Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system
Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system
复制标题
使用人工智能检测内窥镜图像中的结肠息肉，该人工智能是通过自动收集内窥镜报告系统中的带注释图像而构建的
DOI：
10.1111/den.14185
复制
发表时间：
2021
影响因子：
5.3
通讯作者：
Yano Tomonori
中科院分区：
文献类型：
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作者：
Hori Keisuke;Ikematsu Hiroaki;Yamamoto Yoichi;Matsuzaki Hiroki;Takeshita Nobuyoshi;Shinmura Kensuke;Yoda Yusuke;Kiuchi Takayoshi;Takemoto Satoko;Yokota Hideo;Yano Tomonori
背景人工智能（AI）在图像识别，特别是内窥镜图像分析方面取得了长足的进步。大规模注释数据集的可用性有助于该领域的最新进展。高质量带注释的内窥镜图像数据集广泛可用，特别是在日本。收集每日报告的注释数据的系统可以帮助积累大量高质量的注释dataset.AimWe评估了在构建的报告系统中使用每日注释内窥镜图像的有效性，用于息肉检测的原型AI模型。MethodsWe构建了一个自动收集系统，用于从内窥镜报告系统中收集每日注释数据集。仅在日常实践中为每个病例选择和注释关键图像，而不是回顾性地进行。我们使用诊断信息的关键词自动提取已诊断的小型结肠息肉的注释内窥镜图像（研究期为2018年3月至9月），并额外收集正常结肠图像。收集的数据集被设计成训练和验证，以构建和评估AI系统。使用深度学习算法RetinaNet.ResultsThe自动化系统收集了来自结肠镜检查（745）的内窥镜图像（47，391），并提取了带有本地注释的关键结肠息肉图像（1356）。我们的AI模型的灵敏度，特异性和准确性分别为97.0%，97.7%和97.3%（n= 300），分别为conclusionThe自动化系统使开发一个高性能的结肠息肉检测器使用图像在内窥镜报告系统，而无需回顾性注释工作的努力。
BackgroundArtificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The availability of large‐scale annotated datasets has contributed to the recent progress in this field. Datasets of high‐quality annotated endoscopic images are widely available, particularly in Japan. A system for collecting annotated data reported daily could aid in accumulating a significant number of high‐quality annotated datasets.AimWe assessed the validity of using daily annotated endoscopic images in a constructed reporting system for a prototype AI model for polyp detection.MethodsWe constructed an automated collection system for daily annotated datasets from an endoscopy reporting system. The key images were selected and annotated for each case only during daily practice, not to be performed retrospectively. We automatically extracted annotated endoscopic images of diminutive colon polyps that had been diagnosed (study period March–September 2018) using the keywords of diagnostic information, and additionally collect the normal colon images. The collected dataset was devised into training and validation to build and evaluate the AI system. The detection model was developed using a deep learning algorithm, RetinaNet.ResultsThe automated system collected endoscopic images (47,391) from colonoscopies (745), and extracted key colon polyp images (1356) with localized annotations. The sensitivity, specificity, and accuracy of our AI model were 97.0%, 97.7%, and 97.3% (n= 300), respectively.ConclusionThe automated system enabled the development of a high‐performance colon polyp detector using images in endoscopy reporting system without the efforts of retrospective annotation works.