Project Grant 2208759
- The National Science Foundation (NSF) awarded a $275,000 Phase I Small Business Innovation Research (SBIR) grant under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program to KAI TECH LLC. The funding supports the development of an advanced software system called ECG-AID for automated electrocardiogram (ECG) analysis and interpretation. The ECG-AID prototype integrates a comprehensive ECG database, Z-score-based assessments, and machine-learning techniques to enhance the...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $174,740 to the University of Texas at Tyler to enhance the integration of deep learning models for the detection and monitoring of cardiac conditions from electrocardiogram (ECG) data. The project aims to address key challenges in the real-world clinical application of deep learning for ECGs, such as data scarcity for rare conditions,...
- This $275,000 Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development and validation of an automated method for continuous post-operative cardiac rhythm diagnosis. The project, led by Atrility Medical, Inc., aims to improve the accuracy and accessibility of post-operative rhythm monitoring through the use of high-quality atrial electrogram signals. The anticipated outcome is a real-time, continuous...
- This $188,136 Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), supports research to develop an Artificial Intelligence-Enabled Echocardiography Interpretation System (AEIS). The principal investigator, Dr. Chieh-Ju Chao, will train with a multidisciplinary mentoring team led by Drs. Bradley Erickson and Fei-Fei Li to advance his skills in visual-linguistic models and large language models. The goal...
- RCE Technologies, Inc. was awarded a $255,892 Small Business Innovation Research Phase I Project Grant from the National Science Foundation's Technology, Innovation, and Partnerships program to develop a proof-of-concept machine learning-based diagnostic system for detecting myocardial infarction and ischemia in real-time. The system aims to provide point-of-care clinical classification for heart attack cases using a novel, portable device integrating the awardee's proprietary external...
- This $150,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop technologies to improve cardiac care for patients in rural and tribal areas of Arizona. Clemson University is partnering with Northern Arizona University and community leaders to plan an integrated remote heart monitoring system. The system will utilize deep learning and Markov-based methods to predict deterioration in symptoms for patients with conditions...
- This $760,046 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a computationally-efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict structural and functional changes in the heart due to disease progression. The project aims to build fundamental understanding of heart disease by combining techniques from...
- This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $439,954 to Michigan State University (MSU) to develop advanced computational models of the heart that leverage machine learning and artificial intelligence. The goal is to create a multiscale modeling framework that can predict structural and functional changes in the heart due to disease conditions like pathological fibrosis. The...
- This National Science Foundation (NSF) Engineering (CFDA 47.041) program grant, in the amount of $400,000, supports the development of a tissue-like, converged sensing platform for tracking excitation-contraction dynamics in cardiac organoids. The project aims to: Develop a scalable assembly strategy to fabricate an array of three-dimensional sensor structures using planar semiconducting graphene material, designed to detect both electrical and mechanical stimuli. Evaluate the multifunctional...
- This National Science Foundation (NSF) Cooperative Agreement award, under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program, provides $999,553 to Dynocardia, Inc. to develop advanced computer vision and artificial intelligence algorithms for motion artifact correction in their Vitrack wearable blood pressure monitoring device. The project aims to improve the accuracy and reliability of Vitrack's continuous, non-invasive blood pressure measurements in clinical settings, where...
The National Science Foundation awarded Cardiophi LLC $532,000 under the Technology, Innovation, and Partnerships program to develop an artificial intelligence solution for the automated interpretation of electrocardiograms. The project aims to refine existing deep learning-based algorithms for detecting irregular heartbeats, develop methods for predicting the onset of cardiac arrhythmias, and create interpretable and explainable tools to provide insights from ECG signals to clinicians. Leveraging techniques including deep learning, natural language processing, and signal processing, the company will create a trustworthy and automated electrocardiogram analysis tool for prediction and detection of cardiac conditions. The project aligns with the NSF program's goals of advancing research and innovation leading to breakthrough technologies and solutions addressing national challenges in science and engineering.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $20.0k | 3/27/23 | ||
| Not listed | $256.0k | 6/3/22 |