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Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165296/ https://www.ncbi.nlm.nih.gov/pubmed/37192839 http://dx.doi.org/10.1140/epjp/s13360-023-03745-4 |
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author | Hu, Jiaxi Mougiakakou, Stavroula Xue, Song Afshar-Oromieh, Ali Hautz, Wolf Christe, Andreas Sznitman, Raphael Rominger, Axel Ebner, Lukas Shi, Kuangyu |
author_facet | Hu, Jiaxi Mougiakakou, Stavroula Xue, Song Afshar-Oromieh, Ali Hautz, Wolf Christe, Andreas Sznitman, Raphael Rominger, Axel Ebner, Lukas Shi, Kuangyu |
author_sort | Hu, Jiaxi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10165296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101652962023-05-09 Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease Hu, Jiaxi Mougiakakou, Stavroula Xue, Song Afshar-Oromieh, Ali Hautz, Wolf Christe, Andreas Sznitman, Raphael Rominger, Axel Ebner, Lukas Shi, Kuangyu Eur Phys J Plus Review 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. Springer Berlin Heidelberg 2023-05-08 2023 /pmc/articles/PMC10165296/ /pubmed/37192839 http://dx.doi.org/10.1140/epjp/s13360-023-03745-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Hu, Jiaxi Mougiakakou, Stavroula Xue, Song Afshar-Oromieh, Ali Hautz, Wolf Christe, Andreas Sznitman, Raphael Rominger, Axel Ebner, Lukas Shi, Kuangyu Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title | Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title_full | Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title_fullStr | Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title_full_unstemmed | Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title_short | Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
title_sort | artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165296/ https://www.ncbi.nlm.nih.gov/pubmed/37192839 http://dx.doi.org/10.1140/epjp/s13360-023-03745-4 |
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