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

Informations projet

PRE-ACT

N° de convention de subvention: 101057746

DOI

10.3030/101057746

Date de signature de la CE 23 Mai 2022

Date de début 1 Octobre 2022

Date de fin 30 Septembre 2027

Financé au titre de

• Health

Coût total

€ 4 596 615,50

Contribution de l’UE

€ 4 592 114,75

Coordonné par

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

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

Publications

Peer reviewed articles (3)

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

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

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

Auteurs: 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
Publié dans: Information Fusion, Numéro 100, 2023, ISSN 1566-2535
Éditeur: Elsevier
DOI: 10.1016/j.inffus.2023.101945

Transferring CNN features maps to ensembles of explainable neural networks

Auteurs: Guido Bologna
Publié dans: MDPI Information, Numéro Volume 14, no.2, 2023, ISSN 2078-2489
Éditeur: MDPI
DOI: 10.3390/info14020089

Other (3)

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

Auteurs: • 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
Publié dans: 14th European Breast Cancer Conference, 2024
Éditeur: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

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

Auteurs: • 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
Publié dans: 14th European Breast Cancer Conference, 2024
Éditeur: Elsevier
DOI: 10.1016/J.EJCA.2024.113820

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

Auteurs: • 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
Publié dans: European Journal of Surgical Oncology 50, 2024
Éditeur: 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

Auteurs: I. Koutsopoulos
Publié dans: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Éditeur: Springer

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

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

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Auteurs: G. B. Bordes and A. Perotti
Publié dans: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, ISSN 2184-433X
Éditeur: SCITEPRESS

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

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

Lecture Notes in Computer Science

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

Exploring Multi-Task Learning for Explainability

Auteurs: F. Charalampakos and I. Koutsopoulos
Publié dans: 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
Éditeur: Springer