Project Grant R41HL180169
- This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), provides $742,395 to Case Western Reserve University (CWRU) to develop and validate a machine learning-based analysis of coronary artery calcium scans (CTCS) to identify biomarkers for predicting heart failure risk. The 4-year project aims to create an automated tool for extracting CTCS-derived radiomics, develop a comprehensive heart failure risk...
- 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 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 $783,801 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University (CCLCM). The grant, titled "Demonstrating the Feasibility of Democratized Push-Button Autonomous Cardiac MRI Exam in Community Family Health Centers (AutoCMR for FHC)", aims to demonstrate the feasibility of using a push-button, time-resolved 3D...
- 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 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...
- 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 (CFDA 93.837 - Cardiovascular Diseases Research) provides $756,906 to Mayo Clinic to conduct a population-based study on using deep learning methods to detect and quantify breast arterial calcification (BAC) from mammogram images. The study aims to leverage the high rates of mammography screening to explore BAC as a biomarker for predicting cardiovascular disease risk in women. The project will analyze data from 125,519...
- 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 $232,500 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to The Johns Hopkins University to conduct a 30-year follow-up study of the Coronary Artery Calcium Consortium (CAC Consortium). The goal of the project is to quantify long-term survival and cardiovascular mortality risk prediction for individuals aged 30-59 based on their baseline coronary artery calcium (CAC) scores. The research aims to...
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 PulseImaging.AI, Case Western Reserve University, Houston Methodist, and University Hospitals of Cleveland, will create advanced algorithms to analyze various features of coronary calcifications, referred to as "calcium-omics," to more accurately predict future cardiovascular events compared to the standard Agatston scoring method. This research aims to enable cardiologists to provide more personalized treatments and lifestyle guidance to patients based on their individual cardiovascular risk profiles.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $304.4k | 8/9/25 |