No abstract

Glaucoma is an asymptomatic ocular disease at the onset which, if left untreated, can lead to blindness. The World Health Organization (WHO) has estimated that by 2020 glaucoma should affect 80 million people and by 2040 will be 111.5 million. In this context, the present work aims to compare methods of automatic classification of glaucoma to aid in its diagnosis by medical specialists. For this, two models are developed based on the extraction of characteristics of eye fundus images obtained from the public base RIM-ONE v2, and also uses a simple convolutional neural network (CNN). In the first model, texture characteristics, histogram information and image colors are extracted, which are submitted to the Principal Component Analysis (PCA) to evaluate nine classical classifiers including Multilayers perceptron (MLP). In the evaluation are used Accuracy, sensitivity, specificity and area under curve (AUC). The results demonstrate that the best performance is obtained with the MLP classifier with PCA use in the characteristic descriptors, with accuracy of 90.11%, sensitivity 97.44%, specificity of 84.62%, and AUC equal to 0.90. Resumo: O glaucomaé uma doença ocular assintomática no início que, se não for tratada a tempo, pode levar a cegueira. A Organização Mundial de Saúde (OMS) estimou que em 2020 o glaucoma deve afetar 80 milhões de pessoas e em 2040 serão 111,5 milhões. Neste contexto, o presente trabalho visa comparar métodos de classificação automática do glaucoma para auxiliar no seu diagnóstico por médicos especialistas. Para tanto, são desenvolvidos dois modelos com base na extração de características de imagens de fundo de olho, obtidas da base pública RIM-ONE v2, e também se utiliza de uma rede neural convolucional (CNN) simples. No primeiro modelo, extraem-se características de textura, informações de histogramas e cores das imagens, que são submetidasà Análise de Componentes Principais (PCA) para, em seguida, realizar a avaliação de nove classificadores clássicos, incluindo o Multilayers perceptron (MLP). No segundo modelo, adicionam-se características oculares geométricas extraídas: Cup to Disk Ratio (CDR), Neuro-Retinal Rim (NRR) e Blood Vessel Ratio (BVR). Empregam-se na avaliação a acurácia, sensibilidade, especificidade eárea sob a curva (area under curve-AUC). Os resultados demonstram que o melhor desempenhoé obtido com o classificador MLP com uso do PCA nos descritores de características, com acurácia de 90,11%, sensibilidade 97,44%, especificidade de 84,62%, e AUC igual a 0,90.

Background: Noninvasive ventilation (NIV) provides positive pressure through different interfaces. A multifunctional full-face mask prototype was developed to provide NIV from three sources: ICU ventilators, portable ventilators, and high-flow medical gas pipeline systems. This study aimed to evaluate the usability of this prototype mask. Methods: This was a quantitative experimental study, conducted in two phases: the development of a full-face mask prototype NIV interface, and the evaluation of its usability by health professionals (evaluators) using a heuristic approach. The Wolf Mask prototype is a multifunctional full-face mask that makes it possible to deliver positive pressure from three different sources: microprocessor-controlled ICU ventilators, portable ventilators with single-limb circuits, and high-flow medical gas. The evaluation was conducted in three stages: presentation of the prototype to the evaluators; skills testing via simulation in a clinical environment; and a review of skills. Results: The prototype was developed by a multidisciplinary team and patented in Brazil. The evaluators were 10 health professionals specializing in NIV. Seven skills related to handling the prototype were evaluated. Three of the ten evaluators called for (non-urgent) changes to improve recognition of the components of the prototype. Only one evaluator called for (non-urgent) changes to improve recognition of the pieces, assembly, and checking the mask. Conclusions: The newly developed multifunctional full-face mask prototype demonstrated excellent usability for providing noninvasive ventilation from multiple sources. Minor modifications may further improve the design.