Algoritmo de Indicação de Equipamento Coletor para Estomias / Algorithm for Indication of Collector Equipment for Stomas / Algoritmo para la Indicación de Equipos Colectores para Estomas

Objetivo:Relatar a experiência de uma equipe de enfermeiros estomaterapeutas na construção de um algoritmo para a indicação de equipamento coletor para estomias de eliminação. Método: Relato de experiência, do período de janeiro de 2018 a setembro de 2019, sobre o processo de construção de um algoritmo para indicação de equipamento coletor para estomias de eliminação. Resultados: A partir de determinadas características clínicas (parâmetros de avaliação) e da categorização dos equipamentos coletores (solução), foi desenvolvido um algoritmo para indicação de equipamento coletor para estomias de eliminação. Conclusão: Espera-se que esse instrumento possa auxiliar os enfermeiros na sua prática profissional quanto à escolha do equipamento coletor e na construção de protocolos clínicos.

Objective:To report the experience of a team of enterostomal therapists in the construction of an algorithm for the indication of collecting equipment for elimination stomas. Method: Experience report, from January 2018 to September 2019, on the process of building an algorithm to indicate collecting equipment for elimination stomas. Results: Based on certain clinical characteristics (assessment parameters) and the categorization of collecting equipment (solution), an algorithm was developed to indicate collecting equipment for elimination stomas. Conclusion: It is expected that this instrument can help nurses in their professional practice regarding the choice of collecting equipment and the construction of clinical protocols.

Objetivo:Relatar la experiencia de un equipo de enfermeros estomaterapeutas en la construcción de un algoritmo para la indicación de equipos recolectores para estomas de eliminación. Método: Informe de experiencia, de enero de 2018 a septiembre de 2019, sobre el proceso de construcción de un algoritmo para indicar equipos colectores para estomas de eliminación. Resultado: A partir de ciertas características clínicas (parámetros de evaluación) y la categorización de los equipos colectores (solución), se desarrolló un algoritmo para indicar equipos colectores para estomas de eliminación. Conclusión: Se espera que este instrumento pueda ayudar a los enfermeros en su práctica profesional en cuanto a la elección de equipos de recolección y la construcción de protocolos clínicos.

El hombre perfecto / I am your person

La fantasía que impera en este film plantea la ilusión de encontrar un ser complementario que se adapte a nuestras preferencias y nos haga plenos. "Mi algoritmo está diseñado para hacerte feliz" dice el humanoide. Ilusión de que alguien tendría la posibilidad de ser complementario, de saber exactamente lo que el otro requiere. Estamos en las antípodas de la famosa fórmula de Lacan:" (Le Séminaire, Encore, 1975) "No hay relación sexual" (o sea, no hay complementariedad). No habría resto, el sujeto no estaría atravesado por la castración simbólica. La IA compite con Zeus. La fantasía del Uno, organismo previo a la separación del andrógino por parte de Zeus, se podría materializar con la IA

The fantasy that prevails in this film, raises the illusion of finding a complementary being that adapts to our preferences and makes us full. "My algorithm is designed to make you happy," says the humanoid. Illusion that someone would have the possibility of being complementary, of knowing exactly what the other requires. We are at the antipodes of Lacan's famous formula: "(Le Séminaire, Encore, 1975) "There is no sexual intercourse" (that is, there is no complementarity). There would be no rest, the subject would not be pierced by symbolic castration. AI competes with Zeus. The fantasy of the One, an organism prior to the separation of the androgynous by Zeus, could materialize with AI.

Sex Determination by the Machine Learning Algorithms Through Using Morphometric Measurements of the Carpal, Metacarpal, and Phalangeal Bones / Determinación del Sexo Mediante Algoritmos de Aprendizaje Automático Mediante el Uso de Mediciones Morfométricas de los Huesos del Carpo, el Metacarpo y las Falanges

