Project Grant R01HL174650
- 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 $300,000 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports the development of interpretable machine learning tools to uncover imaging biomarkers associated with breast and lung cancer. The project aims to create dimension reduction techniques to visualize features from deep learning models, establish an interpretable...
- 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...
- 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 $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...
- This Project Grant award of $784,362.00, provided by the National Heart, Lung, and Blood Institute (CFDA 93.837 - Cardiovascular Diseases Research) to Brigham & Women's Hospital Inc., will support research investigating the role of body composition, coronary microvascular dysfunction, and heart failure with preserved ejection fraction in characterizing adverse cardiometabolic disease phenotypes. The research aims to leverage novel imaging technologies to identify mechanisms of...
- This Project Grant award was provided by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research federal grant program (CFDA 93.837). The $138,523 award to Brigham & Women's Hospital Inc. (a subsidiary of Partners Healthcare System) will fund research to investigate the distinct mechanisms underlying medial and intimal calcification in blood vessels, which are associated with increased cardiovascular disease risk. The research aims to systematically...
- 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 Project Grant award of $642,454 from the National Cancer Institute (CFDA 93.395 - Cancer Treatment Research) supports research to develop an AI-based system for predicting the risk of treatment-related cardiovascular disease (CVD) in breast cancer patients. The goal is to help clinicians adjust anti-tumor therapies and prescribe interventions to reduce the risk of serious CVD complications, which can be a leading cause of non-malignant death during or after cancer treatment. The research...
- This federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), aims to improve the diagnostic accuracy and cost-effectiveness of breast cancer screening through the development of an AI system. The $672,087 grant, awarded on August 12, 2025, will fund a research project at New York University School of Medicine 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 women across two diverse cohorts in Mississippi and Minnesota, with the goal of validating the use of automated BAC quantification from 2D and 3D mammograms to improve cardiovascular disease risk assessment for women. The award period runs from September 1, 2025 to June 30, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $756.9k | 8/29/25 |