Artificial Intelligence for Colonoscopy: Past, Present, and Future

dc.contributor.author Tavanapong, Wallapak
dc.contributor.author Riegler, Michael A.
dc.contributor.author Khaleel, Mohammed
dc.contributor.author Mittal, Bhuvan
dc.contributor.author de Groen, Piet C.
dc.contributor.department Computer Science
dc.date.accessioned 2022-11-15T22:12:01Z
dc.date.available 2022-11-15T22:12:01Z
dc.date.issued 2022-08
dc.description.abstract During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.
dc.description.comments This article is published as Tavanapong, W., Oh, J., Riegler, M. A., Khaleel, M., Mittal, B., & De Groen, P. C. (2022). Artificial intelligence for colonoscopy: Past, present, and future. IEEE journal of biomedical and health informatics, 26(8), 3950-3965. doi:10.1109/JBHI.2022.3160098. Posted with permission.<br/><br/>This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/5w5p1qXz
dc.language.iso en
dc.publisher Copyright 2022, The Authors
dc.source.uri https://doi.org/10.1109/JBHI.2022.3160098 *
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Artificial Intelligence and Robotics
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Theory and Algorithms
dc.subject.disciplines DegreeDisciplines::Engineering::Electrical and Computer Engineering::Biomedical
dc.subject.keywords Artificial intelligence
dc.subject.keywords medical image analysis
dc.subject.keywords real-time systems
dc.subject.keywords machine learning
dc.subject.keywords colonoscopy
dc.title Artificial Intelligence for Colonoscopy: Past, Present, and Future
dc.type Article
dspace.entity.type Publication
relation.isAuthorOfPublication f9b67a19-5d18-4682-9a80-4f91f92018a2
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
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