9 results on '"Seymour, Karine"'
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2. Exploitation des données pour la recherche et l’intelligence artificielle : enjeux médicaux, éthiques, juridiques, techniques
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Seymour, Karine, Benyahia, Nesrine, Hérent, Paul, and Malhaire, Caroline
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- 2019
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3. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
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Martí-Bonmatí, Luis, Alberich-Bayarri, Ángel, Ladenstein, Ruth, Blanquer, Ignacio, Segrelles, J. Damian, Cerdá-Alberich, Leonor, Gkontra, Polyxeni, Hero, Barbara, García-Aznar, J. M., Keim, Daniel, Jentner, Wolfgang, Seymour, Karine, Jiménez-Pastor, Ana, González-Valverde, Ismael, Martínez de las Heras, Blanca, Essiaf, Samira, Walker, Dawn, Rochette, Michel, Bubak, Marian, Mestres, Jordi, Viceconti, Marco, Martí-Besa, Gracia, Cañete, Adela, Richmond, Paul, Wertheim, Kenneth Y., Gubala, Tomasz, Kasztelnik, Marek, Meizner, Jan, Nowakowski, Piotr, Gilpérez, Salvador, Suárez, Amelia, Aznar, Mario, Restante, Giuliana, and Neri, Emanuele
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- 2020
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4. Data infrastructures for AI in medical imaging:a report on the experiences of five EU projects
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Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, 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, Kondylakis, Haridimos, Kalokyri, Varvara, Sfakianakis, Stelios, Marias, Kostas, Tsiknakis, Manolis, Jimenez-Pastor, Ana, Camacho-Ramos, Eduardo, Blanquer, Ignacio, Segrelles, J. Damian, López-Huguet, Sergio, Barelle, Caroline, Kogut-Czarkowska, Magdalena, Tsakou, Gianna, Siopis, Nikolaos, Sakellariou, Zisis, Bizopoulos, Paschalis, Drossou, Vicky, Lalas, Antonios, Votis, Konstantinos, Mallol, Pedro, Marti-Bonmati, Luis, Alberich, Leonor Cerdá, Seymour, Karine, Boucher, Samuel, Ciarrocchi, Esther, Fromont, Lauren, Rambla, Jordi, Harms, Alexander, Gutierrez, Andrea, Starmans, Martijn P.A., Prior, Fred, Gelpi, Josep Ll, and Lekadir, Karim
- 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.
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- 2023
5. D10.2. Action Plan for Repository Sustainability
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Caputo, Francesca Pia, Tumminello, Lorenzo, Aghakhanyan, Gayane, Cioni, Dania, Neri, Emanuele, Martínez, Ricard, Barquin, Clara, Suarez, Amelia, Sanchez, Ana Blanco, Blanco, Ana Miguel, Marti-Bonmati, Luis, Blanquer, Ignacio, Seymour, Karine, and Krischak Katharina
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The Deliverable D10.2 is part of WP10 “CHAIMELEON Repository Sustainability after project end”, framed under T10.1 “Actions to ensure CHAIMELEON repository Sustainability”. The deliverable has the aim to design the CHAIMELEON repository sustainability plan during and beyond the duration of the project while providing a governance structure to pursue this goal. The sustainability plan can be thought of as made of two different sets of actions that together can guarantee the CHAIMELEON sustainability: the short-term actions and long-term actions. The short-term actions and promotional actions (paragraph 3) consists of including CHAIMELEON metadata in the EIBIR Imaging Biobank Catalogue (paragraph 3.1) and in the creation of specific “Open Challenges “ and/or “Open Competitions” to promote and incentivise the use and popularity of the repository (paragraph 3.2). In the paragraph 3.3, the importance of the legal framework and the design of the European Union’s digital transformations policies are stressed out for the CHAIMELEON sustainability. The long-term sustainability plan can provide a long-term existence of the repository: this goal can be achieved in the context of the EHDS (paragraph 4.1) and thanks to the EUCAIM project (paragraph 4.2). The importance of the inclusion of the CHAIMELEON repository in the EUCAIM framework is presented together with the needed steps to take for the integration process (paragraph 4.3). Legal and ethical issues of the integration are reported in paragraph 4.4. The Governance structure is defined in paragraph 5.1, while the access model (to define the access roles and the responsibilities related to the access itself) and the definitive version of the DSA are presented in paragraph 5.2 and 5.3, respectively. A summary of the cost and revenues is made in the next paragraph, 6. Briefly, the conclusions for the deliverable D 10.2 are drawn in paragraph 7.
