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

Fecha de publicación: Fecha Ahead of Print:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Kondylakis, Haridimos
  • Kalokyri, Varvara
  • Sfakianakis, Stelios
  • Marias, Kostas
  • Tsiknakis, Manolis
  • Jimenez-Pastor, Ana
  • Camacho-Ramos, Eduardo
  • Segrelles, J. Damian
  • Lopez-Huguet, Sergio
  • Barelle, Caroline
  • Kogut-Czarkowska, Magdalena
  • Tsakou, Gianna
  • Siopis, Nikolaos
  • Sakellariou, Zisis
  • Bizopoulos, Paschalis
  • Drossou, Vicky
  • Lalas, Antonios
  • Votis, Konstantinos
  • Mallol, Pedro
  • Alberich, Leonor Cerda
  • Seymour, Karine
  • Boucher, Samuel
  • Ciarrocchi, Esther
  • Fromont, Lauren
  • Rambla, Jordi
  • Harms, Alexander
  • Gutierrez, Andrea
  • Starmans, Martijn P. A.
  • Prior, Fred
  • Gelpi, Josep Ll
  • Lekadir, Karim

Grupos

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.

© 2023. The Author(s).

Datos de la publicación

ISSN/ISSNe:
2509-9280, 2509-9280

European Radiology Experimental  SPRINGERNATURE

Tipo:
Article
Páginas:
20-20
PubMed:
37150779
Factor de Impacto:
1,049 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 2

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Keywords

  • Artificial intelligence; Data anonymization; Data management; Diagnostic imaging; Neoplasms

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