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1.
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
2.
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
3.
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
4.
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
5.
Int. j. morphol ; 40(1): 107-114, feb. 2022. ilus, tab
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.


Subject(s)
Humans , Male , Female , Pelvic Bones/anatomy & histology , Sex Determination by Skeleton/methods , Deep Learning , Thailand , Neural Networks, Computer
6.
Article in Spanish | LILACS-Express | LILACS, CUMED | ID: biblio-1408536

ABSTRACT

La Imagen Fotoacústica (PAI por sus siglas en inglés), es una modalidad de imagen híbrida que fusiona la iluminación óptica y la detección por ultrasonido. Debido a que los métodos de imágenes ópticas puras no pueden mantener una alta resolución, la capacidad de lograr imágenes de contraste óptico de alta resolución en tejidos biológicos hace que la fotoacústica (PA por sus siglas en inglés) sea una técnica prometedora para varias aplicaciones de imágenes clínicas. En la actualidad el Aprendizaje Profundo (Deep Learning) tiene el enfoque más reciente en métodos basados en la PAI, donde existe una gran cantidad de aplicaciones en análisis de imágenes, en especial en el área del campo biomédico, como lo es la adquisición, segmentación y reconstrucciones de imágenes de tomografía computarizada. Esta revisión describe las últimas investigaciones en PAI y un análisis sobre las técnicas y métodos basados en Deep Learning, aplicado en diferentes modalidades para el diagnóstico de cáncer de seno(AU)


Photoacoustic Imaging (PAI) is a hybrid imaging modality that combines optical illumination and ultrasound detection. Because pure optical imaging methods cannot maintain high resolution, the ability to achieve high resolution optical contrast images in biological tissues makes Photoacoustic (PA) a promising technique for various clinical imaging applications. At present, Deep Learning has the most recent approach of methods based on PAI where there are a large number of applications in image analysis especially in the area of ​​the biomedical field, such as acquisition, segmentation and reconstructions of computed tomography imaging. This review describes the latest research in PAI and an analysis of the techniques and methods based on Deep Learning applied in different modalities for the diagnosis of breast cancer(AU)


Subject(s)
Humans , Female , Image Processing, Computer-Assisted/methods , Breast Neoplasms/diagnosis , Photoacoustic Techniques/methods , Deep Learning , Mexico
7.
Frontiers of Medicine ; (4): 496-506, 2022.
Article in English | WPRIM | ID: wpr-939875

ABSTRACT

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients' physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.


Subject(s)
Bone Density , Deep Learning , Diabetes Mellitus/epidemiology , Female , Fractures, Bone/etiology , Humans , Osteoporosis/complications , Risk Factors
8.
Article in Chinese | WPRIM | ID: wpr-928225

ABSTRACT

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.


Subject(s)
Algorithms , Child , Deep Learning , Electroencephalography , Epilepsy/diagnosis , Humans , Seizures/diagnosis , Signal Processing, Computer-Assisted , Wavelet Analysis
9.
Article in Chinese | WPRIM | ID: wpr-928208

ABSTRACT

O 6-carboxymethyl guanine(O 6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O 6-CMG dataset and can accurately identify all sequence segments containing O 6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.


Subject(s)
Deep Learning , Guanine , Nanopore Sequencing , Nanopores , Porins/genetics
11.
Article in Chinese | WPRIM | ID: wpr-939751

ABSTRACT

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.