SUMMARY: In the study, it was aimed to predict sex from hand measurements using machine learning algorithms (MLA). Measurements were made on MR images of 60 men and 60 women. Determined parameters; hand length (HL), palm length (PL), hand width (HW), wrist width (EBG), metacarpal I length (MIL), metacarpal I width (MIW), metacarpal II length (MIIL), metacarpal II width (MIIW), metacarpal III length (MIIL), metacarpal III width (MIIIW), metacarpal IV length (MIVL), metacarpal IV width (MIVW), metacarpal V length (MVL), metacarpal V width (MVW), phalanx I length (PILL), measured as phalanx II length (PIIL), phalanx III length (PIIL), phalanx IV length (PIVL), phalanx V length (PVL). In addition, the hand index (HI) was calculated. Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN) and Naive Bayes (NB) were used as MLAs. In the study, the KNN algorithm's Accuracy, SEN, F1 and Specificity ratios were determined as 88 %. In this study using MLA, it is understood that the highest accuracy belongs to the KNN algorithm. Except for the hand's MIIW, MIIIW, MIVW, MVW, HI variables, other variables were statistically significant in terms of sex difference.

En el estudio, el objetivo era predecir el sexo a partir de mediciones manuales utilizando algoritmos de aprendizaje automático (MLA). Las mediciones se realizaron en imágenes de RM de 60 hombres y 60 mujeres. Parámetros determinados; longitud de la mano (HL), longitud de la palma (PL), ancho de la mano (HW), ancho de la muñeca (EBG), longitud del metacarpiano I (MIL), ancho del metacarpiano I (MIW), longitud del metacarpiano II (MIIL), ancho del metacarpiano II (MIIW), longitud del metacarpiano III (MIIL), ancho del metacarpiano III (MIIIW), longitud del metacarpiano IV (MIVL), ancho del metacarpiano IV (MIVW), longitud del metacarpiano V (MVL), ancho del metacarpiano V (MVW), longitud de la falange I (PILL), medido como longitud de la falange II (PIIL), longitud de la falange III (PIIL), longitud de la falange IV (PIVL), longitud de la falange V (PVL). Además, se calculó el índice de la mano (HI). Regresión logística (LR), Random Forest (RF), Análisis discriminante lineal (LDA), K-vecino más cercano (KNN) y Naive Bayes (NB) se utilizaron como MLA. En el estudio, las proporciones de precisión, SEN, F1 y especificidad del algoritmo KNN se determinaron en un 88 %. En este estudio que utiliza MLA, se entiende que la mayor precisión pertenece al algoritmo KNN. Excepto por las variables MIIW, MIIIW, MIVW, MVW, HI de la mano, otras variables fueron estadísticamente significativas en términos de diferencia de sexo.

Experiencia en la introducción de resultados científicos desde la formación doctoral / Experience in the introduction of scientific results from the doctoral training i: en

Durante estos años, condicionados por los efectos de una pandemia y la situación económica global, la incorporación oportuna de los resultados científico-técnicos es necesidad y responsabilidad de la comunidad científica. En este trabajo se expone una experiencia en la introducción de resultados científicos desde la formación doctoral, dirigida al área de la atención inicial al paciente con traumatismo maxilofacial. La importancia de esta práctica radica en los aportes social, científico y profesional y en la formación de recursos humanos para lograr la transformación y el mejoramiento de la realidad.

During these years, conditioned by the effects of a pandemic and the global economic situation, the opportune incorporation of the scientific technical results is necessity and responsibility of scientific community. An experience in the introduction of scientific results from the doctoral training, directed to the area of initial care to the patient with maxillofacial traumatism, is presented in this work. The importance of this practice resides in the social, scientific, professional contributions and in the formation of human resources to achieve the transformation and improvement of reality.

La inteligencia artificial y la video-vigilancia en la predicción y detección de delitos en espacio-tiempo: una revisión sistemática / Artificial intelligence and video surveillance in space-time crime prediction and detection: a systematic review / Inteligência artificial e vídeo-vigilância na previsão e detecção de crimes no espaço-tempo: uma revisão sistemática

