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STTR Phase I: Computerized System for Detection, Assessment, and Visualization of Intraoperative Bleeding During Robotic and Laparoscopic Surgery

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1953822
Agency Tracking Number: 1953822
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: R
Solicitation Number: N/A
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-06-01
Award End Date (Contract End Date): 2021-05-31
Small Business Information
29777 Telegraph Rd, Suite # 1670
Southfield, MI 48034
United States
DUNS: 080316363
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Madhu Reddiboina
 (248) 376-8670
 madhu.reddiboina@rediminds.com
Business Contact
 Madhu Reddiboina
Phone: (248) 376-8670
Email: madhu.reddiboina@rediminds.com
Research Institution
 Wayne State University
 Abhilash Pandya
 
5057 Woodward 6th Floor
Detroit, MI 48202
United States

 Nonprofit college or university
Abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the morbidity and mortality of patients undergoing surgery. Inadvertent bleeding during surgery represents a critical problem that occurs during all types of procedures on millions of patients around the world. This project will advance a robotic surgical tool system using artificial intelligence (AI) to manage intraoperative bleeding. Reduced blood loss will lead to a reduced demand for blood transfusions, reduced healthcare costs, and improved recuperation from surgery. This technology will advance a universal standard for bolt-on safety utilities to the evolving surgical tool manufacturer market. The Small Business Technology Transfer (STTR) Phase I project will advance the translation of an intelligent intraoperative system. Currently, there is no tool to detect or characterize bleeding, so the surgeon must continually monitor the camera view for bleeding and estimate the source of the bleed, which is often submerged in a pool of blood. The proposed effort will advance a prototype to assist a surgeon in detecting, visualizing, and characterizing arterial bleeding in real time during urological surgery. The source of the bleeding will be presented to the surgeon using 2D or 3D (augmented reality) overlays, enabling him/her to control the bleeding precisely and quickly. The technology fuses robotics, computer vision, and machine learning in a novel manner to produce a surgical tool that will significantly advance the current state-of-the-art. 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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