure of a smart hand sanitizer dispenser to be printed in 3D was designed. Finally, the programming of the system and the circuit that make up the device was carried out. As a result, a smart device that measures and alerts the body temperature, dispenses hand sanitizer and applies facial recognition for the detection of proper face mask wearing was built. The device also incorporates a computer system that processes the collected data that to analyze the behavior of people in compliance with the biosafety protocol for Covid-19 through the Business Intelligence application. The result of the project was a smart and automated device that will provide the institution an innovative, low-cost tool to measure the behavior of the population that makes use of the ITCA-FEPADE Sede Central facilities and will contribute to preventing Covid-19 infections by giving greater safety to a face-to-face return to the facilities.

Deep learning model-assisted detection of kidney stones on computed tomography

ABSTRACT Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.

Development of an application for providing corneal topography reports based on artificial intelligence / Criação de aplicativo para confecção de laudos de topografia corneana baseado na inteligência artificial

ABSTRACT Purpose: To develop an application (TopEye) in the iOS platform for mobile devices to allow the capture and interpretation of color maps generated by corneal topographers using artificial intelligence. Methods: In the execution, follow-up, and assessment of the project, we used the Scrum methodology and interactive and incremental development process for the project management and agile software development. The ge nerated diagnostic pattern bank consists of 1,172 examples of corneal topography, divided into 275 spherical, 302 symmetrical, 295 asymmetrical, and 300 irregular patterns (keratoconus). For the development of the artificial intelligence of the application, network training was established with 240 images of each pattern type, with a total of 960 patterns (81.91%). The remaining 212 images (18.09%) were used to test the application and will be used for the results. The process is semi-automatic, so the topographic image is captured with a smartphone, the examiner performs the contour of the corneal relief manually, and then the neural network performs the diagnosis. Results: The application diagnosed 201 cases (94.81%) correctly. In 212 images, the algorithm missed the classification of 11 cases (5.19%). The major error that occurred was in distinguishing between symmetrical and asymmetrical classes. In keratoconus screening, the application reached 95.00% sensitivity and 98.68% specificity. Conclusion: The work resulted in obtaining an efficient application to capture topographic images using a smartphone camera and their interpretations through applied artificial intelligence.

RESUMO Objetivo: Desenvolver um aplicativo (TopEye) na plataforma iOS para dispositivos móveis que possibilite a captação e interpretação do mapa de cores gerados por qualquer topógrafo corneano através da inteligência artificial (IA). Metodos: A execução, acompanhamento e avaliação do projeto foi utilizada a metodologia Scrum, processo de desenvolvimento interativo e incremental para gerenciamento de projetos e desenvolvimento ágil de software. O banco de padrões de diagnóstico gerado consiste em 1172 exemplos, divididos em: 275 padrões esféricos, 302 regulares simétricos, 295 regulares assimétricos e 300 irregulares (ceratocone). Para o desenvolvimento da inteligência artificial do aplicativo, foi estabelecido o treinamento da rede com 240 imagens de cada tipo de padrão, totalizando 960 (81,91%) padrões. O restante das imagens, 212 (18,09%), foram utilizadas para testar o aplicativo e usadas para gerar os resultados. O processo é semiautomático, assim a captação da imagem topográfica é realizada com smartphone, o examinador realiza o contorno do relevo corneano manualmente para em seguida a rede neural realizar o diagnóstico. Resultados: O aplicativo diagnosticou 201 (94,81%) imagens corretamente. De um total de 212 imagens, o algoritmo errou a classificação de apenas 11 (5,19%). A principal ocorrência de erro foi na distinção das classes simétrica e assimétrica. No rastreio do ceratocone o aplicativo alcançou 95,00% de sensibilidade e 98,68% especificidade. Conclusão: O trabalho resultou na obtenção de um aplicativo eficiente na captura da imagem topográfica pela câmera do smartphone e na interpretação da mesma através da inteligência artificial aplicada.

