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1.
Rev. bras. med. esporte ; 29(spe1): e2022_0194, 2023. tab, graf
Article in English | LILACS | ID: biblio-1394852

ABSTRACT

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.


Subject(s)
Humans , Energy Metabolism/physiology , Gait Analysis , Biomechanical Phenomena , Algorithms
2.
Rev. bras. med. esporte ; 29(spe1): e2022_0198, 2023. tab, graf
Article in English | LILACS | ID: biblio-1394847

ABSTRACT

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.


Subject(s)
Humans , Adult , Young Adult , Algorithms , Exercise/physiology , Athletic Performance/physiology , Deep Learning , Athletic Injuries , Sports/physiology , Muscle Strength , Athletes
3.
Rev. bras. med. esporte ; 29(spe1): e2022_0199, 2023. tab, graf
Article in English | LILACS | ID: biblio-1394846

ABSTRACT

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.


Subject(s)
Humans , Swimming/physiology , Algorithms , Biomarkers/analysis , Deep Learning , Athletic Performance/physiology , Athletes
4.
Rev. bras. med. esporte ; 29(spe1): e2022_0197, 2023. tab, graf
Article in English | LILACS | ID: biblio-1394845

ABSTRACT

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.


Subject(s)
Humans , Algorithms , Computational Biology/methods , Athletic Performance/physiology , Deep Learning , Physical Education and Training/methods
5.
Rev. mex. ing. bioméd ; 43(1): 1208, Jan.-Apr. 2022. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1389187

ABSTRACT

ABSTRACT The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the bi- nary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database's repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.

6.
Biomédica (Bogotá) ; 42(1): 170-183, ene.-mar. 2022. tab, graf
Article in English | LILACS | ID: biblio-1374516

ABSTRACT

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.


Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.


Subject(s)
Coronavirus Infections/diagnosis , Deep Learning , Tomography, X-Ray Computed
7.
Int. j. morphol ; 40(1)feb. 2022.
Article in English | LILACS-Express | LILACS | ID: biblio-1385563

ABSTRACT

SUMMARY: Sex assessment is an important process in forensic identification. A pelvis is the best skeletal element for identifying sexes due to its sexually dimorphic morphology. This study aimed to compare the accuracy of the visual assessment in dry bones as well as 2D images and to test the accuracy of using a deep convolutional neural network (GoogLeNet) for increasing the performance of a sex determination tool in a Thai population. The total samples consisted of 250 left os coxa that were divided into 200 as a 'training' group (100 females, 100 males) and 50 as a 'test' group. In this study, we observed the auricular area, both hands-on and photographically, for visual assessment and classified the images using GoogLeNet. The intra-inter observer reliabilities were tested for each visual assessment method. Additionally, the validation and test accuracies were 85, 72 percent and 79.5, 60 percent, for dry bone and 2D image methods, respectively. The intra- and inter-observer reliabilities showed moderate agreement (Kappa = 0.54 - 0.67) for both visual assessments. The deep convolutional neural network method showed high accuracy for both validation and test sets (93.33 percent and 88 percent, respectively). Deep learning performed better in classifying sexes from auricular area images than other visual assessment methods. This study suggests that deep learning has advantages in terms of sex classification in Thai samples.


