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

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2223976
Agency Tracking Number: 2223976
Amount: $996,413.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: DH
Solicitation Number: NSF 22-552
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-03-01
Award End Date (Contract End Date): 2025-02-28
Small Business Information
12425 W Bell Rd, Suite 110
Surprise, AZ 85378
United States
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Hannah Eherenfeldt
 (619) 784-9777
Business Contact
 Hannah Eherenfeldt
Phone: (619) 784-9777
Research Institution

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_
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_
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.

* Information listed above is at the time of submission. *

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