IEEE J Biomed Health Inform. 2022 Aug;26(8):3950-3965. doi: 10.1109/JBHI.2022.3160098. Epub 2022 Aug 11.
During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.
在过去的几十年中,已经开发出许多用于结肠镜检查的自动化图像分析方法。在临床试验中已经测试了最有前途的方法的实时实现,包括最近的几项多中心研究。所有试验都表明了可能有助于预防结直肠癌的结果。我们总结了结肠镜检查视频分析方法的过去和现在的发展,重点介绍了在临床试验中使用的两类人工智能 (AI) 技术。这些是 (1) 用于改善结肠镜检查质量的分析和反馈,以及 (2) 异常检测。我们的调查包括使用精心设计的手工制作特征的传统机器学习算法以及最近的深度学习方法的方法。最后,我们展示了当前最先进技术与理想临床特征之间的差距,并以内镜 AI 技术发展的未来方向结束,这些方向将缩小当前的差距。