R01HL157235
Project Grant
Overview
Grant Description
Radiomics Approach to Engineering an Artificial Intelligence Based Echocardiography Platform to Predict Cardiovascular Surgery and Heart Failure Outcomes - Summary
In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a wide variety of medical imaging tasks, in applications ranging from disease diagnosis using chest X-rays to survival analyses using histopathology slides.
All current automated echocardiography systems – much like human echocardiography reads – are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculated.
Despite the staggering increase in usable data, the vast majority of information contained in time-resolved echocardiography videos remain woefully underutilized.
As opposed to treating echocardiography studies as videos intended solely for visual interpretation, the ‘radiomics’ approach treats medical images as high-dimensional datasets to be mined with advanced computational tools.
The overall goals of this project are to further develop and validate our novel, generalizable, multi-modal artificial intelligence (AI) platform for analyzing time-resolved echocardiography studies, to address this underutilization.
The impact of such an echo AI system is immediately perceptible in the field of heart failure. An estimated 6.5 million people suffer from heart failure in the United States.
Across the spectrum of severity in this disease, echocardiography remains the cornerstone of screening and clinical diagnosis, a guide for medical management and pharmacotherapy, and an essential tool for planning acute lifesaving surgical interventions.
We propose to build on our preliminary research and ready access to high-quality paired echocardiographic and clinical datasets to achieve the following goals:
1) Develop a surgical decision support system for end-stage heart failure patients considered for left ventricular assist device (LVAD) implant.
2) Expand and generalize our echo AI tools to enable downstream prediction of long-term survival and development of heart failure, in both asymptomatic individuals and patients with pulmonary arterial hypertension.
3) Cloud and hardware integration of our echo AI platform.
The end result of our research will be a powerful echo AI tool that is translational and integrated into clinical practice.
In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a wide variety of medical imaging tasks, in applications ranging from disease diagnosis using chest X-rays to survival analyses using histopathology slides.
All current automated echocardiography systems – much like human echocardiography reads – are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculated.
Despite the staggering increase in usable data, the vast majority of information contained in time-resolved echocardiography videos remain woefully underutilized.
As opposed to treating echocardiography studies as videos intended solely for visual interpretation, the ‘radiomics’ approach treats medical images as high-dimensional datasets to be mined with advanced computational tools.
The overall goals of this project are to further develop and validate our novel, generalizable, multi-modal artificial intelligence (AI) platform for analyzing time-resolved echocardiography studies, to address this underutilization.
The impact of such an echo AI system is immediately perceptible in the field of heart failure. An estimated 6.5 million people suffer from heart failure in the United States.
Across the spectrum of severity in this disease, echocardiography remains the cornerstone of screening and clinical diagnosis, a guide for medical management and pharmacotherapy, and an essential tool for planning acute lifesaving surgical interventions.
We propose to build on our preliminary research and ready access to high-quality paired echocardiographic and clinical datasets to achieve the following goals:
1) Develop a surgical decision support system for end-stage heart failure patients considered for left ventricular assist device (LVAD) implant.
2) Expand and generalize our echo AI tools to enable downstream prediction of long-term survival and development of heart failure, in both asymptomatic individuals and patients with pulmonary arterial hypertension.
3) Cloud and hardware integration of our echo AI platform.
The end result of our research will be a powerful echo AI tool that is translational and integrated into clinical practice.
Funding Goals
THE NATIONAL HEART, LUNG, AND BLOOD INSTITUTE (NHLBI) PROVIDES GLOBAL LEADERSHIP FOR A RESEARCH, TRAINING, AND EDUCATION PROGRAM TO PROMOTE THE PREVENTION AND TREATMENT OF HEART, LUNG, AND BLOOD DISEASES AND ENHANCE THE HEALTH OF ALL INDIVIDUALS SO THAT THEY CAN LIVE LONGER AND MORE FULFILLING LIVES. TO FOSTER HEART AND VASCULAR RESEARCH IN THE BASIC, TRANSLATIONAL, CLINICAL AND POPULATION SCIENCES, AND TO FOSTER TRAINING TO BUILD TALENTED YOUNG INVESTIGATORS IN THESE AREAS, FUNDED THROUGH COMPETITIVE RESEARCH TRAINING GRANTS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION; USE SMALL BUSINESS TO MEET FEDERAL RESEARCH AND DEVELOPMENT NEEDS; FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY SOCIALLY AND ECONOMICALLY DISADVANTAGED PERSONS; AND INCREASE PRIVATE-SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION; FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE R&D BETWEEN SMALL BUSINESSES AND RESEARCH INSTITUTIONS, AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL R&D.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Palo Alto,
California
94304
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 423% from $587,951 to $3,076,107.
The Leland Stanford Junior University was awarded
Advanced AI-Powered Echocardiography Platform Heart Failure Prediction
Project Grant R01HL157235
worth $3,076,107
from National Heart Lung and Blood Institute in January 2021 with work to be completed primarily in Palo Alto California United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.837 Cardiovascular Diseases Research.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 4/6/26
Period of Performance
1/1/22
Start Date
12/31/26
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Transaction History
Modifications to R01HL157235
Additional Detail
Award ID FAIN
R01HL157235
SAI Number
R01HL157235-4201843523
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Funding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Awardee UEI
HJD6G4D6TJY5
Awardee CAGE
1KN27
Performance District
CA-16
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Heart, Lung, and Blood Institute, National Institutes of Health, Health and Human Services (075-0872) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,134,025 | 100% |
Modified: 4/6/26