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- 2023
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6. D10.1. Prospective assessment of the Repository sustainability
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Neri, Emanuele, Ciarrocchi, Esther, Shortrede, Jorge, Tumminello, Lorenzo, Seymour, Karine, Martí-Bonmatí, Luis, Jiménez-Pastor, Ana, Bellvis-Bataller, Fuensanta, Blanco, Ana, and Martinez, Ricard
- Abstract
The D10.1 is undertaken as part of WP10 “CHAIMELEON REPOSITORY SUSTAINABILITY AFTER PROJECT END”, framed under T10.1 “Actions to ensure CHAIMELEON repository sustainability”. This deliverable defines the overall strategy for the sustainability of the CHAIMELEON repository during and beyond the duration of the CHAIMELEON project. Moreover, this document will address the challenges that the consortium will have to overcome and the steps to follow for guaranteeing the sustainability of the CHAIMELEON repository beyond the end of the project. The deliverable is divided in the following main sections that include: sustainability challenge, our vision beyond the project, CHAIMELEON governance, costs and the revenue of the CHAIMELEON repository. The sustainability strategy will be reviewed and updated during the project in order to implement the best actions to guarantee the project’s sustainability after its end., {"references":["Chalmers, Don. \"Has the biobank bubble burst? Withstanding the challenges for sustainable biobanking in the digital era.\" PubMed, 12 July 2016, https://pubmed.ncbi.nlm.nih.gov/27405974/. Accessed 15 February 2022.","Schacter, Brent. \"A framework for biobank sustainability.\" PubMed, 2014, https://pubmed.ncbi.nlm.nih.gov/24620771/. Accessed 14 February 2022.","Yong, William H., and David Geffen. \"Sustainability in biobanking.\" NCBI, 1 January 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918833/. Accessed 14 February 2022.","van der Stijl, Rogier. \"Recommendations for a Dutch Sustainable Biobanking Environment.\" PubMed, 27 May 2021, https://pubmed.ncbi.nlm.nih.gov/34042498/. Accessed 15 February 2022.","Vaught, Jim. \"ISSN 1947-5543 (Online) | Biopreservation and biobanking.\" The ISSN Portal, 2021, https://portal.issn.org/resource/ISSN/1947-5543. Accessed 15 February 2022.","Kumar, Awanish. \"Virtual global biorepository: access for all to speed-up result-oriented research.\" PubMed, 2020, https://pubmed.ncbi.nlm.nih.gov/32270405/. Accessed 15 February 2022.","De Souza, Yvonne G. \"Biobanking past, present and future: responsibilities and benefits.\" PubMed, 28 January 2013, https://pubmed.ncbi.nlm.nih.gov/23135167/. Accessed 15 February 2022.","BBMRI-ERIC Directory. BBMRI-ERIC Directory, https://directory.bbmri-eric.eu/#/. Accessed 15 February 2022.","Ministerio de Ciencia e Innovación de España. \"BOE-A-2021-9488 Resolución de 31 de mayo de 2021, del Instituto de Salud Carlos III, OA, MP, por la que se publica el Convenio con el Ministerio de Ciencia e Innovación, para la participación de España en el Consorcio de Infraestructuras de ...\" BOE.es, 31 May 2021, https://www.boe.es/diario_boe/txt.php?id=BOE-A-2021-9488. Accessed 15 February 2022."]}
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- 2023
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7. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools
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Bonmatí, Luis Martí, primary, Miguel, Ana, additional, Suárez, Amelia, additional, Aznar, Mario, additional, Beregi, Jean Paul, additional, Fournier, Laure, additional, Neri, Emanuele, additional, Laghi, Andrea, additional, França, Manuela, additional, Sardanelli, Francesco, additional, Penzkofer, Tobias, additional, Lambin, Phillipe, additional, Blanquer, Ignacio, additional, Menzel, Marion I., additional, Seymour, Karine, additional, Figueiras, Sergio, additional, Krischak, Katharina, additional, Martínez, Ricard, additional, Mirsky, Yisroel, additional, Yang, Guang, additional, and Alberich-Bayarri, Ángel, additional
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- 2022
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8. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, European Commission, Martí-Bonmatí, Luis, Alberich Bayarri, Ángel, Ladenstein, Ruth, Blanquer Espert, Ignacio, Segrelles Quilis, José Damián, Cerdá-Alberich, Leonor, Gkontra, Polyxeni, Hero, Barbara, García-Aznar, J. M., Keim, Daniel, Jentner, Wolfgang, Seymour, Karine, Jiménez-Pastor, Ana, González-Valverde, Ismael, Martínez de las Heras, Blanca, Essiaf, Samira, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, European Commission, Martí-Bonmatí, Luis, Alberich Bayarri, Ángel, Ladenstein, Ruth, Blanquer Espert, Ignacio, Segrelles Quilis, José Damián, Cerdá-Alberich, Leonor, Gkontra, Polyxeni, Hero, Barbara, García-Aznar, J. M., Keim, Daniel, Jentner, Wolfgang, Seymour, Karine, Jiménez-Pastor, Ana, González-Valverde, Ismael, Martínez de las Heras, Blanca, and Essiaf, Samira
- Abstract
[EN] PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
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- 2020
9. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.
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Kondylakis H, Catalan R, Alabart SM, Barelle C, Bizopoulos P, Bobowicz M, Bona J, Fotiadis DI, Garcia T, Gomez I, Jimenez-Pastor A, Karatzanis G, Lekadir K, Kogut-Czarkowska M, Lalas A, Marias K, Marti-Bonmati L, Munuera J, Nikiforaki K, Pelissier M, Prior F, Rutherford M, Saint-Aubert L, Sakellariou Z, Seymour K, Trouillard T, Votis K, and Tsiknakis M
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Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects., (© 2024. The Author(s).)
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- 2024
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