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. 2022 Jul 1;6(1):29.
doi: 10.1186/s41747-022-00281-1.

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

Haridimos Kondylakis  1 , Esther Ciarrocchi  2 , Leonor Cerda-Alberich  3 , Ioanna Chouvarda  4 , Lauren A Fromont  5 , Jose Manuel Garcia-Aznar  6 , Varvara Kalokyri  7 , Alexandra Kosvyra  4 , Dawn Walker  8 , Guang Yang  9 , Emanuele Neri  7 ; the AI4HealthImaging Working Group on metadata models**
Affiliations

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

Haridimos Kondylakis et al. Eur Radiol Exp. .

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.

Keywords: Artificial intelligence; Diagnostic imaging; Metadata; Radiation therapy; Radiomics.

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Conflict of interest statement

Emanuele Neri is a member of the European Radiology Experimental Editorial Board. He has not taken part in the review or selection process of this article. The remaning authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
An example of a Digital Imaging and Communications in Medicine (DICOM) contrast-enhanced magnetic resonance image (T1-weighted sequence) of the prostate and DICOM metadata about patient demographics, acquisition-related parameters and image-related parameters
Fig. 2
Fig. 2
Level of EuCanImage variable mapping into the Accelerating Research in Genomic Oncology (ARGO) model based on the clinical use cases, at the time of assessment (July 2021). TBC To be confirmed
Fig. 3
Fig. 3
The design of the INCISIVE data model
See this image and copyright information in PMC

References

    1. Kohli MD, Summers RM, Geis JR. Medical image data and datasets in the era of machine learning - whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging. 2017;30:392–399. doi: 10.1007/s10278-017-9976-3. - DOI - PMC - PubMed
    1. Jimenez-del-Toro O, Cirujeda P, Müller H. Combining radiology images and clinical metadata for multimodal medical case-based retrieval. Cham: Cloud-based benchmarking of medical image analysis. Springer International Publishing; 2017. pp. 221–236.
    1. Martí-Bonmatí L, Alberich-Bayarri Á, Ladenstein R, et al (2020) PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp 4:22. 10.1186/s41747-020-00150-9 - PMC - PubMed
    1. The PRIMAGE project (2021). https://www.primageproject.eu/. Accessed 22 Dec 2021
    1. The EuCanImage project (2021). https://eucanimage.eu/. Accessed 22 Dec 2021

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