  This paper explores how graph neural networks (GNNs) can be used to enhance
visual scene understanding and surgical skill assessment. By using GNNs to
analyze the complex visual data of surgical procedures represented as graph
structures, relevant features can be extracted and surgical skill can be
predicted. Additionally, GNNs provide interpretable results, revealing the
specific actions, instruments, or anatomical structures that contribute to the
predicted skill metrics. This can be highly beneficial for surgical educators
and trainees, as it provides valuable insights into the factors that contribute
to successful surgical performance and outcomes. SurGNN proposes two concurrent
approaches -- one supervised and the other self-supervised. The paper also
briefly discusses other automated surgical skill evaluation techniques and
highlights the limitations of hand-crafted features in capturing the
intricacies of surgical expertise. We use the proposed methods to achieve
state-of-the-art results on EndoVis19, and custom datasets. The working
implementation of the code can be found at https://github.com/<redacted>.
