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Under creation, Please refer to the projects page
The aim of surgical evaluation and training axe is addressing the main following challenges: systematic data collection, personalization of training program thanks to machine and deep learning, development of simulators for non-technical skills in robotic and non-robotic surgery, and development of simulators for patients.
Surgical skills are not the only parameters influencing surgical performance. Characteristics specific to each operator can also significantly influence this performance: the physiological state at the time of the task or the intrinsic skills of the operator outside the surgical field. Knowing these characteristics and their influence on performance can make it possible to propose personalized training plans.
The SunSet project aims at developing a software suite based on immersive and collaborative virtual reality technology for training and evaluating non-technical skills. The project is implemented in the context of the training of scrub nurses specialized in neurosurgery.More information
The use of virtual reality (VR) is now proposed for surgical training because it offers a tool at the border between theoretical and practical realities. Arthroscopic surgery is particularly well suited to this VR simulation because of its per-operative visual feedback on a 2D screen. However, its validity when transposing the performance of the simulator to the real patient is not yet proven. To show that the student progressing on the simulator also becomes efficient in the operating room would be a considerable advance in medical training. More information
In this project, we propose to study a solution for the computation of surgical procedural knowledge models from recordings of individual procedures and their execution. The objective is to develop an enabling technology for procedural knowledge-based surgical simulation systems. In this project, we demonstrate its potential added value for high-level abstraction teaching applications. More information
This project has to objective to detect and distinguish surgical process deviations in abdominal surgeries based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. More information