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1.
Rev. Hosp. Ital. B. Aires (2004) ; 42(1): 12-20, mar. 2022. graf, ilus, tab
Article in Spanish | LILACS, BINACIS, UNISALUD | ID: biblio-1368801

ABSTRACT

Introducción: determinar la causa de muerte de los pacientes internados con enfermedad cardiovascular es de suma importancia para poder tomar medidas y así mejorar la calidad su atención y prevenir muertes evitables. Objetivos: determinar las principales causas de muerte durante la internación por enfermedades cardiovasculares. Desarrollar y validar un algoritmo para clasificar automáticamente a los pacientes fallecidos durante la internación con enfermedades cardiovasculares Diseño del estudio: estudio exploratorio retrospectivo. Desarrollo de un algoritmo de clasificación. Resultados: del total de 6161 pacientes, el 21,3% (1316) se internaron por causas cardiovasculares; las enfermedades cerebrovasculares representan el 30,7%, la insuficiencia cardíaca el 24,9% y las enfermedades cardíacas isquémicas el 14%. El algoritmo de clasificación según motivo de internación cardiovascular vs. no cardiovascular alcanzó una precisión de 0,9546 (IC 95%: 0,9351-0,9696). El algoritmo de clasificación de causa específica de internación cardiovascular alcanzó una precisión global de 0,9407 (IC 95%: 0,8866-0,9741). Conclusiones: la enfermedad cardiovascular representa el 21,3% de los motivos de internación de pacientes que fallecen durante su desarrollo. Los algoritmos presentaron en general buena performance, particularmente el de clasificación del motivo de internación cardiovascular y no cardiovascular y el clasificador según causa específica de internación cardiovascular. (AU)


Introduction: determining the cause of death of hospitalized patients with cardiovascular disease is of the utmost importance in order to take measures and thus improve the quality of care of these patients and prevent preventable deaths. Objectives: to determine the main causes of death during hospitalization due to cardiovascular diseases.To development and validate a natural language processing algorithm to automatically classify deceased patients according to their cause for hospitalization. Design: retrospective exploratory study. Development of a natural language processing classification algorithm. Results: of the total 6161 patients in our sample who died during hospitalization, 21.3% (1316) were hospitalized due to cardiovascular causes. The stroke represent 30.7%, heart failure 24.9%, and ischemic cardiac disease 14%. The classification algorithm for detecting cardiovascular vs. Non-cardiovascular admission diagnoses yielded an accuracy of 0.9546 (95% CI 0.9351, 0.9696), the algorithm for detecting specific cardiovascular cause of admission resulted in an overall accuracy of 0.9407 (95% CI 0.8866, 0.9741). Conclusions: cardiovascular disease represents 21.3% of the reasons for hospitalization of patients who die during hospital stays. The classification algorithms generally showed good performance, particularly the classification of cardiovascular vs non-cardiovascular cause for admission and the specific cardiovascular admission cause classifier. (AU)


Subject(s)
Humans , Artificial Intelligence/statistics & numerical data , Cerebrovascular Disorders/mortality , Myocardial Ischemia/mortality , Heart Failure/mortality , Hospitalization , Quality of Health Care , Algorithms , Reproducibility of Results , Factor Analysis, Statistical , Mortality , Cause of Death , Electronic Health Records
3.
Article in Portuguese | ColecionaSUS, LILACS, ColecionaSUS, CONASS, SES-GO | ID: biblio-1358947

ABSTRACT

Introdução: A área de Tecnologia da Informação da Secretaria de Estado da Saúde de Goiás com o intuito de desenvolver soluções tecnológicas para apoio à tomada de decisão iniciou em 2010 o projeto na área de Business Intelligence que engloba, dentre outras ações, a construção de um data warehouse que agrega dados dos sistemas transacionais em conjunto com a elaboração de painéis de monitoramento que permitem uma visão integrada do todo para subsidiar a alta gestão e áreas técnicas da Secretaria de Estado da Saúde de Goiás (SES-GO), garantindo assim, decisões mais assertivas e a elaboração de políticas públicas mais pertinentes e fundamentadas. Este projeto evoluiu e atualmente as áreas de Ciência de Dados e Big Data também compõem área de analytics da SES-GO. Objetivo: o principal objetivo deste artigo é apresentar a plataforma FLINK que foi desenvolvida vislumbrando a democratização de dados e indicadores de saúde da Secretaria de Estado da Saúde de Goiás e compartilhar o processo de construção e implantação da mesma. Metodologia: Trata-se de um estudo de abordagem qualitativa, do tipo relato de experiência, no qual os autores relatam a construção e funcionalidades da plataforma FLINK. Considerações finais: A plataforma FLINK representa a efetivação da transparência da gestão pública no estado de Goiás em dados de saúde. A versatilidade e a flexibilidade do mesmo possibilita a melhoria das ações de saúde e beneficia todos os usuários do SUS


