This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research Federal Grant Program (CFDA 93.837), provides $810,650 to Cedars-Sinai Medical Center to develop and validate an artificial intelligence (AI)-enabled system for characterizing fibrotic and calcific aortic valve tissue from computed tomography angiography (CTA) scans. The goal is to predict rapid aortic valve disease progression and risk of mortality and stroke following...
This federal Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), will provide $1,584,184 to Cedars-Sinai Medical Center to develop deep learning-based methods for precise phenotyping and outcomes prediction in valvular heart disease, particularly mitral valve disease. The key objectives are to: (i) automate the phenotyping of mitral regurgitation severity, systolic dysfunction, diastolic parameters, and...
This federal Project Grant award of $704,611, provided by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837), aims to develop and validate a deep learning model called EchoNext that can accurately detect undiagnosed structural heart disease from electrocardiogram (ECG) data. The project, titled "Capitalizing on Artificial Intelligence to Capture Undiagnosed Structural Heart Disease from Electrocardiograms (CACTUS)",...
This is a Project Grant awarded by the National Institute on Aging (CFDA 93.866 - Aging Research) totaling $813,824.00. The award will fund a multi-center study led by Yale University that aims to develop and evaluate new artificial intelligence (AI)-driven screening and monitoring strategies for aortic stenosis, a common and serious cardiovascular condition in older adults. The key products/services to be delivered include: A pragmatic randomized controlled trial to assess an AI-based,...
The National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research Federal Grant Program (CFDA 93.837), awarded a $299,604 Project Grant to Case Western Reserve University (CWRU) to develop and validate a novel "transfer volume regression" machine learning model for predicting major cardiovascular events, including stroke, heart attack, heart failure, and death. The 4-year project, starting September 1, 2024, aims to create a standardized, fairness-aware,...
This federal Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), provides $117,375.00 to Emory University to conduct research on applying machine learning and time-series analysis to longitudinal electrocardiogram (ECG) data. The goal is to develop algorithms that can detect subtle changes in ECG patterns over time as biomarkers for identifying the optimal timing of cardiac interventions for adults with...
This $250,000 federal Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the CFDA 47.049 Mathematical and Physical Sciences program aims to develop a personalized digital twin technology for monitoring and understanding cardiovascular aging. The central goal is to utilize computational and animal models to create a physics-based, data-driven digital twin that can provide real-time monitoring and prediction of aging-related cardiovascular...
The federal Project Grant award R01HL173186 for $742,395.00, awarded by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837), supports a research project focused on developing and validating a comprehensive machine learning-based model to predict heart failure risk using coronary artery calcium scoring (CT calcium scoring) data. The project aims to leverage a large, well-characterized cohort of over 160,000 participants from...
The National Heart, Lung, and Blood Institute (NHLBI), a division of the National Institutes of Health (NIH), awarded a $294,023 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to Heartlung Corporation. The grant supports the development of AUTOCHAMBER, an FDA-designated Breakthrough AI tool that can opportunistically screen existing chest CT scans to detect enlarged cardiac chambers and left ventricular wall thickness without the use of contrast agents. The...
The National Heart, Lung, and Blood Institute (NHLBI) awarded a $129,612 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to Case Western Reserve University (CWRU) to develop new artificial intelligence (AI) methods for evaluating high-risk coronary atherosclerotic plaque using coronary computed tomography angiography (CCTA) data. The goal is to enable non-invasive detection of high-risk plaque by comparing CCTA image features to the gold standard of intravascular...