Team Coordinator: Pierre Jannin
MediCIS is a INSERM research team of the UMR Inserm U1099 LTSI from University of Rennes, affiliated to INSERM (National Institute of Health and Scientific Research) and is located in Rennes, France at the Medical University.
The MediCIS team’s project aims at improving surgical quality by means of Surgical Data Science for both surgical performance and training. Surgical data science has the potential to revolutionize surgery. Relying upon recent progress in artificial intelligence (AI) and deep learning, surgical data science aims to rely on data collected all along the surgical process, being analyzed to produce explicit knowledge that can be used for decision support, evaluation or training. Following our pioneered research on surgical process modeling and analysis, surgical data science includes analysis of human and effectors processes in addition to patient data. An holistic analysis of the surgical environment is then used for developing the next generation of computer assisted surgical systems and surgical simulators.
Main Research Projects
- « Surgical Data Science for Improving Quality » (PI-4 years) Funding from ANR-PRCI
Past Research Projects
MediCIS News
- 04/04/2024: Call of paper for the Hamlyn Workshop “Applying Surgical Data Science in Robotic Surgery” that we co-organized the 25th June 2024
Involvement in Conferences
- Call of paper for the Hamlyn Workshop “Applying Surgical Data Science in Robotic Surgery” that we co-organized the 25th June 2024
- We are involved in the organization of ISBI international conference in 2023.
Facts
- 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
Methods:
- Surgical Data Science
- Artificial Intelligence and Machine Learning
- Study and understanding of surgical competencies within simulated and real clinical environments
- Study of methods for modeling and evaluation of competencies from observed data
- Implementation of models and methods within systems allowing evaluating, ensuring and/or optimising surgical quality
Our core competencies: Surgical Data Science, Artificial Intelligence and Machine Learning, Data fusion and image processing, Ontologies
For more details about the methodological and applicative objectives on these issues, go to the Scientific Activities page.