RESUMO
Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.
Assuntos
Aprendizado Profundo , Leucemia , Humanos , Leucemia/diagnóstico , Redes Neurais de ComputaçãoRESUMO
BACKGROUND: Cannabidiol (CBD) is one of the main components of Cannabis sativa and has anxiolytic properties, but no study has been conducted to evaluate the effects of CBD on anxiety signs and symptoms in patients with Parkinson's disease (PD). This study aimed to evaluate the impacts of acute CBD administration at a dose of 300 mg on anxiety measures and tremors induced by a Simulated Public Speaking Test (SPST) in individuals with PD. METHODS: A randomised, double-blinded, placebo-controlled, crossover clinical trial was conducted. A total of 24 individuals with PD were included and underwent two experimental sessions within a 15-day interval. After taking CBD or a placebo, participants underwent the SPST. During the test, the following data were collected: heart rate, systemic blood pressure and tremor frequency and amplitude. In addition, the Visual Analog Mood Scales (VAMS) and Self-Statements during Public Speaking Scale were applied. Statistical analysis was performed by repeated-measures analysis of variance (ANOVA) while considering the drug, SPST phase and interactions between these variables. RESULTS: There were statistically significant differences in the VAMS anxiety factor for the drug; CBD attenuated the anxiety experimentally induced by the SPST. Repeated-measures ANOVA showed significant differences in the drug for the variable related to tremor amplitude as recorded by the accelerometer. CONCLUSION: Acute CBD administration at a dose of 300 mg decreased anxiety in patients with PD, and there was also decreased tremor amplitude in an anxiogenic situation.
Assuntos
Ansiedade/tratamento farmacológico , Ansiedade/psicologia , Canabidiol/uso terapêutico , Doença de Parkinson/psicologia , Tremor/tratamento farmacológico , Idoso , Pressão Sanguínea/efeitos dos fármacos , Estudos Cross-Over , Método Duplo-Cego , Feminino , Frequência Cardíaca/efeitos dos fármacos , Humanos , Masculino , Pessoa de Meia-Idade , Doença de Parkinson/complicações , Autorrelato , Fala/efeitos dos fármacos , Resultado do Tratamento , Tremor/complicaçõesRESUMO
Devices and sensors for identification of fallers can be used to implement actions to prevent falls and to allow the elderly to live an independent life while reducing the long-term care costs. In this study we aimed to investigate the accuracy of Timed Up and Go test, for fallers' identification, using fusion of features extracted from accelerometer data. Single and dual tasks TUG (manual and cognitive) were performed by a final sample (94% power) of 36 community dwelling healthy older persons (18 fallers paired with 18 non-fallers) while they wear a single triaxial accelerometer at waist with sampling rate of 200Hz. The segmentation of the TUG different trials and its comparative analysis allows to better discriminate fallers from non-fallers, while conventional functional tests fail to do so. In addition, we show that the fusion of features improve the discrimination power, achieving AUC of 0.84 (Sensitivity = Specificity = 0.83, 95% CI 0.62-0.91), and demonstrating the clinical relevance of the study. We concluded that features extracted from segmented TUG trials acquired with dual tasks has potential to improve performance when identifying fallers via accelerometer sensors, which can improve TUG accuracy for clinical and epidemiological applications.