Subject(s)
Deep Learning , Diagnostic Imaging , Dry Eye Syndromes , Humans , Image Processing, Computer-Assisted , Meibomian Glands/diagnostic imaging
12.
Article in Chinese | WPRIM | ID: wpr-936124

ABSTRACT

OBJECTIVE@#To establish a deep learning algorithm that can accurately determine three-dimensional facial anatomical landmarks, multi-view stacked hourglass convolutional neural networks (MSH-CNN) and to construct three-dimensional facial midsagittal plane automatically based on MSH-CNN and weighted Procrustes analysis algorithm.@*METHODS@#One hundred subjects with no obvious facial deformity were collected in our oral clinic. Three-dimensional facial data were scanned by three-dimensional facial scanner. Experts annotated twenty-one facial landmarks and midsagittal plane of each data. Eighty three-dimensional facial data were used as training set, to train the MSH-CNN in this study. The overview of MSH-CNN network architecture contained multi-view rendering and training the MSH-CNN network. The three-dimensional facial data were rendered from ninety-six views that were fed to MSH-CNN and the output was one heatmap per landmark. The result of the twenty-one landmarks was accurately placed on the three-dimensional facial data after a three-dimensional view ray voting process. The remaining twenty three-dimensional facial data were used as test set. The trained MSH-CNN automatically determined twenty-one three-dimensional facial anatomical landmarks of each case of data, and calculated the distance between each MSH-CNN landmark and the expert landmark, which was defined as position error. The midsagittal plane of the twenty subjects' could be automatically constructed, using the MSH-CNN and Procrustes analysis algorithm. To evaluate the effect of midsagittal plane by automatic method, the angle between the midsagittal plane constructed by the automatic method and the expert annotated plane was calculated, which was defined as angle error.@*RESULTS@#For twenty subjects with no obvious facial deformity, the average angle error of the midsagittal plane constructed by MSH-CNN and weighted Procrustes analysis algorithm was 0.73°±0.50°, in which the average position error of the twenty-one facial landmarks automatically determined by MSH-CNN was (1.13±0.24) mm, the maximum position error of the orbital area was (1.31±0.54) mm, and the minimum position error of the nasal area was (0.79±0.36) mm.@*CONCLUSION@#This research combines deep learning algorithms and Procrustes analysis algorithms to realize the fully automated construction of the three-dimensional midsagittal plane, which initially achieves the construction effect of clinical experts. The obtained results constituted the basis for the independent intellectual property software development.


Subject(s)
Algorithms , Deep Learning , Face , Humans , Neural Networks, Computer , Software
13.
Article in Chinese | WPRIM | ID: wpr-927840

ABSTRACT

Objective To develop a risk prediction model combining pre/intraoperative risk factors and intraoperative vital signs for postoperative healthcare-associated infection(HAI)based on deep learning. Methods We carried out a retrospective study based on two randomized controlled trials(NCT02715076,ChiCTR-IPR-17011099).The patients who underwent elective radical resection of advanced digestive system tumor were included in this study.The primary outcome was HAI within 30 days after surgery.Logistic regression analysis and long short-term memory(LSTM)model based on iteratively occluding sections of the input were used for feature selection.The risk prediction model for postoperative HAI was developed based on deep learning,combining the selected pre/intraoperative risk factors and intraoperative vital signs,and was evaluated by comparison with other models.Finally,we adopted the simulated annealing algorithm to simulatively adjust the vital signs during surgery,trying to explore the adjustment system that can reduce the risk of HAI. Results A total of 839 patients were included in this study,of which 112(13.3%)developed HAI within 30 days after surgery.The selected pre/intraoperative risk factors included neoadjuvant chemotherapy,parenteral nutrition,esophagectomy,gastrectomy,colorectal resection,pancreatoduodenectomy,hepatic resection,intraoperative blood loss>500 ml,and anesthesia time>4 h.The intraoperative vital signs significantly associated with HAI were in an order of heart rate>core body temperature>systolic blood pressure>diastolic blood pressure.Compared with multivariable Logistic regression model,random forest model,and LSTM model including vital signs only,this deep learning-based prediction model performed best(ACC=0.733,F1=0.237,AUC=0.728).The simulation via simulated annealing algorithm reduced the incidence of postoperative HAI.Moreover,the incidence decreased most in the case of reducing the initial annealing temperature and choosing the last 20% of surgery procedure. Conclusions This study developed a risk prediction model for postoperative HAI based on deep learning,which combined pre/intraoperative risk factors and intraoperative basic vital signs.Using simulated annealing algorithm to adjust intraoperative vital signs could reduce the incidence of postoperative HAI to some extent.


