2208248
Project Grant
Overview
Grant Description
Sbir Phase I: Development of Novel Artificial Intelligence (AI)-Enabled, Non-Invasive, Heart Attack Diagnostics - The broader impact of this Small Business Innovation Research (SBIR) Phase I project is a novel, portable, diagnostic device for non-invasively diagnosing myocardial infarctions (MI) and ischemia in real-time with a high level of sensitivity and specificity.
The system aims to provide an accurate point-of-care clinical classification for the 805,000 heart attack cases occurring in the US each year with a real-time diagnostic and monitoring tool. The system aims to significantly reduce the time needed with current invasive sampling and blood analysis measures, thereby improving patient outcomes during the first critical hours of an MI while saving healthcare resources and improving efficiency.
The portable nature of the technology enables other forms of integration including at home, in clinic, hospital bedside use, or field use applications. This Small Business Innovation Research (SBIR) Phase I project will develop a proof-of-concept diagnostic-prognostic machine learning-based system for detecting MI. The scope of activities includes testing multiple techniques and models and producing distribution analyses and event plots of training data in order to optimize performance compared to clinically adjudicated events.
Success measures include a data model which, when using their proprietary external noninvasive transdermal biomarker sensor with their wearable garment electrocardiograms (EKGs), are able to detect MI with > 85% accuracy. The device will also be able to detect clinically relevant ischemia with > 85% accuracy. These results will progress the company's objective of completing a standalone, noninvasive, real-time diagnostic and remote monitoring tool.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
The system aims to provide an accurate point-of-care clinical classification for the 805,000 heart attack cases occurring in the US each year with a real-time diagnostic and monitoring tool. The system aims to significantly reduce the time needed with current invasive sampling and blood analysis measures, thereby improving patient outcomes during the first critical hours of an MI while saving healthcare resources and improving efficiency.
The portable nature of the technology enables other forms of integration including at home, in clinic, hospital bedside use, or field use applications. This Small Business Innovation Research (SBIR) Phase I project will develop a proof-of-concept diagnostic-prognostic machine learning-based system for detecting MI. The scope of activities includes testing multiple techniques and models and producing distribution analyses and event plots of training data in order to optimize performance compared to clinically adjudicated events.
Success measures include a data model which, when using their proprietary external noninvasive transdermal biomarker sensor with their wearable garment electrocardiograms (EKGs), are able to detect MI with > 85% accuracy. The device will also be able to detect clinically relevant ischemia with > 85% accuracy. These results will progress the company's objective of completing a standalone, noninvasive, real-time diagnostic and remote monitoring tool.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Woodstock,
Georgia
30188-7600
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
RCE Technologies was awarded
Project Grant 2208248
worth $255,892
from National Science Foundation in January 2023 with work to be completed primarily in Woodstock Georgia United States.
The grant
has a duration of 5 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Development of novel artificial intelligence (AI)-enabled, non-invasive, heart attack diagnostics
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is a novel, portable, diagnostic device for non-invasively diagnosing myocardial infarctions (MI) and ischemia in real-time with a high level of sensitivity and specificity. The system aims to provide an accurate point-of-care clinical classification for the 805,000 heart attack cases occurring in the US each year with a real-time diagnostic and monitoring tool. The system aims to significantly reduce the time needed with current invasive sampling and blood analysis measures, thereby improving patient outcomes during the first critical hours of an MI while saving healthcare resources and improving efficiency. The portable nature of the technology enables other forms of integration including at home, in clinic, hospital bedside use, or field use applications._x000D_ _x000D_ This Small Business Innovation Research (SBIR) Phase I project will develop a proof-of-concept diagnostic-prognostic machine learning-based system for detecting MI. The scope of activities includes testing multiple techniques and models and producing distribution analyses and event plots of training data in order to optimize performance compared to clinically adjudicated events. Success measures include a data model which, when using their proprietary external noninvasive transdermal biomarker sensor with their wearable garment electrocardiograms (EKGs), are able to detect MI with greater than 85% accuracy.The device will also be able to detect clinically relevant ischemia with greater than 85% accuracy. These results will progress the company’s objective of completing a standalone, noninvasive, real-time diagnostic and remote monitoring tool._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
MD
Solicitation Number
NSF 21-562
Status
(Complete)
Last Modified 1/24/23
Period of Performance
1/15/23
Start Date
6/30/23
End Date
Funding Split
$255.9K
Federal Obligation
$0.0
Non-Federal Obligation
$255.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2208248
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
For-Profit Organization (Other Than Small Business)
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
GK5RCZ9RTDM7
Awardee CAGE
8BQD2
Performance District
Not Applicable
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $255,892 | 100% |
Modified: 1/24/23