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STTR Phase I: AI-assisted Assessment, Tracking, and Reporting of COVID-19 Severity on Chest CT

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2032534
Agency Tracking Number: 2032534
Amount: $256,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: DH
Solicitation Number: N/A
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-01
Award End Date (Contract End Date): 2021-01-31
Small Business Information
HOOVER, AL 35226
United States
DUNS: 117218324
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Robert Jacobus
 (205) 440-2980
Business Contact
 Robert Jacobus
Phone: (205) 440-2980
Research Institution
 University of Alabama at Birmingham
 Srini Tridandapani
AB 1170 1720 2nd Avenue South
Birmingham, AL 35294
United States

 Nonprofit College or University

The broader impact /commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to leverage artificial intelligence (AI) to reduce errors and improve accuracy, standardization, agreement, and reporting in evaluation of COVID-19 lung disease severity on chest computed tomography (CT) images. Chest CT procedures play a critical role in COVID-19 patients but current methods for evaluating chest CT images lack accurate, quantitative, or consistent information, leading to text-based reports that are difficult to interpret. The proposed AI-assisted COVID-19 chest CT workflow will efficiently capture the fraction of lung involvement and improve communication with clinicians by providing a standardized graphical report, key images of important findings, and structured text. The quantitative data will standardize reporting on an individual patient basis and provide data for population-level analyses, thereby offering the potential to significantly advance scientific knowledge of COVID-19 lung disease on a national level. This STTR Phase I project proposes to develop an AI-assisted COVID-19 chest CT workflow to rapidly and objectively quantify the percentage of lung involvement, classify lung involvement using the COVID-19 Reporting and Data System (CO-RADS), track common and uncommon COVID-19 lung findings, and automatically generate summary reports with a graph, key images, and structured text. The standard-of-care for assessing and reporting COVID-19 lung disease severity on chest CT images involves dictated text-based reports that are subjective, highly variable, inefficient to generate and interpret, prone to errors, incomplete, and qualitative with data provided in an unstandardized format. The proposed AI-assisted COVID-19 chest CT workflow will reduce interpretation errors and omissions and improve accuracy, standardization, inter-observer agreement, efficiency, and reporting in evaluation of COVID-19 disease severity and response to treatment. This project will validate the working prototype with a team of expert clinicians. 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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