AIxSuture: vision-based assessment of open suturing skills

Hanna Hoffmann, Isabel Funke, Philipp Peters, Danush Kumar Venkatesh, Jan Egger, Dominik Rivoir, Rainer Röhrig, Frank Hölzle, Sebastian Bodenstedt, Marie-Christin Willemer, Stefanie Speidel, Behrus Puladi

Abstract: Efficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is gaining traction for minimally invasive techniques, this cannot be said for open surgery skills. Open surgery generally has more degrees of freedom when compared to minimally invasive surgery, making it more difficult to interpret. In this paper, we present novel approaches for skill assessment for open surgery skills.