RESUMEN: La evaluación del sexo es un proceso importante en la identificación forense. La pelvis es el mejor elemento esquelético para identificar sexos debido a su morfología sexualmente dimórfica. Este estudio tuvo como objetivo comparar la precisión de la evaluación visual en huesos secos, así como imágenes 2D y probar la precisión del uso de una red neuronal convolucional profunda (GoogLeNet) para aumentar el rendimiento de una herramienta de determinación de sexo en una población tailandesa. Las muestras consistieron en 250 huesos coxales izquierdos, los que fueron dividi- das de la siguiente manera: 200 como un grupo de "entrenamiento" (100 mujeres, 100 hombres) y 50 como un grupo de "prueba". En este estudio, observamos el área auricular, tanto de forma práctica como fotográfica, para una evaluación visual y clasificamos las imágenes utilizando GoogLeNet. Se analizó la confiabilidad intra-interobservador para cada método de evaluación visual. Además, las precisiones de validación y prueba fueron del 85, 72 por ciento y 79,5, 60 por ciento, para los métodos de hueso seco y de imágenes 2D, respectivamente. Las confiabilidades intra e interobservador mostraron un acuerdo moderado (Kappa = 0.54 - 0.67) para ambas evaluaciones visuales. El método de red neuronal convolucional profunda mostró una alta precisión tanto para la validación como para los conjuntos de prueba (93,33 por ciento y 88 por ciento, respectivamente). El aprendizaje se desempeñó mejor en la clasificación de sexos a partir de imágenes del área auricular que otros métodos de evaluación visual. Este estudio sugiere que el aprendizaje profundo tiene ventajas en términos de clasificación por sexo en muestras tailandesas.

8.
Article in Chinese | WPRIM | ID: wpr-904795

ABSTRACT

@#TNM(tumor node metastasis)classification is a common way to evaluate the prognosis of patients with oral cancer; however, many years of application have proven this method to be confined merely in clinical and pathological data and it cannot be adapted to the development of modern medicine. Deep learning (DL) has been widely used in various aspects of human life, has advantages for conducting efficient and intelligent searches and can explore and analyze substantial medical information well. Additionally, the application of DL to medical practice is quickly increasing. In the field of oral cancer prognosis, DL can efficiently process and analyze the pathological, radiographic and molecular data of oral cancer patients represented by lymphocytes, gray level cooccurrence matrix (GLCM) and gene maps and make accurate prognostic judgments accordingly. By assisting physicians in optimizing treatment plans, DL can effectively improve patients’ survival. Although DL lacks sufficient data and practical clinical application in prognostic studies, it has shown good clinical application prospects.

9.
Article in Chinese | WPRIM | ID: wpr-920594

ABSTRACT

@#With the improvement of computer computing capability and the accumulation of a large amount of medical data, artificial intelligence is gradually being applied in the diagnosis of oral and maxillofacial tumors. Artificial intelligence technology can assist doctors in clinical diagnosis and improve the efficiency of clinical work and the accuracy of diagnosis. In recent years, researchers have focused primarily on the recognition of medical images. The commonly used method is to annotate a large number of images by experts for learning image features by machines. The available literature has been able to utilize artificial intelligence technology to diagnose tumors by analyzing medical images, pathological sections, and tumor photos. The main issues in the current research are uneven labeling data quality, small data size, limited research problems, and single data modalities. These problems need to be solved through the continuous improvement of algorithms and the accumulation of high-quality data. The future direction of artificial intelligence applications should be to integrate medical data from multiple sources, assist doctors in diagnosis, and explore a variety of noninvasive and easy-to-use new methods for the early diagnosis of tumors. This may completely change the existing diagnosis and treatment model of oral and maxillofacial tumors.

10.
Article in Chinese | WPRIM | ID: wpr-913153

ABSTRACT

With the advent of the era of 5G and big data, complex medical data with multiple dimensions and a large sample size bring both opportunities and challenges for clinical medicine in the new era. Compared with conventional methods, artificial intelligence can detect the hidden patterns within large datasets, and more and more scholars are applying such advanced technology in the diagnosis and treatment of diseases. After development and perfection for more than half a century, liver transplantation has become the most effective treatment method for end-stage liver diseases. Unlike the analysis of "single-patient" data in other fields, liver transplantation usually requires the consideration of the features of both the donor and the recipient and the variables during transplantation, thus generating a larger volume of medical data than other diseases, which is particularly in line with the advantages of artificial intelligence. Effective application of artificial intelligence and its combination with clinical research will usher in the new era of precision medicine. The advantages and limitations of artificial intelligence technology should be comprehensively discussed for the cross-application of artificial intelligence in liver transplantation, and the future directions of this field should also be proposed.

