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EPIONE Research team

E-Patient: Images, Data & MOdels for e-MediciNE

Team presentation


Our long-term goal is to contribute to the development of what we call the e-patient (digital patient) for e-medicine (digital medicine).

The personalized e-patient for e-medicine

  • the e-patient (or digital patient) is a set of computational models of the human body able to describe and simulate the anatomy and the physiology of the patient’s organs and tissues, at various scales, for an individual or a population. The e-patient can be seen as a framework to integrate and analyze in a coherent manner the heterogeneous information measured on the patient from disparate sources: imaging, biological, clinical, sensors, …
  • e-medicine (or digital medicine) is defined as the computational tools applied to the e-patient to assist the physician and the surgeon in their medical practice,  to assess the diagnosis/prognosis, and to plan, control and evaluate the therapy.

The models that govern the algorithms designed for e-patients and e-medicine come from various disciplines: informatics, mathematics, medicine, statistics, physics, biology, chemistry, etc. The parameters of those models must be adjusted to an individual or a population based on the available images, signals and data. This adjustment is called personalization and usually requires the resolution of difficult inverse problems. The overall picture of the construction of the personalized e-patient for e-medicine was presented at the College de France through an inaugural lecture (fr) and a series of courses (fr) and seminars (fr), concluded by an international workshop.


The research organization in our field is often built on a virtuous triangle. On one vertex, academic research requires multidisciplinary collaborations associating informatics and mathematics to other disciplines: medicine, biology, physics, chemistry… On a second vertex, a clinical partnership is required to help defining pertinent questions, to get access to clinical data, and to clinically evaluate any proposed solution. On the third vertex, an industrial partnership can be introduced for the research activity itself, and also to transform any proposed solution into a validated product that can ultimately be transferred to the clinical sites for an effective use on the patients.

Keeping this triangle in mind, we choose our research directions within a virtuous circle: we look at difficult problems raised by our clinical or industrial partners, and then try to identify some classes of generic fundamental/theoretical problems associated to their resolution. We also study some fundamental/theoretical problems per se in order to produce fundamental scientific advances that can help in turn to promote new applications.

Research themes

Scientific Axes:

  1. Biomedical Image Analysis & Machine Learning
  2. Imaging & Phenomics, Biostatistics
  3. Computational Anatomy, Geometric Statistics
  4. Computational Physiology & Image-Guided Therapy
  5. Computational Cardiology & Image-Based Cardiac Intervention

Technological Axes:

  1. Software Platform for Medical Image Analysis & Simulation
  2. Infostructure for Large Scale Biomedical Data Science

The chosen axes have non-null intersections, and the progress made in one axis can contribute to the progress of the other axes.

International and industrial relations




Keywords: Biomedical Image Analysis Machine Learning Imaging & Phenomics Biostatistics Computational Anatomy Geometric Statistics Computational Physiology Image-Guided Therapy Computational Cardiology Image-Based Cardiac Intervention