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Fine-tuning the artificial intelligence experience in endoscopy


Editorial

. 2022 May 20.


doi: 10.1002/ueg2.12253.


Online ahead of print.

Affiliations

Item in Clipboard

Editorial

Keith Siau et al.


United European Gastroenterol J.


.

No abstract available


Keywords:

artificial intelligence; colonoscopy; endoscopy; polypectomy.

References

REFERENCES

    1. Wang P, Liu X, Berzin TM, Brown JRG, Liu P, Zhou C, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5(4):343-51. https://doi.org/10.1016/s2468-1253(19)30411-x

    1. Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68(10):1813-19. https://doi.org/10.1136/gutjnl-2018-317500

    1. Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology. 2022. https://doi.org/10.1053/j.gastro.2022.03.007

    1. Hassan C, Balsamo G, Lorenzetti R, Zullo A, Antonelli G. Artificial intelligence for leaving-in-situ colorectal polyps: results of a real-time clinical trial. Endoscopy. 2022;54(S 01):OP140.

    1. Siau K, Hayee BH, Gayam S. Endoscopy’s current carbon footprint. Techniques Innovations Gastrointestinal Endoscopy. 2021;23(4):344-52. https://doi.org/10.1016/j.tige.2021.06.005



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