Upcoming Horizon 2020 callsWe have identified seven upcoming calls relevant to biomedical imaging
Below are the full call texts, including information on the budget and prospective estimates on how many projects will be funded. You can scroll down or use the quick menu on the right side of the screen.
We are pleased to present open calls for European project proposals relevant to the field of biomedical imaging
BHC-06: Digital diagnostics – developing tools for supporting clinical decisions by integrating various diagnostic data (7/4/2020)
BHC-37: Towards the new generation of clinical trials – trials methodology research (7/4/2020)
DTH-12: Use of Real-World Data to advance research on the management of complex chronic conditions (7/4/2020)
TDS-05: AI for Health Imaging (13/11/2019)
HCC-10: Towards a Health research and innovation Cloud: Capitalising on data sharing initiatives in health research (7/4/2020)
DTH-02: Personalised early risk prediction, prevention and intervention based on Artificial Intelligence and Big Data technologies (22/4/2020)
TDS-04: AI for Genomics and Personalised Medicine (22/4/2020)
ICT-36: Disruptive photonics technologies (22/4/2020)
BHC-06: Digital diagnostics – developing tools for supporting clinical decisions by integrating various diagnostic data
Research and Innovation Action – 8-15 million Euro funding, 3-5 projects funded
Deadline April 7, 2020
The availability of appropriate decision support tools for healthcare practitioners can promote uptake of personalised medicine in health care. There is a need to carry out research activities aiming to develop and validate such decision tools that would integrate available and/or emerging diagnostic means for the area concerned, enabling increased precision of diagnostics and clinical decision making. On-going progress in the fields of bioinformatics and biostatistics, advanced analytical tools (e.g. machine learning) up to Artificial Intelligence (AI) solutions, should make possible the development of devices, platforms or novel approaches leading to highly personalised diagnosis, based on the integration of data available from various sources. The ultimate result would be a detailed health status assessment from a multitude of viewpoints, in a systemic way and easy to use for clinical purposes, leading to better diagnostic accuracy, increased effectiveness and efficiency of treatments. Novel hardware enabling truly innovative, integrative diagnostic platforms can also be considered.
Proposals should develop tools, platforms or services that will use information provided by most relevant diagnostic means for a particular area, resulting in an accurate, detailed, structured, systemic and prioritised assessment of the health status in a patient. The proposed solutions should integrate various data sources such as medical records, in vitro and/or in vivo diagnostics, medical imaging, -omics data, functional tests (lab-on-a-chip) etc., while taking into account the actual needs of healthcare practitioners, and should be tested and validated in real-life settings in pilot centres, facilitating future Health Technology Assessment. These tools/platforms/services should contribute to improving diagnosis and clinical decision, not only integrate existing data, and should involve intelligent human computer interface solutions to facilitate its daily use in clinical practice. Any medical data relevant for a particular disease (textual data, numerical measurements, recorded signals, images etc.) may be considered. The aim is to steer the development of solutions towards concrete patient and public sector needs, having the citizen and healthcare providers at the centre. Careful attention should be paid to appropriately addressing ethical and legal concerns, providing adequate information to health professionals and patients to support informed decisions, and ensuring data safety and privacy, in line with existing European and international standards and legislation. Gender and sex differences should be taken into consideration when relevant.
- Increase EU’s capacity to innovate in the area of medical instruments technologies through the development of new diagnostic tools, platforms or services integrating various diagnostic data and providing quick, detailed, accurate and highly personalized diagnostics for optimal decision in clinical practice.
- Improve the quality and sustainability of healthcare systems through quicker and more encompassing diagnosis of medical conditions, leading to quicker and better clinical decisions and timely delivery of effective personalised treatments, with reduction of errors and delays (and costs associated to them).
- Contribute to the growth of the European diagnostics sector, in particular for SMEs.
- Reinforce EU’s role among world leaders in the production of medical diagnostic devices.
BHC-37: Towards the new generation of clinical trials – trials methodology research
Research and Innovation Action Lump Sum – 4-6 million Euro funding, 1 project funded
Deadline April 7, 2020
Efficient and effective clinical trials are the primary means to provide scientific evidence to ensure optimal health interventions. Although the randomized controlled trial (RTC) design is regarded as the gold standard for evaluating the effectiveness of intervention in clinical research, there is a need for new trial methodologies that address current challenges such as:
- Globalization of clinical research;
- Use of emerging health technologies
- Defining patient populations and patient enrolment strategies;
- Data management.
