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

本研究使用真实的临床数据集，提出了一种基于神经网络的自动化框架，通过 COF 的代理来预测外科手术技能。该方法在与初级外科医生的人类表现相比可达到 0.55 的斯皮尔曼相关性。