Project Grant K08HL175205
- This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research federal grant program (CFDA 93.837), will support a $704,611 research project to develop and validate an AI-based model, named EchoNext, that can accurately detect undiagnosed structural heart disease (SHD) from electrocardiogram (ECG) data. The project aims to: 1) prospectively validate the accuracy of EchoNext in detecting undiagnosed SHD in patients presenting to...
- This Project Grant award of $1,526,628.00 from the National Heart Lung and Blood Institute (CFDA 93.837 Cardiovascular Diseases Research) supports research by Cedars-Sinai Medical Center to develop Artificial Intelligence (AI) methods for analyzing echocardiography data to predict biological cardiovascular age and identify trajectories of accelerated versus delayed cardiovascular aging. The research aims to capture an aggregate measure of cardiac aging and identify potential interventions to...
- This $1,302,060 federal Project Grant awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health (CFDA 93.286) program aims to develop an AI-assisted strategy for monitoring pulmonary congestion in acute heart failure patients in emergency settings. The primary goals are to automate lung ultrasound congestion scoring using AI/ML methods and a dataset of heart failure patients,...
- This federal Project Grant award from the National Heart, Lung, and Blood Institute under the Cardiovascular Diseases Research program (CFDA 93.837) provides $793,868 to Tufts Medical Center to develop and validate innovative machine learning methods for automating the diagnosis of aortic stenosis (AS) from cardiac ultrasound imaging. The goal is to improve the identification and treatment of this life-threatening cardiovascular condition that affects over 12.6 million adults annually in the...
- This federal Project Grant award, with a total funding amount of $304,415.00, was provided by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837). The goal of this 1-year project is to develop a software solution that uses artificial intelligence and image analytics to improve cardiovascular risk prediction from screening CT calcium score (CTCS) images. The project team, which includes engineers and clinicians from...
- This federal Project Grant award, totaling $753,813 and provided by the National Heart Lung and Blood Institute (CFDA 93.837 Cardiovascular Diseases Research), supports research to develop novel machine learning techniques for predicting cardiovascular outcomes in patients with clonal hematopoiesis of indeterminate potential (CHIP). The key goals are to use cardiac MRI imaging and genomic data to: 1) Develop a machine learning model to accurately identify CHIP patients and predict their risk...
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $526,890 Project Grant to Mayo Clinic to address differential performance by socioeconomic status in artificial intelligence (AI) models for childhood asthma care. The funding, provided through the Cardiovascular Diseases Research federal grant program (CFDA 93.837), aims to develop a framework and tool for measuring differential AI performance, explore the role of electronic health record (EHR) quality on disparities, and create...
- The U.S. National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), has awarded a $522,695 Project Grant to Kennesaw State University (KSU) to develop and validate a generative AI framework that can efficiently and accurately predict the biomechanical outputs of the bi-ventricular myocardium. The 3-year project aims to enhance personalized diagnosis and treatment of ventricular diseases, such as hypertrophic cardiomyopathy, heart failure,...
- 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...
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $164,127 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to the University of Texas Health Science Center at Houston (UTHealth) to develop an artificial intelligence-based facial recognition tool for screening and early detection of heritable thoracic aortic disease (HTAD). The project aims to leverage facial image data from the Montalcino Aortic Consortium's patient registry to build a...
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 is to create an AEIS that can automate the interpretation of echocardiography, a critical diagnostic tool for the 5.5% of the U.S. population with cardiac issues. The research plan involves pre-training visual-linguistic models on medical literature, fine-tuning them on Mayo Clinic's echocardiography data, and optimizing large language models for factual correctness and expert preferences to summarize echocardiography reports. This award aims to develop innovative AI solutions that can improve the timeliness and accuracy of echocardiography interpretation, ultimately enhancing patient outcomes.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $188.1k | 9/10/25 |