Applied Sciences (Aug 2022)
The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography
- Ivan Lazic,
- Ferran Agullo,
- Susanna Ausso,
- Bruno Alves,
- Caroline Barelle,
- Josep Ll. Berral,
- Paschalis Bizopoulos,
- Oana Bunduc,
- Ioanna Chouvarda,
- Didier Dominguez,
- Dimitrios Filos,
- Alberto Gutierrez-Torre,
- Iman Hesso,
- Nikša Jakovljević,
- Reem Kayyali,
- Magdalena Kogut-Czarkowska,
- Alexandra Kosvyra,
- Antonios Lalas,
- Maria Lavdaniti,
- Tatjana Loncar-Turukalo,
- Sara Martinez-Alabart,
- Nassos Michas,
- Shereen Nabhani-Gebara,
- Andreas Raptopoulos,
- Yiannis Roussakis,
- Evangelia Stalika,
- Chrysostomos Symvoulidis,
- Olga Tsave,
- Konstantinos Votis,
- Andreas Charalambous
Affiliations
- Ivan Lazic
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
- Ferran Agullo
- Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Susanna Ausso
- Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain
- Bruno Alves
- European Dynamics, 1466 Luxembourg, Luxembourg
- Caroline Barelle
- European Dynamics, 1466 Luxembourg, Luxembourg
- Josep Ll. Berral
- Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Paschalis Bizopoulos
- Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
- Oana Bunduc
- Telesto IoT Solutions, London N7 7PX, UK
- Ioanna Chouvarda
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- Didier Dominguez
- Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain
- Dimitrios Filos
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- Alberto Gutierrez-Torre
- Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Iman Hesso
- Department of Pharmacy, Kingston University London, London KT1 2EE, UK
- Nikša Jakovljević
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
- Reem Kayyali
- Department of Pharmacy, Kingston University London, London KT1 2EE, UK
- Magdalena Kogut-Czarkowska
- Timelex BV/SRL, 1000 Brussels, Belgium
- Alexandra Kosvyra
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- Antonios Lalas
- Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
- Maria Lavdaniti
- Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece
- Tatjana Loncar-Turukalo
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
- Sara Martinez-Alabart
- Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain
- Nassos Michas
- European Dynamics, 1466 Luxembourg, Luxembourg
- Shereen Nabhani-Gebara
- Department of Pharmacy, Kingston University London, London KT1 2EE, UK
- Andreas Raptopoulos
- Telesto IoT Solutions, London N7 7PX, UK
- Yiannis Roussakis
- German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus
- Evangelia Stalika
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- Chrysostomos Symvoulidis
- Telesto IoT Solutions, London N7 7PX, UK
- Olga Tsave
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- Konstantinos Votis
- Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
- Andreas Charalambous
- Department of Nursing, Cyprus University of Technology, Limassol 3036, Cyprus
- DOI
- https://doi.org/10.3390/app12178755
- Journal volume & issue
-
Vol. 12,
no. 17
p. 8755
Abstract
Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.
Keywords
