SADC

Regroupement en IA
soins aigus pour l'enfant

Les articles du laboratoire

Le laboratoire croit en une vision basée sur un modèle d’innovation ouverte pour les membres du groupe et partiellement ouverte ou contrôlé à l’extérieur du groupe. Dans cette optique, nous souhaitons partager nos connaissances et faire rayonner le milieu académique avec la publication d'articles.

Nos articles
 

Remote design of a pediatric intensive care unit dashboard in time of pandemics

Année de parution: 2021

To support the pediatric intensive care unit with the COVID-19 pandemic, we followed a user-centered design process to create a dashboard in a context where direct access to users was impossible. To this end, we applied contextual inquiry, user interview, requirement definition, iterative design with user validation and usability testing in a remote fashion.

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Dense-Unet: a light model for lung fields segmentation in Chest X-Ray images

Année de parution: 2020

Automatic and accurate lung segmentation in chest X-ray (CXR) images is fundamental for computer-aided diagnosis systems since the lung is the region of interest in many diseases and also it can reveal useful information by its contours.

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Arterial Partial Pressures of Carbon Dioxide Estimation Using Non-Invasive Parameters in Mechanically Ventilated Children

Année de parution: 2020

Objective: We aim to create a predictive model capable of giving a noninvasive, immediate and reliable estimate of the arterial partial pressure of carbon dioxide (PaCO2) in mechanically ventilated children with a better reliability than its estimation from end-tidal CO2 (PetCO2) and minute ventilation volume (Vmin) evolution.

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Automatic eye localization for hospitalized infants and children using convolutional neural networks

Année de parution: 2020

Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence of medical equipment.

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