Artificial Intelligence for Colonoscopy: Past, Present, and Future
Date
2022-08
Authors
Oh, JungHwan
Riegler, Michael A.
Khaleel, Mohammed
Mittal, Bhuvan
de Groen, Piet C.
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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.
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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.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.