SBIR/STTR Research Award

SBIR Phase II:Quantification of Operative Performance via Simulated Surgery, Capacitive Sensing, and Machine Learning to Improve Surgeon Performance andMedical Device Development

Award identifier 0774ec6a6c6369cd1db67be77a6807b5d51ae78cf4cb2b595e5db8250769628bAwarding agency National Science Foundation

Award amount

$996,413

Program and phase

SBIR · Phase II

Award year

2023

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
RESUTURE INC.
Proposal award date
2023-03-01
Contract end date
2025-02-28
UEI
DD4NZFP3KVW8
Solicitation number
NSF 22-552
Topic code
DH

Research 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. _x000D_ _x000D_ 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._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.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

2 SBIR/STTR records share this UEI (2021–2023). Reported award amounts total $1,249,875. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: National Science Foundation

Agency intelligence

National Science Foundation

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