Post written by Wai K. Leung, MD, from the University of Hong Kong, Hong Kong, China.

In this study, we developed a novel artificial intelligence (AI)-based quantitative measurement for effective withdrawal time (EWT) during colonoscopy. This measurement also takes into consideration the quality of the images obtained during colonoscope withdrawal.
Standard withdrawal time, a simple measurement of the time taken from the cecum to the rectum, has been shown to have good correlation with adenoma detection rate (ADR) and even risk of interval cancer. A 6-minute withdrawal time is generally considered the minimum.
However, withdrawal time could be subjected to inter- and intra-individual variations including uneven withdrawal speed and inability to obtain a stable luminal view. Hence, we developed this AI-derived metric to quantify exact duration of EWT more accurately.
Based on video recordings of 350 colonoscopies, we showed that ADR progressively increased with each quintile of EWT from 10% to 85.7%. Each minute increase in EWT was associated with a 49% increase in ADR (adjusted odds ratio, 1.49; 95% confidence interval [CI], 1.36-1.65). EWT also was superior to standard withdrawal time in terms of area under the curve for adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], P < .01). Similar results are observed for polyp detection as well as sessile serrated lesion detection.
In conclusion, this AI-derived EWT measurement may be a better surrogate marker for quality of colonoscopy than standard withdrawal time.

Adjusted odds ratio for the detection of adenomas (top), polyps (middle), and serrated lesions (bottom) according to quintile of EWT (in red) and SWT (in blue). EWT, Effective withdrawal time; ADR, adenoma detection rate; SWT, standard withdrawal time; OR, odds ratio; aOR, adjusted odds ratio.
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