Prediction of heart failure decompensations using artificial intelligence and machine learning techniques / Predicción de las descompensaciones de la insuficiencia cardíaca mediante técnicas de inteligencia artificial y aprendizaje automático

Abstract Introduction: Heart failure (HF) is a major concern in public health. We have used artificial intelligence to analyze information and improve patient outcomes. Method: An Observational, retrospective, and non-randomized study with patients enrolled in our telemonitoring program (May 2014-February 2018). We collected patients’ clinical data, telemonitoring transmissions, and HF decompensations. Results: A total of 240 patients were enrolled with a follow-up of 13.44 ± 8.65 months. During this interval, 527 HF decompensations in 148 different patients were detected. Significant weight increases, desaturation below 90% and perception of clinical worsening are good predictors of HF decompensation. We have built a predictive model applying machine learning (ML) techniques, obtaining the best results with the combination of "Weight + Ankle + well-being plus alerts of systolic and diastolic blood pressure, oxygen saturation, and heart rate." Conclusions: ML techniques are useful tools for the analysis of HF datasets and the creation of predictive models that improve the accuracy of the actual remote patient telemonitoring programs.

Resumen Introducción: La insuficiencia cardíaca (IC) es un motivo de gran preocupación en la salud pública. Hemos utilizado técnicas de aprendizaje automático para analizar información y mejorar los resultados. Métodos: Estudio observacional, retrospectivo y no aleatorizado, con los pacientes incluidos en el programa de telemonitorización de IC de nuestro centro desde mayo 2014 hasta febrero 2018. Se han analizado datos clínicos, transmisiones de telemonitorización y descompensaciones de IC. Resultados: 240 pacientes incluidos con un seguimiento de 13.44 ± 8.65 meses. En este intervalo se han detectado 527 descompensaciones de IC en 148 pacientes diferentes. Los aumentos significativos de peso, la desaturación inferior al 90% y la percepción de empeoramiento clínico, han resultado buenos predictores de la descompensación de IC. Hemos construido un modelo predictivo aplicando técnicas de aprendizaje automático obteniendo los mejores resultados con la combinación de "Peso + Edemas en EEII + empeoramiento clínico + alertas de tensión arterial sistólica y diastólica, saturación de oxígeno y frecuencia cardiaca". Conclusiones: Las técnicas de inteligencia artificial son herramientas útiles para el análisis de las bases de datos de IC y para la creación de modelos predictivos que mejoran la precisión de los programas de telemonitorización actuales.

Uso de Python para detecção de fake news sobre a covid-19: desafios e possibilidades / Using Python to detect fake news about covid-19: challenges and possibilities / Usar Python para detectar noticias falsas sobre covid-19: desafíos y posibilidades

Este trabalho tem como objetivo relatar estratégias para coleta de um conjunto de dados em português para treinamento de modelos de Inteligência Artificial com vistas a identificar de forma automática fake news sobre covid-19 disseminadas durante a pandemia, a partir de código Python. Analisamos um método de detecção de fake news baseado em uma Rede Neural Recorrente e de aprendizagem supervisionada. Selecionamos um corpus com 7,2 mil textos coletados em websites e agências de notícias por Monteiro et al. (2018) com cada um previamente catalogado como verdadeiro ou falso como conjunto de dados de treino e validação. O modelo foi usado para detecção de fake news sobre covid-19 em um conjunto de notícias coletadas e classificadas pelos autores deste trabalho. O índice de acerto foi de 70%, ou seja, essa foi a taxa de sucesso da detecção dos itens catalogados.

This work aims to report strategies for collecting a dataset in Portuguese for training Artificial Intelligence models to automatically identify fake news about covid-19 disseminated during the pandemic, using Python code. We analyze a fake news detection method based on a Recurrent Neural Network and supervised learning. We selected a corpus with 7,200 texts collected on websites and news agencies by Monteiro et al. (2018), each one of them previously cataloged as true or false as a training and validation dataset. This model was used to detect fake news about covid-19 in a set of news collected and classified by the authors of this work. The hit rate was 70%.

Este trabajo tiene como objetivo informar estrategias para recopilar un conjunto de datos en portugués para entrenar modelos de Inteligencia Artificial para identificar automáticamente noticias falsas sobre covid-19 difundidas durante la pandemia, utilizando el código Python. Analizamos un método de detección de noticias falsas basado en una Red Neuronal Recurrente y de aprendizaje supervisado. Seleccionamos un corpus de 7.200 textos recogidos en webs y agencias de noticias por Monteiro et al. (2018) con cada uno catalogado previamente como verdadero o falso como un conjunto de datos de entrenamiento y validación. El modelo se utilizó para detectar noticias falsas sobre covid-19 en un conjunto de noticias recopiladas y clasificadas por los autores de este trabajo. La tasa de acierto fue del 70%, es decir, esta fue la tasa de éxito de detección de los artículos catalogados.

Radiografías periapicales y panorámicas como herramientas para la predicción temprana de osteoporosis / Periapical and panoramic radiographs as tools for early prediction of osteoporosis

Introducción: La osteoporosis es una enfermedad del sistema esquelético provocada por una disminución progresiva de la densidad mineral ósea y el deterioro de la microarquitectura, que aumenta el riesgo de fracturas. Por tanto, se hace necesario adoptar medidas de diagnóstico que permitan la detección temprana de alteraciones de la densidad mineral. Dado que las radiografías dentales son rutinarias y permiten examinar las estructuras óseas de los maxilares, se han propuesto como herramientas primarias de diagnóstico de osteoporosis. Objetivo: Examinar la viabilidad y el avance del uso de radiografías periapicales y panorámicas como predictoras tempranas de osteoporosis. Comentarios principales: Fue realizada una revisión bibliográfica sobre cómo las radiografías periapicales y panorámicas, junto con técnicas de aprendizaje automático e índices morfométricos, pueden ser predictores tempranos de osteoporosis. Consideraciones globales: Radiografías panorámicas y periapicales pueden ayudar en la predicción precoz de osteoporosis. Para ello el odontólogo debe contar con amplia experiencia en la interpretación de imágenes radiográficas o ser especialista en radiología oral o cirugía maxilofacial. Por otro lado, existen herramientas computacionales fundamentadas en aprendizaje automático que han mostrado resultados de identificación de osteoporosis comparables a los arrojados por radiólogos. Estas herramientas pueden servir de apoyo a profesionales menos experimentados. Los odontólogos están llamados a ser los primeros inspectores de cambios anómalos en la densidad ósea. Deben remitir oportunamente los pacientes con sospecha de osteoporosis al médico especialista(AU)

Introduction: Osteoporosis is a disease of the skeletal system caused by a gradual reduction in bone mineral density and deterioration of the microarchitecture, raising the risk of fracture. It is therefore necessary to implement diagnostic actions allowing early detection of mineral density alterations. Given the fact that dental radiographs are routine practice and make it possible to examine the bone structure of maxillae and mandibles, they have been proposed as primary tools for osteoporosis diagnosis. Objective: Examine the viability of and progress in the use of periapical and panoramic radiographs as early predictors of osteoporosis. Main remarks: A review was conducted about the combined use of panoramic and periapical radiographs. Both are machine learning techniques and morphometric indices. General considerations: Panoramic and periapical radiographs may be useful for early prediction of osteoporosis. To achieve this end, dentists should have broad experience interpreting radiographic images or be specialists in oral radiology or maxillofacial surgery. On the other hand, computer tools based on machine learning are available which have obtained results in osteoporosis identification comparable to those obtained by radiologists. Those tools may support the work of less experienced professionals. Dentists should be the first to detect anomalous bone density changes, timely referring patients suspected of osteoporosis to the corresponding specialist(AU)

Estado del Arte de la Predicción de Variables en Sistemas de Ingeniería Eléctrica Basada en Inteligencia Artificial / State of the Art of Predicting Electrical Engineering Variables Based on Artificial Intelligence

Resumen Existe una gran cantidad de sistemas que se estudian y desarrollan en el campo de la Ingeniería Eléctrica en los que se realizan análisis que tienen como uno de sus fines principales la predicción de sus variables, tanto para procesos de planificación como de toma de decisiones. Con el advenimiento de la Inteligencia Artificial, se ha observado cómo distintas técnicas relacionadas con el aprendizaje automático y la optimización se han incorporado a estas tareas de predicción, con las cuales se obtienen generalmente mejores resultados en los valores estimados que aquellos generados a partir de técnicas más tradicionales. La presente investigación tiene como objetivo realizar una revisión de lo publicado sobre predicciones de variables en sistemas de Ingeniería Eléctrica en las bases de datos EBSCO, SciELO, RedAlyc, Springer Link, IEEE Xplorer, y Google Académico, a partir de una delimitación temporal y de palabras clave del área. A partir del análisis de la literatura se obtuvo la tendencia sobre el tema a partir de los años más productivos, áreas de impacto e idiomas más frecuentes. Se observó que los estudios desarrollados han crecido en años recientes, y que las áreas de mayor impacto, de acuerdo con el número de publicaciones y de citas son la predicción del consumo y producción de energía eléctrica, y las variables relacionadas con energías renovables.

