Search Results - akos+rudas

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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

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

Detection of Dicrotic Notch in Arterial Pressure and Photoplethysmography Signals Using Iterative Envelope Mean Filter (Case No. 2024-034)

Summary: UCLA Researchers in the Department of Anesthesiology have developed an iterative envelope mean (IEM) method for the detection of specific features in arterial pressure monitoring applications. Background: The cardiac cycle consists of the distinct systolic and diastolic phases. The transition from the contracted, systolic phase to the...

Published: 10/24/2024 | Inventor(s): Maxime Cannesson, Ravi Pal, Akos Rudas, Jeffrey Chiang, Sungsoo (Danny) Kim

Keywords(s): arrhythmia, arterial blood pressure (ABP), cardiac cycle, Cardiac Electrophysiology, Cardiac failure, Cardiac Magnetic Resonance Imaging, cardiometabolic disease, cardiopulmonary illness, Cardiovascular, Cardiovascular Disease, Cardiovascular Disease Nephropathy, cardiovascular modeling, cardiovascular prediction, cardiovascular therapeutic solution, diastolic, diastolic phase peak, dicrotic notch, feature extraction tool, iterative envelope method (IEM), Medical Device, medical device cardiac monitoring, Medical Device Poly(Methyl Methacrylate), Medical Devices and Materials, non-invasive cardiac monitoring, photoplethysmography (PPG), Smart medical device, systolic, tachycardia, wearable medical device, wearable medical devices

Category(s): Medical Devices, Medical Devices > Monitoring And Recording Systems, Platforms, Platforms > Diagnostic Platform Technologies, Software & Algorithms, Diagnostic Markers > Targets And Assays, Diagnostic Markers