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A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [(18)F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammator...

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Autores principales: Schwenck, Johannes, Kneilling, Manfred, Riksen, Niels P., la Fougère, Christian, Mulder, Douwe J., Slart, Riemer J. H. A., Aarntzen, Erik H. J. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/
https://www.ncbi.nlm.nih.gov/pubmed/36045228
http://dx.doi.org/10.1186/s41824-022-00138-1
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author Schwenck, Johannes
Kneilling, Manfred
Riksen, Niels P.
la Fougère, Christian
Mulder, Douwe J.
Slart, Riemer J. H. A.
Aarntzen, Erik H. J. G.
author_facet Schwenck, Johannes
Kneilling, Manfred
Riksen, Niels P.
la Fougère, Christian
Mulder, Douwe J.
Slart, Riemer J. H. A.
Aarntzen, Erik H. J. G.
author_sort Schwenck, Johannes
collection PubMed
description The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [(18)F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.
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spelling pubmed-94335582022-09-02 A role for artificial intelligence in molecular imaging of infection and inflammation Schwenck, Johannes Kneilling, Manfred Riksen, Niels P. la Fougère, Christian Mulder, Douwe J. Slart, Riemer J. H. A. Aarntzen, Erik H. J. G. Eur J Hybrid Imaging Review The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [(18)F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment. Springer International Publishing 2022-09-01 /pmc/articles/PMC9433558/ /pubmed/36045228 http://dx.doi.org/10.1186/s41824-022-00138-1 Text en © The Author(s) 2022 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
Schwenck, Johannes
Kneilling, Manfred
Riksen, Niels P.
la Fougère, Christian
Mulder, Douwe J.
Slart, Riemer J. H. A.
Aarntzen, Erik H. J. G.
A role for artificial intelligence in molecular imaging of infection and inflammation
title A role for artificial intelligence in molecular imaging of infection and inflammation
title_full A role for artificial intelligence in molecular imaging of infection and inflammation
title_fullStr A role for artificial intelligence in molecular imaging of infection and inflammation
title_full_unstemmed A role for artificial intelligence in molecular imaging of infection and inflammation
title_short A role for artificial intelligence in molecular imaging of infection and inflammation
title_sort role for artificial intelligence in molecular imaging of infection and inflammation
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/
https://www.ncbi.nlm.nih.gov/pubmed/36045228
http://dx.doi.org/10.1186/s41824-022-00138-1
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