En la sociedad de hoy los delitos vienen incrementándose y particularmente en la ciudad de Bogotá, lo que ha causado muchos inconvenientes a la Policía Nacional de Colombia, así como también a los centros de seguridad ciudadana. Ante esta situación, se ha propuesto una predicción de tiempo-espacio en los puntos críticos de crímenes y delitos, con la ayuda de inteligencia artificial. Por consiguiente, este trabajo tiene como objetivo analizar, resumir, interpretar y evaluar las distintas técnicas de predicción espacio-temporal de la delincuencia con un panorama inteligente. Por la propia naturaleza de la investigación, se utilizó una metodología de enfoque descriptivo-cualitativo, con la cual se diseñaron fichas de observación estructurada para sistematizar información de cinco bases de datos: Scopus, Web of Science, IEEE, ACM, Springer; dichas publicaciones comprenden desde 2019 hasta junio de 2021. En consecuencia, se encontraron en total 3015 estudios, después del proceso de cribado y verificación de los criterios de exclusión e inclusión, se seleccionaron 132 artículos, luego se aplicaron preguntas Psicólogo Interno Residente (PIR), quedando así 18 artículos. Los principales hallazgos encontrados indican que los algoritmos de redes neuronales resultaron ser uno de los métodos más eficaces para la detección de puntos críticos de delincuencia, dado que los grandes avances de la tecnología coadyuvarían en los próximos años a predecir de forma rápida y eficaz los actos delictivos y los crímenes ubicados en cualquier región del continente latinoamericano.

In today's society, crimes are increasing, particularly in the city of Bogota, which has caused many inconveniences to the National Police of Colombia, as well as to the citizen security centers. Given this situation, a time-space prediction of crime and crime hotspots has been proposed with the help of artificial intelligence. Therefore, this paper aims to analyze, summarize, interpret and evaluate the various techniques of space-time prediction of crime with an intelligent view. Due to the very nature of the research, a descriptive-qualitative approach methodology was used, with which structured observation sheets were designed to systematize information from five da-tabases: Scopus, Web of Science, IEEE, ACM, Springer; these publications span from 2019 to June 2021. Consequently, a total of 3015 studies were found, after the screening process and verification of exclusion and inclusion criteria, 132 articles were selected, then questions were applied Psychologist Internal Resident (PIR), thus leaving 18 articles. The main findings indicate that neural network algorithms proved to be one of the most effective methods for the detection of crime hotspots, given that the great advances in technology would help in the coming years to quickly and effectively predict criminal acts and crimes located in any region of the Latin American continent.

Na sociedade de hoje, a criminalidade está aumentando, particularmente na cidade de Bogotá, o que tem causado muitos inconvenientes para a Polícia Nacional Colombiana, bem como para os centros de segurança do cidadão. Diante desta situação, foi proposta uma previsão tempo-espacial de hotspots de crime com a ajuda da inteligência artificial. Portanto, este documento visa analisar, resumir, interpretar e avaliar as diversas técnicas de previsão espaço-temporal do crime com uma visão inteligente. Devido à própria natureza da pesquisa, foi utilizada uma metodologia de abordagem descritiva-qualitativa, com a qual foram elaboradas fichas de observação estrutura-das para sistematizar informações de cinco bancos de dados: Scopus, Web of Science, IEEE, ACM, Springer; estas publicações abrangem o período de 2019 a junho de 2021. Consequentemente, foi encontrado um total de 3015 estudos, após o processo de triagem e verificação dos critérios de exclusão e inclusão, 132 artigos foram selecionados, depois foram aplicadas perguntas ao Psicólogo em Residência (PIR), deixando 18 artigos. As principais descobertas indicam que os algoritmos de redes neurais provaram ser um dos métodos mais eficazes para a detecção de hotspots de crime, dado que os grandes avanços na tecnologia ajudarão nos próximos anos a prever rápida e efetivamente atos criminosos e crimes localizados em qualquer região do continente latino-americano.

