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SBIR Phase I:Digitizing The Pathologist In The Operating Room

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2126919
Agency Tracking Number: 2126919
Amount: $256,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DH
Solicitation Number: NSF 21-562
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-15
Award End Date (Contract End Date): 2023-12-31
Small Business Information
500 E 63RD ST APT 20A
NEW YORK, NY 10065
United States
DUNS: 080990952
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel Gareau
 (503) 708-7078
 dan@surgivance.com
Business Contact
 Daniel Gareau
Phone: (503) 708-7078
Email: dan@surgivance.com
Research Institution
N/A
Abstract

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the quality and reproducibility of cancer diagnosis and margin screening.Surgery currently represents the best opportunity for curing invasive cancers but is limited by modern pathology methods. Mohs surgery is the treatment of choice for skin cancer because it results in the highest cure rates, but it often takes place in stages, extending surgical procedures. The use of ex vivo confocal microscopy (XVM) is potentially faster, less costly, and inherently both 3-D and digitized, with benefits including: 1) enhanced accuracy, precision, and margin control via enriched 3D information content and simplified specimen orientation maintenance, decreasing error and improving functional outcomes; 2) decreased duration of open surgical wounds potentially reducing the rate of complications; and (3) enhanced and accelerated surgical workflows.This Small Business Innovation Research (SBIR) Phase I project aims to improve Mohs surgery and other margin screening applications. Using confocal image processing software, the proposed software will improve current pathology processing methods by enabling rapid processing and re-coloring of XVM images, visualization without the need for retraining, and automated detection of residual tumor at the patient’s bedside, all within minutes. The research described here will result in the production of a fully functional and robust software product through the following development projects: 1) Artificial intelligence (AI)-based software for image preprocessing and colorization/enhancement, and 2) AI-based software for automated identification of key morphological features and diagnosis of basal cell and squamous cell carcinoma. The proposed technology will streamline cancer specimen processing, ultimately improving patient outcomes.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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