Project Grant R01HL180937
- 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...
- This $722,299 Project Grant award from the National Heart, Lung, and Blood Institute (CFDA 93.837 - Cardiovascular Diseases Research) supports a research initiative to use deep learning techniques to study normal variation in aortic valve hemodynamics and its link to the development of aortic stenosis. The primary objectives are to: 1) Measure aortic valve parameters in 100,000 UK Biobank participants using deep learning models, validate the measurements at UCSF, and develop models to identify...
- 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 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...
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $443,544 Project Grant under the Cardiovascular Diseases Research (CFDA 93.837) program to Dasisimulations LLC, a minority-owned small business located in Dublin, Ohio. The grant supports the development and validation of an innovative, AI-driven software platform to automatically perform complex measurements for pre-planning transcatheter aortic valve replacement (TAVR) procedures. The project aims to (1) develop and validate the...
- 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, 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 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 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 $144,989 Project Grant (CFDA 93.837 - Cardiovascular Diseases Research) to The General Hospital Corporation (doing business as Massachusetts General Hospital) to develop, train, and test deep generative models using computed tomography (CT) images to predict lung injury progression and treatment responses in patients with acute respiratory distress syndrome (ARDS). The goal is to leverage artificial intelligence and image...
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 U.S. The research will focus on improving machine learning networks to reliably identify severe AS cases and training the networks to work with portable handheld ultrasound devices for screening in primary care settings. This award aims to establish tools that can help address the large number of undiagnosed AS cases, as 50% of symptomatic patients who go untreated will die within 2 years.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $2.0k | 8/8/25 | ||
| Not listed | $791.9k | 7/24/25 |