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In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : Recent meta-analysis showed that up to 26% of adenoma could be missed during colonoscopy. We investigated whether the use of artificial intelligence (AI) assisted real-time detection could provide new insights into mechanisms underlying missed lesions during colonoscopy.

METHODS : A validated real-time deep learning AI model for detection of colonic polyps was first tested in the videos of tandem colonoscopy of the proximal colon for missed lesions. The real-time AI model was then prospectively validated in total colonoscopy in which endoscopist was blinded to the real-time AI findings. Segmental unblinding of the AI findings were provided and that colonic segment would be re-examined when there were missed lesions detected by AI but not the endoscopist. All polyps were removed for histological examination as the criterion standard.

RESULTS : Sixty-five videos of tandem examination of the proximal colon were reviewed by AI. AI could detect 79.1% (19/24) of missed proximal adenoma in the video of the first-pass examination. In the 52 prospective colonoscopies, real-time AI detection could detect at least one missed adenoma in 14 (26.9%) patients and increased total number of adenomas detected by 23.6%. Multivariable analysis showed that missed adenoma(s) was more likely when there were multiple polyps (adjusted OR, 1.05; 95% CI, 1.02-1.09; p < 0.0001) or colonoscopy by less experienced endoscopists (adjusted OR, 1.30; 95% CI, 1.05-1.62; p=0.02).

CONCLUSION : Our findings provide new insights on the prominent role of human factors, including inexperience and distraction, play on missed colonic lesions. With the use of real-time AI assistance, up to 80% of missed adenoma could be prevented.

Lui Thomas Kl, Hui Cynthia Ky, Tsui Vivien Wm, Cheung Ka Shing, Ko Michael Kl, aCC Foo Dominic, Mak Lung Yi, Yeung Chun Kwong, Lui Tim Hw, Wong Siu Yin, Leung Wai K