Subject(s)
Cross Infection , Deep Learning , Delivery of Health Care , Humans , Postoperative Period , Retrospective Studies
14.
Prensa méd. argent ; 107(5): 282-286, 20210000.
Article in English | LILACS, BINACIS | ID: biblio-1359365

ABSTRACT

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


Subject(s)
Humans , Artificial Intelligence , Neural Networks, Computer , Speech Recognition Software , Deep Learning
15.
Braz. oral res. (Online) ; 35: e094, 2021. graf
Article in English | LILACS, BBO | ID: biblio-1285723

ABSTRACT

Abstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as "deep learning", which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.


Subject(s)
Humans , Artificial Intelligence , Deep Learning , Neural Networks, Computer , Dentistry , Machine Learning
16.
Braz. arch. biol. technol ; 64: e21200221, 2021. tab, graf
Article in English | LILACS | ID: biblio-1285550

ABSTRACT

HIGHLIGHTS Novel whale optimization algorithm is proposed for prediction of breast cancer. Deep learning-based WOA adjusts the CNN structure as per maximum detection accuracy. Proposed method achieves 92.4% accuracy in comparison to 90.3%. Validity of method is evaluated with magnifying factors like 40x, 100 x, 200x, 400x.


Abstract Breast cancer is one of the most common cancers among women that cause billions of deaths worldwide. Identification of breast cancer often depends on the examination of digital biomedical photography such as the histopathological images of various health professionals, and clinicians. Analyzing histopathological images is a unique task and always requires special knowledge to conclude investigating these types of images. In this paper, a novel efficient technique has been proposed for the detection and prediction of breast cancer at its early stage. Initially, the dataset of images is used to carry out the pre-processing phase, which helps to transform a human pictorial image into a computer photographic image and adjust the parameters appropriate to the Convolutional neural network (CNN) classifier. Afterward, all the transformed images are assigned to the CNN classifier for the training process. CNN classifies incoming breast cancer clinical images as malignant and benign without prior information about the occurrence of cancer. For parameter optimization of CNN, a deep learning-based whale optimization algorithm (WOA) has been proposed which proficiently and automatically adjusts the CNN network structure by maximizing the detection accuracy. We have also compared the obtained accuracy of the proposed algorithm with a standard CNN and other existing classifiers and it is found that the proposed algorithm supersedes the other existing algorithms.


Subject(s)
Humans , Breast Neoplasms/prevention & control , Early Detection of Cancer , Whales , Neural Networks, Computer , Deep Learning
17.
Rev. bras. educ. méd ; 45(3): e169, 2021. tab
Article in Portuguese | LILACS | ID: biblio-1288301

ABSTRACT

Resumo: Introdução: A qualidade de aprendizado de estudantes de escolas médicas tem sido tema recorrente da literatura mundial nas últimas décadas, mas há escassez de estudos nacionais acerca do assunto. O ambiente de ensino deve favorecer o aprendizado profundo, por estar intimamente relacionado com uma aprendizagem significativa. Metodologias ativas de aprendizagem são vinculadas a maior qualidade de aprendizado, por propiciarem ambiente favorável ao aprendizado profundo. Objetivo: Este estudo teve como objetivos avaliar a qualidade do aprendizado de estudantes de Medicina de um curso que adota metodologias ativas de aprendizagem e correlacioná-la com as percepções dos alunos acerca do ambiente educacional e com dados sociodemográficos. Método: Trata-se de estudo descritivo transversal realizado com estudantes do curso de Medicina da Universidade Federal de São Carlos (UFSCar), do primeiro ao sexto ano. Foram utilizados os instrumentos R-SPQ-2F, DREEM e questionário sociodemográfico. Realizou-se análise descritiva, e compararam-se as frequências por meio do teste do qui-quadrado ou teste exato de Fisher. As diferenças entre médias foram avaliadas por meio de teste t de Student ou teste de Mann-Whitney, quando se compararam somente dois grupos, ou por meio de análise de variância (ANOVA) ou teste de Kruskal-Wallis, quando comparados mais de dois grupos. As associações entre as variáveis quantitativas foram verificadas por meio do coeficiente de correlação de Pearson ou de Spearman. A análise estatística foi realizada com auxílio do programa IBM SPSS Statistics versão 25.0, e adotou-se como parâmetro de significância um valor de p < 0,05. Resultado: Entrevistaram-se 226 alunos. A pontuação média para abordagem profunda foi de 33,52 e, para a abordagem superficial, 17,42. Em relação à percepção do ambiente educacional, a média foi de 129,77 pontos. As variáveis objetivas que demonstraram influência sobre o aprendizado foram: sexo, idade de início do curso, contato prévio com metodologias ativas, prática de idiomas, ter graduação ou pós-graduação prévia, receber auxílio financeiro de familiares e ano da graduação. Conclusão: As metodologias ativas de aprendizagem podem estimular a adoção de estratégias de aprendizado profundo. O estudo dos fatores que influenciam na abordagem de aprendizado é complexo e envolve questões individuais subjetivas.


