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Surgical skill assessment is important for surgery training and qualitycontrol. Prior works on this task largely focus on basic surgical tasks such assuturing and knot tying performed in simulation settings. In contrast, surgicalskill assessment is studied in this paper on a real clinical dataset, whichconsists of fifty-seven in-vivo laparoscopic surgeries and corresponding skillscores annotated by six surgeons. From analyses on this dataset, the clearnessof operating field (COF) is identified as a good proxy for overall surgicalskills, given its strong correlation with overall skills and highinter-annotator consistency. Then an objective and automated framework based onneural network is proposed to predict surgical skills through the proxy of COF.The neural network is jointly trained with a supervised regression loss and anunsupervised rank loss. In experiments, the proposed method achieves 0.55Spearman's correlation with the ground truth of overall technical skill, whichis even comparable with the human performance of junior surgeons.

Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field