Abstract In many systems that are studied and developed in the field of Electrical Engineering, analyzes are carried out that have as one of their main purposes the prediction of their variables, both for planning and decision-making processes. With the advent of Artificial Intelligence, it has been observed how different techniques related to machine learning and optimization have been incorporated into these prediction tasks. Those new techniques generally obtained better results in the estimation of values ​​than those generated from more traditional techniques. The objective of this research is to review what has been published on predictions of variables in Electrical Engineering systems in the databases EBSCO, SciELO, RedAlyc, Springer Link, IEEE Xplorer, and Google Scholar, given specific temporal and keyworks delimitations for the area. From the analysis of the literature, the trend on the subject was obtained from the most productive years, areas of impact, and most frequent languages. It was observed that the studies developed have grown in recent years and that the areas of greatest impact, according to the number of publications and citations, are the prediction of electricity consumption and production, and the variables related to renewable energy.

Inteligencia artificial en otorrinolaringología: estado actual y perspectivas a futuro / Artificial intelligence in otolaryngology: current status and future perspectives

La inteligencia artificial posee una larga historia, llena de innovaciones que han dado como resultado diferentes recursos diagnósticos de alto rendimiento, que se encuentran disponibles actualmente. En este artículo se presenta una revisión sobre la inteligencia artificial y sus aplicaciones en medicina. El trabajo se centra en la especialidad de otorrinolaringología con el objetivo de informar a la comunidad médica la importancia y las aplicaciones más destacadas en los diferentes procesos diagnósticos dentro de la especialidad. Incluimos una sección para el análisis del estado actual de la inteligencia artificial en otorrinolaringología en Chile, así como los desafíos a enfrentar a futuro para utilizar la inteligencia artificial en la práctica médica diaria.

Artificial intelligence has a long history full of innovations that have resulted in different high-performance diagnostic resources currently available. This work has reviewed the artificial intelligence definition and its applications to medicine. We focused our review on otolaryngology's specialty to inform the medical community of the importance and the most relevant applications in the different diagnostic processes. We include an analysis of the current state of artificial intelligence in otolaryngology in Chile, and the challenges to be faced in the future to use artificial intelligence into daily medical practice.

Identificação de evasão fiscal utilizando dados abertos e inteligência artificial / Identificación de la evasión fiscal mediante datos abiertos e inteligencia artificial / Tax evasion identification using open data and artificial intelligence

Resumo A evasão fiscal é a consequência da prática da sonegação. Apenas no Brasil, estima-se que ela corresponda a 8% do PIB. Com isso, os governos necessitam de sistemas inteligentes para apoiar os auditores fiscais na identificação de sonegadores. Tais sistemas dependem de dados sensíveis dos contribuintes para o reconhecimento dos padrões, que são protegidos por lei. Com isso, o presente trabalho apresenta uma solução inteligente, capaz de identificar os perfis de potenciais sonegadores com o uso apenas de dados abertos, públicos, disponibilizados pela Receita Federal e pelo Conselho Administrativo Tributário do Estado de Goiás, entre outros cadastros públicos. Foram gerados três modelos que utilizaram os recursos Random Forest, Redes Neurais e Grafos. Em validação depois de melhorias finas, foi possível obter acurácia superior a 98% na predição do perfil inadimplente. Por fim, criou-se uma solução de software visual para uso e validação pelos auditores fiscais do estado de Goiás.

Resumen La evasión fiscal es la consecuencia de la práctica de la defraudación tributaria. En Brasil, se estima que corresponde al 8% del PIB. Por lo tanto, los gobiernos necesitan y utilizan sistemas inteligentes para ayudar a los agentes de hacienda a identificar a los defraudadores fiscales. Dichos sistemas se basan en datos confidenciales de los contribuyentes para el reconocimiento de patrones, que están protegidos por ley. Este trabajo presenta una solución inteligente, capaz de identificar perfiles de potenciales defraudadores fiscales, utilizando únicamente datos públicos abiertos, puestos a disposición por la Hacienda Federal y por el Consejo Administrativo Tributario del Estado de Goiás, entre otros registros públicos. Se generaron tres modelos utilizando random forest y neural networks. En la validación después de finas mejoras, fue posible obtener una precisión superior al 98% en la predicción del perfil moroso. Finalmente, se creó una solución de software visual para uso y validación por parte de los auditores fiscales del estado de Goiás.