Given that all clinical research relies on voluntary contribution of patients, new designs may reduce the operational complexity, assure transparency and build trust, meeting all ethics standards and protecting the individuals’ personal identity and privacy.
Additionally, non-commercial trials often show suboptimal performance as compared to large commercial trials in terms of data collection, management and processing, good clinical practice compliance, and pharmacovigilance, there is a need of a new methodology that improves their legislative compliance and encourage clinical trials conducted by noncommercial sponsors.
Proposals should focus on methodology research and develop innovative solutions to improve the design, conduct and analysis of clinical trials. Proposals should identify and validate methods that will improve the generalizability of evidence generated through differently designed trials, including personalized medicine approaches and combinatorial interventions. In order to draw meaningful conclusions following state of the art of statistical analyses, applicants need to demonstrate access to adequate clinical trial data sets that will be included into the proposed research.
The proposed methodology should allow sound extrapolation in various subgroups of disease of high public health burden as well as integration of RTC data and post-approval evidence generation. Furthermore, applicants should identify best practices to prevent bottlenecks in execution of clinical trial, including issues related to patient recruitment, adherence and compliance, governance, ethics, sex and gender-based analysis and data sharing.
The special attention should be put on non-commercial trials, including quantifiable indicators to measure the qualitative improvement in terms of trial management, data processing, and reporting. Whenever relevant, proposals should cover different aspects of training exercises, including hands-on trainings and closer monitoring of the scientific and technical staff involved in the conduct, management and analysis of the trial. All literature analyses to define the current state of the art in the clinical trial methodology research must be completed at the time of submission of the proposal. Methodology research related to clinical studies exclusively on medical devices is not in the scope of this topic. In this topic, the European Medicines Agency (EMA) and the Commission Expert Group on Clinical Trials will support the selected applicant consortium in the implementation of the action. Successful applicants under this topic are also expected to liaise with the successful applicants of the relevant coordination and support action (CSA) topics, in order to exchange information, avoid potential overlapping activities, create synergies and support the CSA goals. To maintain the interactions with the CSA consortia, specific tasks and a dedicated budget should be foreseen in the proposal. Additionally, consultations with the European Centre for Disease Prevention and Control should also be envisaged as additional relevant activities of the successful proposals.Please note that this topic is part of the lump sum funding pilot scheme. Funding for grants awarded under this topic will take the form of lump sums as defined in the Commission Decision C(2017)7151 of 27 October 2017. Details of the lump sum funding pilot scheme are published on the Funding & tender opportunities website together with the specific Model Grant Agreement for Lump Sums applicable.
- Improved relevance, quality and efficiency of clinical trials conducted with public funding.
- Potential to establish a novel clinical trial methodology supported by regulatory authorities.
DTH-12: Use of Real-World Data to advance research on the management of complex chronic conditions
Research and Innovation Action – 4-6 Million Euro funding, 7-10 projects funded
Deadline April 7, 2020
The number of people with chronic illness is growing and almost half of them have multiple chronic conditions. Patients with complex chronic conditions (CCCs) have chronic multi-morbidities or chronic disease complications that require the attention of multiple health care providers or facilities as well as home-based care. A patient with CCC presents to the health care system with unique constellation of needs, disabilities, or functional limitations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2150604/).
Managing patients with complex chronic conditions therefore needs approaches that ensure multi-disciplinary, personalised and well accepted by the patient ways of care and monitoring.
The controlled randomised clinical trials on chronic diseases provide important information that can be translated in the daily clinical practice, but they often do not comprise sufficient breadth and depth commensurate to the complexity of diseases, and to the degree of personalisation of treatment needed.
Real World Data (referring specifically to any type of data not collected in a randomized clinical trial) can complement these to fill the knowledge gap between controlled clinical trials results and clinical practice needs in real environments. They can provide new insights into disease patterns and help improve the safety and effectiveness of health interventions. Tapping into this rich resource of ‘real world data’ issued from daily clinical practice, either collected on a permanent/regular basis by public bodies or through devices and mobile applications, , and smartly assembled in combination with clinical studies, should boost both output and relevance of controlled clinical research results.
