French
English
Aims
Among heart muscle diseases, ischemic heart disease remains the leading cause of death worldwide, with a 21% increase over the last decade (Roth et al, Lancet 2018). In France, 10-year mortality after myocardial infarction (MI) has fallen to 10% thanks to multifaceted efforts, including regional networks for faster coronary intervention, specialized intensive care after MI, and iterative public education efforts to reduce risk factors and recognize early symptoms (joint initiatives of French national public health agencies and cardiology societies over the past two decades) (Grave C et al. 2019). However, cardiovascular disease (CVD) remains a major public health problem. With more survivors after AMI, approximately 60% of cases of heart failure (HF) in France are ischemic in origin, and the cost associated with the management of ischemic HF is estimated at €3.6 billion per year in France. Heart failure is expected to increase by 25% every four years in the coming years due to the aging population. In this context, we are developing multimodal and multiparametric approaches based on innovative acquisition techniques, combining advanced magnetic resonance imaging and spectroscopy, metabolic and functional imaging, and machine learning techniques for the integration of multiple data sets and population analysis.
Magnetic Resonance Imaging & Spectroscopy (MRI & MRS)
We are interested in the multifactorial progression of heart and cardiovascular diseases, addressing not only the limitations of current acquisition strategies, the reconstruction of imaging and spectroscopy data, but also working on the extraction of data derived from modeling in order to obtain unique and reliable in vivo quantitative measurements of myocardial lesions and the structure-function relationship of the heart muscle. We are setting up preclinical models and clinical studies to understand and remedy pathophysiological changes in the heart muscle and their consequences in terms of public health.
Diagnosis and prognosis based on routine clinical images
The richness of cardiac data from existing protocols in clinical practice remains underutilized. We are therefore exploring:
- increasing the robustness and reliability of current measurements extracted from conventional imaging systems;
- integrating complex descriptors using representative learning solutions;
- exploiting advanced statistical models to improve diagnosis and prognosis.
Current Projects
GENESIS: "Long-term fasting: Multi-system adaptations in humans". While human life expectancy has steadily increased over the past few centuries thanks to improvements in medical care, this is no longer the case today. Our modern, unhealthy Western lifestyle has hindered this progress by promoting an epidemic of chronic metabolic and inflammatory diseases, of which overweight and obesity are only the tip of the iceberg. This forces us to seek therapeutic strategies, ideally low-tech and low-cost, to improve the quality of life of populations. In this regard, fasting (intermittent and long-term) and calorie-restricted diets are proving to be very promising approaches for inducing and maintaining good health. This study uses advanced magnetic resonance imaging techniques to probe muscle physiology and the body's remarkable ability to adapt to low-calorie situations. There are clear parallels between the experiences of endurance athletes and those who fast, and the benefits they derive, with overcompensation opening up extremely interesting avenues of research.
INTELLIGENCIA: "An integrated pipeline powered by Artificial Intelligence for the exploitation of cardiac contrast-enhanced imaging health data warehouses with living meta-analysis for myocardial injury prognosis".
This project aims to work with the medical community to build a unified public health solution that will enable better use of cardiac MRI data. Its objective is to create an automatic, standardized, open-source, and reproducible AI-driven quantification platform to analyze myocardial injuries using living meta-analyses. The project will focus on implementing methods that guarantee explainability, interpretability, and confidence levels.
hashtag#Recherche hashtag#SantéPublique hashtag#Innovation hashtag#DonnéesDeSanté hashtag#EssaisCliniques hashtag#MESSIDORE2024 Inserm CNRS Hospices Civils de Lyon - HCL CHU de Saint-Etienne INSA Lyon - Institut National des Sciences Appliquées de Lyon IReSP - Institut pour la Recherche en Santé Publique