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Review
. 2023 May 8;7(1):20.
doi: 10.1186/s41747-023-00336-x.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Haridimos Kondylakis  1 , Varvara Kalokyri  2 , Stelios Sfakianakis  2 , Kostas Marias  2 , Manolis Tsiknakis  2 , Ana Jimenez-Pastor  3 , Eduardo Camacho-Ramos  3 , Ignacio Blanquer  4 , J Damian Segrelles  4 , Sergio López-Huguet  4 , Caroline Barelle  5 , Magdalena Kogut-Czarkowska  6 , Gianna Tsakou  7 , Nikolaos Siopis  8 , Zisis Sakellariou  8 , Paschalis Bizopoulos  8 , Vicky Drossou  8 , Antonios Lalas  8 , Konstantinos Votis  8 , Pedro Mallol  9 , Luis Marti-Bonmati  9 , Leonor Cerdá Alberich  9 , Karine Seymour  10 , Samuel Boucher  10 , Esther Ciarrocchi  11 , Lauren Fromont  12 , Jordi Rambla  12 , Alexander Harms  13 , Andrea Gutierrez  13 , Martijn P A Starmans  13 , Fred Prior  14 , Josep Ll Gelpi  15 , Karim Lekadir  15
Affiliations
Review

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Haridimos Kondylakis et al. Eur Radiol Exp. .

Abstract

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Keywords: Artificial intelligence; Data anonymization; Data management; Diagnostic imaging; Neoplasms.

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Conflict of interest statement

AJP and ECR are employees of Quibim S.L. CB is an employee of European Dynamics. MKC is an employee of Timelex BV/SRL. GT is an employee of Maggioli Spa. KS and SB are employees of Medexprim. The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
INCISIVE common data model backbone
Fig. 2
Fig. 2
The Extract-Transform-Load (ETL) process in INCISIVE
See this image and copyright information in PMC

References

    1. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15:504–508. doi: 10.1016/j.jacr.2017.12.026. - DOI - PubMed
    1. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–1057. doi: 10.1007/s10278-013-9622-7. - DOI - PMC - PubMed
    1. Fedorov A, Longabaugh WJR, Pot D, et al. NCI imaging data commons. Cancer Res. 2021;81:4188–4193. doi: 10.1158/0008-5472.CAN-21-0950. - DOI - PMC - PubMed
    1. Martí-Bonmatí L, Alberich-Bayarri Á, Ladenstein R, et al. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp. 2020;4:1–11. doi: 10.1186/s41747-020-00150-9. - DOI - PMC - PubMed
    1. Martí-Bonmatí L, Miguel A, Suárez A et al (2022) CHAIMELEON project: creation of a pan-European repository of health imaging data for the development of AI-powered cancer management tools. Front Oncol 515. 10.3389/fonc.2022.742701 - PMC - PubMed

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