11.
Article in Chinese | WPRIM | ID: wpr-913142

ABSTRACT

Deep learning is a process in which machine learning obtains new knowledge and skills by simulating the learning behavior of human brain through massive data training and analysis. With the development of medical technology, a large amount of data has been accumulated in the medical field, and the research on data may help to understand the relationships and rules within data and predict the onset and prognosis of human diseases. Deep learning can find the hidden information in data and has been increasingly used in the medical field. Primary liver cancer is a malignant tumor with high incidence and mortality rates, poor prognosis, and a high recurrence rate, and early diagnosis, timely treatment, and prediction of recurrence have always been the research hotspots in recent years. This article reviews the advances in the application of deep learning in the diagnosis and recurrence of liver cancer from the aspects of risk prediction, postoperative recurrence, and survival risk prediction.

12.
Article in Chinese | WPRIM | ID: wpr-941013

ABSTRACT

OBJECTIVE@#To build a helical CT projection data restoration model at random low-dose levels.@*METHODS@#We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared.@*RESULTS@#Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05).@*CONCLUSION@#The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.


Subject(s)
Algorithms , Artifacts , Tomography, Spiral Computed , Tomography, X-Ray Computed/methods
13.
Article in Chinese | WPRIM | ID: wpr-939616

ABSTRACT

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.


Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography/methods , Humans , Imagery, Psychotherapy , Imagination
14.
Article in Chinese | WPRIM | ID: wpr-939612

ABSTRACT

Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.


Subject(s)
Humans , Lung/pathology , Lung Neoplasms/pathology , Neural Networks, Computer , Tomography, X-Ray Computed/methods
15.
Article in Chinese | WPRIM | ID: wpr-934372

ABSTRACT

Artificial neural network (ANN) is a network framework that drives artificial intelligence (AI). Classical convolutional neural networks (CNN) are mainly used for cell count and image recognition at fixed time in embryo evaluation. Fully connected deep neural networks (DNN), with increased accuracy of image recognition, are suitable for the units equipped with high configuration hardware and need comprehensive prediction according to the integrated clinical information. Residual networks improve the accuracy by increasing layers and solving the gradient disappearance problem through jump connection to realize dynamic embryo assessment. Bayesian networks (BN) and multi-layer perceptron (MLP) are two machine learning methods. The former is especially used for comprehensive prediction combined with complex clinical information in case of lack of conditions. The latter has gradient disappearance and explosion problem, and is easy to lose some spatial features of images, so it is used for small sample volumes. ANN has advantages in the prediction of implantation rate and aneuploidy and reducing invasive detection in quality assessment of embryos, which is an important research direction of human-assisted reproductive technology (ART).

16.
Article in Chinese | WPRIM | ID: wpr-934283

ABSTRACT

Objective:To build a small-sample ultra-widefield fundus images (UWFI) multi-disease classification artificial intelligence model, and initially explore the ability of artificial intelligence to classify UWFI multi-disease tasks.Methods:A retrospective study. From 2016 to 2021, 1 608 images from 1 123 patients who attended the Eye Center of the Renmin Hospital of Wuhan University and underwent UWFI examination were used for UWFI multi-disease classification artificial intelligence model construction. Among them, 320, 330, 319, 268, and 371 images were used for diabetic retinopathy (DR), retinal vein occlusion (RVO), pathological myopia (PM), retinal detachment (RD), and normal fundus images, respectively. 135 images from 106 patients at the Tianjin Medical University Eye Hospital were used as the external test set. EfficientNet-B7 was selected as the backbone network for classification analysis of the included UWFI images. The performance of the UWFI multi-task classification model was assessed using the receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity, and accuracy. All data were expressed using numerical values and 95% confidence intervals ( CI). The datasets were trained on the network models ResNet50 and ResNet101 and tested on an external test set to compare and observe the performance of EfficientNet with the 2 models mentioned above. Results:The overall classification accuracy of the UWFI multi-disease classification artificial intelligence model on the internal and external test sets was 92.57% (95% CI 91.13%-92.92%) and 88.89% (95% CI 88.11%-90.02%), respectively. These were 96.62% and 92.59% for normal fundus, 95.95% and 95.56% for DR, 96.62% and 98.52% for RVO, 98.65% and 97.04% for PM, and 97.30% and 94.07% for RD, respectively. The mean AUC on the internal and external test sets was 0.993 and 0.983, respectively, with 0.994 and 0.939 for normal fundus, 0.999 and 0.995 for DR, 0.985 and 1.000 for RVO, 0.991 and 0.993 for PM and 0.995 and 0.990 for RD, respectively. EfficientNet performed better than the ResNet50 and ResNet101 models on both the internal and external test sets. Conclusion:The preliminary UWFI multi-disease classification artificial intelligence model using small samples constructed in this study is able to achieve a high accuracy rate, and the model may have some value in assisting clinical screening and diagnosis.

