Federate Learning and mUlti-party computation Techniques for prostatE cancer

FLUTE项目将通过开发保护隐私的数据中心跨境利用的新方法，推动和扩大数据驱动的医疗保健。将进行高级研究，以推动联邦学习中安全多方计算的性能极限，包括相关的人工智能模型和安全执行环境。这些技术创新将被整合到一个隐私执法平台中，该平台将为创新者提供一个非常安全的环境，用于联合医疗人工智能解决方案的开发、测试和部署，包括集成来自数据中心的真实世界健康数据，以及生成和利用合成数据。为了最大限度地提高成果的影响力、采用率和可复制性，该项目将为全球HL7 FHIR标准的制定做出贡献，并为医疗保健领域符合GDPR的跨境联合学习创建新的指导方针。为了展示结果的实际用途和影响，该项目将把FLUTE平台与位于三个不同国家的健康数据中心整合起来，利用他们的数据开发用于诊断具有临床意义的前列腺癌的新型联合人工智能工具集，并对其有效性进行多国临床验证，这将有助于提高侵袭性前列腺癌的预测，同时避免不必要的活检，从而提高患者的福利，并显著降低相关成本。团队。这个由11人组成的财团将包括来自三个不同国家的三个临床/数据合作伙伴、三个技术中小企业、三个技术研究合作伙伴、一个法律/伦理合作伙伴和一个标准组织。协作。根据欧盟委员会确定的优先事项，该项目的目标将是与相关的欧洲国家和国际项目进行协作、相互促进和协同增效。

The FLUTE project will advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments. The technical innovations will be integrated in a privacy-enforcing platform that will provide innovators with a provenly secure environment for federated healthcare AI solution development, testing and deployment, including the integration of real world health data from the data hubs and the generation and utilization of synthetic data. To maximize the impact, adoption and replicability of the results, the project will contribute to the global HL7 FHIR standard development, and create novel guidelines for GDPR-compliant cross-border Federated Learning in healthcare. To demonstrate the practical use and impact of the results, the project will integrate the FLUTE platform with health data hubs located in three different countries, use their data to develop a novel federated AI toolset for diagnosis of clinically significant prostate cancer and perform a multi-national clinical validation of its efficacy, which will help to improve predictions of aggressive prostate cancer while avoiding unnecessary biopsies, thus improving the welfare of patients and significantly reducing the associated costs. Team. The 11- strong consortium will include three clinical / data partners from three different countries, three technology SMEs, three technology research partners, a legal/ethics partner and a standards organization. Collaboration. In accordance with the priorities set by the European Commission, the project will target collaboration, cross-fertilization and synergies with related national and international European projects.