BACKGROUND AND AIMS The quality of bowel preparation is an important factor that can affect the effectiveness of a colonoscopy. Several tools, such as the Boston Bowel Preparation Scale (BBPS) and Ottawa Bowel Preparation Scale, have been developed to evaluate bowel preparation. However, understanding the differences between evaluation methods and consistently applying them can be challenging for endoscopists. There are also subjective biases and differences among endoscopists. Therefore, this study aimed to develop a novel, objective, and stable method for the assessment of bowel preparation through artificial intelligence. METHODS We used a deep convolutional neural network to develop this novel system. First, we retrospectively collected colonoscopy images to train the system and then compared its performance with endoscopists via a human-machine contest. Then, we applied this model to colonoscopy videos and developed a system named ENDOANGEL to provide bowel preparation scores every 30 seconds and to show the cumulative ratio of frames for each score during the withdrawal phase of the colonoscopy. RESULTS ENDOANGEL achieved 93.33% accuracy in the human-machine contest with 120 images, which was better than that of all endoscopists. Moreover, ENDOANGEL achieved 80.00% accuracy among 100 images with bubbles. In 20 colonoscopy videos, accuracy was 89.04%, and ENDOANGEL continuously showed the accumulated percentage of the images for different BBPS scores during the withdrawal phase and prompted us for bowel preparation scores every 30 seconds. CONCLUSIONS We provided a novel and more accurate evaluation method for bowel preparation and developed an objective and stable system-ENDOANGEL-that could be applied reliably and steadily in clinical settings.

中文翻译：

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用于评估肠道准备的新型人工智能系统（带视频）。

背景和目的肠准备的质量是可能影响结肠镜检查的有效性的重要因素。已经开发了多种工具来评估肠道准备，例如波士顿肠道准备量表（BBPS）和渥太华肠道准备量表。然而，对于内镜医师而言，了解评估方法之间的差异并持续应用它们可能会带来挑战。内镜医师之间也存在主观偏见和差异。因此，本研究旨在开发一种新颖，客观，稳定的方法，以通过人工智能评估肠道准备。方法我们使用了深度卷积神经网络来开发这个新颖的系统。第一的，我们回顾性地收集了结肠镜检查图像以训练该系统，然后通过人机竞赛将其性能与内窥镜检查人员进行了比较。然后，我们将此模型应用于结肠镜检查视频，并开发了一个名为ENDOANGEL的系统，该系统每30秒提供一次肠道准备评分，并显示结肠镜检查退出阶段每个评分的帧的累积比率。结果ENDOANGEL在人机竞赛中以120张图像获得了93.33％的准确度，比所有内镜医师都高。此外，ENDOANGEL在100张带有气泡的图像中达到了80.00％的准确度。在20例结肠镜检查视频中，准确性为89.04％，并且ENDOANGEL持续显示戒断阶段不同BBPS评分的图像累积百分比，并提示我们每30秒进行一次肠道准备评分。