Gilabert Pere, Vitrià Jordi, Laiz Pablo, Malagelada Carolina, Watson Angus, Wenzek Hagen, Segui Santi
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
Digestive System Research Unit, University Hospital Vall d'Hebron, Barcelona, Spain.
Front Med (Lausanne). 2022 Oct 13;9:1000726. doi: 10.3389/fmed.2022.1000726. eCollection 2022.
Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.
结肠胶囊内镜检查（CCE）是一种微创手术，越来越多地被用作传统结肠镜检查的替代方法。胶囊相机录制的视频很长，需要一位或多位专家花费时间进行查看，以识别可能导致重大健康问题的息肉或其他潜在肠道问题。我们开发并测试了一个多平台网络应用程序AI-Tool，它嵌入了卷积神经网络（CNN）来帮助CCE审查人员。借助人工智能，AI-Tool能够检测出极有可能包含息肉的图像，并在审查过程中对其进行优先排序。在3位专家合作审查18个视频的过程中，我们比较了使用RAPID Reader Software v9.0的传统线性审查方法和我们展示的新软件。应用新策略后，审查时间减少了6倍，息肉检测灵敏度从81.08%提高到了87.80%。