Introduction: The servers in the Business Intelligence (BI) area of the State Department of Health of Goiás, in order to develop technological solutions to support decision-making, started in 2010 to build a data warehouse that would aggregate data from traditional systems, and of monitoring panels, allowing a more integrated view of the whole, to support the senior management and technical areas of State Department of Health of Goiás (SES-GO), thus ensuring more assertive decisions and the development of more relevant and grounded public policies. Objective: The main objective of this article is to present the flink plataform that was developed with a view to the democratization of data and health status indicators of the Goiás state health department and to share the process of construction and implementation of the same. Method: This is a study with a qualitative approach, of the experience report type, in which the authors report the construction and functionalities of the FLINK plataform. Final considerations: FLINK represents the effectiveness of transparency in public management in the state of Goiás. Its versatility and flexibility enables the improvement of health actions and benefits all SUS users


Subject(s)
Humans , Artificial Intelligence , Public Health Informatics , Health Status Indicators
4.
Article in English | LILACS, BBO | ID: biblio-1365230

ABSTRACT

ABSTRACT Over the past year and a half dental education has been conducted primarily online due to the SARS-CoV-2 pandemic. During the pandemic, we have spent many hours a day on our computers, mobile phones, and tablets to gather information and participate in online seminars and classrooms. Health consequences resulting from the overuse of these devices include carpal tunnel syndrome as well as computer vision syndrome (CVS). Computer vision syndrome, also known as digital eye strain, has several associated features such as eye burning, strained vision, dry eye, blurred vision, and associated neck and shoulder pain. Several predisposing factors have been linked with CVS, but often this problem gets ignored. The management of this syndrome is aimed at educating dentists on computer use, position, and the surrounding environment. Considering all this, we must ensure that we spend some time away from these devices every day to avoid any significant vision problems. The objective of preparing this manuscript was to provide a brief overview of the increased prevalence of computer vision syndrome and its associated features.


Subject(s)
Vision Disorders/prevention & control , Artificial Intelligence , Dentists , Eye Diseases/prevention & control , COVID-19/complications , Microcomputers , Carpal Tunnel Syndrome , Prevalence , Risk Factors , Education, Dental , Screen Time , India
5.
Edumecentro ; 13(4): 274-287, 2021.
Article in Spanish | LILACS | ID: biblio-1345962

ABSTRACT

RESUMEN Introducción: la enfermedad por SARS-Cov-2 refuerza la importancia del uso de las nuevas tecnologías de la información y las comunicaciones en función del desarrollo e implementación de sistemas de inteligencia artificial que favorecen el diagnóstico. Objetivo: describir la posibilidad del uso de la inteligencia artificial como una herramienta en la imagenología para los pacientes positivos a la COVID-19. Métodos: se realizó una revisión de fuentes bibliográficas en Infomed, SciELO, PubMed y Google Académico, comprendidas en los años 2015 al 2020 con el uso de palabras claves: coronavirus, COVID-19, neumonía, radiografía e inteligencia artificial. Se seleccionaron 28 documentos por su pertinencia en el estudio. Desarrollo: la creación de sistemas de inteligencia artificial que ayuden al diagnóstico médico requiere un enfoque interprofesional de la ciencia y constituye una de las líneas de trabajo en Cuba durante la pandemia. Una condición indispensable para la introducción de la inteligencia artificial en el diagnóstico radiológico es la capacitación que deben recibir los médicos para interactuar con ella, a través de un proceso formativo que incluya una evaluación y explicación de la calidad de los datos asociada tanto al aprendizaje como a las nuevas predicciones. Conclusiones: la utilización de inteligencia artificial mejorará el rendimiento del radiólogo para distinguir la COVID-19; la integración de estas tecnologías en el flujo de trabajo clínico de rutina puede ayudar a los radiólogos a diagnosticar con precisión.