17.
Article in Chinese | WPRIM | ID: wpr-934279

ABSTRACT

Objective:To study a deep learning-based dual-modality fundus camera which was used to study retinal blood oxygen saturation and vascular morphology changes in eyes with branch retinal vein occlusion (BRVO).Methods:A prospective study. From May to October 2020, 31 patients (31 eyes) of BRVO (BRVO group) and 20 healthy volunteers (20 eyes) with matched gender and age (control group) were included in the study. Among 31 patients (31 eyes) in BRVO group, 20 patients (20 eyes) received one intravitreal injection of anti-vascular endothelial growth factor drugs before, and 11 patients (11 eyes) did not receive any treatment. They were divided into treatment group and untreated group accordingly. Retinal images were collected with a dual-modality fundus camera; arterial and vein segments were segmented in the macular region of interest (MROI) using deep learning; the optical density ratio was used to calculate retinal blood oxygen saturation (SO 2) on the affected and non-involved sides of the eyes in the control group and patients in the BRVO group, and calculated the diameter, curvature, fractal dimension and density of arteriovenous in MROI. Quantitative data were compared between groups using one-way analysis of variance. Results:There was a statistically significant difference in arterial SO 2 (SO 2 -A) in the MROI between the affected eyes, the fellow eyes in the BRVO group and the control group ( F=4.925, P<0.001), but there was no difference in the venous SO 2 (SO 2-V) ( F=0.607, P=0.178). Compared with the control group, the SO 2-A in the MROI of the affected side and the non-involved side of the untreated group was increased, and the difference was statistically significant ( F=4.925, P=0.012); there was no significant difference in SO 2-V ( F=0.607, P=0.550). There was no significant difference in SO 2-A and SO 2-V in the MROI between the affected side, the non-involved side in the treatment group and the control group ( F=0.159, 1.701; P=0.854, 0.197). There was no significant difference in SO 2-A and SO 2-V in MROI between the affected side of the treatment group, the untreated group and the control group ( F=2.553, 0.265; P=0.088, 0.546). The ophthalmic artery diameter, arterial curvature, arterial fractal dimension, vein fractal dimension, arterial density, and vein density were compared in the untreated group, the treatment group, and the control group, and the differences were statistically significant ( F=3.527, 3.322, 7.251, 26.128, 4.782, 5.612; P=0.047, 0.044, 0.002, <0.001, 0.013, 0.006); there was no significant difference in vein diameter and vein curvature ( F=2.132, 1.199; P=0.143, 0.321). Conclusion:Arterial SO 2 in BRVO patients is higher than that in healthy eyes, it decreases after anti-anti-vascular endothelial growth factor drugs treatment, SO 2-V is unchanged.

