| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | February 17, 2023 |
| Latest Amendment Date: | August 9, 2024 |
| Award Number: | 2223976 |
| Award Instrument: | Cooperative Agreement |
| Program Manager: |
Alastair Monk
amonk@nsf.gov (703)292-4392 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | March 1, 2023 |
| End Date: | February 28, 2026 (Estimated) |
| Total Intended Award Amount: | $996,413.00 |
| Total Awarded Amount to Date: | $1,194,616.00 |
| Funds Obligated to Date: |
FY 2024 = $198,203.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
12425 W BELL RD STE 110 SURPRISE AZ US 85378-9022 (619)784-9777 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
12425 W BELL RD STE 110 Surprise AZ US 85378-9022 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase II |
| Primary Program Source: |
01AB2324DB R&RA DRSA DEFC AAB |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve surgical skill acquisition, assessment of surgical performance, and medical device training. The apprenticeship-based model of surgical training has created inefficiencies in the medical device and healthcare industries. This problem is exacerbated by the evolving complexity and specialization of surgical procedures and devices. The proposed technology combines lifelike, physical simulated procedures, novel sensing technologies, and machine-learned data analytics to address a universal market need for data-driven training. The technology developed during this project will result in surgical simulation platforms to improve procedural competency and the ability to practice device deployment outside of the operating room, while providing critical data-driven insight into surgical performance and quantitative evaluation. Ultimately, this solution could reduce patient costs, improve outcomes, and expedite medical device development and adoption.
The proposed project will result in the development of a comprehensive system that collects data and evaluates vascular surgical operative performance in both the open and endovascular fields. An open vascular surgery simulation platform previously developed to train surgeons will be expanded to include endovascular procedures and the integration of capacitive sensors to capture a comprehensive set of operative performance data. This project aims to use artificial intelligence to classify key performance metrics from the collected dataset to build a comprehensive model to classify operative performance. A data-driven platform for surgical training and medical device development is not currently commercially available and the industry currently relies on increasingly cost-prohibitive means to provide vital surgical training.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