ABSTRACT Introduction: SARS-Cov-2 disease reinforces the importance of the use of new information and communication technologies based on the development and implementation of artificial intelligence systems that favor diagnosis. Objective: to describe the possibility of using artificial intelligence as a tool in imaging for COVID-19 positive patients. Methods: a review of bibliographic sources was carried out in Infomed, SciELO, PubMed and Google Scholar, from 2015 to 2020 with the use of keywords: coronavirus, COVID-19, pneumonia, radiography and artificial intelligence. 28 documents were selected for their relevance in the study. Development: the creation of artificial intelligence systems that help medical diagnosis requires an interprofessional approach to science and constitutes one of the lines of work in Cuba during the pandemic. An essential condition for the introduction of artificial intelligence in radiological diagnosis is the training that doctors must receive to interact with it, through a training process that includes an evaluation and explanation of the quality of the data associated with both learning and to new predictions. Conclusions: the use of artificial intelligence will improve the radiologist's performance to distinguish COVID-19; integrating these technologies into routine clinical workflow can help radiologists diagnose accurately.


Subject(s)
Radiology , Artificial Intelligence , Coronavirus Infections , Imaging, Three-Dimensional
6.
Rev. cuba. anestesiol. reanim ; 20(3): e713, 2021.
Article in Spanish | LILACS, CUMED | ID: biblio-1351983

ABSTRACT

Introducción: La administración manual en bolo ha evolucionado desde la infusión volumétrica basada en regímenes farmacológicos estandarizados, hasta los sistemas de infusión controlada por objetivo y los más sofisticados sistemas de circuito cerrado. Objetivo: Describir los principios tecnológicos y aplicaciones clínicas extendidas de la infusión controlada por objetivo y los sistemas de circuito cerrado. Métodos: Se realizó una revisión no sistemática de la literatura, en bases de datos científicas como Cochrane Database of Systematic Reviews, Pubmed/Medline, EMBASE, Scopus, Web of Science, EBSCOhost, Science Direct, OVID y el buscador académico Google Scholar, en el mes de septiembre del año 2020. Desarrollo: La disponibilidad y portabilidad de dispositivos electrónicos con capacidad de procesamiento avanzado a precios relativamente accesibles, el perfeccionamiento del aprendizaje automático e inteligencia artificial aplicado a las decisiones médicas, y las iteraciones tecnológicas complejas incorporadas en los sistemas de circuito abierto y cerrado, desarrollados originalmente en el campo de la Anestesiología, han posibilitado su expansión a otras especialidades y entornos clínicos tan disímiles como el tratamiento de la diabetes mellitus, administración de fármacos antineoplásicos, ventilación mecánica, control de las variables hemodinámicas y la terapia antimicrobiana en pacientes críticos. Conclusiones: La infusión controlada por objetivo y los sistemas de circuito cerrado se han convertido en tecnologías maduras, seguras y viables, aplicadas clínicamente en múltiples naciones y escenarios, con un desempeño superior a los sistemas manuales tradicionales(AU)


Introduction: Manual bolus administration has evolved from volumetric infusion based on standardized pharmacological regimens to target-controlled infusion systems and the most sophisticated closed-loop systems. Objective: To describe the technological principles and extended clinical applications of target-controlled infusion and closed-loop systems. Methods: A nonsystematic review of the literature was carried out, during September 2020, in scientific databases such as Cochrane Database of Systematic Reviews, Pubmed/Medline, EMBASE, Scopus, Web of Science, EBSCOhost, Science Direct, OVID and the academic search engine Google Scholar. Development: The availability and portability of electronic devices with advanced processing capacity at relatively affordable prices, the refinement of machine learning and artificial intelligence applied to medical decisions, as well as the complex technological iterations incorporated into open and closed-loop systems, originally developed in the field of anesthesiology, have enabled their expansion to other specialties and clinical settings so diverse as treatment of diabetes mellitus, administration of antineoplastic drugs, mechanical ventilation, control of hemodynamic variables and antimicrobial therapy in critical patients. Conclusions: Target-controlled infusion and closed-loop systems have become mature, safe and viable technologies, applied clinically in multiple nations and settings, with superior performance compared to traditional manual systems(AU)


