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      MediCIS       MediCIS
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-**Team Coordinator**, [[:members:pierre.jannin:index|Pierre Jannin]].+**Team Coordinator**: [[:members:pierre.jannin:index|Pierre Jannin]].
-MediCIS is a research team of the [[http://www.ltsi.univ-rennes1.fr|UMR Inserm U1099 LTSI]] from [[http://www.univ-rennes1.fr|University of Rennes I]], affiliated to [[http://www.inserm.fr|INSERM]] (National Institute of Health and Scientific Research) and is located in Rennes, France at the medical school.+MediCIS is a INSERM research team of the [[http://www.ltsi.univ-rennes1.fr|UMR Inserm U1099 LTSI]] from [[http://www.univ-rennes1.fr|University of Rennes I]], affiliated to [[http://www.inserm.fr|INSERM]] (National Institute of Health and Scientific Research) and is located in Rennes, France at the Medical University.
-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.+The MediCIS team’s project aims at improving surgical quality by means of [[https://www.nature.com/articles/s41551-017-0132-7|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.
 +**[[HTTP://WWW.CARS2019.ORG|... WE ORGANIZED CARS 2019 CONFERENCE IN RENNES. See how successful it was ...]]
 +**
 +[[HTTP://WWW.CARS2019.ORG|{{ :cars_2019_-_d.png?100|}}]]
====== News ====== ====== News ======
{{rss>https://medicis.univ-rennes1.fr/feed.php?type=rss3&num=10&ns=news&mode=list&content=html 5}} {{rss>https://medicis.univ-rennes1.fr/feed.php?type=rss3&num=10&ns=news&mode=list&content=html 5}}
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  * [[https://medicis.univ-rennes1.fr/activities/theme1/dbscomp|DBS-COMP: « Tractography and Deep Brain Stimulation » (PI-3 years) Funding from FRM]]   * [[https://medicis.univ-rennes1.fr/activities/theme1/dbscomp|DBS-COMP: « Tractography and Deep Brain Stimulation » (PI-3 years) Funding from FRM]]
  * [[http://www.incr.fr|M-DBS: « Model Guided Deep Brain Stimulation : Understanding of underlying phenomenon and improvement of surgical procedure » (PI-4 years) Funding from INCR]]   * [[http://www.incr.fr|M-DBS: « Model Guided Deep Brain Stimulation : Understanding of underlying phenomenon and improvement of surgical procedure » (PI-4 years) Funding from INCR]]
-  * [[http://www.sunset.cominlabs.ueb.eu|SunSet: « Surgical Non-Technical Skills Training System » (PI-4 years) Funding from LabEx CominLabs]]+  * [[https://project.inria.fr/sunset/|SunSet: « Surgical Non-Technical Skills Training System » (PI-4 years) Funding from LabEx CominLabs]]
  * [[https://condor-project.eu|CONDOR (Partner-3 years)]]   * [[https://condor-project.eu|CONDOR (Partner-3 years)]]
-  * [[http://www.s3pm.cominlabs.ueb.eu|S3PM: Surgical Process Model for Surgical Training (PI-4 years)]]+  * [[https://project.inria.fr/s3pm/|S3PM: Surgical Process Model for Surgical Training (PI-4 years)]]
  * [[http://cami-labex.fr|CAMI LabEx: Computer Assisted Medical Interventions]]   * [[http://cami-labex.fr|CAMI LabEx: Computer Assisted Medical Interventions]]
  * [[http://b-com.org|B-COM: Augmented Healthcare and Connected Healthcare]]   * [[http://b-com.org|B-COM: Augmented Healthcare and Connected Healthcare]]
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====== Involvement in Conferences ====== ====== Involvement in Conferences ======
-  * Participation à l'organisation de la [[http://www.cars-int.org/|conférence internationale CARS 2017]] Barcelona en Juin 2017 (Spain) +  * We are applying to host the international conference [[HTTP://WWW.miccai.ORG|MICCAI]] 2024 in Rennes (France) ... Fingers crossed 
-  * P. Jannin: Program Chair for [[http://www.miccai2017.org/|MICCAI 2017 conference]], Quebec, Canada +  * [[HTTP://WWW.CARS2019.ORG|Organization of CARS 2019 CONFERENCE in RENNES June 17-21 2019...]] 
-  * P. Jannin gives an invited talk on "Surgical Process Modeling: Methods and Applications" on October 17 2016, at [[http://care2016.imaging.robarts.ca/|the CARE workshop at MICCAI 2016]], Athens, Greece +  * Participation à l'organisation de la [[http://www.cars-int.org/|conférence internationale CARS 2018]] Berlin en Juin 2018 (Germany)
-  * P. Jannin gives a plenary talk on "Surgical Skill Analysis and Modeling" on October 14 2016, at [[http://ksmr.or.kr/accas/index.php/|the Asian Conference on Computer Aided Surgery]], Daejeon, Korea +
-  * P. Jannin: Program Committee member for [[http://www.miccai2016.org/|MICCAI 2016 conference]], Athens, Greece+
[[:confs_page:index|See all ...]] [[:confs_page:index|See all ...]]
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Methods: Methods:
 +  - Surgical Data Science
 +  - Artificial Intelligence and Machine Learning
  - Study and understanding of surgical competencies within simulated and real clinical environments   - Study and understanding of surgical competencies within simulated and real clinical environments
  - Study of methods for modeling and evaluation of competencies from observed data   - 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   - 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+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 [[:activities|Scientific Activities]] page. For more details about the methodological and applicative objectives on these issues, go to the [[:activities|Scientific Activities]] page.
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