
CompSURG aims to develop a novel computational methodology for analyzing intra-operative adverse events (IAEs) from surgical videos on a large scale. Currently, IAEs are under-reported, hindering their thorough analysis, the establishment of appropriate safety measures, and the development of intraoperative assistance systems to reduce their occurrence. Recent studies have shown that IAEs, while previously considered inconsequential, may in fact be indicative of serious complications and poor surgical outcomes.
Building on these findings, CompSURG will propose a radically new computational approach to improve intra-operative surgical safety, with a specific focus on automatic recognition and analysis of surgical activities and IAEs in endoscopic videos. This will involve developing novel cutting-edge computer vision and machine learning techniques to achieve groundbreaking models of the intricate interactions between surgical tools and anatomy, examine activity patterns and variability on a large scale, and identify critical steps requiring safety measures.

The joint IHU – University of Strasbourg CAMMA research team led by Prof. Nicolas Padoy aims at developing new tools and methods based on computer vision, medical image analysis and machine learning to perceive, model, analyze and support clinician and staff activities in the operating room using the vast amount of digital data generated during surgeries. Nested in the IHU building, the team benefits from a unique infrastructure and eco-system with the university hospital and IRCAD.









