Abstract Tax evasion is the practice of the non-payment of taxes. In Brazil alone, it is estimated as 8% of GDP. Thus, governments must use intelligent systems to support tax auditors to identify tax evaders. Such systems seek to recognize patterns and rely on sensitive taxpayer data that is protected by law and difficult to access. This research presents a smart solution, capable of identifying the profile of potential tax evaders, using only open and public data, made available by the Brazilian internal revenue service, the administrative council of tax appeals of the State of Goiás, and other public sources. Three models were generated using Random Forest, Neural Networks, and Graphs. The validation after fine improvements offered an accuracy greater than 98% in predicting tax evading companies. Finally, a web-based solution was created to be used and validated by tax auditors of the State of Goiás.

Artificial intelligence and Big Data in neurology / Inteligência artificial e Big Data em neurologia

ABSTRACT Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.

RESUMO Os recentes avanços na tecnologia nos permitiram acessar uma infinidade de conjuntos de dados pertencentes a várias dimensões da neurologia. Juntamente com as enormes oportunidades, também enfrentamos desafios relacionados à qualidade dos dados, ética e dificuldades intrínsecas relacionadas à aplicação da ciência de dados na área da saúde. Neste artigo descreveremos os principais avanços no campo da inteligência artificial e Big Data aplicados à neurologia com foco nas neurociências baseadas em imagens médicas. Dados do mundo real (RWD) e análises relacionadas ao grande volume de informações serão descritos, bem como algumas das iniciativas científicas mais relevantes no momento da redação deste artigo.

Differentiation of affected and nonaffected ovaries in ovarian torsion with magnetic resonance imaging texture analysis

SUMMARY OBJECTIVE: This study aimed to evaluate the feasibility of texture analysis on T2-weighted axial images in differentiating affected and nonaffected ovaries in ovarian torsion. METHODS: We included 22 torsioned ovaries and 19 healthy ovaries. All patients were surgically proven ovarian torsion cases. On T2-weighted axial images, ovarian borders were delineated by the consensus of two radiologists for magnetic resonance imaging-based texture analysis. Statistical differences between texture features of affected and nonaffected ovaries were assessed. RESULTS: A total of 44 texture features were extracted from each ovary using LIFEx software. Of these, 17 features were significantly different between affected and nonaffected ovaries in ovarian torsion. NGLDM_Coarseness and NGLDM_Contrast, which are the neighborhood gray-level difference matrix parameters, had the largest area under the curve: 0.923. The best cutoff values for the NGLDM_Contrast and NGLDM_Coarseness were 0.45 and 0.01, respectively. With these cutoff levels, NGLDM_Contrast had the best accuracy (85.37%). CONCLUSION: Magnetic resonance imaging-based texture analysis on axial T2-weighted images may help differentiate affected and nonaffected ovaries in ovarian torsion.

Inteligencia artificial en la evaluación y manejo de pacientes con epilepsia / Artificial intelligence in the evaluation and management of patients with epilepsy

RESUMEN La epilepsia es una enfermedad que frecuentemente conlleva significativos niveles de morbi-mortalidad, afecta seriamente la calidad de vida y, en cerca de un tercio de los pacientes, es refractaria a diversos tratamientos. La inteligencia artificial (IA) ha beneficiado el estudio, tratamiento y pronóstico de los pacientes con epilepsia a través de los años. Estos logros abarcan diagnóstico, predicción de crisis automatizada, monitoreo avanzado de crisis epilépticas y electroencefalograma, uso de recursos genéticos en manejo y diagnóstico, algoritmos en imagen y tratamiento, neuromodulación y cirugía robótica. La presente revisión explica de forma práctica los avances actuales y futuros de la inteligencia artificial, rama de la ciencia que ha mostrado resultados prometedores en el diagnóstico y tratamiento de pacientes con epilepsia.

SUMMARY Epilepsy is a condition that frequently coexists with significant morbi-mortality levels, seriously affects the quality of life and, in up to one third of patients, is refractory to a variety of treatment approaches. Artificial intelligence (AI) has largely benefitted the study, treatment, and prognosis of patients with epilepsy through the course of recent years. These achievements applied the fields of diagnosis, automated seizure prediction, advanced seizure monitoring and electroencephalogram, use of genetics in diagnosis and management, imaging algorithms in the treatment, neuromodulation, and robotic surgery. This review conveys the actual and future directions of AI. a branch of science that has shown promising results in the treatment and diagnosis of patients with epilepsy.

