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Intraoperative Deep Learning Model for Imputation of the Continuous Central Venous Pressure (CVP) and Pulmonary Arterial Pressure (PAP) Waveforms From (Case No. 2024-224)

Summary: Researchers in the UCLA Department of Anesthesiology have developed a deep learning model to accurately represent and visualize hemodynamic waveforms, or blood flow patterns, with minimally invasive approaches. Background: Swan-Ganz (SG) catheters are used for precise cardiac hemodynamic evaluations. Indicated for patients with severe...

Published: 9/3/2024 | Inventor(s): Maxime Cannesson, Sungsoo (Danny) Kim, Akos Rudas, Jeffrey Chiang, Ravi Pal

Keywords(s): active learning, Algorithm, algorithm-based testing, arterial blood pressure (ABP), Artifical Intelligence (Machine Learning, Data Mining), artificial intelligence algorithms, blood cancers, blood flow management, Blood Pressure, Blood Proteins, cardiovascular monitoring, catheter, Catheterization, central venous pressure (CVP), Computer Aided Learning, Continuous blood pressure monitoring, critical care, curriculum learning, Deep Learning, Deep learning-based sensing, deep-learning analysis algorithms, heart failure, hemodynamic monitoring, Machine Learning, non-invasive monitoring, Perceptual Learning, pulmonary arterial pressure (PAP), Software & Algorithms, Swan-Ganz catheter

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