Big Data
Big data has become a major challenge for building the medicine of tomorrow. The advent of Omics, which are massive data generation techniques whose path was opened by genomics but which has been supplemented by other disciplines such as proteomics, metabolomics, lipidomics, transcriptomics, allows the accumulation of a large number of heterogeneous data which represent a real treasure for research but it is still necessary to be able to use them. In addition to Omics, there is another form of data that is just as valuable, patient health data. Advances in science and technology allow the production and storage of data to create large cohorts.
The whole challenge of big data today is to be able to structure data, characterize it, annotate it and store it in accordance with the regulations in force on the protection of personal data in order to be able to make it accessible to research teams for exploit thus contribute to the scientific progress of tomorrow.
This booming field has led to changes in regulations requiring close work between investigators, IT and legal teams. The IHU-ICAN took up the challenge and set up a data expertise group. At the heart of the concerns of this working group, anticipating the needs of researchers, informing patients and sharing know-how at the international level. Drawing inspiration from structures in other fields (particularly industrial and agricultural), ICAN explores hypotheses for the platformization of data and services for health solutions, in several fields: diagnosis of atrial fibrillation, management of NASH, diabetic foot care, etc.
Finally, the ICAN, through its board of directors and, in particular its president Thierry Tuot, shares its feedback on the use of mass patient data with regulators, thus participating in regulatory developments in the field.
The advantages of the IHU ICAN
Through large-scale innovative projects, ICAN is positioning itself as the leader in cardiometabolism health data.
Maestria an innovative and daring project of international scope to build the medicine of tomorrow
The MAESTRIA (Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation) research project, coordinated by Prof. Stéphane Hatem, director of the IHU-ICAN and director of the Inserm UMR_S1166 research unit, aims to develop a new approach for rapid detection of atrial cardiomyopathy by creating multiparametric digital tools based on a new generation of biomarkers, in particular medical imaging associated with artificial intelligence, with the aim of meeting the major challenges of integrating data and personalized medicine focused on atrial cardiomyopathy, atrial fibrillation and stroke.
This project takes the form of a consortium of 18 partners from Europe, the United States and Canada responding to a H2020 call for projects (13.9 million euros) on digital diagnosis. It perfectly illustrates the interest of imaging in the medicine of the future and the new perspectives it offers.
MAESTRIA will allow the constitution of a patient database on atrial cardiomyopathy structured in strict compliance with European regulations (anonymization of patient data, security) and usable to intensify research activity on atrial cardiomyopathy which is making the bed atrial fibrillation and embolic strokes. This is a springboard project integrating public and private sector actors and using very sensitive health data. MAESTRIA paves the way for inventing the medicine of the future. https://maestria-h2020.com/
MAESTRIA, a very innovative project to better detect the atrial cardiomyopathy responsible for the occurrence of atrial fibrillation and embolic vascular accidents
Atrial fibrillation is the most common heart rhythm disorder. Its incidence and prevalence are increasing rapidly, mainly in relation to the aging of the population. It can be estimated that in France around 750,000 people are victims of atrial fibrillation. This importance leads to a high cost of care that can be estimated at around 2.5 billion euros per year.
What are the challenges for this platform?
Atrial Fibrillation (AF), a heart rhythm disorder, is the most common cardiac arrhythmia and the first cardiac cause of cerebrovascular accidents (CVA).
Frequently associated with heart failure, high blood pressure, but also with obesity and diabetes, AF affects approximately 1% of the general population and up to 8% of people over 80 years of age. The challenge today in the clinical management of Atrial Fibrillation is to intervene before the onset of the arrhythmia, that is to say from the first signs of atrial cardiomyopathy.
Coordinated by Prof. Stéphane Hatem, director of the IHU-ICAN and of research unit 1166 at Sorbonne University, the MAESTRIA project therefore aims to develop a new approach for the rapid detection of atrial cardiomyopathy.
Lasting 5 years, this project requires several expertise within the IHU-ICAN:
– Our scientific operations department (SOS) for setting up the project
– Our legal department (PJV) for regulatory and ethical aspects
– The ICAN Imaging imaging platform for the interpretation of imaging and ECG data and the identification of the most suitable algorithms to integrate into the MAESTRIA demonstrator
– The clinical investigation platform and the ICAN Analytics platform to define the electrophysiological signature of the disease
– The Biological Resource Center (CRB), ICAN Collection for the proper conservation of the biological data of the study
By combining imaging data with physiological data (omics, clinical, etc.) of patients, this platform will be able to identify new therapeutic targets, in order to obtain improved diagnostic precision and will increase the effectiveness and efficiency of treatments by enabling better prevention of complications of atrial cardiomyopathy, such as atrial fibrillation and stroke.
What is a H2020 call for projects?
Horizon 2020 is the research and innovation funding program of the European Union. This program aims to support resolutely interdisciplinary research projects, likely to respond to major economic and social challenges.
