Purpose

Automatic surgical skill assessment is an emerging field beneficial to both efficiency and quality of surgical education and practice. Prior works largely evaluate skills on elementary tasks performed in the simulation laboratory, which cannot fully reflect the variety of intraoperative circumstances in the real operating room. In this paper, we attempt to fill this gap by expanding surgical skill assessment onto a clinical dataset including fifty-seven in vivo surgeries.

Methods

To tackle the workflow and device constraints in the clinical setting, we propose a robust and non-interruptive surrogate for surgical skills, namely the clearness of operating field (COF), which shows strong correlation with overall skills and high inter-annotator consistency on our clinical data. Then, an automatic model based on neural networks is developed to regress surgical skills through the surrogate of COF using only video as input.

Results

The automatic model achieves 0.595 Spearman’s correlation with the ground truth of overall technical skill, which even exceeds the human performance of junior surgeons. Moreover, an exploratory study is conducted to validate the skill predictions against the clinical outcomes of patients.

Conclusion

Our results demonstrate that the surrogate of COF is promising and the approach is potentially applicable to clinical practice.

中文翻译：

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手术视野清晰：体内临床数据手术技能的替代品

目的

自动手术技能评估是一个新兴领域，有利于提高手术教育和实践的效率和质量。先前的工作主要评估模拟实验室中执行的基本任务的技能，不能完全反映真实手术室中的各种术中情况。在本文中，我们试图通过将手术技能评估扩展到包括 57 个体内手术在内的临床数据集来填补这一空白。

方法

为了解决临床环境中的工作流程和设备限制，我们提出了一种稳健且无中断的手术技能替代方法，即手术区域的清晰度 (COF)，它与整体技能和高度的注释者间一致性在我们的临床数据。然后，开发了一个基于神经网络的自动模型，通过仅使用视频作为输入的 COF 替代来回归手术技能。

结果

自动模型与整体技术技能的基本事实实现了 0.595 的 Spearman 相关性，甚至超过了初级外科医生的人类表现。此外，还进行了一项探索性研究，以根据患者的临床结果验证技能预测。

结论

我们的结果表明 COF 的替代方法是有希望的，并且该方法可能适用于临床实践。