Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China.
J Digit Imaging. 2023 Dec;36(6):2578-2601. doi: 10.1007/s10278-023-00844-7. Epub 2023 Sep 21.
With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.
随着内镜技术和人工智能的进步，大量内镜成像数据集已经向全世界的研究人员公开。本研究旨在回顾和介绍这些数据集。在 PubMed 中进行了广泛的文献检索以确定合适的数据集，并在 GitHub、Kaggle 和 Simula 中进行了其他有针对性的搜索以直接确定数据集。我们对每个数据集进行了简要介绍，并评估了所包括数据集的特征。此外，还讨论了两个正在进行的国家数据集。共纳入 40 个内镜图像数据集，其中 34 个可用于使用。报告了每个数据集的基本和详细信息。在所有数据集中，16 个专注于息肉，6 个专注于小肠病变。大多数数据集（n=16）仅通过结肠镜检查构建，其次是常规胃肠内镜和胶囊内镜（n=9）。本综述可能有助于内镜研究中使用公共数据集资源。
