SBIR/STTR Research Award

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

Award identifier 4d1f2468f1165aa21f438d270374027ce6a12172d4ab7c44fd396eca7f31e115Awarding agency National Science Foundation

Award amount

$256,000

Program and phase

STTR · Phase I

Award year

2020

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
AI METRICS, LLC
Proposal award date
2020-08-01
Contract end date
2021-01-31
UEI
MD66UGK4QL33
Solicitation number
Not reported
Topic code
DH

Research abstract

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.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

2 SBIR/STTR records share this UEI (2020–2021). Reported award amounts total $562,499. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Health and Human Services, National Science Foundation

Agency intelligence

National Science Foundation

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