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

Information

  • NSF Award
  • 2032534
Owner
  • Award Id
    2032534
  • Award Effective Date
    8/1/2020 - 4 years ago
  • Award Expiration Date
    1/31/2021 - 3 years ago
  • Award Amount
    $ 256,000.00
  • Award Instrument
    Standard Grant

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

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. <br/><br/>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.<br/><br/>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.

  • Program Officer
    Alastair Monkamonk@nsf.gov7032924392
  • Min Amd Letter Date
    8/25/2020 - 4 years ago
  • Max Amd Letter Date
    10/21/2020 - 4 years ago
  • ARRA Amount

Institutions

  • Name
    AI METRICS, LLC
  • City
    HOOVER
  • State
    AL
  • Country
    United States
  • Address
    432 RENAISSANCE DR
  • Postal Code
    352264231
  • Phone Number
    2055733332

Investigators

  • First Name
    Srini
  • Last Name
    Tridandapani
  • Email Address
    srinit@yahoo.com
  • Start Date
    8/25/2020 12:00:00 AM
  • First Name
    Maqbool
  • Last Name
    Patel
  • Email Address
    maqbool@aimetrics.com
  • Start Date
    8/25/2020 12:00:00 AM
  • End Date
    10/21/2020
  • First Name
    Robert
  • Last Name
    Jacobus
  • Email Address
    bob@aimetrics.com
  • Start Date
    10/21/2020 12:00:00 AM

Program Element

  • Text
    STTR Phase I
  • Code
    1505

Program Reference

  • Text
    INSTRUMENTATION & DIAGNOSTICS
  • Text
    COVID-19 Research
  • Text
    Health Care Enterprise Systems
  • Code
    8023
  • Text
    Software Services and Applications
  • Code
    8032
  • Text
    Health and Safety
  • Code
    8042
  • Text
    EXP PROG TO STIM COMP RES
  • Code
    9150