Due to the enormous amount of imaging data that is becoming available, there is a wide range of possible improvements that can be provided by artificial intelligence (AI) algorithms in clinical care covering diagnosis and decision support. Therefore, it is crucial how we proceed in managing and handling this data, e.g. medical images, and to define which metadata must be considered to use this data to their full potential. The authors of this study report on the state of the art in metadata models for medical imaging, current limitations, and future developments, as well as describe the strategy adopted by the Horizon 2020 “AI for Health Imaging” projects.
Key points
- Metadata are essential for the correct use and interpretation of medical images.
- An appropriate and possibly standardised data model is necessary to represent these data and their correlations.
- We report the state of the art of metadata models and the position of Horizon 2020 “AI for Health Imaging” projects.
Article: Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks
Authors: Haridimos Kondylakis, Esther Ciarrocchi, Leonor Cerda-Alberich, Ioanna Chouvarda, Lauren A. Fromont, Jose Manuel Garcia-Aznar, Varvara Kalokyri, Alexandra Kosvyra, Dawn Walker, Guang Yang, Emanuele Neri & the AI4HealthImaging Working Group on metadata models**
