人工智能辅助结肠镜检查有效退镜时间计算系统的构建及临床应用价值

Development and clinical application value of an artificial intelligence‑assisted system for calculating effective colonoscopy withdrawal time

摘要:

目的 开发一套人工智能辅助结肠镜检查有效退镜时间的计算系统，并评估其临床应用价值。方法 首先使用来自武汉大学人民医院的17 118张肠镜图片作为训练集和测试集，构建识别不同肠镜视野的深度卷积神经网络模型；随后将此模型与课题组前期开发的体内外识别模型、盲肠识别模型相结合，构建自动计算有效退镜时间的人工智能系统；最后将2020年7月1日至2020年10月10日武汉大学人民医院内镜中心944例结肠镜检查视频纳入回顾性分析，使用人工智能自动计算系统计算有效退镜时间，其中89例追加人工计算，用以评价人工智能自动计算系统的准确度，剩余855例根据人工智能自动计算系统计算结果分成2组，即有效退镜时间<6 min组（n=615）和有效退镜时间≥6 min组（n=240），对比分析腺瘤总体检出率、息肉总体检出率的组间差异。结果 有效退镜时间人工智能自动计算系统的准确度达92.1%（82/89）。有效退镜时间≥6 min组的腺瘤总体检出率为37.5%（90/240），有效退镜时间<6 min组为19.0%（117/615），组间差异有统计学意义（χ2=32.11，P<0.001）；有效退镜时间≥6 min组的息肉总体检出率为75.0%（180/240），有效退镜时间<6 min组为45.2%（278/615），组间差异也有统计学意义（χ2=61.62，P<0.001）。结论 人工智能自动计算系统能够准确计算结肠镜检查有效退镜时间，可用于临床结肠镜检查有效退镜时间的监测。此外，有效退镜时间≥6 min能有效提高腺瘤及息肉的检出率。

Abstract:

Objective To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value. Methods First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group (n=615) and the effective withdrawal time ≥6 min group (n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant (χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance (χ2=61.62, P<0.001). Conclusion AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.