Artificial intelligence (AI) has the potential to change many aspects of health-care practice. Two newly published trials explore the potential applications of AI to improve polyp detection and mucosal visualization in gastrointestinal endoscopy — both show the benefits of AI to improve detection in gastrointestinal endoscopy.
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References
Morris, E. J. A. et al. Post-colonoscopy colorectal cancer (PCCRC) rates vary considerably depending on the method used to calculate them: a retrospective observational population-based study of PCCRC in the English National Health Service. Gut 64, 1248–1256 (2015).
Menon, S. & Trudgill, N. How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis. Endosc. Int. Open 2, E46–E50 (2014).
Rees, C. J. et al. Expert opinions and scientific evidence for colonoscopy key performance indicators. Gut 65, 2045–2060 (2016).
Ngu, W. S. et al. Improved adenoma detection with Endocuff Vision: the ADENOMA randomised controlled trial. Gut 68, 280–288 (2019).
Rees, C. J. et al. Narrow band imaging optical diagnosis of small colorectal polyps in routine clinical practice: the Detect Inspect Characterise Resect and Discard 2 (DISCARD 2) study. Gut 66, 887–895 (2016).
Byrne, M. F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 68, 94–100 (2019).
Ruffle, J. K., Farmer, A. D. & Aziz, Q. Artificial intelligence-assisted gastroenterology— promises and pitfalls. Am. J. Gastroenterol. 114, 422–428 (2019).
Wang, P. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut https://doi.org/10.1136/gutjnl-2018-317500 (2019).
Wu, L. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut https://doi.org/10.1136/gutjnl-2018-317366 (2019).
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The authors thank Y. Guan and K. Montague for their assistance with this article.
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Rees, C.J., Koo, S. Artificial intelligence — upping the game in gastrointestinal endoscopy?. Nat Rev Gastroenterol Hepatol 16, 584–585 (2019). https://doi.org/10.1038/s41575-019-0178-y
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DOI: https://doi.org/10.1038/s41575-019-0178-y
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