The topic will support clinical research integrating Real World Data from clinical practice or from patient’s daily life and linking them with data collected with a research purpose if relevant.).
The research focus will be on the use of real world data, either newly acquired or from existing sources (such as data from clinical professional societies/associations, cohorts, registers, biobanks or collected through genome research initiatives) to improve the clinical management of adults with complex chronic conditions. The use of new technologies for data analytics and interpretation such as artificial intelligence and computer modelling are encouraged.
The proposed intervention should allow better treatment or monitoring of the person and thus changes in disease progression and/or therapy response. Quality of life, patient safety, psychosocial aspects and well-being are important determinants of complex health conditions and should be addressed whenever relevant. The research should also assess the potential and use of RWD for different health authorities like regulators of safety and quality or health technology assessment bodies. Nevertheless, research has to take duly into account sex and gender differences.
The proposed intervention must add clinical value as well as societal benefits and show feasibility and sustainability in real-life settings. In order to ensure acceptability and sustainability of the intervention early involvement of patients and care providers in the design of the research is considered essential. Similarly, proposals should duly take into account the diversity of health systems in different regions of Europe.
Data protection, data privacy and ethical issues have to be carefully considered as personal data from different sources are to be linked in the course of the proposed research. Data sets assembled under the project, including the linkage to ‘real world data’ should be preserved in a sustainable and accessible way so as to enable future research on the targeted CCC, thus contributing to the overall imperative of Open Science.
Research that focuses on self-management only is not in the scope of this topic. Research on rare and/or infectious diseases are supported through other sections of the programme and are excluded from the scope of this topic.
- Demonstrate the potential of the use multi-disciplinary multi-source Real World Data to advance clinical research on complex chronic conditions;
- Demonstrate potential and use of RWD, in particular RWD from disease-specific professional societies/associations, by health authorities to understand safety, quality and effectiveness of therapies;
- Improve the clinical outcomes as well as quality of life of patients living with CCCs;
- Advance the understanding of management of complex diseases including the
- interdependence of co-morbidities, thus underpinning evidence based therapies and prognostic approaches;
- Further development of new technological tools and platforms for advanced data management;
- Contribution to the cross-border health data exchange and to the goals of the Digital Single Market.
TDS-05: AI for Health Imaging
Research and Innovation Action – 8-10 million Euro funding, 3-4 projects funded
Deadline November 13, 2019
Artificial Intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalised care. Analysis of health images is one of the most promising fields for applying AI in healthcare, contributing to better prediction, diagnosis and treatment of diseases. In order to develop and test reliable AI applications in the field, access to large-volume of high- quality data is needed.
This action should contribute to testing and developing AI tools and analytics focused on the prevention, prediction and treatment of the most common forms of cancer while providing solutions to securely share health images across Europe.
Proposals should set up and contribute to populate a large interoperable repository of health images, enabling the development, testing and validation of AI–based health imaging solutions to improve diagnosis, disease prediction and follow-up of the most common forms of cancer294.
The repository should include high quality, interoperable, anonymised or pseudo-anonymised data sets of annotated cases, based on data donorship, and should comply with relevant ethics, security requirements and data protection legislation. Gender aspects should be considered appropriately. It should ensure data quality and interoperability based on common standards and open Application Programming Interfaces (APIs).
Proposers should specify measures for validating AI-based solutions for health images, such as the effectiveness of clinical decision making. There should be rigorous, peer-reviewed scientific evidence establishing their safety, validity, reproducibility, usability, reliability and usefulness for better health outcomes. It is critical to show how AI-based solutions will deal with and inform about possible failures, inaccuracies and errors. Adequate performance metrics, monitoring and evaluation criteria and procedures should be put in place. The reasoning behind AI-based conclusions and recommendations should be explained so that users can understand their situation and be able to consent or challenge any proposed course of action.
The consortium should build on relevant national and EU activities and bring together: 1) expertise to set up the infrastructure, ensuring the appropriate sharing of data quality and interoperability, 2) AI developers/expertise to experiment its content while ensuring compliance with relevant legislations.
- Contributing towards the creation of a EU-wide repository of health images dedicated to the most common forms of cancer, enabling experimentation of AI-based solutions to improve diagnosis, treatment and follow-up and contribute to a more precise and personalised management of cancer.
