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Deep Neural Networks for Real-Time Non-invasive Continuous Peripheral Oxygen Saturation Monitoring (Case No. 2024-227)

Summary: UCLA researchers in the Department of Anesthesiology have developed a novel pulse oximetry methodology utilizing deep neural networks for non-invasive monitoring. Background: In the US alone, over 5 million patients are admitted to the ICU for oxygen saturation monitoring. They, as well as the more than 15 million patients undergoing surgery,...

Published: 7/26/2024 | Inventor(s): Sungsoo (Danny) Kim, Sohee Kwon, Mia Markey, Alan Bovik, Akos Rudas, Ravi Pal, Maxime Cannesson

Keywords(s): Artifical Intelligence (Machine Learning, Data Mining), Blood Pressure, cardiovascular monitoring, central venous pressure (CVP), Continuous blood pressure monitoring, critical care, Deep learning-based sensing, deep-learning analysis algorithms, heart failure, hemodynamic monitoring, machine learning modeling, Monitoring (Medicine), neural network, non-invasive monitoring, Oxygen, Oxygen Saturation, pulmonary arterial pressure (PAP), Swan-Ganz catheter

Category(s): Medical Devices > Monitoring And Recording Systems, Software & Algorithms > Digital Health