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This paper explores how graph neural networks (GNNs) can be used to enhancevisual scene understanding and surgical skill assessment. By using GNNs toanalyze the complex visual data of surgical procedures represented as graphstructures, relevant features can be extracted and surgical skill can bepredicted. Additionally, GNNs provide interpretable results, revealing thespecific actions, instruments, or anatomical structures that contribute to thepredicted skill metrics. This can be highly beneficial for surgical educatorsand trainees, as it provides valuable insights into the factors that contributeto successful surgical performance and outcomes. SurGNN proposes two concurrentapproaches -- one supervised and the other self-supervised. The paper alsobriefly discusses other automated surgical skill evaluation techniques andhighlights the limitations of hand-crafted features in capturing theintricacies of surgical expertise. We use the proposed methods to achievestate-of-the-art results on EndoVis19, and custom datasets. The workingimplementation of the code can be found at https://github.com/<redacted>.

SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks