Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease
Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
2 ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008, Bern, Switzerland
3 Department of University Emergency Center of Inselspital, University of Bern, Freiburgstrasse 15, 3010, Bern, Switzerland
4 Department of Radiology, Inselspital, Bern University Hospital, University of Bern, 3012, Bern, Switzerland
Accepted: 25 January 2023
Published online: 8 May 2023
Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT–PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health.
© The Author(s) 2023
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