A role for artificial intelligence in molecular imaging of infection and inflammation
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 infl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-09-01
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| Series: | European Journal of Hybrid Imaging |
| Online Access: | https://doi.org/10.1186/s41824-022-00138-1 |
| _version_ | 1798032531276169216 |
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| author | Johannes Schwenck Manfred Kneilling Niels P. Riksen Christian la Fougère Douwe J. Mulder Riemer J. H. A. Slart Erik H. J. G. Aarntzen |
| author_facet | Johannes Schwenck Manfred Kneilling Niels P. Riksen Christian la Fougère Douwe J. Mulder Riemer J. H. A. Slart Erik H. J. G. Aarntzen |
| author_sort | Johannes Schwenck |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:15:32Z |
| format | Article |
| id | doaj.art-1010a0baffd24cc79a3cd66832c4813c |
| institution | Directory Open Access Journal |
| issn | 2510-3636 |
| language | English |
| last_indexed | 2024-04-11T20:15:32Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Journal of Hybrid Imaging |
| spelling | doaj.art-1010a0baffd24cc79a3cd66832c4813c2022-12-22T04:04:59ZengSpringerOpenEuropean Journal of Hybrid Imaging2510-36362022-09-016111610.1186/s41824-022-00138-1A role for artificial intelligence in molecular imaging of infection and inflammationJohannes Schwenck0Manfred Kneilling1Niels P. Riksen2Christian la Fougère3Douwe J. Mulder4Riemer J. H. A. Slart5Erik H. J. G. Aarntzen6Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls UniversityDepartment of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls UniversityDepartment of Internal Medicine, Radboud University Medical CenterDepartment of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls UniversityDepartment of Biomedical Photonic Imaging, Faculty of Science and Technology, University of TwenteDepartment of Nuclear Medicine and Molecular Imaging, University Medical Center GroningenDepartment of Internal Medicine, University of Groningen, University Medical Center GroningenAbstract 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.https://doi.org/10.1186/s41824-022-00138-1 |
| spellingShingle | Johannes Schwenck Manfred Kneilling Niels P. Riksen Christian la Fougère Douwe J. Mulder Riemer J. H. A. Slart Erik H. J. G. Aarntzen A role for artificial intelligence in molecular imaging of infection and inflammation European Journal of Hybrid Imaging |
| 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 |
| url | https://doi.org/10.1186/s41824-022-00138-1 |
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