Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

Abstract

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 “AI for Health Imaging” projects, which are all dedicated to the creation of imaging biobanks.

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Item Type:	Article
Authors/Creators:	Kondylakis, H. Ciarrocchi, E. Cerda-Alberich, L. Chouvarda, I. Fromont, L.A. Garcia-Aznar, J.M. Kalokyri, V. Kosvyra, A. Walker, D. https://orcid.org/0000-0001-8801-8093 Yang, G. Neri, E.
Copyright, Publisher and Additional Information:	© 2022 The Author(s) under exclusive licence to European Society of Radiology. Open Access. This 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/.
Keywords:	Artificial intelligence; Diagnostic imaging; Metadata; Radiation therapy; Radiomics; Algorithms; Artificial Intelligence; Biological Specimen Banks; Diagnostic Imaging; Metadata
Dates:	Published: 1 July 2022 Published (online): 1 July 2022 Accepted: 20 April 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:	Funder Grant number European Commission - HORIZON 2020 826494
Depositing User:	Symplectic Sheffield
Date Deposited:	08 Aug 2022 11:07
Last Modified:	08 Aug 2022 11:07
Status:	Published
Publisher:	Springer Nature
Refereed:	Yes
Identification Number:	10.1186/s41747-022-00281-1
Related URLs:	PubMed URL
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:189781

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Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

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