Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility and usability.

BACKGROUND AND AIMS: Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization and quality of colonoscopy.

METHODS: A systematic literature search was performed in MEDLINE and Embase to identify AI-studies describing publicly available colonoscopic imaging datasets published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub and Figshare was done to identify datasets directly. Datasets were included if they contained data about polyp detection, polyp characterization or quality of colonoscopy. To assess accessibility of datasets the following categories were defined: open access, open access with barriers and regulated access. To assess the potential usability of the included datasets, essential details of each dataset were extracted using a checklist derived from the CLAIM-checklist.

RESULTS: We identified 22 datasets with open access, 3 datasets open access with barriers and 15 datasets with regulated access. The 22 open access databases containing 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization and/or segmentation, six on polyp characterization and three on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train or benchmark their AI-system. Although technical details were in general well-reported, important details such as polyp and patient demographics and the annotation process were underreported in almost all databases.

CONCLUSION: This review provides greater insight on public availability of colonoscopic imaging databases for AI-research. Incomplete reporting of important details limits the ability of researchers to assess the usability of the current databases.