18.
Article in Chinese | WPRIM | ID: wpr-934107

ABSTRACT

Objective:To construct a deep learning-based artificial intelligence endoscopic ultrasound (EUS) bile duct scanning substation system to assist endoscopists in learning multi-station imaging and improve their operation skills.Methods:A total of 522 EUS videos in Renmin Hospital of Wuhan University and Wuhan Union Hospital from May 2016 to October 2020 were collected, and images were captured from these videos, including 3 000 white light images and 31 003 EUS images from Renmin Hospital of Wuhan University, and 799 EUS images from Wuhan Union Hospital. The pictures were divided into training set and test set in the EUS bile duct scanning system. The system included filtering model of white light gastroscopy images (model 1), distinguishing model of standard station images and non-standard station images (model 2) and substation model of EUS bile duct scanning standard images (model 3), which were used to classify the standard images into liver window, stomach window, duodenal bulb window, and duodenal descending window. Then 110 pictures were randomly selected from the test set for a man-machine competition to compare the accuracy of multi-station imaging by experts, advanced endoscopists and the artificial intelligence model.Results:The accuracies of model 1 and model 2 were 100.00% (1 200/1 200) and 93.36% (2 938/3 147) respectively. Those of model 3 on the internal validation dataset in each classification were 97.23% (1 687/1 735) in liver window, 96.89% (1 681/1 735) in stomach window, 98.73% (1 713/1 735) in duodenal bulb window, and 97.18% (1 686/1 735) in duodenal descending window. And those on the external validation dataset were 89.61% (716/799) in liver window, 92.74% (741/799) in stomach window, 90.11% (720/799) in duodenal bulb window, and 92.24% (737/799) in duodenal descending window. In the man-machine competition, the accuracy of the substation model was 89.09% (98/110), which was higher than that of senior endoscopists [85.45% (94/110), 74.55% (82/110), and 85.45% (94/110)] and close to the level of experts [92.73% (102/110) and 90.00% (99/110)].Conclusion:The deep learning-based EUS bile duct scanning system constructed in the current study can assist endoscopists to perform standard multi-station scanning in real time more accurately and improve the completeness and quality of EUS.

19.
Article in Chinese | WPRIM | ID: wpr-932665

ABSTRACT

Objective:Hybrid attention U-net (HA-U-net) neural network was designed based on U-net for automatic delineation of craniospinal clinical target volume (CTV) and the segmentation results were compared with those of U-net automatic segmentation model.Methods:The data of 110 craniospinal patients were reviewed, Among them, 80 cases were selected for the training set, 10 cases for the validation set and 20 cases for the test set. HA-U-net took U-net as the basic network architecture, double attention module was added at the input of U-net network, and attention gate module was combined in skip-connection to establish the craniospinal automatic delineation network model. The evaluation parameters included Dice similarity coefficient (DSC), Hausdorff distance (HD) and precision.Results:The DSC, HD and precision of HA-U-net network were 0.901±0.041, 2.77±0.29 mm and 0.903±0.038, respectively, which were better than those of U-net (all P<0.05). Conclusion:The results show that HA-U-net convolutional neural network can effectively improve the accuracy of automatic segmentation of craniospinal CTV, and help doctors to improve the work efficiency and the consistent delineation of CTV.

20.
Article in Chinese | WPRIM | ID: wpr-932626

ABSTRACT

Objective:Due to the low contrast between tumors and surrounding tissues in CBCT images, this study was designed to propose an automatic segmentation method for central lung cancer in CBCT images.Methods:There are 221 patients with central lung cancer were recruited. Among them, 176 patients underwent CT localization and 45 patients underwent enhanced CT localization. The enhanced CT images were set as the lung window and mediastinal window, and elastic registration was performed with the first CBCT validation images to obtain the paired data set. The CBCT images could be transformed into" enhanced CT" under the lung window and mediastinal window by loading the paired data sets into cycleGAN network for style transformation. Finally, the transformed images were loaded into the UNET-attention network for deep learning of GTV. The results of segmentation were evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD) and the area under the receiver operating characteristic curve (AUC).Results:The contrast between tumors and surrounding tissues was significantly improved after style transformation. The DSC value of cycleGAN+ UNET-attention network was 0.78±0.05, HD value was 9.22±3.42 and AUC value was 0.864, respectively.Conclusion:The cycleGAN+ UNET-attention network can effectively segment central lung cancer in CBCT images.

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