HistoBCAD: herramienta de código abierto para detección de cáncer de mama en imágenes histopatológicas

RESUMEN Fundamento: la detección y clasificación precisa del cáncer de mama mediante el diagnóstico histopatológico es de vital importancia para el tratamiento efectivo de la enfermedad. Entre los tipos de cáncer de mama, el carcinoma ductal invasivo es el más frecuente. El análisis visual de las muestras de tejido en el microscopio es un proceso manual que consume tiempo y depende del observador. Sin embargo, en muchos países, incluido Cuba, es escaso el uso de herramientas software para asistir el diagnóstico. Objetivo: desarrollar una herramienta software para detectar tejido de cáncer de mama, del subtipo carcinoma ductal invasivo, en imágenes histopatológicas. Métodos: la herramienta se implementó en Python e incluye métodos de detección de carcinoma ductal invasivo en imágenes histopatológicas, basados en algoritmos de extracción de características de color y textura en combinación con un clasificador de bosques aleatorios. Resultados: la herramienta de código abierto brinda una serie de facilidades para la lectura, escritura y visualización de imágenes histopatológicas, delineación automática y manual de zonas cancerígenas, gestión de los datos diagnósticos del paciente y evaluación colaborativa a distancia. Fue evaluada en una base de datos con 162 imágenes de pacientes diagnosticados con carcinoma ductal invasivo y se obtuvo una exactitud balanceada de 84 % y factor F1 de 75 %. Conclusiones: la herramienta permitió un análisis interactivo, rápido, reproducible y colaborativo mediante una interfaz gráfica sencilla e intuitiva. En versiones futuras se prevé incluir nuevos métodos de aprendizaje automático incremental para el análisis de imágenes histopatológicas digitales.

ABSTRACT Background: the accurate detection and classification of breast cancer through histopathological diagnosis is of vital importance for the effective treatment of the disease. Among the types of breast cancer, invasive ductal carcinoma (IDC) is the most common. Visual analysis of tissue samples under the microscope is a manual, time-consuming and observer-dependent process. However, in many countries, including Cuba, the use of software tools to assist diagnosis is scarce. Objective: to develop a software tool to detect IDC subtype breast cancer tissue in histopathological images. Methods: the tool is implemented in Python and includes IDC detection methods in histopathological images, based on algorithms for extraction of color and texture features in combination with a random forest classifier. Results: the open source tool provides a series of facilities for the reading, writing and visualization of histopathological images, automatic and manual delineation of cancer areas, management of patient diagnostic data and collaborative remote evaluation. It was evaluated in a database with 162 images of patients diagnosed with IDC, obtaining a balanced accuracy of 84 % and a F1 factor of 75 %. Conclusions: the tool allowed an interactive, fast, reproducible, precise and collaborative analysis through a simple and intuitive graphical interface. Future versions are expected to include new incremental machine learning methods for the analysis of digital histopathology images.

Inteligencia artificial explicable, una perspectiva al problema de la clasificación automática de COVID-19 mediante radiografías de tórax

RESUMEN Esta investigación pretende dilucidar, a partir del análisis de técnicas de inteligencia artificial explicables, la robustez y el nivel de generalización de los métodos de visión por computadora propuestos para identificar COVID-19 utilizando imágenes de radiografías de tórax. Asimismo, alertar a los investigadores y revisores sobre el problema del aprendizaje por atajos. En este estudio se siguen recomendaciones para identificar si los modelos de clasificación automática de COVID-19 se ven afectados por el aprendizaje por atajos. Para ello, se revisaron los artículos que utilizan métodos de inteligencia artificial explicable en dicha tarea. Se evidenció que al utilizar la imagen de radiografía de tórax completa o el cuadro delimitador de los pulmones, las regiones de la imagen que más contribuyen a la clasificación aparecen fuera de la región pulmonar, algo que no tiene sentido. Los resultados indican que, hasta ahora, los modelos existentes presentan el problema de aprendizaje por atajos, lo cual los hace inapropiados para ser usados en entornos clínicos.

ABSTRACT This research aims to elucidate, from the analysis of explainable artificial intelligence techniques, the robustness and level of generalization of the proposed computer vision methods to identify COVID-19 using chest X-ray images. Also, alert researchers and reviewers about the problem of learning by shortcuts. In this study, recommendations are followed to identify if the automatic classification models of COVID-19 are affected by shortcut learning. To do this, articles that use explainable artificial intelligence methods were reviewed. It was shown that when using the full chest X-ray image or the bounding box of the lungs, the regions of the image that contribute the most to the classification appear outside the lung region, something that does not make sense. The results indicate that, so far, the existing models present the problem of learning by shortcuts, which makes them inappropriate to be used in clinical settings.