Deep learning analysis on the resulting impacts of weekly load training on students' biological system / Análise do aprendizado profundo nos impactos resultantes dos treinos semanais de carga sobre o sistema biológico dos estudantes / Análisis de aprendizaje profundo sobre los impactos resultantes de los entrenamientos semanales de carga en el sistema biológico de los estudiantes

ABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students' biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes' training load in the annual preparation period. The characteristics of athletes' training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of "teaching" and "training" in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução O recente desenvolvimento do algoritmo de aprendizado profundo como um novo algoritmo de aprendizado de máquina de rede multicamadas reduziu o problema dos algoritmos de treinamento tradicionais, que facilmente caiam em locais mínimos, tornando-se uma direção recente no campo do aprendizado. Objetivo Desenvolver e validar um modelo de inteligência artificial para aprendizado profundo dos impactos resultantes dos treinos semanais de carga sobre o sistema biológico dos estudantes. Métodos De acordo com os índices fisiológicos e bioquímicos dos atletas no processo de treinamento, este artigo analisa os dados reais da carga de treinamento dos atletas no período anual de preparação. As características da carga de treinamento dos atletas no período de preparação foram discutidas. O valor, significância, fatores de composição, princípio de arranjo e método de cálculo e determinação da densidade de carga semanal usando o algoritmo de aprendizado profundo são discutidos. Resultados Os resultados mostraram que a carga diária de 24 horas de amostragem aleatória foi de intensidade moderada, treinamento de baixa densidade e alta intensidade, e o sistema físico-motor e a reatividade neural foram aprimorados. Conclusão A pesquisa mostra que pode haver duas atividades de "ensino" e "treinamento" na área de educação física e no treinamento esportivo. A pesquisa de monitoramento da biologia esportiva revela-se um ponto de crescimento da pesquisa de treinamento esportivo com grande potencial de expansão para pesquisas futuras. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción El reciente desarrollo del algoritmo de aprendizaje profundo como un nuevo algoritmo de aprendizaje automático de red multicapa ha reducido el problema de los algoritmos de entrenamiento tradicionales, que caen fácilmente en lugares mínimos, convirtiéndose en una dirección reciente en el campo del aprendizaje. Objetivo Desarrollar y validar un modelo de inteligencia artificial para el aprendizaje profundo de los impactos resultantes del entrenamiento de la carga semanal en el sistema biológico de los estudiantes. Métodos De acuerdo con los índices fisiológicos y bioquímicos de los atletas en el proceso de entrenamiento, este artículo analiza los datos reales de la carga de entrenamiento de los atletas en el período de preparación anual. Se analizaron las características de la carga de entrenamiento de los atletas en el periodo de preparación. Se analizan el valor, el significado, los factores de composición, el principio de disposición y el método de cálculo y determinación de la densidad de carga semanal mediante el algoritmo de aprendizaje profundo. Resultados Los resultados mostraron que la carga diaria de 24 horas de muestreo aleatorio era de intensidad moderada, de baja densidad y de alta intensidad de entrenamiento, y que el sistema físico-motor y la reactividad neural mejoraban. Conclusión La investigación muestra que puede haber dos actividades de "enseñanza" y "formación" en la educación física y el entrenamiento deportivo. La investigación sobre el seguimiento de la biología del deporte demuestra ser un punto de crecimiento de la investigación sobre el entrenamiento deportivo con un gran potencial de expansión para futuras investigaciones. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Deep learning for analysis of changes in vital capacity and blood markers after swimming matches based on blended learning / Aprendizado profundo para análise das alterações da capacidade vital e marcadores sanguíneos após jogos de natação baseados no aprendizado combinado / Aprendizaje profundo para el análisis de los cambios en la capacidad vital y los marcadores sanguíneos después de los partidos de natación basados en el aprendizaje combinado

