Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy

doi:10.3748/wjg.119276

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Jul 21, 2026; 32(27): 119276
Published online Jul 21, 2026. doi: 10.3748/wjg.119276

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Figure 1 Image classification. Annotation: In the pre-experiment, labeled images were annotated by multiple experts and then converted into a dataset. Model test precision/recall confirmed images with ≥ 2/3 mucosa touch meeting the mucosa touch image criteria.

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Figure 2 Receiver operating characteristic curve of the artificial intelligence-mucosa touch rate model. ROC: Receiver operating characteristic.

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Figure 3 Scatter plot of mucosa touch rate vs polyp detection rate. MTR: Mucosa touch rate; PDR: Polyp detection rate.

Citation: Chen W, Wu M, Wang HY, Wu HB, Li J, Sun YH, Huang F, Gao M, Zhong ZH, Wu YM, Chen L. Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy. World J Gastroenterol 2026; 32(27): 119276
URL: https://www.wjgnet.com/1007-9327/full/v32/i27/119276.htm
DOI: https://dx.doi.org/10.3748/wjg.119276

Chen W, Wu M, Wang HY, Wu HB, Li J, Sun YH, Huang F, Gao M, Zhong ZH, Wu YM, Chen L. Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy. World J Gastroenterol 2026; 32(27): 119276 [DOI: 10.3748/wjg.119276]

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