Purpose

Inexpensive benchtop training systems offer significant advantages to meet the increasing demand of training surgeons and gastroenterologists in flexible endoscopy. Established scoring systems exist, based on task duration and mistake evaluation. However, they require trained human raters, which limits broad and low-cost adoption. There is an unmet and important need to automate rating with machine learning.

Method

We present a general and robust approach for recognizing training tasks from endoscopic training video, which consequently automates task duration computation. Our main technical novelty is to show the performance of state-of-the-art CNN-based approaches can be improved significantly with a novel semi-supervised learning approach, using both labelled and unlabelled videos. In the latter case, we assume only the task execution order is known a priori.

Results

Two video datasets are presented: the first has 19 videos recorded in examination conditions, where the participants complete their tasks in predetermined order. The second has 17 h of videos recorded in self-assessment conditions, where participants complete one or more tasks in any order. For the first dataset, we obtain a mean task duration estimation error of 3.65 s, with a mean task duration of 159 s (\(2.3\%\) relative error). For the second dataset, we obtain a mean task duration estimation error of 3.67 s. We reduce an average of 5.63% in error to 3.67% thanks to our semi-supervised learning approach.

Conclusion

This work is the first significant step forward to automate rating of flexible endoscopy students using a low-cost benchtop trainer. Thanks to our semi-supervised learning approach, we can scale easily to much larger unlabelled training datasets. The approach can also be used for other phase recognition tasks.

中文翻译：

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具有半监督学习的灵活内窥镜台式训练器中的自动任务识别。

目的

廉价的台式培训系统具有显着优势，可以满足外科医生和胃肠病学家在灵活内窥镜检查方面不断增长的需求。现有的评分系统基于任务持续时间和错误评估。但是，它们需要经过培训的人工评估员，这限制了广泛和低成本的采用。使用机器学习自动化评级有一个未满足且重要的需求。

方法

我们提出了一种通用且稳健的方法，用于从内窥镜训练视频中识别训练任务，从而自动计算任务持续时间。我们的主要技术创新是展示最先进的基于 CNN 的方法的性能可以通过使用标记和未标记视频的新型半监督学习方法显着提高。在后一种情况下，我们假设只有任务执行顺序是先验已知的。

结果

提供了两个视频数据集：第一个在检查条件下记录了 19 个视频，参与者按预定顺序完成任务。第二个在自我评估条件下录制了 17 小时的视频，参与者以任何顺序完成一项或多项任务。对于第一个数据集，我们获得了 3.65 秒的平均任务持续时间估计误差，平均任务持续时间为 159 秒（\(2.3\%\)相对误差）。对于第二个数据集，我们获得了 3.67 秒的平均任务持续时间估计误差。由于我们的半监督学习方法，我们将平均错误率从 5.63% 减少到 3.67%。

结论

这项工作是使用低成本台式培训器自动评估柔性内窥镜学生的第一个重要步骤。由于我们的半监督学习方法，我们可以轻松扩展到更大的未标记训练数据集。该方法还可用于其他相位识别任务。