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A scientific team at Yokohama City University Hospital has used facial recognition technology to develop an automated system that can predict with moderate accuracy (75%) when patients in the intensive care unit (ICU) are at high risk of unsafe behavior. The automated risk detection tool has the potential to continuously monitor patients’ safety when limited capacity makes it difficult for staff to continuously observe critically ill patients at the bedside.
Dr. Akane Sato, who led the research, said that the researchers used images of a patient’s face and eyes to train computer systems to recognize high-risk arm movement that could lead to behavior such as accidentally removing a breathing tube. A proof-of-concept model…READ MORE