Assessing the agitation of critically ill patients with RGB video acquisition and artificial intelligence
Critically ill patients are continuously and closely monitored in order to quickly intervene in cases where their state of health decreases abruptly. The Pediatric Intensive Care Unit (PICU) at the Centre Hospitalier Universitaire (CHU) Ste-Justine receives patients in critical condition ranging from newborns to 18-year-old.
Critically ill patients are also visually monitored, which is equally crucial for both remotely assessing a trauma patient’s state and for transporting them. Visual monitoring aims to assess various critical parameters: the general motility of the patient and his/her agitation; the level of consciousness via the motion analysis of the patient’s eye movements and gaze; and any neurological deficit by evaluating the decrease in motility of one part of the body such as the leg, arm, or face. All these elements are assessed to evaluate the patient’s vital distress level.
Physicians observe and assess the patient’s agitation and motility visually, by watching the patient when they are within the proximity of their bed. This method of observation is subjective, qualitative, and is limited to a very short time window.
Our objective consists in using video analysis and machine learning to help in the assessment of patients’ distress in the Intensive Care Unit. More specifically, we intend to focus on assessing patients’ agitation and motility from video acquired with RGB-D cameras. We plan to use the joints’ displacements and speed as an assessment of the patient’s agitation and will validate the agitation assessment by comparing it with a score as assessed by the clinicians.
• Une connaissance des algorithmes d’intelligence artificielle
• Une excellente motivation
• Un bon dossier en recherche
• Capacité de travailler en équipe et d’une façon autonome
• Capacité de bien communiquer par écrit
• Excellente connaissance de programmation