IDIOMS: Infectious Disease Imaging Outbreak Monitoring System
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Aryya Gangopadhyay, Michael Morris, Babak Saboury, Eliot Siegel, and Yelena Yesha. 2020. IDIOMS: Infectious Disease Imaging Outbreak Monitoring System. Digit. Gov.: Res. Pract. 2, 1, Article 15 (November 2020), 5 pages. https://doi.org/10.1145/3428092
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
In this commentary, we propose a framework for convergence accelerator research leveraging AI models with medical images for effective diagnosis, monitoring, and treatment of diseases with pandemic potential. The goal is to create a novel Infectious Disease Imaging Outbreak Monitoring System (IDIOMS) to prospectively anticipate, identify, and characterize potential infectious disease outbreaks across a population of patients in real-time as patients receive medical imaging examinations. IDIOMS will provide critical surveillance before an outbreak is widely identified and before adequate testing resources are available. This can be achieved through the creation of an infectious disease medical imaging library resource and the implementation of a computer
vision approach to infectious disease medical imaging classification using Artificial Intelligence (AI). Improved characterization of Infectious Disease (ID) by medical imaging could provide an earlier indicator for a recurrent or future pandemic, even before the underlying pathogen is identified clinically or before an alternative commercially available reliable laboratory test can be developed and distributed. Such an infectious disease medical imaging classifier could have altered the course of the COVID-19 pandemic caused by SARS-CoV-2.