- Contributing to developing technical, organisational and ethical standards for AI for health imaging
- Promoting access to anonymised health image data sets to be made more openly reusable across the EU for training AI applications.
- Increasing trust in AI solutions among users (healthcare professionals and patients), investors and stakeholders at industry and academia.
HCC-10: Towards a Health research and innovation Cloud: Capitalising on data sharing initiatives in health research
Coordinator and Support Action – 2-3 million Euro funding, 1 project funded
Deadline April 7, 2020
Technological innovation has triggered an unprecedented increase in data production in health research and healthcare. The need to make EU health research data FAIR (i.e., Findable, Accessible, Interoperable and Re-usable) becomes more pressing than ever before if European health research is to reap the full benefits of this valuable resource. The stakes are high because making optimal use of this health data is expected to both accelerate research discoveries and bring them closer to clinical application for the benefit of EU citizens.
A wide range of challenges needs to be overcome before this vision becomes a reality. To be able to seamlessly integrate and analyse health data coming from different sources and different health sub-disciplines, individual research institutes and/or hospitals would need a potent IT infrastructure and interoperability solutions as well as powerful data analytics tools. Services in the Internet Cloud (i.e., Cloud Services) are a promising starting point to build these systems.
Properly addressing the security and privacy of health research data, and the compliance with various levels of legislations, in particular the General Data Protection Regulation (GDPR) together with the applicable National legislations in the EU Member States/Associated Countries and with different jurisdictions is a critical step for the design of a Health Research and Innovation Cloud (HRIC). These aspects need to be an integral part of the proposal so that the collection, governance, sharing, analysis and curation of health research data across different application domains can be achieved in ways that are technologically robust, scientifically reliable, and ethically and legally sound.
The successful project should bring together data-intensive EU health research initiatives to design an implementation roadmap /strategic agenda for a one-stop shop, a HRIC FAIR data portal respecting legal and ethics requirements. It should also define and promote, among research projects, procedures to make data FAIR as well as a standard way of communicating such data, so that any IT-system can easily provide metadata to the portal. This portal would serve as catalogue of all relevant publicly-funded health research databases, registries and infrastructures (e.g., ESFRI) and allow access to high quality health research data. The proposal is expected to build a community (i.e., a wider forum) in order to align strategies and capitalise on the work done by relevant European and international initiatives. The proposal should develop two use cases, where all the aforementioned aspects will be integrated and analysed. These use cases should link health research data, and if relevant, health research data with curated clinical data and health administrative data. The participation of experts in ethics and law as well as patient representatives is strongly recommended.
The proposal should also produce guidelines for researchers to contribute to the proper application of the GDPR regulation, taking into account the specific features of processing personal data in the area of health.
The HRIC should contribute to the European Open Science Cloud.
Project results should be widely disseminated to the relevant stakeholders across the Member States and Associated Countries.
The implementation roadmap of the HRIC FAIR data portal will define how to address the specific requirements of health research data. In this sense, the selected proposal is expected to collaborate with the projects funded under topics ‘INFRAEOSC-04-2018’ and ‘INFRAEOSC-06-2019-2020: Enhancing the EOSC portal and connecting thematic clouds’, in particular with those in the health field. Grants awarded under these topics will be complementary. The respective options of Article 2, Article 31.6 and Article 41.4 of the Model Grant Agreement will be applied.
- A HRIC FAIR data portal respecting legal and ethics requirements. This portal should serve as catalogue of all relevant publicly-funded health research databases, registries and infrastructures (e.g., ESFRI) and allow access to high quality health research data.
- Through use cases, demonstrate the added value of close collaboration of health researchers with healthcare providers and other actors in health care systems.
- Guidelines on application of the GDPR and the EU Member States and Associated Countries national legislations. The developed guidelines should cover the processing and further processing of health research data.
- Contribute to the setup of a Health Research and Innovation Cloud, the Health thematic cloud of the European Open Science Cloud.
- Contribute to the Digital Single Market through piloting IT health research solutions.
DTH-02: Personalised early risk prediction, prevention and intervention based on Artificial Intelligence and Big Data technologies
Research and Innovation Action – 4-6 million Euro funding, 4-6 projects funded
Opening November 19, 2019 – Deadline April 22, 2020
The ageing of the population together with the rising burden of chronic conditions (incl. mental diseases) and multi-morbidity bring an ever increasing demand to strengthen disease prevention and integrate service delivery around people’s needs for health and social care.
