A novel artificial intelligence system for the assessment of bowel preparation
A novel artificial intelligence system for the assessment of bowel preparation
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DOI：
10.1016/j.gie.2019.11.026
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发表时间：
2020-02-01
影响因子：
7.7
通讯作者：
Yu, Honggang
中科院分区：
文献类型：
--
作者：
Zhou, Jie;Wu, Lianlian;Yu, Honggang
背景和目的：肠道准备的质量是影响结肠镜检查效果的重要因素。已经开发了几种工具，如波士顿肠道准备量表（BBPS）和渥太华肠道准备量表，用于评价肠道准备。然而，了解评价方法之间的差异并始终如一地应用它们对内窥镜医生来说可能是一个挑战。内镜医师之间也存在主观偏见和差异。因此，本研究旨在通过人工智能开发一种新颖，客观，稳定的方法来评估肠道准备。方法：我们使用了深度卷积神经网络来开发这个新系统。首先，我们回顾性地收集结肠镜检查图像来训练系统，然后通过人机竞赛将其性能与内窥镜医生进行比较。然后，我们将此模型应用于结肠镜视频，并开发了一个系统名为ENDOANGEL提供肠道准备分数每30秒，并显示在结肠镜撤出阶段的每一个分数的帧的累积比例。结果：ENDOANGEL达到93.33%的准确率，在人机竞赛的120幅图像，这是优于所有的内镜医生。此外，ENDOANGEL在100张气泡图像中的准确率为80.00%。在20个结肠镜检查视频中，准确率为89.04%，ENDOANGEL连续显示撤药期间不同BBPS评分的图像累积百分比，并每30秒提示我们进行肠道准备评分。我们为肠道准备提供了一种新的、更准确的评价方法，并开发了一种客观、稳定的系统--ENDOANGEL，可以在临床环境中可靠和稳定地应用。
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 systemd-ENDOANGEL-that could be applied reliably and steadily in clinical settings.