Artificial Intelligence and Infectious Disease Imaging

Author:

Chu, Winston T [ORCID]

Reza, Syed M S

Anibal, James T

Landa, Adam

Crozier,Ian

Bagci, Ulas

Wood, Bradford J

Solomon, Jeffrey
Author Address

Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland, USA., Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA., Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA., Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.,

Abstract:

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions. Published by Oxford University Press on behalf of Infectious Diseases Society of America 2023.

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Author Address