Subject(s)
Humans , Male , Female , Artificial Intelligence , Machine Learning , Anesthesiology , Anesthesia, Closed-Circuit/methods , Early Goal-Directed Therapy
8.
Prensa méd. argent ; 107(5): 282-286, 20210000.
Article in English | LILACS, BINACIS | ID: biblio-1359365

ABSTRACT

El aprendizaje profundo es un tipo de inteligencia artificial computarizada que tiene como objetivo entrenar a una computadora para que realice tareas que normalmente realizan los humanos basándose en redes neuronales artificiales. Los avances tecnológicos recientes han demostrado que las redes neuronales artificiales se pueden aplicar a campos como el reconocimiento de voz y audio, la traducción automática, los juegos de mesa, el diseño de fármacos y el análisis de imágenes médicas. El desarrollo de estas técnicas ha sido extremadamente rápido en los últimos años y las redes neuronales artificiales hoy en día superan a los humanos en muchas de estas tareas. Las redes neuronales artificiales se inspiraron en la función de sistemas biológicos como el cerebro y los nodos conectados dentro de estas redes que modelan las neuronas. El principio de tales redes es que están capacitadas con conjuntos de datos donde se conoce la verdad fundamental. Como ejemplo, la red debe estar capacitada para identificar imágenes donde se representa una bicicleta. Esto requiere una gran cantidad de imágenes donde las bicicletas se etiquetan manualmente (la llamada verdad fundamental) que luego son analizadas por la computadora. Si se utilizan suficientes imágenes con bicicleta o sin bicicleta, la red neuronal artificial puede entrenarse para identificar bicicletas en otros conjuntos de imágenes. En las imágenes médicas, los enfoques clásicos incluyen la extracción de características semánticas definidas por expertos humanos o características agonísticas definidas por ecuaciones. Las características semánticas pueden proporcionar una buena especificidad para el diagnóstico de enfermedades, pero pueden diferir entre diferentes médicos dependiendo de su nivel de experiencia, requieren mucho tiempo y son costosas. Las características agonísticas pueden tener una especificidad limitada, pero ofrecen la ventaja de una alta reproducibilidad. El aprendizaje profundo tiene un enfoque diferente. Se requiere un conjunto de datos de entrenamiento donde se conoce la verdad básica, en este caso el diagnóstico. El número de datos necesarios es elevado y, por lo general, se utilizan 100.000 imágenes o más. Una vez que se entrena la red neuronal artificial, se puede aplicar a un conjunto de datos de validación en el que también se conoce el diagnóstico, pero no se informa a la computadora. La salida de la red neuronal artificial es, en el caso más simple, una enfermedad o ninguna enfermedad que pueda compararse con la verdad fundamental. La concordancia con la verdad del terreno se cuantifica utilizando medidas como el área bajo la curva (AUC, puede tomar valores entre 0 y 1, siendo 1 la discriminación perfecta entre salud y enfermedad), especificidad (puede tomar valores entre 0% y 100% y la proporción de negativos reales que se identifican correctamente) y la sensibilidad (puede tomar valores entre 0% y 100% y cuantifica la proporción de positivos reales que se identifican correctamente). Si se requiere una alta sensibilidad o una alta especificidad depende de la enfermedad, la prevalencia de la enfermedad, así como el entorno clínico real donde se debe emplear esta red


Subject(s)
Humans , Artificial Intelligence , Neural Networks, Computer , Speech Recognition Software , Deep Learning
9.
Gac. méd. Méx ; 157(3): 311-314, may.-jun. 2021.
Article in Spanish | LILACS | ID: biblio-1346112

ABSTRACT

Resumen En internet ha crecido la inteligencia artificial hasta convertirse en un programa con códigos y algoritmos que aprenden y se reprograman para efectuar tareas preestablecidas con mayor eficiencia; si bien lo anterior se traduce en mejoría, el programador desconoce los alcances de los resultados y de la reprogramación. Ante el riesgo de desviación de los objetivos preestablecidos y de los reglamentos éticos, se tienen que implementar filtros al inicio, durante y al final del proceso, como alarmas cuando existan desviaciones con implicación bioética. La interacción de la inteligencia humana con la inteligencia artificial ha tenido desencuentros negativos y positivos. Al principio, bastó con adecuar normas, leyes laborales y derechos humanos; ahora se requiere establecer normas éticas, como las formuladas en la Declaración de Barcelona para el Adecuado Desarrollo y Uso de la Inteligencia Artificial en Europa.


