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| Funder | European Commission |
|---|---|
| Recipient Organization | Eibir Gemeinnutzige Gmbh Zur Forderung Der Erforschung Der Biomedizinischen Bildgebung |
| Country | Austria |
| Start Date | Jan 01, 2023 |
| End Date | Dec 31, 2027 |
| Duration | 1,825 days |
| Number of Grantees | 12 |
| Roles | Participant; Associated Partner; Coordinator |
| Data Source | European Commission |
| Grant ID | 101057091 |
ArtifArtificial Intelligence (AI) will revolutionize healthcare as its diagnostic performance approaches that of clinical experts. In particular, in cancer screening, AI helps patients to make better-informed decisions and reduce medical error. However, this requires large datasets whose collection faces severe practical, ethical and legal obstacles.
These obstacles can be overcome with swarm learning (SL) where partners jointly train AI models without sharing any data.
Yet, access to SL technology is seriously limited because no studies have implemented SL in a true multinational setup, no practically usable implementation of SL is available, researchers & healthcare providers have no experience with setting up SL networks and policymakers are currently unaware of the broader implications of SL.
ODELIA will address & solve these issues: ODELIA will build the first open-source software framework for SL, providing an assembly line for the streamlined development of AI solutions.
To serve as a blueprint for future SL-based AI systems, ODELIA partners collaborate as a swarm to develop the first clinically useful AI algorithm for the detection of breast cancer in magnetic resonance imaging (MRI).
The size of ODELIA's distributed database will exceed all previous studies and ODELIA's AI models will reach expert-level performance for breast cancer screening.
Thereby, ODELIA will not only deliver a useful medical application, but prove the clinical benefit of SL in terms of accelerated development, increased performance and robust generalizability to ultimately save thousands of lives of European patients.
ODELIA's success will push partners to serve as nuclei for the exponential growth of the SL network and extend SL to a multitude of medical applications.
Thus, patients, healthcare providers and citizens in Europe will be provided with a digital infrastructure that enables development of expert-level AI tools on big data without compromising data safety and data privacy.
Mitera Idiotiki Geniki, Maieytiki,Gynaikologiki Kai Paidiatriki Kliniki Anonymi Etaireia; Fundacio Privada Institut D'Investigacio Oncologica de Vall-Hebron (Vhio); The Chancellor Masters and Scholars of the University of Cambridge; Universitaetsklinikum Aachen; Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev; Stratifai Gmbh; Stichting Radboud Universitair Medisch Centrum; Eibir Gemeinnutzige Gmbh Zur Forderung Der Erforschung Der Biomedizinischen Bildgebung; Ribera Salud Sa; Universitair Medisch Centrum Utrecht; Universitat Zurich; Technische Universitaet Dresden
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