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

Project Information

PRE-ACT

Grant agreement ID: 101057746

DOI

10.3030/101057746

EC signature date 23 May 2022

Start date 1 October 2022

End date 30 September 2027

Funded under

• Health

Total cost

€ 4 596 615,50

EU contribution

€ 4 592 114,75

Coordinated by

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

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Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

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

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

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

Author(s): 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
Published in: Information Fusion, Issue 100, 2023, ISSN 1566-2535
Publisher: Elsevier
DOI: 10.1016/j.inffus.2023.101945

Transferring CNN features maps to ensembles of explainable neural networks

Author(s): Guido Bologna
Published in: MDPI Information, Issue Volume 14, no.2, 2023, ISSN 2078-2489
Publisher: MDPI
DOI: 10.3390/info14020089

Other (3)

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

Author(s): • 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
Published in: 14th European Breast Cancer Conference, 2024
Publisher: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

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

Author(s): • 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
Published in: 14th European Breast Cancer Conference, 2024
Publisher: Elsevier
DOI: 10.1016/J.EJCA.2024.113820

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

Author(s): • 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
Published in: European Journal of Surgical Oncology 50, 2024
Publisher: 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

Author(s): I. Koutsopoulos
Published in: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Publisher: Springer

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

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

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Author(s): G. B. Bordes and A. Perotti
Published in: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, ISSN 2184-433X
Publisher: SCITEPRESS

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

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

Lecture Notes in Computer Science

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

Exploring Multi-Task Learning for Explainability

Author(s): F. Charalampakos and I. Koutsopoulos
Published in: 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
Publisher: Springer