TY - JOUR
T1 - Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research
T2 - availability, accessibility, and usability
AU - Houwen, Britt B. S. L.
AU - Nass, Karlijn J.
AU - Vleugels, Jasper L. A.
AU - Fockens, Paul
AU - Hazewinkel, Yark
AU - Dekker, Evelien
N1 - Funding Information: DISCLOSURE: The following authors disclosed financial relationships: P. Fockens: Research support from Boston Scientific ; consultant for Olympus and Cook Endoscopy . E. Dekker: Research support from Fujifilm ; consultant for Olympus, Fujifilm, GI Supply , PAION, Ambu , and CPP-FAP ; speaker for Olympus, Roche, GI Supply, Norgine, Fujifilm, and IPSEN. All other authors disclosed no financial relationships. Funding Information: DISCLOSURE: The following authors disclosed financial relationships: P. Fockens: Research support from Boston Scientific; consultant for Olympus and Cook Endoscopy. E. Dekker: Research support from Fujifilm; consultant for Olympus, Fujifilm, GI Supply, PAION, Ambu, and CPP-FAP; speaker for Olympus, Roche, GI Supply, Norgine, Fujifilm, and IPSEN. All other authors disclosed no financial relationships.We are grateful for the support of F. S. van Etten Jamaludin, librarian with the University of Amsterdam, who assisted with the systematic literature search for this review. We also thank Djuna de Jong for her diligent proofreading of the manuscript. Publisher Copyright: © 2023 American Society for Gastrointestinal Endoscopy
PY - 2023/2
Y1 - 2023/2
N2 - 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 databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging. Results: We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 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 under-reported in almost all databases. Conclusions: 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 current databases.
AB - 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 databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging. Results: We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 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 under-reported in almost all databases. Conclusions: 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 current databases.
KW - Artificial Intelligence
KW - Colonic Polyps/diagnostic imaging
KW - Colonoscopes
KW - Colonoscopy/methods
KW - Humans
KW - Radiography
UR - http://www.scopus.com/inward/record.url?scp=85144456157&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.gie.2022.08.043
DO - https://doi.org/10.1016/j.gie.2022.08.043
M3 - Review article
C2 - 36084720
SN - 0016-5107
VL - 97
SP - 184-199.e16
JO - Gastrointestinal Endoscopy
JF - Gastrointestinal Endoscopy
IS - 2
ER -