Moen Sarah, Vuik Fanny E R, Kuipers Ernst J, Spaander Manon C W
Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center, 3015 CE Rotterdam, The Netherlands.
Diagnostics (Basel). 2022 Aug 17;12(8):1994. doi: 10.3390/diagnostics12081994.
: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability. Automated reviewing of colon capsule endoscopy images using artificial intelligence could be timesaving while providing an objective and reproducible outcome. This systematic review aims to provide an overview of the available literature on artificial intelligence for reviewing colonic mucosa by colon capsule endoscopy and to assess the necessary action points for its use in clinical practice. : A systematic literature search of literature published up to January 2022 was conducted using Embase, Web of Science, OVID MEDLINE and Cochrane CENTRAL. Studies reporting on the use of artificial intelligence to review second-generation colon capsule endoscopy colonic images were included. : 1017 studies were evaluated for eligibility, of which nine were included. Two studies reported on computed bowel cleansing assessment, five studies reported on computed polyp or colorectal neoplasia detection and two studies reported on other implications. Overall, the sensitivity of the proposed artificial intelligence models were 86.5-95.5% for bowel cleansing and 47.4-98.1% for the detection of polyps and colorectal neoplasia. Two studies performed per-lesion analysis, in addition to per-frame analysis, which improved the sensitivity of polyp or colorectal neoplasia detection to 81.3-98.1%. By applying a convolutional neural network, the highest sensitivity of 98.1% for polyp detection was found. : The use of artificial intelligence for reviewing second-generation colon capsule endoscopy images is promising. The highest sensitivity of 98.1% for polyp detection was achieved by deep learning with a convolutional neural network. Convolutional neural network algorithms should be optimized and tested with more data, possibly requiring the set-up of a large international colon capsule endoscopy database. Finally, the accuracy of the optimized convolutional neural network models need to be confirmed in a prospective setting.
结肠胶囊内镜在日常实践中的应用受到伴随而来的高强度阅片时间以及观察者间变异性风险的限制。使用人工智能对结肠胶囊内镜图像进行自动阅片可以节省时间，同时提供客观且可重复的结果。本系统评价旨在概述关于使用人工智能通过结肠胶囊内镜检查来评估结肠黏膜的现有文献，并评估其在临床实践中应用的必要关键点。：使用Embase、Web of Science、OVID MEDLINE和Cochrane CENTRAL对截至2022年1月发表的文献进行了系统的文献检索。纳入了报告使用人工智能评估第二代结肠胶囊内镜结肠图像的研究。：对1017项研究进行了资格评估，其中9项被纳入。两项研究报告了计算机化肠道清洁评估，五项研究报告了计算机化息肉或结直肠肿瘤检测，两项研究报告了其他影响。总体而言，所提出的人工智能模型对肠道清洁的敏感性为86.5% - 95.5%，对息肉和结直肠肿瘤检测的敏感性为47.4% - 98.1%。两项研究除了逐帧分析外还进行了逐病变分析，这将息肉或结直肠肿瘤检测的敏感性提高到了81.3% - 98.1%。通过应用卷积神经网络，发现息肉检测的最高敏感性为98.1%。：使用人工智能评估第二代结肠胶囊内镜图像具有前景。通过卷积神经网络进行深度学习实现了息肉检测的最高敏感性98.1%。卷积神经网络算法应使用更多数据进行优化和测试，可能需要建立一个大型国际结肠胶囊内镜数据库。最后，需要在前瞻性环境中确认优化后的卷积神经网络模型的准确性。