ABSTRACT Introduction Nowadays, more people are concerned with physical exercise and swimming competitions, as a major sporting event, have become a focus of attention. Such competitions require special attention to their athletes and the use of computational algorithms assists in this task. Objective To design and validate an algorithm to evaluate changes in vital capacity and blood markers of athletes after swimming matches based on combined learning. Methods The data integration algorithm was used to analyze changes in vital capacity and blood acid after combined learning swimming competition, followed by the construction of an information system model to calculate and process this algorithm. Results Comparative experiments show that the neural network algorithm can reduce the calculation time from the original initial time. In the latest tests carried out in about 10 seconds, this has greatly reduced the total calculation time. Conclusion According to the model requirements of the designed algorithm, practical help has been demonstrated by building a computational model. The algorithm can be optimized and selected according to the calculation model according to the reality of the application. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução Atualmente, mais pessoas preocupam-se com o exercício físico e as competições de natação, como evento esportivo de destaque, tornou-se foco de atenção. Tais competições exigem atenção especial aos seus atletas e o uso de algoritmos computacionais auxiliam nessa tarefa. Objetivo Projetar e validar um algoritmo para avaliação das alterações da capacidade vital e marcadores sanguíneos dos atletas após os jogos de natação baseados no aprendizado combinado. Métodos O algoritmo de integração de dados foi usado para analisar as mudanças de capacidade vital e ácido sanguíneo após competição de natação de aprendizado combinado, seguido à construção de um modelo de sistema de informação para calcular e processar esse algoritmo. Resultados Experiências comparativas mostram que o algoritmo de rede neural pode reduzir o tempo de cálculo a partir do tempo inicial original. Nos últimos testes levados à cabo em cerca de 10 segundos, isto reduziu muito o tempo total de cálculo. Conclusão De acordo com os requisitos do modelo do algoritmo projetado, foi demonstrada a ajuda prática pela construção de um modelo computacional. O algoritmo pode ser otimizado e selecionado de acordo com o modelo de cálculo, segundo a realidade da aplicação. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción Hoy en día, cada vez más personas se preocupan por el ejercicio físico y las competiciones de natación, como evento deportivo destacado, se han convertido en un foco de atención. Estas competiciones requieren una atención especial para sus atletas y el uso de algoritmos computacionales ayuda en esta tarea. Objetivo Diseñar y validar un algoritmo para evaluar los cambios en la capacidad vital y los marcadores sanguíneos de los atletas después de los partidos de natación basado en el aprendizaje combinado. Métodos Se utilizó el algoritmo de integración de datos para analizar los cambios de la capacidad vital y la acidez de la sangre tras la competición de natación de aprendizaje combinado, seguido de la construcción de un modelo de sistema de información para calcular y procesar este algoritmo. Resultados Los experimentos comparativos muestran que el algoritmo de la red neuronal puede reducir el tiempo de cálculo con respecto al tiempo inicial. En las últimas pruebas realizadas en unos 10 segundos, esto redujo en gran medida el tiempo total de cálculo. Conclusión De acuerdo con los requisitos del modelo del algoritmo diseñado, se ha demostrado la ayuda práctica mediante la construcción de un modelo computacional. El algoritmo puede optimizarse y seleccionarse según el modelo de cálculo en función de la realidad de la aplicación. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Swarm algorithms applied to fitness testing of athletes in competition / Algoritmos de enxame aplicados ao exame do condicionamento físico de atletas em competição / Algoritmos de enjambre aplicados a las pruebas de aptitud física de atletas en competición

