Journal Article (Review Article)

DKFZ-2023-00725

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Is There a Role of Artificial Intelligence in Preclinical Imaging?

Küper, A.DKFZ* ; Blanc-Durand, P. ; Gafita, A. ; Kersting, D.DKFZ* ; Fendler, W. P.DKFZ* ; Seibold, C. ; Moraitis, A.DKFZ* ; Lückerath, K.DKFZ* ; James, M. L. ; Seifert, R.DKFZ*

2023
Saunders Duluth, Minn.

This record in other databases:  

Please use a persistent id in citations: doi:10.1053/j.semnuclmed.2023.03.003

Abstract: This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.

Note: 2023 Sep;53(5):687-693

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Contributing Institute(s):

1. DKTK Koordinierungsstelle Essen/Düsseldorf (ED01)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Appears in the scientific report 2023

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Database coverage:
; BIOSIS Previews ; BIOSIS Reviews Reports And Meetings ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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