The work carried out throughout the project lifetime concluded to the following main results:
1. Τhe final version of the AI toolbox comprising AI services offering decision-support for all 4 cancer types addressed by the project and combining several AI models per AI service. The main decision-support services offered relate to classification of abnormalities, patient prioritization, lesion segmentation and localization assistance, cancer diagnosis and staging and risk for metastasis prediction. The AI toolbox includes a total of 28 AI models and is integrated with a set of intuitive and interactive visualisation and reporting tools that support usability of the AI services, facilitating the delivery of the AI tools’ outcomes to healthcare professionals, such as image-to-report transformation, explainable AI features, the implemented approach on federated learning, and AR-enhanced visualizations.
2. The final version of the INCISIVE federated data repository, supporting the data sharing of more than 4 million interoperable cancer images and accompanying clinical data from 9 data providers in a GDPR-compliant way. The repository allows health data sharing among registered stakeholders in compliance with legal, ethical, privacy and security requirements, for AI-related training and research experimentation. The repository aggregates anonymised cancer image data and accompanying clinical data from more than 10.000 individuals. It relies on federated data storage abiding to high data privacy and security standards, allowing data sharing through a central node and 5 distributed federated nodes located at the data holders’ site, depending on data holders’ preference. The repository is extensible and scalable and can easily integrate additional federated data nodes.
3. The final version of the INCISIVE integrated platform, integrating all planned functionalities and making them available to end users, following appropriate authorisation depending on their role (data provider, data user, AI services user).
4. A GDPR-compliant data sharing mechanism, covering both technical and operational aspects of data sharing, and complying with legal and ethical norms before data is shared. This mechanism supports interested data providers to share their data within the INCISIVE hybrid/federated repository and allows data users to search and reuse INCISIVE data, respecting the respective data access rights imposed by data holders.
5. A federated learning mechanism, which enables the training of AI models by leveraging distributed data stored in different locations.
6. A data interoperability framework including a methodology for data integration, as well as a documented DICOM- and FHIR-based Common Data Model and tools supporting integration of heterogeneous cancer image and clinical data from multiple data providers.
Extensive dissemination and exploitation activities have been carried out to bring out the full potential of the aforementioned project results. All project results will be sustained, made accessible for further reuse and extension through the cancer image digital infrastructure (
https://cancerimage.eu/(odnośnik otworzy się w nowym oknie)) co-funded by the EC, that is currently being implemented by the EUCAIM project.