Abstract On the internet, artificial intelligence has grown to become a program with codes and algorithms that learn and reprogram themselves to carry out pre-established tasks with greater efficiency; although this translates into improvements, the scope of the results and reprogramming are unknown to the programmer. Given the risk of deviation from pre-established objectives and ethical regulations, filters must be installed at the beginning, during and at the end of the process, as alarms for detecting deviations with bioethical implications. The interaction of human intelligence with artificial intelligence has had negative and positive disagreements. Initially, adapting regulations, labor laws and human rights was enough; now it is necessary for ethical standards to be established, such as those formulated in the Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe.


Subject(s)
Humans , Artificial Intelligence , Intelligence , Algorithms , Human Rights , Morals
10.
Arq. bras. cardiol ; 116(6): 1091-1098, Jun. 2021. tab, graf
Article in English, Portuguese | LILACS | ID: biblio-1278330

ABSTRACT

Resumo Fundamento A quantificação não invasiva da reserva fracionada de fluxo miocárdico (FFR TC ) através de software baseado em inteligência artificial em versão mais atualizada e tomógrafo de última geração (384 cortes) apresenta elevada performance na detecção de isquemia coronariana. Objetivos Avaliar o desempenho diagnóstico da FFR TC na detecção de doença arterial coronariana (DAC) significativa em relação ao FFRi, em tomógrafos de gerações anteriores (128 e 256 cortes). Métodos Estudo retrospectivo com pacientes encaminhados à angiotomografia de artérias coronárias (TCC) e cateterismo (FFRi). Foram utilizados os tomógrafos Siemens Somatom Definition Flash (256 cortes) e AS+ (128 cortes). A FFR TC e a área luminal mínima (ALM) foram avaliadas em software (cFFR versão 3.0.0, Siemens Healthineers, Forchheim, Alemanha). DAC obstrutiva foi definida como TCC com redução luminal ≥50% e DAC funcionalmente obstrutiva como FFRi ≤0,8. Todos os valores de p reportados são bicaudais; e quando <0,05, foram considerados estatisticamente significativos. Resultados Noventa e três pacientes consecutivos (152 vasos) foram incluídos. Houve boa concordância entre FFR TC e FFRi, com mínima superestimação da FFR TC (viés: -0,02; limites de concordância: 0,14 a 0,09). Diferentes tomógrafos não modificaram a relação entre FFR TC e FFRi (p para interação = 0,73). A FFR TC demonstrou performance significativamente superior à classificação visual de estenose coronariana (AUC 0,93 vs. 0,61, p <0,001) e à ALM (AUC 0,93 vs. 0,75, p <0,001) reduzindo o número de casos falso-positivos. O melhor ponto de corte para a FFR TC utilizando um índice de Youden foi de 0,85 (sensiblidade, 87%; especificidade, 86%; VPP, 73%; NPV, 94%), com redução de falso-positivos. Conclusão FFR TC baseada em inteligência artificial, em tomógrafos de gerações anteriores (128 e 256 cortes), apresenta boa performance diagnóstica na detecção de DAC, podendo ser utilizada para reduzir procedimentos invasivos.


Abstract Background The non-invasive quantification of the fractional flow reserve (FFRCT) using a more recent version of an artificial intelligence-based software and latest generation CT scanner (384 slices) may show high performance to detect coronary ischemia. Objectives To evaluate the diagnostic performance of FFRCT for the detection of significant coronary artery disease (CAD) in contrast to invasive FFR (iFFR) using previous generation CT scanners (128 and 256- detector rows). Methods Retrospective study with patients referred to coronary artery CT angiography (CTA) and catheterization (iFFR) procedures. Siemens Somatom Definition Flash (256-detector rows) and AS+ (128-detector rows) CT scanners were used to acquire the images. The FFRCT and the minimal lumen area (MLA) were evaluated using a dedicated software (cFFR version 3.0.0, Siemens Healthineers, Forchheim, Germany). Obstructive CAD was defined as CTA lumen reduction ≥ 50%, and flow-limiting stenosis as iFFR ≤0.8. All reported P values are two-tailed, and when <0.05, they were considered statistically significant. Results Ninety-three consecutive patients (152 vessels) were included. There was good agreement between FFRCT and iFFR, with minimal FFRCT overestimation (bias: -0.02; limits of agreement:0.14-0.09). Different CT scanners did not modify the association between FFRCT and FFRi (p for interaction=0.73). The performance of FFRCT was significantly superior compared to the visual classification of coronary stenosis (AUC 0.93vs.0.61, p<0.001) and to MLA (AUC 0.93vs.0.75, p<0.001), reducing the number of false-positive cases. The optimal cut-off point for FFRCT using a Youden index was 0.85 (87% Sensitivity, 86% Specificity, 73% PPV, 94% NPV), with a reduction of false-positives. Conclusion Machine learning-based FFRCT using previous generation CT scanners (128 and 256-detector rows) shows good diagnostic performance for the detection of CAD, and can be used to reduce the number of invasive procedures.


