Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Jiaxi, Mougiakakou, Stavroula, Xue, Song, Afshar-Oromieh, Ali, Hautz, Wolf, Christe, Andreas, Sznitman, Raphael, Rominger, Axel, Ebner, Lukas, Shi, Kuangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
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
_version_ 1785038239092965376
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
work_keys_str_mv AT hujiaxi artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT mougiakakoustavroula artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT xuesong artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT afsharoromiehali artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT hautzwolf artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT christeandreas artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT sznitmanraphael artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT romingeraxel artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT ebnerlukas artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease
AT shikuangyu artificialintelligenceforreducingtheradiationburdenofmedicalimagingforthediagnosisofcoronavirusdisease