It is widely recognised that health systems must put more emphasis on prevention and adopt a person-centred rather than a disease-centred approach. The goal must be to overcome service fragmentation and to move towards integration and coordination of interventions along the continuum of care.
Personalised early risk prediction models, estimating the probability that a specific event occurs in a given individual over a predefined time, can enable earlier and better intervention, prevent negative consequences on a person’s quality of life and thus result in improved individual health outcomes.
The challenge is to develop and validate these comprehensive models based on AI or other state of the art technologies for prediction, prevention and intervention using multiple available data resources and to integrate them in personalised health and care pathways that empower individuals to actively contribute to risk mitigation, prevention and targeted intervention.
Proposals should build on results of projects (e.g. PHC-21-2015) and the state of the art in ICT for early
risk prediction and introduce innovative ICT solutions through data, data analytics, advanced or novel digital technologies, services, products, organisational changes, and citizens data ownership, that lead to more effective health and care systems. These innovative ICT based solutions may address one or multiple conditions and explore ways of inducing adequate personalised preventive measures (e.g. behavioural change, diet, interventions, medication, primary prevention) from advanced predictive models. Sustainable behaviour change refers to efforts to change people’s personal habits to prevent disease, stimulate healthy people to monitor their health parameters and thus lowering the risk of developing (chronic) conditions. Proposals should build on the use of already existing and/or new data generated by individuals, health professionals and other service providers (including but not limited to data collected through IoT enabled devices, wearables, mobile devices, data source networks or data lakes etc. collected outside the controlled environment of clinical trials) by citizens, healthcare professionals, public authorities and industry, with a view to developing personalised early risk prediction, prevention and intervention approaches that meet the needs of individuals while providing them with adequate information to support informed decision making, improve the uptake of preventive approaches and lead to better health outcomes. Proposals should also include actions aimed at increasing health literacy, including the role of the citizen as owner of his or her own personal data, as well as advancing health and care professionals’ proficiency in novel, data-oriented health services through the use of digital solutions to increase knowledge about diseases and help them in the interpretation of symptoms and effects (e.g. with visualisations like dashboards, etc.), notably of early warning signs and medical information. Early warning signs relay to either healthy people monitoring several body parameters e.g. to conduct healthy life styles and increase physical activity levels or to the detection of the deterioration of the condition of already diseased patients. The latter could include advanced prediction models from aggregated patient data of certain health events/complications.
Proposals are expected to be built on realistic scenarios for new health and care pathways, and should integrate multi-disciplinary research involving behavioural, sociological, medical and other relevant disciplines. Stakeholder engagement (esp. considering vulnerable user groups, i.e. persons belonging, or perceived to belong, to groups that are in a disadvantaged position or marginalised, for example, elderly people, persons with special needs or chronic diseases) should be part of the research design for an agile approach to ensuring that relevant user needs (including social, age and gender aspects) are met and solutions find acceptance by users. Full account should be taken of ethical and legal aspects e.g. data protection, privacy and data security. This action should create a clear and coherent set of recommendations or guidelines for public health authorities in Europe together with a strategy to support their implementation.
No large-scale piloting or clinical trials are expected in this Research and Innovation Action. However, proposals should include validation (testing on a prototype and/or proof of concept) and demonstration of feasibility of their respective models, technologies and scenarios.
- Evidence of the benefits of delivering adequate information regarding personalised risk prediction, prevention and intervention, based on proof of concept and involvement and specified roles of relevant stakeholders.
- Clear improvements of outcomes for individuals, care systems and wider society from prevention measures and interventions based on personalised early risk prediction in comparison with current practices.
- Usefulness and effectiveness of integration and coordination of interventions in new health and care pathways based on person-centred early risk prediction, prevention and intervention models.
- Realise large-scale collection of user-generated data in compliance with data protection, privacy and security rules and principles.
- Support integration with tools and services under the European Open Science Cloud.
