Objective

According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons’ deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows.

Methods

We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation.

Results

The best results have over 90% accuracy. Recall and precision for event deviations, i.e. related to adverse events, are respectively below 80% and 40%. To understand these results, we have provided a detailed analysis of the incorrectly-detected observations.

Conclusion

Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method.

Significance

Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems.

中文翻译：

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腹腔镜直肠固定术中手术偏差的离线识别。

目标

根据对 7 项研究的荟萃分析，手术期间发生至少一次不良事件的患者中位数为 14.4%，其中三分之一是可以预防的。不良事件的发生迫使外科医生实施纠正策略，从而偏离标准的手术过程。因此，很明显，不良事件的自动识别是患者安全的主要挑战。在本文中，我们提出了一种使我们能够识别此类偏差的方法。我们专注于确定外科医生由于手术事件而不是解剖学特征而偏离标准手术过程。鉴于典型外科手术工作流程的高度可变性，这尤其具有挑战性。

方法

我们引入了一种新方法，旨在基于多维非线性时间标度自动检测和区分手术过程偏差，并使用手动注释手术过程的隐藏半马尔可夫模型。然后使用交叉验证评估该方法。

结果

最好的结果有超过 90% 的准确率。事件偏差（即与不良事件相关）的召回率和精确度分别低于 80% 和 40%。为了理解这些结果，我们对错误检测到的观察结果进行了详细分析。

结论

带有隐藏半马尔可夫模型的多维非线性时间缩放为检测偏差提供了有希望的结果。我们对错误检测到的观察结果的误差分析提供了不同的线索，以进一步改进我们的方法。

意义

我们的方法证明了自动检测手术偏差的可行性，可用于技能分析和开发基于情境感知的计算机辅助手术系统。