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Models of Surgical and Interventional Competencies
Modélisation des compétences chirurgicales et interventionnelles

Team Coordinator, Pierre Jannin.

MediCIS is a research team of the UMR Inserm U1099 LTSI from University of Rennes I, affiliated to INSERM (National Institute of Health and Scientific Research) and is located in Rennes, France at the medical school.

We aim at improving surgical quality through two directions. First, by computer assistance of the surgical decision-making process including managerial decisions, process of care, and outcome-based decisions. Second, by developing simulation systems for better surgical training and evaluation. A common methodological approach will be studied for both: the study of methods for symbolic and numeric modeling of the different technical and non-technical surgical skills. One aim is also to study low-cost and low-complexity technologies for, but not exclusively, neurosurgery.



Main Research Projects and Supports

Involvement in Conferences

  • Participation à l'organisation de la conférence internationale CARS 2018 Berlin en Juin 2018 (Germany)
  • Invited talk at “Autonomy in Surgical robots” Hamlyn Symposium Imperial College London, June 24-27, 2018
  • Invited talk at 2nd Biannual Summer School on Control of Surgical Robots (COSUR) 2018 9-13 July 2018 Department of Computer Science, University of Verona, Italy

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  • Between 6 and 8 millions of surgical procedures per year in France (250 millions worldwide)
  • More patients in an older population need for more surgical procedures with better health care
  • 10 000 to 15 000 avoidable serious adverse events occur in the peri-operative process per year in France

Global Vision

Improving surgical quality by means of:

  • Surgical decision support with predictive models of best practice
  • Evaluation and training of both technical and non-technical surgical skills


  1. Study and understanding of surgical competencies within simulated and real clinical environments
  2. Study of methods for modeling and evaluation of competencies from observed data
  3. Implementation of models and methods within systems allowing evaluating, ensuring and/or optimising surgical quality

Our core competencies: Data fusion and image processing, Machine learning, Supervized and unsupervised analysis, Knowledge modeling

For more details about the methodological and applicative objectives on these issues, go to the Scientific Activities page.

inserm rennes1 ltsi