ABSTRACT Introduction Many countries have increased their investments in human resources and technology for the internal development of competitive sports, leading the world sports scene to increasingly fierce competition. Coaches and research assistants must place importance on feedback tools for frequent training of college athletes, and deep learning algorithms are an important resource to consider. Objective To develop and validate a swarm algorithm to examine the fitness of athletes during periods of competition. Methods Based on the swarm intelligence algorithm, the concept, composition, and content of physical exercises were analyzed. Combined with the characteristics of events, the body function files and the comprehensive evaluation system for high-level athletes were established. Results The insight was obtained that the constant mastery of the most advanced techniques and tactics by athletes is an important feature of modern competitive sports. Physical fitness is not only a valuable asset for athletes but also one of the keys to success in competition. Conclusion Fitness has become an increasingly prominent issue in competition, and the scientific training of contemporary competitive sports has been increasingly refined. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução Muitos países aumentaram seus investimentos em recursos humanos e tecnologia para o desenvolvimento interno de esportes competitivos, levando o cenário esportivo mundial a uma disputa cada vez mais acirrada. Treinadores e assistentes de pesquisa devem dar importância às ferramentas de feedback para o treinamento frequente dos atletas universitários e os algoritmos de aprendizado profundo são um importante recurso a ser levado em consideração. Objetivo Desenvolver e validar um algoritmo de enxame para examinar o condicionamento físico dos atletas em períodos de competição. Métodos Com base no algoritmo de inteligência de enxame, o conceito, composição e conteúdo de exercícios físicos foram analisados. Combinado com as características dos eventos, os arquivos de funções corporais e o sistema abrangente de avaliação de atletas de alto nível foram estabelecidos. Resultados Obteve-se a percepção de que o constante domínio das técnicas e táticas mais avançadas pelos atletas é uma característica importante dos esportes competitivos modernos. A aptidão física não é apenas um ativo valioso para os atletas, mas também uma das chaves para o sucesso nas competições. Conclusão A aptidão física tem se tornado cada vez mais um problema proeminente na competição, sendo o treinamento científico dos esportes competitivos contemporâneos cada vez mais aperfeiçoado. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción Muchos países han aumentado sus inversiones en recursos humanos y tecnología para el desarrollo interno del deporte de competición, lo que ha llevado al panorama deportivo mundial a una competencia cada vez más feroz. Los entrenadores y asistentes de investigación deben dar importancia a las herramientas de retroalimentación para el entrenamiento frecuente de los atletas universitarios y los algoritmos de aprendizaje profundo son un recurso importante a tener en cuenta. Objetivo Desarrollar y validar un algoritmo de enjambre para examinar el estado físico de los atletas durante los periodos de competición. Métodos A partir del algoritmo de inteligencia de enjambre, se analizó el concepto, la composición y el contenido de los ejercicios físicos. En combinación con las características de los eventos, se establecieron los archivos de funciones corporales y el sistema de evaluación integral de los atletas de alto nivel. Resultados Se obtuvo la conclusión de que el dominio constante de las técnicas y tácticas más avanzadas por parte de los atletas es una característica importante de los deportes de competición modernos. La forma física no sólo es un activo valioso para los deportistas, sino también una de las claves del éxito en las competiciones. Conclusión La aptitud física se ha convertido en una cuestión cada vez más importante en la competición, y el entrenamiento científico de los deportes de competición contemporáneos es cada vez mejor. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Investigation of tactile gait parameters based on deep learning of energy consumption estimation algorithm in sport / Investigação sobre parâmetros tácteis de marcha baseados no aprendizado profundo do algoritmo de estimativa de consumo energético no esporte / Investigación sobre los parámetros táctiles de marcha basados en el aprendizaje profundo del algoritmo de estimación del consumo de energía en el deporte

ABSTRACT Introduction In medicine, Deep Learning is a type of machine learning that aims to train computers to perform human tasks by simulating the human brain. Gait recognition and gait motion simulation is one of the most interesting research areas in the field of biometrics and can benefit from this technological feature. Objective To use Deep Learning to format and validate according to the dynamic characteristics of gait. Methods Gait was used for identity recognition, and gait recognition based on kinematics and dynamic gait parameters was performed through pattern recognition, including the position and the intensity value of maximum pressure points, pressure center point, and pressure ratio. Results The investigation shows that the energy consumption of gait as modeled analyzed, and the model of gait energy consumption can be obtained, which is comprehensively affected by motion parameters and individual feature parameters. Conclusion Real-time energy measurement is obtained when most people walk. The research shows that the gait frequency and body parameters obtained from the tactile parameters of gait biomechanics can more accurately estimate the energy metabolism of exercise and obtain the metabolic formula of exercise. There is a good application prospect for assessing energy metabolism through the tactile parameters of gait. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução Na medicina, o aprendizado profundo é um tipo de aprendizado de máquina que visa treinar computadores para a realização de tarefas humanas simulando o cérebro humano. O reconhecimento da marcha e a simulação do movimento de marcha são um dos pontos de maior interesse da investigação no campo da biometria e pode ser beneficiado com esse recurso tecnológico. Objetivo Utilizar o aprendizado profundo para formatar e validar, de acordo com as características dinâmicas da marcha. Métodos A marcha foi utilizada para o reconhecimento da identidade, e o reconhecimento da marcha baseado na cinemática e parâmetros dinâmicos de marcha foi realizado através do reconhecimento de padrões, incluindo a posição e o valor de intensidade dos pontos de pressão máxima, ponto central de pressão e relação de pressão. Resultados A investigação mostra que o consumo de energia da marcha como modelado analisado, e o modelo de consumo de energia da marcha pode ser obtido, o qual é afetado de forma abrangente pelos parâmetros de movimento e pelos parâmetros de características individuais. Conclusão A medição de energia em tempo real é obtida quando a maioria das pessoas caminha. A investigação mostra que a frequência da marcha e os parâmetros corporais obtidos a partir dos parâmetros tácteis da biomecânica da marcha podem estimar com maior precisão o metabolismo energético do exercício e obter a fórmula metabólica do exercício. Há uma boa perspectiva de aplicação para avaliar o metabolismo energético através dos parâmetros tácteis da marcha. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción En medicina, el aprendizaje profundo es un tipo de aprendizaje que pretende entrenar a los ordenadores para que realicen tareas humanas simulando el cerebro humano. El reconocimiento de la marcha y la simulación de su movimiento es uno de los puntos más interesantes de la investigación en el campo de la biometría y puede beneficiarse de este recurso tecnológico. Objetivo Utilizar el aprendizaje profundo para formatear y validar según las características dinámicas de la marcha. Métodos Se utilizó la marcha para el reconocimiento de la identidad, y el reconocimiento de la marcha basado en la cinemática y los parámetros dinámicos de la marcha se realizó mediante el reconocimiento de patrones, incluyendo la posición y el valor de la intensidad de los puntos de presión máxima, el punto de presión central y la relación de presión. Resultados La investigación muestra que el consumo de energía de la marcha, tal y como se analizó, y el modelo de consumo de energía de la marcha se puede obtener, que es ampliamente afectado por los parámetros de movimiento y los parámetros de las características individuales. Conclusión La medición de la energía en tiempo real se obtiene cuando la mayoría de la gente camina. La investigación muestra que la frecuencia de la marcha y los parámetros corporales obtenidos a partir de los parámetros táctiles de la biomecánica de la marcha pueden estimar con mayor precisión el metabolismo energético del ejercicio y obtener la fórmula metabólica del mismo. Existe una buena perspectiva de aplicación para evaluar el metabolismo energético a través de los parámetros táctiles de la marcha. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry

Abstract Introduction The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. Methods The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). Results A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. Conclusions Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma / 中华肿瘤杂志

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Using machine learning algorithm to predict the risk of post-traumatic stress disorder among firefighters in Changsha / 中南大学学报(医学版)

OBJECTIVES@#Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset.@*METHODS@#This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score.@*RESULTS@#The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism.@*CONCLUSIONS@#PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.

Application of Novel Down-sampling Method in Retinal Vessel Segmentation / 中国医疗器械杂志

Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.

Automatic determination of mandibular landmarks based on three-dimensional mandibular average model / 北京大学学报(医学版)

OBJECTIVE@#To explore an efficient and automatic method for determining the anatomical landmarks of three-dimensional(3D) mandibular data, and to preliminarily evaluate the performance of the method.@*METHODS@#The CT data of 40 patients with normal craniofacial morphology were collected (among them, 30 cases were used to establish the 3D mandibular average model, and 10 cases were used as test datasets to validate the performance of this method in determining the mandibular landmarks), and the 3D mandibular data were reconstructed in Mimics software. Among the 40 cases of mandibular data after the 3D reconstruction, 30 cases that were more similar to the mean value of Chinese mandibular features were selected, and the size of the mandibular data of 30 cases was normalized based on the Procrustes analysis algorithm in MATLAB software. Then, in the Geomagic Wrap software, the 3D mandibular average shape model of the above 30 mandibular data was constructed. Through symmetry processing, curvature sampling, index marking and other processing procedures, a 3D mandible structured template with 18 996 semi-landmarks and 19 indexed mandibular anatomical landmarks were constructed. The open source non-rigid registration algorithm program Meshmonk was used to match the 3D mandible template constructed above with the tested patient's 3D mandible data through non-rigid deformation, and 19 anatomical landmark positions of the patient's 3D mandible data were obtained. The accuracy of the research method was evaluated by comparing the distance error of the landmarks manually marked by stomatological experts with the landmarks marked by the method of this research.@*RESULTS@#The method of this study was applied to the data of 10 patients with normal mandibular morphology. The average distance error of 19 landmarks was 1.42 mm, of which the minimum errors were the apex of the coracoid process [right: (1.01±0.44) mm; left: (0.56±0.14) mm] and maximum errors were the anterior edge of the lowest point of anterior ramus [right: (2.52±0.95) mm; left: (2.57±1.10) mm], the average distance error of the midline landmarks was (1.15±0.60) mm, and the average distance error of the bilateral landmarks was (1.51±0.67) mm.@*CONCLUSION@#The automatic determination method of 3D mandibular anatomical landmarks based on 3D mandibular average shape model and non-rigid registration algorithm established in this study can effectively improve the efficiency of automatic labeling of 3D mandibular data features. The automatic determination of anatomical landmarks can basically meet the needs of oral clinical applications, and the labeling effect of deformed mandible data needs to be further tested.

Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia / 中华医学杂志(英文版)

BACKGROUND@#Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.@*METHODS@#We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models.@*RESULTS@#The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749).@*CONCLUSIONS@#The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population.@*TRIAL REGISTRATION@#Chictr.org, ChiCTR 1800018895.

Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer / 南方医科大学学报

OBJECTIVE@#To screen the risk factors for death in patients with nasopharyngeal carcinoma (NPC) using artificial intelligence (AI) technology and establish a risk prediction model.@*METHODS@#The clinical data of NPC patients obtained from SEER database (1973-2015). The patients were randomly divided into model building and verification group at a 7∶3 ratio. Based on the data in the model building group, R software was used to identify the risk factors for death in NPC patients using 4 AI algorithms, namely eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Least absolute shrinkage and selection operator (LASSO) and random forest (RF), and a risk prediction model was constructed based on the risk factor identified. The C-Index, decision curve analysis (DCA), receiver operating characteristic (ROC) curve and calibration curve (CC) were used for internal validation of the model; the data in the validation group and clinical data of 96 NPC patients (collected from First Affiliated Hospital of Bengbu Medical College) were used for internal and external validation of the model.@*RESULTS@#The clinical data of a total of 2116 NPC patients were included (1484 in model building group and 632 in verification group). Risk factor screening showed that age, race, gender, stage M, stage T, and stage N were all risk factors of death in NPC patients. The risk prediction model for NPC-related death constructed based on these factors had a C-index of 0.76 for internal evaluation, an AUC of 0.74 and a net benefit rate of DCA of 9%-93%. The C-index of the model in internal verification was 0.740 with an AUC of 0.749 and a net benefit rate of DCA of 3%-89%, suggesting a high consistency of the two calibration curves. In external verification, the C-index of this model was 0.943 with a net benefit rate of DCA of 3%-97% and an AUC of 0.851, and the predicted value was consistent with the actual value.@*CONCLUSIONS@#Gender, age, race and TNM stage are risk factors of death of NPC patients, and the risk prediction model based on these factors can accurately predict the risks of death in NPC patients.

Application of extended reality technology for real-time navigation in clinical operation / 南方医科大学学报

OBJECTIVE@#To explore the application of extended reality (XR) technology in clinical surgeries for improving the success rate of surgeries.@*METHODS@#To assist the surgeons to better understand the location, size and geometric shape of the lesions and reduce potential radiation exposure in minimally invasive surgical navigation based on two-dimensional images, we constructed three-dimensional models based on CT data and used XR technology to achieve intraoperative navigation. An improved quaternion method was used to improve the accuracy of electromagnetic positioning, with which the system error of positioning accuracy was reduced to below 2 mm. A 5G network was used to optimize the server GPU programming algorithm, and real-time video stream coding strategy and network design were adopted to reduce data transmission jam and delay in the remote surgery network, which achieved an average delay of less than 60 ms. A Gaussian distribution deformation model was used to simulate collision detection and stress deformation of the tissues to achieve a tactile perception effect.@*RESULTS AND CONCLUSION@#The intraoperative navigation system based on XR technology allowed more accurate determination of the location of the lesions, effectively reduced the surgical risk, and avoided the risk of intraoperative radiation exposure. The low latency and high fidelity of 5G network achieved real-time interaction during the surgery to provide a technical basis for multi-terminal remote cooperative surgery. The combination of force feedback technology and XR technology enables the surgeons to conduct deep immersion preoperative planning and virtual surgery to improve the success rate of surgery and shorten the learning curve.