TDS-04: AI for Genomics and Personalised Medicine
Research and Innovation Action – up to 10 million Euro funding, 3-4 projects funded
Opening November 19, 2020 – Deadline April 22, 2020
Several national and regional initiatives already support the pooling of genomic and other health data to advance research and personalised medicine. The next step is to make use of the existing infrastructures and initiatives for the successful exploitation of genomic data to facilitate personalised medicine.
The challenge is to demonstrate the potential and benefits of AI technologies for identifying new knowledge, support clinical research and decision making by linking Europe’s relevant genomic repositories, while ensuring full compliance with data protection legislation and ethical principles.
Proposals should demonstrate the potential and benefits of AI technologies for advancing research and personalised medicine through the linking of relevant genomics data and repositories, according to adequate organisational, regulatory, security, ethical and technical requirements.
Proposals should develop and test AI solutions for linking genomics repositories across the EU, including banks of “-omics” and health related data, biobanks and other registries (including e.g. rare disease registries), with the view of supporting clinical research and decision making. By combining sequenced genomic data and other medical data, physicians and researchers can understand better diseases at a personal level and can determine the most appropriate treatment for a particular person. The focus should be to reduce the burden of diseases for which a treatment exists and to apply such treatments in a more targeted way, to identify new evidences on the predictive value of the AI solutions and to enhance the diagnostic capacity e.g. for rare or low prevalence and complex diseases.
Proposals should demonstrate a potential to build a large-scale distributed repository of relevant genomic data and other -omics and medical data that will enable to advance validation of the new clinically impactful insights supported trough AI solutions. Proposals should ensure compliance with the relevant privacy, cybersecurity, ethical and legal rules. Sex and gender aspects should be considered appropriately. The European Open Science Cloud Initiative (EOSC) may facilitate the access of researchers to the newest data managing technologies, High Performance Computing facilities to process and analyse data and to a European Open Science Cloud list of ICT services while ensuring the appropriate data safety and protection. Proposals should address technical specifications and standards for the secure access and exchange of cross-border genomic and other health data, and collaborate with actions selected under the topic SC1-HCC-06-2020 as relevant for achieving progress towards the expected impacts.
- Supporting the development and testing of AI technologies on genomics and other linked –omics and health data repositories for identifying new knowledge, support clinical research and decision making, leading to more reliable and meaningful outcomes for advancing research and personalised medicine.
- Promoting the sharing of data and infrastructure for prevention and personalized medicine research, concretely a European network on genomics, seeking to link it with ongoing ‘-omics’ and human cell mapping initiatives.
- Effectiveness of AI technologies for genomics and personalised medicine.
- Measuring patient-based value healthcare outcomes for impact assessment on how genomics, personalised medicine and patient outcomes can help to implement valuebased healthcare in Europe.
- Contributing to developing technical specifications for secure access and cross-border exchange of genomic and other –omics and health datasets in Europe for research purposes.
- Facilitating interoperability of relevant registries (including e.g. rare disease registries) and and databases in support of genomics and personalised medicine research.
- Supporting the pooling of health data and resources across the EU, and demonstrate the benefits for advancing research, disease prevention and personalised medicine.
- Contributing to standards for genomic data generation, analysis, privacy and sharing of genomic and associated clinical and other phenotype data, including self-reported data, data from wearables, omics, and imaging.
- Contributing to the European Cloud Initiative, notably by providing open, reusable data for prevention, genomics and personalised medicine research.
- Increasing the trust of users (healthcare professionals and patients) and other stakeholders on AI solutions to process and link genomics data with other –omics and health related data for better decision-making and value-based patient health outcomes.
ICT-36: Disruptive photonics technologies
Research and Innovation Action – 3-6 million Euro funding, at least one project funded
Opening November 19, 2020 – Deadline April 22, 2020
The challenge is the development of advanced photonics technologies which have the potential to revolutionise an existing application sector or to create completely new applications and markets.
Next generation biophotonics methods and devices as research tools to understand the cellular origin of diseases: Actions will focus on photonics-based in-vivo/in-vitro imaging systems and techniques which deliver greatly increased penetration, resolution, sensitivity, specificity and depth of focus. Real time data handling and processing may also be addressed as appropriate. Actions should include medical/clinical doctors or research laboratories with relevant experience.
- Significant gain in understanding of inter- and/or intra-cellular processes; strengthen Europe’s industrial position in the biophotonics-related market for microscopes and research and development tools.