Abstract: Introduction: The quality of learning of medical students has been a recurring subject of research in recent decades, but there are scarce national studies on it. The learning environment should encourage deep learning, as it is intimately related to meaningful learning. Active learning methods are linked to better quality of learning, since they provide a favorable environment for deep learning. Objective: The aim of this study was to evaluate the quality of learning of students at a medical school that adopts active learning methods for all course years and correlate it to students' views on educational environment and sociodemographic data. Method: This is a cross-sectional descriptive study with 1st to 6th-year medical students from the Federal University of São Carlos. The R-SPQ-2f, DREEM and sociodemographic questionnaires were applied. Descriptive analysis was performed, and frequencies were compared using chi-square test or Fisher's exact test. Differences between means were evaluated with either Student's t-test or Mann-Whitney's test, when comparing only two groups, and with one-way analysis of variance (ANOVA) or Kruskal-Wallis' test, when comparing more than two groups. Associations between quantitative variables were verified with either Pearson's or Spearman's correlation coefficient. Statistical analysis was performed with IBM SPSS Statistics version 25.0. For significance, p-value<0.05 was adopted. Result: 226 students were interviewed. The average deep learning score was 33.52 points, and, for superficial learning, 17.42 points. As for how the environment was perceived, the average score was 129.77. The objective variables that showed influence in learning were sex, course start age, previous contact with active learning methods, language study, previous undergraduate training, or post-graduate degree, receiving financial help from family and current course year. Conclusion: Active learning methods may stimulate the adoption of deep learning strategies. The evaluation of factors that influence study approaches is complex, involving subjective individual parameters.


Subject(s)
Humans , Male , Female , Adolescent , Adult , Young Adult , Problem-Based Learning/statistics & numerical data , Education, Medical/methods , Deep Learning/statistics & numerical data , Schools, Medical , Socioeconomic Factors , Cross-Sectional Studies , Surveys and Questionnaires
18.
Clin. biomed. res ; 41(1): 27-32, 2021. ilus, tab
Article in Portuguese | LILACS | ID: biblio-1280803

ABSTRACT

Introdução: A inteligência artificial (IA) está revolucionando a área da saúde. Na oftalmologia, esta tecnologia pode possibilitar diagnósticos mais rápidos e precisos, impedindo a progressão das alterações na visão. Médicos e algoritimosalgoritmos podem ser mais eficientes quando trabalham juntos. Desenvolver um software de IA com alta especificidade e sensibilidade para apoio no diagnóstico de algumas patologias oftalmológicas. Métodos: O software de deep learning foi construído através de redes neurais valendo de duas bases computacionais MobileNet e Inception. Para o treinamento do banco de dados foram utilizadas 2.520 imagens de glaucoma, retinopatia diabética, toxoplasmose ocular, papiledema, descolamento de retina e retina normal. Para a validação foi utilizado 428 imagens patológicas e normais para os cálculos de sensibilidade e de especificidade. Todas as imagens foram cedidas da Sociedade Americana de Especialistas da Retina. Resultados: Os resultados de sensibilidade e especificidade foram no MobileNet de 91% (IC 95%, 89-92%) e 98,5% (IC 95%, 98-99%); no Inception, de 91,4% (IC 95%, 89-93,5%) de 98,4% (IC 95%, 98-98,8%), respectivamente. Não houve diferença significativa entre os dois métodos utilizados. Conclusão: O software apresentou resultados promissores na distinção das condições oftalmológicas pesquisadas. (AU)