Subject(s)
Humans , Coronary Artery Disease , Coronary Stenosis , Fractional Flow Reserve, Myocardial , Severity of Illness Index , Artificial Intelligence , Tomography, X-Ray Computed , Predictive Value of Tests , Retrospective Studies , Coronary Angiography , Constriction, Pathologic , Coronary Vessels , Machine Learning , Computed Tomography Angiography
11.
Rev. cuba. oftalmol ; 34(2): e1152, 2021.
Article in Spanish | LILACS, CUMED | ID: biblio-1341465

ABSTRACT

El presente trabajo aborda el uso de la inteligencia artificial en la cirugía de catarata y la incursión de Cuba en este campo. La inteligencia artificial tiene como objetivo dotar a un agente con la capacidad de tomar decisiones correctas. Dentro de los campos de la inteligencia artificial se encuentra el aprendizaje de máquinas cuyo propósito es entrenar a las computadoras para aprender de un conjunto de datos las decisiones que han de tomar, dada una situación específica. Uno de los métodos más utilizados para el entrenamiento y el aprendizaje de máquinas es el desarrollo de redes neuronales artificiales. Desde un enfoque social, se explica cómo la influencia sobre el resultado visual que puede lograrse con esta tecnología repercute en el individuo y la sociedad, y se resaltan las ventajas y las desventajas de su utilización(AU)


The study addresses the use of artificial intelligence in cataract surgery and Cuba's incorporation into this field. The purpose of artificial intelligence is to develop agents with the ability to take appropriate decisions. One of the branches of artificial intelligence is machine learning, whose aim is to train computers to draw from a set of data the decisions to be taken in response to a specific situation. One of the most common methods in machine training and learning is the development of artificial neural networks. A social explanation is provided of the effect of the visual outcomes obtained by this technology on the individual and society, highlighting the advantages and disadvantages of its use(AU)


Subject(s)
Humans , Artificial Intelligence , Cataract Extraction/methods , Machine Learning
12.
Ciênc. Saúde Colet ; 26(5): 1885-1898, maio 2021. tab, graf
Article in English, Portuguese | LILACS | ID: biblio-1249510

ABSTRACT

Resumo O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.


Abstract This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.


Subject(s)
Humans , Aged , COVID-19 Vaccines , COVID-19 , Brazil , Artificial Intelligence , Cross-Sectional Studies , Vaccination , Cities , SARS-CoV-2 , Intelligence
13.
Säo Paulo med. j ; 139(5): 535-542, May 2021. tab, graf
Article in English | LILACS | ID: biblio-1290267

ABSTRACT

BACKGROUND: The new coronavirus of 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally and has repercussions within ophthalmological care. It has caused ocular manifestations in some patients, which can spread through eye secretions. OBJECTIVES: The purpose of this review was to summarize the currently available evidence on COVID-19 with regard to its implications for ophthalmology. DESIGN AND SETTING: Narrative review developed by a research group at Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil, and at Ludwig-Maximilians-Universität, Munich, Germany. METHODS: We searched the literature on the repercussions of COVID-19 within ophthalmological care, using the MEDLINE and LILACS databases, with the keywords "COVID-19", "ophthalmology" and "coronavirus", from January 1, 2020, to March 27, 2021. Clinical trials, meta-analysis, randomized controlled trials, reviews and systematic reviews were identified. RESULTS: We retrieved 884 references, of which 42 were considered eligible for intensive review and critical analysis. Most of the studies selected reported the evidence regarding COVID-19 and its implications for ophthalmology. CONCLUSIONS: Knowledge of eye symptoms and ocular transmission of the virus remains incomplete. New clinical trials with larger numbers of patients may answer these questions in the future. Moreover, positively, implementation of innovative changes in medicine such as telemedicine and artificial intelligence may assist in diagnosing eye diseases and in training and education for students.


