000118994 001__ 118994
000118994 005__ 20230914083641.0
000118994 0247_ $$2doi$$a10.1186/s41747-022-00281-1
000118994 0248_ $$2sideral$$a130362
000118994 037__ $$aART-2022-130362
000118994 041__ $$aeng
000118994 100__ $$aKondylakis, Haridimos
000118994 245__ $$aPosition of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks
000118994 260__ $$c2022
000118994 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118994 5203_ $$aA 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.
000118994 536__ $$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/EC/H2020/952103/EU/Novel pan-European imaging platform for artificial intelligence advances in oncology/EuCanImage$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 952103-EuCanImage$$9info:eu-repo/grantAgreement/EC/H2020/952159/EU/An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum/ ProCAncer-I$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 952159- ProCAncer-I$$9info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer management/CHAIMELEON$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 952172-CHAIMELEON$$9info:eu-repo/grantAgreement/EC/H2020/952179/EU/A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer/INCISIVE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 952179-INCISIVE
000118994 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000118994 592__ $$a0.789$$b2022
000118994 593__ $$aRadiology, Nuclear Medicine and Imaging$$c2022$$dQ2
000118994 594__ $$a6.2$$b2022
000118994 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118994 700__ $$aCiarrocchi, Esther
000118994 700__ $$aCerda-Alberich, Leonor
000118994 700__ $$aChouvarda, Ioanna
000118994 700__ $$aFromont, Lauren A.
000118994 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, Jose Manuel$$uUniversidad de Zaragoza
000118994 700__ $$aKalokyri, Varvara
000118994 700__ $$aKosvyra, Alexandra
000118994 700__ $$aWalker, Dawn
000118994 700__ $$aYang, Guang
000118994 700__ $$aNeri, Emanuele
000118994 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000118994 773__ $$g6 (2022), 29 [15 pp.]$$pEuropean radiol. exp.$$tEuropean radiology experimental$$x2509-9280
000118994 8564_ $$s1065183$$uhttps://zaguan.unizar.es/record/118994/files/texto_completo.pdf$$yVersión publicada
000118994 8564_ $$s2356465$$uhttps://zaguan.unizar.es/record/118994/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118994 909CO $$ooai:zaguan.unizar.es:118994$$particulos$$pdriver
000118994 951__ $$a2023-09-13-14:09:27
000118994 980__ $$aARTICLE