Abstract

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [18F]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.

Details

Title
A role for artificial intelligence in molecular imaging of infection and inflammation
Author
Schwenck, Johannes 1   VIAFID ORCID Logo  ; Kneilling, Manfred 2 ; Riksen, Niels P. 3 ; la Fougère, Christian 4 ; Mulder, Douwe J. 5 ; Slart, Riemer J. H. A. 6 ; Aarntzen, Erik H. J. G. 7 

1  Eberhard Karls University, Department of Nuclear Medicine and Clinical Molecular Imaging, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Eberhard Karls University, Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Eberhard Karls University, Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447) 
2  Eberhard Karls University, Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Eberhard Karls University, Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Eberhard Karls University, Department of Dermatology, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447) 
3  Radboud University Medical Center, Department of Internal Medicine, Nijmegen, The Netherlands (GRID:grid.10417.33) (ISNI:0000 0004 0444 9382) 
4  Eberhard Karls University, Department of Nuclear Medicine and Clinical Molecular Imaging, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Eberhard Karls University, Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447) 
5  University of Twente, Department of Biomedical Photonic Imaging, Faculty of Science and Technology, Enschede, The Netherlands (GRID:grid.6214.1) (ISNI:0000 0004 0399 8953) 
6  University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Groningen, The Netherlands (GRID:grid.4494.d) (ISNI:0000 0000 9558 4598); Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands (GRID:grid.10417.33) (ISNI:0000 0004 0444 9382) 
7  University of Groningen, University Medical Center Groningen, Department of Internal Medicine, Groningen, The Netherlands (GRID:grid.4494.d) (ISNI:0000 0000 9558 4598) 
Pages
17
Publication year
2022
Publication date
Dec 2022
Publisher
Springer Nature B.V.
e-ISSN
3005-074X
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s41824-022-00138-1
ProQuest document ID
2708612430
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.