Subject(s)
Humans , Ophthalmology , COVID-19 , Brazil , Artificial Intelligence , SARS-CoV-2
16.
Rev. colomb. gastroenterol ; 36(1): 7-17, ene.-mar. 2021. tab, graf
Article in Spanish | LILACS | ID: biblio-1251516

ABSTRACT

Resumen El cáncer colorrectal (CCR) es uno de los tumores malignos con mayor prevalencia en Colombia y el mundo. Estas neoplasias se originan en lesiones adenomatosas o pólipos que deben resecarse para prevenir la enfermedad, lo cual se puede realizar con una colonoscopia. Se ha reportado que durante una colonoscopia se detectan pólipos en el 40 % de los hombres y en el 30 % de las mujeres (hiperplásicos, adenomatosos, serrados, entre otros), y, en promedio, un 25 % de pólipos adenomatosos (principal indicador de calidad en colonoscopia). Sin embargo, estas lesiones no son fáciles de observar por la multiplicidad de puntos ciegos en el colon y por el error humano asociado con el examen. Diferentes investigaciones han reportado que alrededor del 25 % de pólipos colorrectales no son detectados o se pasan por alto durante la colonoscopia y, como consecuencia, el paciente puede tener un cáncer de intervalo. Estas cifras muestran la necesidad de contar con un segundo observador (sistema de inteligencia artificial) que reduzca al mínimo la posibilidad de no detectar estos pólipos y, de este modo, sea posible prevenir al máximo el cáncer de colon. Objetivo: crear un método computacional para la detección automática de pólipos colorrectales usando inteligencia artificial en videos grabados de procedimientos reales de colonoscopia. Metodología: se usaron bases de datos públicas con pólipos colorrectales y una colección de datos construida en un Hospital Universitario. Inicialmente, se normalizan todos los cuadros de los videos para disminuir la alta variabilidad entre bases de datos. Posteriormente, la tarea de detección de pólipos se hace con un método de aprendizaje profundo usando una red neuronal convolucional. Esta red se inicia con pesos aprendidos en millones de imágenes naturales de la base de datos ImageNet. Los pesos de la red se actualizan usando imágenes de colonoscopia, siguiendo la técnica de ajuste fino. Finalmente, la detección de pólipos se realiza asignando a cada cuadro una probabilidad de contener un pólipo y determinando el umbral que define cuando el pólipo se encuentra presente en un cuadro. Resultados: este enfoque fue entrenado y evaluado con 1875 casos recopilados de 5 bases de datos públicas y de la construida en el hospital universitario, que suman aproximadamente 123 046 cuadros. Los resultados obtenidos se compararon con las marcaciones de diferentes expertos en colonoscopia y se obtuvo 0,77 de exactitud, 0,89 de sensibilidad, 0,71 de especificidad y una curva ROC (receiver operating characteristic) de 0,87. Conclusión: este método logra detectar pólipos de manera sobresaliente, superando la alta variabilidad dada por los distintos tipos de lesiones, condiciones diferentes de la luz del colon (asas, pliegues o retracciones) con una sensibilidad muy alta, comparada con un gastroenterólogo experimentado, lo que podría hacer que se disminuya el error humano, el cual es uno de los principales factores que hacen que no se detecte o se escapen los pólipos durante un examen de colonoscopia.


