PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

Información del proyecto

PRE-ACT

Identificador del acuerdo de subvención: 101057746

DOI

10.3030/101057746

Fecha de la firma de la CE 23 Mayo 2022

Fecha de inicio 1 Octubre 2022

Fecha de finalización 30 Septiembre 2027

Financiado con arreglo a

• Health

Coste total

€ 4 596 615,50

Aportación de la UE

€ 4 592 114,75

Coordinado por

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Documents, reports (6)

Report on panels & templates

Report on membership of the scientific advisory and patient advisory groups, and on production and distribution of internal reporting templates

Context analysis

Report on context analysis of AI-assisted development of therapeutic strategies for cancer patients

Plan for exploitation & dissemination

Material for the website and plan for exploitation and dissemination

Co-design framework

Report on dedicated co-design framework for trustworthy AI decision support systems

User requirements

Report on user requirements from different stakeholders

Operational ethics and fairness

Report on operational ethics and fairness metrics

Websites, patent fillings, videos etc. (1)

Project website launched

Project website is active with all relevant material

Other (1)

Data management plan

Publicaciones

Peer reviewed articles (3)

Co-design of Human-centered, Explainable AI for Clinical Decision Support

Autores: C. Panigutti, A. Beretta, D. Fadda, F. Giannotti, D. Pedreschi, A. Perotti and S. Rinzivillo
Publicado en: ACM Transactions on Interactive Intelligent Systems, Edición Volume 13Edición 4Article No.: 21pp 1–35, 2023, ISSN 2160-6455
Editor: Association for Computing Machinery (ACM)
DOI: 10.1145/3587271

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Autores: J.M. Górriz; I. Álvarez-Illán; A. Álvarez-Marquina; J.E. Arco; M. Atzmueller; F. Ballarini; E. Barakova; G. Bologna; P. Bonomini; G. Castellanos-Dominguez; D. Castillo-Barnes; S.B. Cho; R. Contreras; J.M. Cuadra; E. Domínguez; F. Domínguez-Mateos; R.J. Du
Publicado en: Information Fusion, Edición 100, 2023, ISSN 1566-2535
Editor: Elsevier
DOI: 10.1016/j.inffus.2023.101945

Transferring CNN features maps to ensembles of explainable neural networks

Autores: Guido Bologna
Publicado en: MDPI Information, Edición Volume 14, no.2, 2023, ISSN 2078-2489
Editor: MDPI
DOI: 10.3390/info14020089

Other (3)

Development of an explainable AI prediction model for arm lymphoedema following breast cancer surgery and radiotherapy

Autores: • T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Panisson, A. Perotti, B.L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, A. Traverso, F. Tohidinezhad, K. Verhoeven, A.J. Webb, I. Koutsopoulos
Publicado en: 14th European Breast Cancer Conference, 2024
Editor: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

Breast cancer patients’ communication needs and wishes for an explainable Artificial Intelligence prediction model for lymphedema

Autores: • C. Roumen , J. Rainbird , K. Verhoeven , G. Bologna , A. Bombezin-Domino , T. Rattay , J. van Soest, A. Dekker , M. Joore , A. Panisson, A. Perotti, B.L.T. Ramaekers , S. Rivera A. Romita , A. Traverso, A.J. Webb, I. Koutsopoulos, C.J. Talbot, G. Cortel
Publicado en: 14th European Breast Cancer Conference, 2024
Editor: Elsevier
DOI: 10.1016/J.EJCA.2024.113820

Development of an AI prediction model for arm lymphoedema following breast cancer surgery and radiotherapy

Autores: • T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Panisson, A. Perotti, B. L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, H. Stobart, F. Tohidinezhad, A. Traverso, K. Verhoeven, A. J. Webb, I
Publicado en: European Journal of Surgical Oncology 50, 2024
Editor: EJSO
DOI: 10.1016/J.EJSO.2024.108216

Conference proceedings (6)

PRE-ACT: Prediction of Radiotherapy Side Effects using Explainable AI for Patient Communication and Treatment Modification

Autores: I. Koutsopoulos
Publicado en: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Editor: Springer

Research Challenges in Trustworthy Artificial Intelligence and Computing for Health: The Case of the PRE-ACT project

Autores: F. Charalampakos, T. Tsouparopoulos, I. Papageorgiou, G. Bologna, A. Panisson, A. Perotti and I. Koutsopoulos
Publicado en: Proceedings of the Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), IEEE, Gothenburg, Sweden, 2023, 2023, ISSN 2575-4912
Editor: IEEE
DOI: 10.1109/EuCNC/6GSummit58263.2023.10188239

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Autores: G. B. Bordes and A. Perotti
Publicado en: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, ISSN 2184-433X
Editor: SCITEPRESS

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

Autores: G. Bologna, J-M. Boutay, Q. Leblanc and D. Boquete
Publicado en: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, 2024, ISBN 978-3-031-61137-7
Editor: ACM

Lecture Notes in Computer Science

Autores: T. Tsouparopoulos and I. Koutsopoulos
Publicado en: 1st Workshop on Advancements in Federated Learning (WAFL) of the ECML/PKDD, Turin, Italy, 2023, 2023, ISSN 0302-9743
Editor: Springer PKDD WAFL workshop

Exploring Multi-Task Learning for Explainability

Autores: F. Charalampakos and I. Koutsopoulos
Publicado en: 3rd International Workshop on Explainable and Interpretable Machine Learning (XI-ML) of the 26th European Conference on Artificial Intelligence (ECAI), Krakow, Poland, 2023, 2024, ISBN 978-3-031-50395-5
Editor: Springer