Search Results - cardiometabolic+disease

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Synchronized Biventricular Heart Pacing Using Wirelessly Powered Leadless Pacemakers (Cases 2019-237 & 2020-401)

Summary: UCLA researchers in the department of electrical and computer engineering have developed a device for synchronized biventricular pacing. Background: Many patients with impaired cardiac function suffer from ventricular desynchrony, a condition that involves a lack of synchronization between the contractions of the left and right ventricles...

Published: 9/19/2024 | Inventor(s): Aydin Babakhani, Hongming Lyu, Medhi Razavi, Mathews John, Allison Post

Keywords(s): acute myocardial infarction, cardiac cycle, Cardiac failure, cardiometabolic disease, cardiopulmonary illness, Cardiovascular, Cardiovascular Disease, cardiovascular diseases, cardiovascular modeling, cardiovascular monitoring, cardiovascular prediction, cardiovascular therapeutic solution, medical device cardiac monitoring, non-invasive cardiac monitoring, tachycardia

Category(s): Medical Devices, Medical Devices > Cardiac, Medical Devices > Monitoring And Recording Systems

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification (Case No. 2024-179)

Summary: UCLA researchers from the Departments of Electrical and Computer Engineering and Bioengineering have developed a novel assay for point-of-care testing for acute myocardial infarction. Background: Cardiovascular diseases are responsible for a substantial number of deaths and economic burdens. Acute myocardial infarction (AMI) is an event...

Published: 11/13/2024 | Inventor(s): Aydogan Ozcan, Gyeo-Re Han, Artem Goncharov, Hyou-Arm Joung, Dino Di Carlo

Keywords(s): acute myocardial infarction, Assay, Bioassay, biological assays, cardiometabolic disease, cardiopulmonary illness, Cardiovascular, Cardiovascular Disease, Cardiovascular Disease Nephropathy, cardiovascular diseases, cardiovascular modeling, cardiovascular prediction, cardiovascular therapeutic solution, deep-learning analysis algorithms, Flow Device, heart disease mitigation, high-sensitivity cardiac troponin I, Immunoassay, Laminar flow, nanoparticle amplification chemistry, point-of-care testing, Targets And Assays, vertical flow assay

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

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

2022-088 Neural Networks for Adipose Tissue Segmentation on Magnetic Resonance Images

Summary: UCLA researchers in the Department of Radiological Sciences have developed a novel neural network that can segment and quantify visceral and subcutaneous adipose tissues on magnetic resonance images automatically, accurately, and rapidly, to enhance clinical methods for characterizing and monitoring risk for cardiometabolic diseases in patients...

Published: 9/25/2024 | Inventor(s): Holden Wu, Sevgi Gokce Kafali-Tekat

Keywords(s): Advanced Computing / AI, Artificial Neural Network, Artificial Neural Network Artificial Neuron, cardiometabolic disease, Hypertension, Imaging, Machine Learning, Magnetic Resonance Imaging, Magnetic Resonance Imaging Pathology, Medical Imaging, MRI, multi-contrast imaging, obesity, Tissue (Biology), tissue annotation, tissue detection, tissue ribbons alignment, tissue segmentation

Category(s): Medical Devices > Medical Imaging, Platforms > Diagnostic Platform Technologies, Medical Devices > Medical Imaging > MRI, Therapeutics > Metabolism And Endocrinology