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Active STANDARD GRANT National Science Foundation (US)

SBIR Phase I: Semi-autonomous Image-guided Robotic Suturing System

$2.64M USD

Funder National Science Foundation (US)
Recipient Organization Surgical Vision Systems, Inc.
Country United States
Start Date Jun 01, 2025
End Date May 31, 2026
Duration 364 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2423546
Grant Description

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be a novel robotic technology for improving and automating vascular surgical procedures. A persistently high failure rate exists in microvascular surgery with anastomotic thromboses adversely affecting some of the most vulnerable populations.

The system aims to define a new paradigm by creating a novel system leveraging state-of-the-art imaging, machine learning, and advanced robotics to exceed the capabilities of human performance. The system aims to semi automate these procedures to improve the rates of microvascular anastomotic success, reducing hospital lengths of stay, decreasing re-operations, diminish pain and suffering, and improve longevity of at-risk populations.

The technology presents a novel technological platform for the $6 billion market robotic surgery growing at 15% each year.

This Small Business Innovation Research (SBIR) Phase I project will design and develop a novel robotic surgery system reducing the rates of anastomotic thrombosis in microsurgery. The first objective is to develop a novel optical coherence tomography imaging system integrated with a robotic microvascular suturing tool for microvascular surgery. The second objective will be to test the accuracy of an optical coherence tomography imaging for needle placement in microvascular anastomosis, using synthetic and explanted biologic blood vessels of the images.

Optical coherence tomography images will be compared to electron micrographs for detection of vascular intima, media, and adventitia. The third objective will be to develop machine learning algorithms for accurate image interpretation to guide needle placement using a series of experimental acquired data to achieve the level of control precision needed for micro-anastomosis.

The end result of this project will be to demonstrate technical feasibility for the prototype system.

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.

All Grantees

Surgical Vision Systems, Inc.

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