Introduction: Artificial intelligence (AI) is revolutionizing health care. In ophthalmology, this technology can enable faster and more accurate diagnoses, preventing the progression of vision Physicians and algorithms are most effective when working together. To develop an AI software with high specificity and sensitivity to support the diagnosis of some ophthalmic diseases. Methods: A deep learning software was built through neural networks using two computational bases, MobileNet and Inception. For training the database, 2520 images of glaucoma, diabetic retinopathy, ocular toxoplasmosis, papilledema, retinal detachment, and normal retina were used. For validation, 428 pathological and normal images were used for calculations of sensitivity and specificity. All images were obtained from the American Society of Retina Specialists. Results: The results of sensitivity and specificity were 91% (95% confidence interval [CI], 89-92%) and 98.5% (95% CI, 98-99%) on MobileNet, and 91.4% (95% CI, 89-93.5%) and 98.4% (95% CI, 98-98.8%) on Inception, respectively. There was no significant difference between the two methods. Conclusion: The software showed promising results in distinguishing the main ophthalmic conditions surveyed. (AU)


Subject(s)
Retinal Detachment/diagnosis , Papilledema/diagnosis , Toxoplasmosis, Ocular/diagnosis , Glaucoma , Diabetic Retinopathy/diagnosis , Software , Artificial Intelligence , Sensitivity and Specificity , Deep Learning
19.
Article in English | WPRIM | ID: wpr-921870

ABSTRACT

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.


Subject(s)
Algorithms , Deep Learning , Gastritis/diagnosis , Humans , ROC Curve , Retrospective Studies
20.
Clinics ; 76: e3198, 2021. tab, graf
Article in English | LILACS | ID: biblio-1345808

ABSTRACT

OBJECTIVES: This study aims to evaluate the ability of deep learning algorithms to detect and grade prostate cancer (PCa) in radical prostatectomy specimens. METHODS: We selected 12 whole-slide images of radical prostatectomy specimens. These images were divided into patches, and then, analyzed and annotated. The annotated areas were categorized as follows: stroma, normal glands, and Gleason patterns 3, 4, and 5. Two analyses were performed: i) a categorical image classification method that labels each image as benign or as Gleason 3, Gleason 4, or Gleason 5, and ii) a scanning method in which distinct areas representative of benign and different Gleason patterns are delineated and labeled separately by a pathologist. The Inception v3 Convolutional Neural Network architecture was used in categorical model training, and a Mask Region-based Convolutional Neural Network was used to train the scanning method. After training, we selected three new whole-slide images that were not used during the training to evaluate the model as our test dataset. The analysis results of the images using deep learning algorithms were compared with those obtained by the pathologists. RESULTS: In the categorical classification method, the trained model obtained a validation accuracy of 94.1% during training; however, the concordance with our expert uropathologists in the test dataset was only 44%. With the image-scanning method, our model demonstrated a validation accuracy of 91.2%. When the test images were used, the concordance between the deep learning method and uropathologists was 89%. CONCLUSION: Deep learning algorithms have a high potential for use in the diagnosis and grading of PCa. Scanning methods are likely to be superior to simple classification methods.


Subject(s)
Humans , Male , Prostatic Neoplasms/surgery , Prostatic Neoplasms/diagnostic imaging , Deep Learning , Prostatectomy , Neural Networks, Computer , Neoplasm Grading
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