The operating room is the most frequent location for hospital-related errors leading to increased morbidity, mortality, and costs. However, intra-operative adverse events (IAEs) are seldom well documented, which impedes their thorough root-cause analysis, the definition of appropriate safety measures, and the development of intra-operative support systems to reduce their occurrence. Recent manual video-based assessments of surgical procedures have shown that not only are IAEs frequent, but also that intra-operative near miss events, previously thought to be inconsequential, are in fact predictors of major errors and correlate with complications and poor surgical outcomes. CompSURG leverages these recent findings to propose a radically new, computational approach to improve intra-operative surgical safety.
The CompSURG project proposes to focus on automatically detecting and analyzing surgical variability and IAEs in endoscopic videos via novel computer vision methods that model the surgical tool-tissue interactions and infer the quality of intra-operative events, as needed to study and prevent the activity patterns leading to poor outcomes. It also aims to introduce new machine learning training methods for scaling these approaches to different types of surgeries and centers using a limited set of manual annotations.
These computational methods will allow the automated reporting and analysis of surgical activities and IAEs at a scale unfeasible with manual methods. They will be used to analyze patterns of IAEs, to identify the activities and phases of the surgical procedures that would benefit from new safety measures, and to define and implement best practices for surgical safety.
