In a situation of respiratory failure (RF), patients show signs of increased work of breathing leading to the involvement of accessory respiratory muscles and a desynchronization between rib cage and abdomen known as thoraco-abdominal asynchrony (TAA). Proper assessment of these signs requires sufficiently skilled and trained medical staff. However, human assessment is subjective and is practically impossible to audit. A new non-contact method is proposed for TAA visualization and quantification, in children with RF. The surface variations are analyzed by calculating the 3-dimensional motion of the thorax and abdomen regions during the breathing process. A high-fidelity mannequin was used to simulate thoraco-abdominal asynchrony. The proposed system uses depth information recorded by an RGB-D (Red Green Blue-Depth) camera. Furthermore, surface displacement was calculated in four simulated modes from the normal to the severe TAA mode. Respiratory rates were also calculated based on the analysis of the surface movements. The proposed method was compared to a highly precise laser-ranging system with 1 mm resolution. The resulting root mean square deviation (RMSD) showed an error of 1.78 ml in normal mode, 2.83 mm in mild mode, 2.23 mm in severe mode and 2.34 mm in irregular mode. The results showed a high correlation between the two methods in estimating the retraction distance and respiratory rate (ρ >0.985).
The appearance and behaviour of the eye region are important windows into a patient's condition and level of consciousness, particularly for patients too young to speak. Unfortunately, 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. The overall aim of this research project is to develop a clinical decision support system that uses bedside cameras to detect signs of consciousness and distress due to pain. This work focuses on the first problem to be solved, namely how to locate the eyes in an image of a pediatric patient in a hospital bed.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. Few studies have examined the application of computer vision and facial analysis techniques to young children in a hospital environment.To develop an appropriate solution for eye localization, a new training dataset, formed of images of young children from internet searches, is added to adult facial images to train cascade classifiers and convolutional neural networks. Another novel dataset, consisting of 59 recordings of patients in a pediatric intensive care unit, is used to evaluate the performance of these models. This dataset will also serve future work on this and other research projects in pediatric computer vision.
The Translational Research Committee of the French Intensive Care Society organized the first Young Investigator’s Day on October 18th 2019.
This seminar gave young Intensive Care students the opportunity to present their Master’s or PhD research work to a college of expert researchers.
For this first event, Professors Jean-Marc Cavaillon (Paris), Laurent Papazian (Marseille), Peter Radermacher (Ulm) et Hafid Ait-Oufella (Paris) kindly accepted to give young candidates their critical support.
The subjects of presentations, covering the fields of neuroscience, immunology, hemodynamics and pharmacology illustrated the richness and diversity of translational research in Intensive Care Medicine.
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