The University of California, San Francisco (UCSF) was awarded a $1,453,500 Project Grant by the National Institutes of Health (NIH) under the Trans-NIH Research Support program (CFDA 93.310). The funding will support the development of a novel, physiologically-focused approach to train multi-modal artificial intelligence (AI) algorithms for medical applications. Specifically, the project aims to create a new deep neural network architecture that can accept and learn from multiple...
This federal Project Grant award of $393,750 from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837) supports a research project focused on developing a computational approach for predicting and preventing arrhythmias, a leading cause of sudden cardiac death. The project aims to discover effective antiarrhythmic drug therapies and optimal clinical markers for arrhythmia risk prediction by leveraging mathematical modeling,...
This $1,526,628 Project Grant awarded by the National Heart Lung and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), supports research to develop artificial intelligence (AI) methods to predict biological cardiovascular age from echocardiography imaging data. The key objectives are to: (I) identify trajectories of accelerated versus delayed cardiovascular aging, (II) discern contributors to accelerated cardiovascular aging phenotypes, and (III)...
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....
This federal Project Grant award from the National Institutes of Health (NIH) Office of the Director under the Trans-NIH Research Support program (CFDA 93.310) aims to develop an AI-augmented, image-based hemodynamic modeling platform to enhance cardiovascular research and healthcare. The $150,000 award, effective September 1, 2024 through May 31, 2028, will be used by the University of Notre Dame to automate the transformation of medical images into 3D geometries for computational fluid...
This $2,377,839 federal Project Grant award from the Defense Health Agency under the Military Medical Research and Development (CFDA 12.420) program will support evidence-based research to predict cardiovascular risk in immune-mediated inflammatory diseases. The grant was awarded on June 1, 2025 to The Regents of the University of California, San Francisco (UCSF), a prominent academic medical center with extensive biomedical research capabilities. Through this 4-year grant, UCSF researchers will...
This Project Grant award from the National Heart, Lung, and Blood Institute (CFDA 93.837 Cardiovascular Diseases Research) provides $671,544 to develop and validate a cardiac ultrasound radiomics-guided deep neural network model for precision phenotyping and risk stratification of acute myocardial infarction (AMI) patients. The key objectives are to: 1) Benchmark the radiomics-based model against conventional risk assessment for predicting 1-year mortality, 2) Assess if point-of-care cardiac...
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 Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research Federal Grant Program (CFDA 93.837) supports a $704,611 research project to develop and validate a deep learning model, named EchoNext, that can accurately detect undiagnosed structural heart disease from electrocardiogram (ECG) waveforms. The project aims to conduct a large prospective study to validate the accuracy of EchoNext in identifying undiagnosed structural...