Abstract Colorectal cancer (CRC) is one of the most prevalent malignant tumors worldwide. These neoplasms originate from adenomatous lesions or polyps that must be resected to prevent the development of the disease, and that can be done through a colonoscopy. Polyps are reported during colonoscopy in 40% of men and 30% of women (hyperplastic, adenomatous, serrated, among others), and, on average 25% are adenomatous polyps (the main indicator of quality in colonoscopy). However, these lesions are not easy to visualize because of the multiplicity of blind spots in the colon and human errors associated with the performance of the procedure. Several research works have reported that about 25% of colorectal polyps are overlooked or undetected during colonoscopy, and as a result, the patient may have interval cancer. These figures show the need for a second observer (artificial intelligence system) to reduce the possibility of not detecting polyps and prevent colon cancer as much as possible. Objective: To create a computational method for the automatic detection of colorectal polyps using artificial intelligence using recorded videos of colonoscopy procedures. Methodology: Public databases of colorectal polyps and a data collection constructed in a university hospital were used. Initially, all the frames in the videos were normalized to reduce the high variability between databases. Subsequently, polyps were detected using a deep learning method with a convolutional neural network. This network starts with weights learned from millions of natural images taken from the ImageNET database. Network weights are updated using colonoscopy images, following the fine-tuning technique. Finally, polyps are detected by assigning each box a probability of polyp presence and determining the threshold that defines when the polyp is present in a box. Results: This approach was trained and evaluated with 1 875 cases collected from 5 public databases and the one built in the university hospital, which total approximately 123 046 frames. The results obtained were compared with the markings of different experts in colonoscopy, obtaining 0.77 accuracy, 0.89 sensitivity, 0.71 specificity, and a receiver operating characteristic curve of 0.87. Conclusion: This method detected polyps in an outstanding way, overcoming the high variability caused by the types of lesions and bowel lumen condition (loops, folds or retractions) and obtaining a very high sensitivity compared with an experienced gastroenterologist. This may help reduce the incidence of human error, as it is one of the main factors that cause polyps to not be detected or overlooked during a colonoscopy.


Subject(s)
Humans , Polyps , Artificial Intelligence , Adenomatous Polyps , Audiovisual Aids , Colorectal Neoplasms , Determination
17.
Rev. Assoc. Med. Bras. (1992) ; 67(2): 248-259, Feb. 2021. tab, graf
Article in English | LILACS | ID: biblio-1287808

ABSTRACT

SUMMARY OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute of Nuclear Medicine and Oncology Hospital. METHODS: Ninety workers from the Radiology and Nuclear Medicine departments were included. A high-capacity thermoluminescent was used for annual average effective radiation dose measurements. The liver function tests were conducted for all subjects and controls. Three supervised learning models (multilayer precentron; logistic regression; and random forest) were applied and cross-validated to predict any alteration in liver enzymes. The t-test was applied to see if subjects and controls were significantly different in liver function tests. RESULTS: The annual average effective doses were in the range of 0.07-1.15 mSv. Alanine transaminase was 50% high and aspartate transaminase was 20% high in radiation workers. There existed a significant difference (p=0.0008) in Alanine-aminotransferase between radiation-exposed and radiation-unexposed workers. Random forest model achieved 90-96.6% accuracies in Alanine-aminotransferase and Aspartate-aminotransferase predictions. The second best classifier model was the Multilayer perceptron (65.5-80% accuracies). CONCLUSION: As there is a need of regular monitoring of hepatic function in radiation-exposed people, our artificial intelligence-based predicting model random forest is proved accurate in prediagnosing alterations in liver enzymes.


Subject(s)
Humans , Artificial Intelligence , Occupational Exposure/adverse effects , Radiation Dosage , Algorithms , Liver
18.
Article in English | WPRIM | ID: wpr-922595

ABSTRACT

OBJECTIVES@#The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification.@*METHODS@#The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model.@*RESULTS@#This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm.@*CONCLUSIONS@#The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.


Subject(s)
Artificial Intelligence , Diabetes Mellitus , Diabetic Foot , Humans
19.
Article in Chinese | WPRIM | ID: wpr-922071

ABSTRACT

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. The software platform is divided into three modules including data management module, data receiving module and data processing module. The software platform can be used to query and set the subjects information. It has realized the functions of data receiving, signal processing and the display, analysis and storage of ECG data. The software platform is easy to operate and meets the basic needs of scientific research. It is of great significance to the research of AI-ECG algorithm.


Subject(s)
Algorithms , Artificial Intelligence , Electrocardiography , Humans , Signal Processing, Computer-Assisted , Software
20.
Article in English | WPRIM | ID: wpr-921869

ABSTRACT

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.


Subject(s)
Artificial Intelligence , Female , Humans , Magnetic Resonance Imaging , Ovarian Neoplasms/diagnostic imaging , Prognosis , Tomography, X-Ray Computed
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