Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease.


Journal

European physical journal plus
ISSN: 2190-5444
Titre abrégé: Eur Phys J Plus
Pays: Germany
ID NLM: 101673272

Informations de publication

Date de publication:
2023
Historique:
received: 31 03 2022
accepted: 25 01 2023
pubmed: 17 5 2023
medline: 17 5 2023
entrez: 16 5 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 37192839
doi: 10.1140/epjp/s13360-023-03745-4
pii: 3745
pmc: PMC10165296
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

391

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

Conflict of interestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Jiaxi Hu (J)

Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland.
ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.

Stavroula Mougiakakou (S)

ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.

Song Xue (S)

ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.

Ali Afshar-Oromieh (A)

Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland.

Wolf Hautz (W)

Department of University Emergency Center of Inselspital, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland.

Andreas Christe (A)

Department of Radiology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland.

Raphael Sznitman (R)

ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.

Axel Rominger (A)

Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland.

Lukas Ebner (L)

Department of Radiology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland.

Kuangyu Shi (K)

Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland.

Classifications MeSH