Master of Science, Universite De Grenoble (2009)
Master of Science, Ecole Polytechnique (2010)
Doctor of Philosophy, Universite De Grenoble (2013)
Currently a resident in Anesthesia and Critical Care at Grenoble University Hospital, I joined the Anesthesia Informatics and Media laboratory at Stanford University for a one year post-doctoral position starting from November 2017. The post-doctoral research project is untitled ?Personalization of education for fundamentals in anesthesia and critical care on e-learning platforms? and melts competencies held by Stanford University and Grenoble Alpes University.
Previous to my residency, my academic education in health-care and medical engineering led to a PhD and gave me a multidisciplinary knowledge of medicine and research. I now intend to extend my PhD work in the field of medical quality evaluation by shifting my focus from surgery to anesthesia and critical care. I specifically seek to develop new approaches to evaluate the improvements made possible by innovative medical devices and procedures in anesthesia, using simulation as a research tool. Stanford?s AIM lab, with its pioneering and present leading role in medical education, is offering me an outstanding environment to develop my research in this field.
My initial higher education was at the E ?cole Polytechnique, the top French engineering school. It provided me with a solid knowledge of fundamental and applied science, strong analytical and summarizing skills as well as methodological rigor. In the last year of my engineering curriculum, I changed my orientation to medicine, being attracted to the diversity of experiences it could offer, from patient care to core science. In parallel with my medical studies, I undertook a PhD in Applied Mathematics at the Computer Assisted Medical Interventions laboratory at Grenoble University (Laboratoire TIMC-IMAG). My PhD, focused on the development of a technical skills assessment device for laparoscopic surgery using a priori knowledge, was one brick in the design of a more general system dedicated to the analysis of the surgical process, enabling the identification of risky situations and improving the quality of the surgery.
My residency, through simulation sessions coaching and daily bedside mentoring with interns, has convinced me of the potential of innovative medical devices and pedagogic tools in anesthesia and critical care, and of the importance of developing validation processes for these. I intend to continue to use the skills and methodological background acquired during my PhD to build up such processes.
I contacted Dr. Larry Chu of AIM lab after having read his work on the START program. This educational program centered on the students? needs, using numeric tools and simulation to enhance assimilation of contents, first aroused my curiosity and then boosted my desire to take part in the development of such innovations. Dr. Chu offered me to join his team to work on a research project aiming to maximize learners? engagement in their curriculum, by developing extensions to a new e- learning platform called Learnly, used across the USA by a growing number of anesthesia residents.
This post-doctoral position offers me an excellent opportunity to further develop my understanding of novel pedagogic approaches in anesthesia, that I will be able to mobilize when I return. Indeed, a nation-wide e-learning platform (SIDES 3.0) is currently being developed for French medical residents, and will be deployed in September 2017. I will initiate the collaboration between the research teams in order to share several functionalities and learning experimentations between the two platforms. On the other hand, I will contribute to AIM laboratory research with my experience in non-supervised evaluation methods based on visual tracking and signal processing.
Evaluation of surgical technical abilities is a major issue in minimally invasive surgery. Devices such as training benches offer specific scores to evaluate surgeons but cannot transfer in the operating room (OR). A contrario, several scores measure performance in the OR, but have not been evaluated on training benches. Our aim was to demonstrate that the GOALS score, which can effectively grade in the OR the abilities involved in laparoscopy, can be used for evaluation on a laparoscopic testbench (MISTELS). This could lead to training systems that can identify more precisely the skills that have been acquired or must still be worked on.32 volunteers (surgeons, residents and medical students) performed the 5 tasks of the MISTELS training bench and were simultaneously video-recorded. Their performance was evaluated with the MISTELS score and with the GOALS score based on the review of the recording by two experienced, blinded laparoscopic surgeons. The concurrent validity of the GOALS score was assessed using Pearson and Spearman correlation coefficients with the MISTELS score. The construct validity of the GOALS score was assessed with k-means clustering and accuracy rates. Lastly, abilities explored by each MISTELS task were identified with multiple linear regression.GOALS and MISTELS scores are strongly correlated (Pearson correlation coefficient = 0.85 and Spearman correlation coefficient = 0.82 for the overall score). The GOALS score proves to be valid for construction for the tasks of the training bench, with a better accuracy rate between groups of level after k-means clustering, when compared to the original MISTELS score (accuracy rates, respectively, 0.75 and 0.56).GOALS score is well suited for the evaluation of the performance of surgeons of different levels during the completion of the tasks of the MISTELS training bench.
View details for DOI 10.1007/s11548-017-1645-y
View details for PubMedID 28825199
View details for Web of Science ID 000392740800003
During a laparoscopic surgery, the endoscope can be manipulated by an assistant or a robot. Several teams have worked on the tracking of surgical instruments, based on methods ranging from the development of specific devices to image processing methods. We propose to exploit the instruments' insertion points, which are fixed on the patients abdominal cavity, as a geometric constraint for the localization of the instruments. A simple geometric model of a laparoscopic instrument is described, as well as a parametrization that exploits a spherical geometric grid, which offers attracting homogeneity and isotropy properties. The general architecture of our proposed approach is based on the probabilistic Condensation algorithm.
View details for PubMedID 22003618