Surgical skills have a great influence on surgical safety and patients'
well-being. Traditional assessment of surgical skills involves strenuous manual
efforts, which lacks efficiency and repeatability. Therefore, we attempt to
automatically predict how well the surgery is performed using the surgical
video. In this paper, a unified multi-path framework for automatic surgical
skill assessment is proposed, which takes care of multiple composing aspects of
surgical skills, including surgical tool usage, intraoperative event pattern,
and other skill proxies. The dependency relationships among these different
aspects are specially modeled by a path dependency module in the framework. We
conduct extensive experiments on the JIGSAWS dataset of simulated surgical
tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed
framework achieves promising results on both datasets, with the
state-of-the-art on the simulated dataset advanced from 0.71 Spearman's
correlation to 0.80. It is also shown that combining multiple skill aspects
yields better performance than relying on a single aspect.

提出了一种统一的多路径框架，用于通过手术视频自动预测手术的成功程度，该框架考虑了手术技能的多个组成方面，包括手术工具使用、术中事件模式和其他技能代理，并通过路径依赖模块特别建模这些不同方面之间的依赖关系。在模拟手术任务的 JIGSAWS 数据集和新的真实腹腔镜手术的临床数据集上进行了大量实验，提出的框架在两个数据集上均取得了良好的成果，模拟数据集的最先进水平从 0.71 Spearman 相关性提高到了 0.80，同时还表明结合多个技能方面比依赖单个方面获得更好的性能。