MediCIS is a research team of the University of Rennes I, jointly affiliated to INSERM (National Institute of Health and Scientific Research). MediCIS belongs to the LTSI Institute (UMR Inserm 1099), and is located in Rennes, France at the University Medical School.

MediCIS activities concern the development of new information science algorithms dedicated to the modeling of surgical knowledge and processes.Our objective is to study methods for modelling and analysis surgical and interventional knowledge and processes allowing the development of a new generation of computer assisted surgical (CAS) systems with full decision-making support. To date, most computer assisted surgical systems focused on providing the clinician with pre and intra operative multimodal medical images and signals of the patient, whereas it is well admitted that the decision making process additionally requires information and knowledge. Throughout the peri-operative workflow, from diagnosis, planning, intervention, and postoperative evaluation, the clinician relies not only on images, but on multiple different sources of data, information, and knowledge to take his/her surgical decision. Some sources are implicit; some are explicit. Implicit ones need to be made explicit and available in digital form, in order to enable automating processing and reasoning. Knowledge is usually broken down into conceptual and procedural knowledge. For instance, some knowledge is related to anatomy; some knowledge is related to his/her surgical practice, and some stems from the literature. Modelling approaches enable the explicit representation and formalization of these sources and, consequently, facilitate the system development to support the decision-making process. Whereas modelling of patient information from data is widely studied, few research address modelling of knowledge involved in the surgical decision process.

We are studying and developing new methods to construct models of both domain and procedural knowledge in order to empower the decision making process. These knowledge models are build either from analysis of a large ensemble of patient specific models stored in data repositories, or from formalization of expert’s knowledge or literature. Such models combine and synthesize knowledge used during the surgical decision making process. We address the issue of modelling through two complementary approaches: numeric with data fusion inspired approaches and symbolic with conceptual modelling and formal ontologies. There are three types of decision that may be supported by the models and the systems we both want to develop: managerial decision, process of care, and outcome-based decision. The main long-term challenge we want to address is to enable patient-adapted anticipation and, somewhat, prediction of the interventional or surgical procedure and its outcome. To design the future of the operating room focusing on creating real surgical decision making support systems. For personalized and predictive model-guided interventional and surgical procedures. Our ultimate goal is to address the need for model-guided intervention that is currently emerging at the intersection between the Virtual Physiological Human and Personalized, Predictive and Preventive Medicine.

more details on our scientific topics

Main clinical applications are on cranial and spinal surgery with special focus on functional neurosurgery (such as Deep Brain Stimulation), surgery of brain tumors, spinal surgery, and interventional neuroradiology.

more details on our application domains

Computer Assisted Surgery, Medical Ontologies, Data Fusion, Image guided Surgery, Image Processing, Surgical Process Models, Knowledge Modeling, Surgical Workflow, e-health & HealthGrids, Decision Support Systems